Decoding the exposome: data science methodologies and implications in exposome-wide association studies (ExWASs).

This paper explores the exposome concept and its role in elucidating the interplay between environmental exposures and human health. We introduce two key concepts critical for exposomics research. Firstly, we discuss the joint impact of genetics and environment on phenotypes, emphasizing the variance attributable to shared and nonshared environmental factors, underscoring the complexity of quantifying the exposome's influence on health outcomes. Secondly, we introduce the importance of advanced data-driven methods in large cohort studies for exposomic measurements. Here, we introduce the exposome-wide association study (ExWAS), an approach designed for systematic discovery of relationships between phenotypes and various exposures, identifying significant associations while controlling for multiple comparisons. We advocate for the standardized use of the term "exposome-wide association study, ExWAS," to facilitate clear communication and literature retrieval in this field. The paper aims to guide future health researchers in understanding and evaluating exposomic studies. Our discussion extends to emerging topics, such as FAIR Data Principles, biobanked healthcare datasets, and the functional exposome, outlining the future directions in exposomic research. This abstract provides a succinct overview of our comprehensive approach to understanding the complex dynamics of the exposome and its significant implications for human health.

EHR-BERT: A BERT-based model for effective anomaly detection in electronic health records.

OBJECTIVE: Physicians and clinicians rely on data contained in electronic health records (EHRs), as recorded by health information technology (HIT), to make informed decisions about their patients. The reliability of HIT systems in this regard is critical to patient safety. Consequently, better tools are needed to monitor the performance of HIT systems for potential hazards that could compromise the collected EHRs, which in turn could affect patient safety. In this paper, we propose a new framework for detecting anomalies in EHRs using sequence of clinical events. This new framework, EHR-Bidirectional Encoder Representations from Transformers (BERT), is motivated by the gaps in the existing deep-learning related methods, including high false negatives, sub-optimal accuracy, higher computational cost, and the risk of information loss. EHR-BERT is an innovative framework rooted in the BERT architecture, meticulously tailored to navigate the hurdles in the contemporary BERT method; thus, enhancing anomaly detection in EHRs for healthcare applications. METHODS: The EHR-BERT framework was designed using the Sequential Masked Token Prediction (SMTP) method. This approach treats EHRs as natural language sentences and iteratively masks input tokens during both training and prediction stages. This method facilitates the learning of EHR sequence patterns in both directions for each event and identifies anomalies based on deviations from the normal execution models trained on EHR sequences. RESULTS: Extensive experiments on large EHR datasets across various medical domains demonstrate that EHR-BERT markedly improves upon existing models. It significantly reduces the number of false positives and enhances the detection rate, thus bolstering the reliability of anomaly detection in electronic health records. This improvement is attributed to the model's ability to minimize information loss and maximize data utilization effectively. CONCLUSION: EHR-BERT showcases immense potential in decreasing medical errors related to anomalous clinical events, positioning itself as an indispensable asset for enhancing patient safety and the overall standard of healthcare services. The framework effectively overcomes the drawbacks of earlier models, making it a promising solution for healthcare professionals to ensure the reliability and quality of health data.

Seminar: Scalable Preprocessing Tools for Exposomic Data Analysis.

