Child health promotion in underserved communities: The FAMILIA trial

Background: Preschool-based interventions offer promise to instill healthy behaviors in children, which can be a strategy to reduce the burden of cardiovascular disease later. However, their efficacy in underserved communities is not well established. Objectives: The purpose of this study was to assess the impact of a preschool-based health promotion educational intervention in an underserved community. Methods: This cluster-randomized controlled study involved 15 Head Start preschools in Harlem, New York. Schools and their children were randomized 3:2 to receive either a 4-month (50 h) educational intervention to instill healthy behaviors in relation to diet, physical activity, body/heart awareness, and emotion management; or their standard curriculum (control). The primary outcome was the change from baseline in the overall knowledge, attitudes, and habits (KAH) score of the children at 5 months. As secondary outcomes, we evaluated the changes in KAH subcomponents and emotion comprehension. Linear mixed-effects models were used to test for intervention effects. Results: The authors enrolled 562 preschool children age 3 to 5 years, 51% female, 54% Hispanic/Latino, and 37% African-American. Compared with the control group, the mean relative change from baseline in the overall KAH score was ∼2.2 fold higher in the intervention group (average absolute difference of 2.86 points; 95% confidence interval: 0.58 to 5.14; p = 0.014). The maximal effect was observed in children who received >75% of the curriculum. Physical activity and body/heart awareness components, and knowledge and attitudes domains, were the main drivers of the effect (p values <0.05). Changes in emotion comprehension trended toward favoring intervened children. Conclusions: This multidimensional school-based educational intervention may be an effective strategy for establishing healthy behaviors among preschoolers from a diverse and socioeconomically disadvantaged community. Early primordial prevention strategies may contribute to reducing the global burden of cardiovascular disease. (Family-Based Approach in a Minority Community Integrating Systems-Biology for Promotion of Health [FAMILIAThis study is funded by the American Heart Association under grant No. 14SFRN20490315. The CNIC is supported by the Ministerio de Ciencia, Innovación y Universidades and the Pro CNIC Foundation, and is a Severo Ochoa Center of Excellence (SEV-2015-0505). Dr. Fernandez-Jimenez is a recipient of funding from the European Union Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No. 707642. Dr. Bansilal is an employee of Bayer Pharmaceutical

Pharmacological risk factors associated with hospital readmission rates in a psychiatric cohort identified using prescriptome data mining

Background Worldwide, over 14% of individuals hospitalized for psychiatric reasons have readmissions to hospitals within 30 days after discharge. Predicting patients at risk and leveraging accelerated interventions can reduce the rates of early readmission, a negative clinical outcome (i.e., a treatment failure) that affects the quality of life of patient. To implement individualized interventions, it is necessary to predict those individuals at highest risk for 30-day readmission. In this study, our aim was to conduct a data-driven investigation to find the pharmacological factors influencing 30-day all-cause, intra- and interdepartmental readmissions after an index psychiatric admission, using the compendium of prescription data (prescriptome) from electronic medical records (EMR). Methods The data scientists in the project received a deidentified database from the Mount Sinai Data Warehouse, which was used to perform all analyses. Data was stored in a secured MySQL database, normalized and indexed using a unique hexadecimal identifier associated with the data for psychiatric illness visits. We used Bayesian logistic regression models to evaluate the association of prescription data with 30-day readmission risk. We constructed individual models and compiled results after adjusting for covariates, including drug exposure, age, and gender. We also performed digital comorbidity survey using EMR data combined with the estimation of shared genetic architecture using genomic annotations to disease phenotypes. Results Using an automated, data-driven approach, we identified prescription medications, side effects (primary side effects), and drug-drug interaction-induced side effects (secondary side effects) associated with readmission risk in a cohort of 1275 patients using prescriptome analytics. In our study, we identified 28 drugs associated with risk for readmission among psychiatric patients. Based on prescription data, Pravastatin had the highest risk of readmission (OR = 13.10; 95% CI (2.82, 60.8)). We also identified enrichment of primary side effects (n = 4006) and secondary side effects (n = 36) induced by prescription drugs in the subset of readmitted patients (n = 89) compared to the non-readmitted subgroup (n = 1186). Digital comorbidity analyses and shared genetic analyses further reveals that cardiovascular disease and psychiatric conditions are comorbid and share functional gene modules (cardiomyopathy and anxiety disorder: shared genes (n = 37; P = 1.06815E-06)). Conclusions Large scale prescriptome data is now available from EMRs and accessible for analytics that could improve healthcare outcomes. Such analyses could also drive hypothesis and data-driven research. In this study, we explored the utility of prescriptome data to identify factors driving readmission in a psychiatric cohort. Converging digital health data from EMRs and systems biology investigations reveal a subset of patient populations that have significant comorbidities with cardiovascular diseases are more likely to be readmitted. Further, the genetic architecture of psychiatric illness also suggests overlap with cardiovascular diseases. In summary, assessment of medications, side effects, and drug-drug interactions in a clinical setting as well as genomic information using a data mining approach could help to find factors that could help to lower readmission rates in patients with mental illness

Different Lifestyle Interventions in Adults From Underserved Communities: The FAMILIA Trial

BACKGROUND: The current trends of unhealthy lifestyle behaviors in underserved communities are disturbing. Thus, effective health promotion strategies constitute an unmet need. OBJECTIVES: The purpose of this study was to assess the impact of 2 different lifestyle interventions on parents/caregivers of children attending preschools in a socioeconomically disadvantaged community. METHODS: The FAMILIA (Family-Based Approach in a Minority Community Integrating Systems-Biology for Promotion of Health) study is a cluster-randomized trial involving 15 Head Start preschools in Harlem, New York. Schools, and their children's parents/caregivers, were randomized to receive either an "individual-focused" or "peer-to-peer-based" lifestyle intervention program for 12 months or control. The primary outcome was the change from baseline to 12 months in a composite health score related to blood pressure, exercise, weight, alimentation, and tobacco (Fuster-BEWAT Score [FBS]), ranging from 0 to 15 (ideal health = 15). To assess the sustainability of the intervention, this study evaluated the change of FBS at 24 months. Main pre-specified secondary outcomes included changes in FBS subcomponents and the effect of the knowledge of presence of atherosclerosis as assessed by bilateral carotid/femoral vascular ultrasound. Mixed-effects models were used to test for intervention effects. RESULTS: A total of 635 parents/caregivers were enrolled: mean age 38 ± 11 years, 83% women, 57% Hispanic/Latino, 31% African American, and a baseline FBS of 9.3 ± 2.4 points. The mean within-group change in FBS from baseline to 12 months was ∼0.20 points in all groups, with no overall between-group differences. However, high-adherence participants to the intervention exhibited a greater change in FBS than their low-adherence counterparts: 0.30 points (95% confidence interval: 0.03 to 0.57; p = 0.027) versus 0.00 points (95% confidence interval: -0.43 to 0.43; p = 1.0), respectively. Furthermore, the knowledge by the participant of the presence of atherosclerosis significantly boosted the intervention effects. Similar results were sustained at 24 months. CONCLUSIONS: Although overall significant differences were not observed between intervention and control groups, the FAMILIA trial highlights that high adherence rates to lifestyle interventions may improve health outcomes. It also suggests a potential contributory role of the presentation of atherosclerosis pictures, providing helpful information to improve future lifestyle interventions in adults.AGENCIA FINANCIADORA: The American Heart Association, under grant No 14SFRN20490315, funded this study. R.F-J is a recipient of funding from the European Union Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 707642. The CNIC is supported by the Instituto de Salud Carlos III (ISCIII), the Ministerio de Ciencia, Innovación y Universidades (MCNU) and the Pro CNIC Foundation, and is a Severo Ochoa Center of Excellence (SEV-2015-0505).S

A comprehensive transcript index of the human genome generated using microarrays and computational approaches

BACKGROUND: Computational and microarray-based experimental approaches were used to generate a comprehensive transcript index for the human genome. Oligonucleotide probes designed from approximately 50,000 known and predicted transcript sequences from the human genome were used to survey transcription from a diverse set of 60 tissues and cell lines using ink-jet microarrays. Further, expression activity over at least six conditions was more generally assessed using genomic tiling arrays consisting of probes tiled through a repeat-masked version of the genomic sequence making up chromosomes 20 and 22. RESULTS: The combination of microarray data with extensive genome annotations resulted in a set of 28,456 experimentally supported transcripts. This set of high-confidence transcripts represents the first experimentally driven annotation of the human genome. In addition, the results from genomic tiling suggest that a large amount of transcription exists outside of annotated regions of the genome and serves as an example of how this activity could be measured on a genome-wide scale. CONCLUSIONS: These data represent one of the most comprehensive assessments of transcriptional activity in the human genome and provide an atlas of human gene expression over a unique set of gene predictions. Before the annotation of the human genome is considered complete, however, the previously unannotated transcriptional activity throughout the genome must be fully characterized

Amyotrophic lateral sclerosis: an emerging era of collaborative gene discovery.