BACKGROUND: The exposome serves as a popular framework in which to study exposures from chemical and nonchemical stressors across the life course and the differing roles that these exposures can play in human health. As a result, data relevant to the exposome have been used as a resource in the quest to untangle complicated health trajectories and help connect the dots from exposures to adverse outcome pathways. OBJECTIVES: The primary aim of this methods seminar is to clarify and review preprocessing techniques critical for accurate and effective external exposomic data analysis. Scalability is emphasized through an application of highly innovative combinatorial techniques coupled with more traditional statistical strategies. The Public Health Exposome is used as an archetypical model. The novelty and innovation of this seminar's focus stem from its methodical, comprehensive treatment of preprocessing and its demonstration of the positive effects preprocessing can have on downstream analytics. DISCUSSION: State-of-the-art technologies are described for data harmonization and to mitigate noise, which can stymie downstream interpretation, and to select key exposomic features, without which analytics may lose focus. A main task is the reduction of multicollinearity, a particularly formidable problem that frequently arises from repeated measurements of similar events taken at various times and from multiple sources. Empirical results highlight the effectiveness of a carefully planned preprocessing workflow as demonstrated in the context of more highly concentrated variable lists, improved correlational distributions, and enhanced downstream analytics for latent relationship discovery. The nascent field of exposome science can be characterized by the need to analyze and interpret a complex confluence of highly inhomogeneous spatial and temporal data, which may present formidable challenges to even the most powerful analytical tools. A systematic approach to preprocessing can therefore provide an essential first step in the application of modern computer and data science methods. https://doi.org/10.1289/EHP12901.

Detecting anomalous sequences in electronic health records using higher-order tensor networks.

Detecting anomalous sequences is an integral part of building and protecting modern large-scale health information technology (HIT) systems. These HIT systems generate a large volume of records of patients' state and significant events, which provide a valuable resource to help improve clinical decisions, patient care processes, and other issues. However, detecting anomalous sequences in electronic health records (EHR) remains a challenge in healthcare applications for several reasons, including imbalances in the data, complexity of relationships between events in the sequence, and the curse of dimensionality. Conventional anomaly detection methods use the finite sequence of events to discriminate sequences. They fail to incorporate salient event details under variable higher-order dependencies (e.g., duration between events) that can provide better discrimination of sequences in their models. To address this problem, we propose event sequence and subsequence anomaly detection algorithms that (1) use network-based representations of interactions in the data, (2) account for variable higher-order dependencies in the data, and (3) incorporate events duration for adequate discrimination of the data. The proposed approach identifies anomalies by monitoring the change in the graph after the test sequence is removed from the network. The change is quantified using graph distance metrics so that dramatic changes in the network can be attributed to the removed sequence. Furthermore, the proposed subsequence algorithm recommends plausible paths and salient information for the detected anomalous subsequences. Our results show that the proposed event sequence anomaly detection algorithm outperforms the baseline methods for both synthetic data and real-world EHR data.

Domination based classification algorithms for the controllability analysis of biological interaction networks.

Deciding the size of a minimum dominating set is a classic NP-complete problem. It has found increasing utility as the basis for classifying vertices in networks derived from protein-protein, noncoding RNA, metabolic, and other biological interaction data. In this context it can be helpful, for example, to identify those vertices that must be present in any minimum solution. Current classification methods, however, can require solving as many instances as there are vertices, rendering them computationally prohibitive in many applications. In an effort to address this shortcoming, new classification algorithms are derived and tested for efficiency and effectiveness. Results of performance comparisons on real-world biological networks are reported.

Examining the Drivers of Racial/Ethnic Disparities in Non-Adherence to Antihypertensive Medications and Mortality Due to Heart Disease and Stroke: A County-Level Analysis.

Background: Prior research has identified disparities in anti-hypertensive medication (AHM) non-adherence between Black/African Americans (BAAs) and non-Hispanic Whites (nHWs) but the role of determinants of health in these gaps is unclear. Non-adherence to AHM may be associated with increased mortality (due to heart disease and stroke) and the extent to which such associations are modified by contextual determinants of health may inform future interventions. Methods: We linked the Centers for Disease Control and Prevention (CDC) Atlas of Heart Disease and Stroke (2014-2016) and the 2016 County Health Ranking (CHR) dataset to investigate the associations between AHM non-adherence, mortality, and determinants of health. A proportion of days covered (PDC) with AHM < 80%, was considered as non-adherence. We computed the prevalence rate ratio (PRR)-the ratio of the prevalence among BAAs to that among nHWs-as an index of BAA-nHW disparity. Hierarchical linear models (HLM) were used to assess the role of four pre-defined determinants of health domains-health behaviors, clinical care, social and economic and physical environment-as contributors to BAA-nHW disparities in AHM non-adherence. A Bayesian paradigm framework was used to quantify the associations between AHM non-adherence and mortality (heart disease and stroke) and to assess whether the determinants of health factors moderated these associations. Results: Overall, BAAs were significantly more likely to be non-adherent: PRR = 1.37, 95% Confidence Interval (CI):1.36, 1.37. The four county-level constructs of determinants of health accounted for 24% of the BAA-nHW variation in AHM non-adherence. The clinical care (ß = -0.21, p < 0.001) and social and economic (ß = -0.11, p < 0.01) domains were significantly inversely associated with the observed BAA-nHW disparity. AHM non-adherence was associated with both heart disease and stroke mortality among both BAAs and nHWs. We observed that the determinants of health, specifically clinical care and physical environment domains, moderated the effects of AHM non-adherence on heart disease mortality among BAAs but not among nHWs. For the AHM non-adherence-stroke mortality association, the determinants of health did not moderate this association among BAAs; the social and economic domain did moderate this association among nHWs. Conclusions: The socioeconomic, clinical care and physical environmental attributes of the places that patients live are significant contributors to BAA-nHW disparities in AHM non-adherence and mortality due to heart diseases and stroke.

Molecular Subtyping and Outlier Detection in Human Disease Using the Paraclique Algorithm.

Recent discoveries of distinct molecular subtypes have led to remarkable advances in treatment for a variety of diseases. While subtyping via unsupervised clustering has received a great deal of interest, most methods rely on basic statistical or machine learning methods. At the same time, techniques based on graph clustering, particularly clique-based strategies, have been successfully used to identify disease biomarkers and gene networks. A graph theoretical approach based on the paraclique algorithm is described that can easily be employed to identify putative disease subtypes and serve as an aid in outlier detection as well. The feasibility and potential effectiveness of this method is demonstrated on publicly available gene co-expression data derived from patient samples covering twelve different disease families.

Structural and Social Determinants of Health Factors Associated with County-Level Variation in Non-Adherence to Antihypertensive Medication Treatment.

BACKGROUND: Non-adherence to antihypertensive medication treatment (AHM) is a complex health behavior with determinants that extend beyond the individual patient. The structural and social determinants of health (SDH) that predispose populations to ill health and unhealthy behaviors could be potential barriers to long-term adherence to AHM. However, the role of SDH in AHM non-adherence has been understudied. Therefore, we aimed to define and identify the SDH factors associated with non-adherence to AHM and to quantify the variation in county-level non-adherence to AHM explained by these factors. METHODS: Two cross-sectional datasets, the Centers for Disease Control and Prevention (CDC) Atlas of Heart Disease and Stroke (2014-2016 cycle) and the 2016 County Health Rankings (CHR), were linked to create an analytic dataset. Contextual SDH variables were extracted from the CDC-CHR linked dataset. County-level prevalence of AHM non-adherence, based on Medicare fee-for-service beneficiaries' claims data, was extracted from the CDC Atlas dataset. The CDC measured AHM non-adherence as the proportion of days covered (PDC) with AHM during a 365 day period for Medicare Part D beneficiaries and aggregated these measures at the county level. We applied confirmatory factor analysis (CFA) to identify the constructs of social determinants of AHM non-adherence. AHM non-adherence variation and its social determinants were measured with structural equation models. RESULTS: Among 3000 counties in the U.S., the weighted mean prevalence of AHM non-adherence (PDC < 80%) in 2015 was 25.0%, with a standard deviation (SD) of 18.8%. AHM non-adherence was directly associated with poverty/food insecurity (ß = 0.31, P-value < 0.001) and weak social supports (ß = 0.27, P-value < 0.001), but inversely with healthy built environment (ß = -0.10, P-value = 0.02). These three constructs explained one-third (R2 = 30.0%) of the variation in county-level AHM non-adherence. CONCLUSION: AHM non-adherence varies by geographical location, one-third of which is explained by contextual SDH factors including poverty/food insecurity, weak social supports and healthy built environments.