Amyotrophic lateral sclerosis (ALS) is the most common form of motor neuron disease (MND). It is currently incurable and treatment is largely limited to supportive care. Family history is associated with an increased risk of ALS, and many Mendelian causes have been discovered. However, most forms of the disease are not obviously familial. Recent advances in human genetics have enabled genome-wide analyses of single nucleotide polymorphisms (SNPs) that make it possible to study complex genetic contributions to human disease. Genome-wide SNP analyses require a large sample size and thus depend upon collaborative efforts to collect and manage the biological samples and corresponding data. Public availability of biological samples (such as DNA), phenotypic and genotypic data further enhances research endeavors. Here we discuss a large collaboration among academic investigators, government, and non-government organizations which has created a public repository of human DNA, immortalized cell lines, and clinical data to further gene discovery in ALS. This resource currently maintains samples and associated phenotypic data from 2332 MND subjects and 4692 controls. This resource should facilitate genetic discoveries which we anticipate will ultimately provide a better understanding of the biological mechanisms of neurodegeneration in ALS

Whole-genome sequencing identifies emergence of a quinolone resistance mutation in a case of Stenotrophomonas maltophilia bacteremia

Whole-genome sequences for Stenotrophomonas maltophilia serial isolates from a bacteremic patient before and after development of levofloxacin resistance were assembled de novo and differed by one single-nucleotide variant in smeT, a repressor for multidrug efflux operon smeDEF. Along with sequenced isolates from five contemporaneous cases, they displayed considerable diversity compared against all published complete genomes. Whole-genome sequencing and complete assembly can conclusively identify resistance mechanisms emerging in S. maltophilia strains during clinical therapy

Uncovering the Genetic Landscape for Multiple Sleep-Wake Traits

Despite decades of research in defining sleep-wake properties in mammals, little is known about the nature or identity of genes that regulate sleep, a fundamental behaviour that in humans occupies about one-third of the entire lifespan. While genome-wide association studies in humans and quantitative trait loci (QTL) analyses in mice have identified candidate genes for an increasing number of complex traits and genetic diseases, the resources and time-consuming process necessary for obtaining detailed quantitative data have made sleep seemingly intractable to similar large-scale genomic approaches. Here we describe analysis of 20 sleep-wake traits from 269 mice from a genetically segregating population that reveals 52 significant QTL representing a minimum of 20 genomic loci. While many (28) QTL affected a particular sleep-wake trait (e.g., amount of wake) across the full 24-hr day, other loci only affected a trait in the light or dark period while some loci had opposite effects on the trait during the light vs. dark. Analysis of a dataset for multiple sleep-wake traits led to previously undetected interactions (including the differential genetic control of number and duration of REM bouts), as well as possible shared genetic regulatory mechanisms for seemingly different unrelated sleep-wake traits (e.g., number of arousals and REM latency). Construction of a Bayesian network for sleep-wake traits and loci led to the identification of sub-networks of linkage not detectable in smaller data sets or limited single-trait analyses. For example, the network analyses revealed a novel chain of causal relationships between the chromosome 17@29cM QTL, total amount of wake, and duration of wake bouts in both light and dark periods that implies a mechanism whereby overall sleep need, mediated by this locus, in turn determines the length of each wake bout. Taken together, the present results reveal a complex genetic landscape underlying multiple sleep-wake traits and emphasize the need for a systems biology approach for elucidating the full extent of the genetic regulatory mechanisms of this complex and universal behavior

The Metagenomics and Metadesign of the Subways and Urban Biomes (MetaSUB) International Consortium inaugural meeting report

The Metagenomics and Metadesign of the Subways and Urban Biomes (MetaSUB) International Consortium is a novel, interdisciplinary initiative comprised of experts across many fields, including genomics, data analysis, engineering, public health, and architecture. The ultimate goal of the MetaSUB Consortium is to improve city utilization and planning through the detection, measurement, and design of metagenomics within urban environments. Although continual measures occur for temperature, air pressure, weather, and human activity, including longitudinal, cross-kingdom ecosystem dynamics can alter and improve the design of cities. The MetaSUB Consortium is aiding these efforts by developing and testing metagenomic methods and standards, including optimized methods for sample collection, DNA/RNA isolation, taxa characterization, and data visualization. The data produced by the consortium can aid city planners, public health officials, and architectural designers. In addition, the study will continue to lead to the discovery of new species, global maps of antimicrobial resistance (AMR) markers, and novel biosynthetic gene clusters (BGCs). Finally, we note that engineered metagenomic ecosystems can help enable more responsive, safer, and quantified cities