The Effects of Social, Personal, and Behavioral Risk Factors and PM2.5 on Cardio-Metabolic Disparities in a Cohort of Community Health Center Patients.

(1) Background: Cardio-metabolic diseases (CMD), including cardiovascular disease, stroke, and diabetes, have numerous common individual and environmental risk factors. Yet, few studies to date have considered how these multiple risk factors together affect CMD disparities between Blacks and Whites. (2) Methods: We linked daily fine particulate matter (PM2.5) measures with survey responses of participants in the Southern Community Cohort Study (SCCS). Generalized linear mixed modeling (GLMM) was used to estimate the relationship between CMD risk and social-demographic characteristics, behavioral and personal risk factors, and exposure levels of PM2.5. (3) Results: The study resulted in four key findings: (1) PM2.5 concentration level was significantly associated with reported CMD, with risk rising by 2.6% for each µg/m3 increase in PM2.5; (2) race did not predict CMD risk when clinical, lifestyle, and environmental risk factors were accounted for; (3) a significant variation of CMD risk was found among participants across states; and (4) multiple personal, clinical, and social-demographic and environmental risk factors played a role in predicting CMD occurrence. (4) Conclusions: Disparities in CMD risk among low social status populations reflect the complex interactions of exposures and cumulative risks for CMD contributed by different personal and environmental factors from natural, built, and social environments.

Biclique: an R package for maximal biclique enumeration in bipartite graphs.

OBJECTIVE: Bipartite graphs are widely used to model relationships between pairs of heterogeneous data types. Maximal bicliques are foundational structures in such graphs, and their enumeration is an important task in systems biology, epidemiology and many other problem domains. Thus, there is a need for an efficient, general purpose, publicly available tool to enumerate maximal bicliques in bipartite graphs. The statistical programming language R is a logical choice for such a tool, but until now no R package has existed for this purpose. Our objective is to provide such a package, so that the research community can more easily perform this computationally demanding task. RESULTS: Biclique is an R package that takes as input a bipartite graph and produces a listing of all maximal bicliques in this graph. Input and output formats are straightforward, with examples provided both in this paper and in the package documentation. Biclique employs a state-of-the-art algorithm previously developed for basic research in functional genomics. This package, along with its source code and reference manual, are freely available from the CRAN public repository at https://cran.r-project.org/web/packages/biclique/index.html.

MicroRNA Profiling in Adipose Before and After Weight Loss Highlights the Role of miR-223-3p and the NLRP3 Inflammasome.

OBJECTIVE: Adipose tissue plays a key role in obesity-related metabolic dysfunction. MicroRNA (miRNA) are gene regulatory molecules involved in intercellular and inter-organ communication. It was hypothesized that miRNA levels in adipose tissue would change after gastric bypass surgery and that this would provide insights into their role in obesity-induced metabolic dysregulation. METHODS: miRNA profiling (Affymetrix GeneChip miRNA 2.0 Array) of omental and subcutaneous adipose (n = 15 females) before and after gastric bypass surgery was performed. RESULTS: One omental and thirteen subcutaneous adipose miRNAs were significantly differentially expressed after gastric bypass, including downregulation of miR-223-3p and its antisense relative miR-223-5p in both adipose tissues. mRNA levels of miR-223-3p targets NLRP3 and GLUT4 were decreased and increased, respectively, following gastric bypass in both adipose tissues. Significantly more NLRP3 protein was observed in omental adipose after gastric bypass (P = 0.02). Significant hypomethlyation of NLRP3 and hypermethylation of miR-223 were observed in both adipose tissues after gastric bypass. In subcutaneous adipose, significant correlations were observed between both miR-223-3p and miR-223-5p and glucose and between NLRP3 mRNA and protein levels and blood lipids. CONCLUSIONS: This is the first report detailing genome-wide miRNA profiling of omental adipose before and after gastric bypass, and it further highlights the association of miR-223-3p and the NLRP3 inflammasome with obesity.

A robustness metric for biological data clustering algorithms.

BACKGROUND: Cluster analysis is a core task in modern data-centric computation. Algorithmic choice is driven by factors such as data size and heterogeneity, the similarity measures employed, and the type of clusters sought. Familiarity and mere preference often play a significant role as well. Comparisons between clustering algorithms tend to focus on cluster quality. Such comparisons are complicated by the fact that algorithms often have multiple settings that can affect the clusters produced. Such a setting may represent, for example, a preset variable, a parameter of interest, or various sorts of initial assignments. A question of interest then is this: to what degree do the clusters produced vary as setting values change? RESULTS: This work introduces a new metric, termed simply "robustness", designed to answer that question. Robustness is an easily-interpretable measure of the propensity of a clustering algorithm to maintain output coherence over a range of settings. The robustness of eleven popular clustering algorithms is evaluated over some two dozen publicly available mRNA expression microarray datasets. Given their straightforwardness and predictability, hierarchical methods generally exhibited the highest robustness on most datasets. Of the more complex strategies, the paraclique algorithm yielded consistently higher robustness than other algorithms tested, approaching and even surpassing hierarchical methods on several datasets. Other techniques exhibited mixed robustness, with no clear distinction between them. CONCLUSIONS: Robustness provides a simple and intuitive measure of the stability and predictability of a clustering algorithm. It can be a useful tool to aid both in algorithm selection and in deciding how much effort to devote to parameter tuning.

On Finding and Enumerating Maximal and Maximum k-Partite Cliques in k-Partite Graphs.

Let k denote an integer greater than 2, let G denote a k-partite graph, and let S denote the set of all maximal k-partite cliques in G. Several open questions concerning the computation of S are resolved. A straightforward and highly-scalable modification to the classic recursive backtracking approach of Bron and Kerbosch is first described and shown to run in O(3 n/3) time. A series of novel graph constructions is then used to prove that this bound is best possible in the sense that it matches an asymptotically tight upper limit on |S|. The task of identifying a vertex-maximum element of S is also considered and, in contrast with the k = 2 case, shown to be NP-hard for every k ≥ 3. A special class of k-partite graphs that arises in the context of functional genomics and other problem domains is studied as well and shown to be more readily solvable via a polynomial-time transformation to bipartite graphs. Applications, limitations, potentials for faster methods, heuristic approaches, and alternate formulations are also addressed.

Integration of heterogeneous functional genomics data in gerontology research to find genes and pathway underlying aging across species.

Understanding the biological mechanisms behind aging, lifespan and healthspan is becoming increasingly important as the proportion of the world's population over the age of 65 grows, along with the cost and complexity of their care. BigData oriented approaches and analysis methods enable current and future bio-gerontologists to synthesize, distill and interpret vast, heterogeneous data from functional genomics studies of aging. GeneWeaver is an analysis system for integration of data that allows investigators to store, search, and analyze immense amounts of data including user-submitted experimental data, data from primary publications, and data in other databases. Aging related genome-wide gene sets from primary publications were curated into this system in concert with data from other model-organism and aging-specific databases, and applied to several questions in genrontology using. For example, we identified Cd63 as a frequently represented gene among aging-related genome-wide results. To evaluate the role of Cd63 in aging, we performed RNAi knockdown of the C. elegans ortholog, tsp-7, demonstrating that this manipulation is capable of extending lifespan. The tools in GeneWeaver enable aging researchers to make new discoveries into the associations between the genes, normal biological processes, and diseases that affect aging, healthspan, and lifespan.

Altitude and Variable Effects on Infant Mortality in the United States.

Levine, Robert S., Jason L. Salemi, Maria C. Mejia de Grubb, Sarah K. Wood, Lisa Gittner, Hafiz Khan, Michael A. Langston, Baqar A. Husaini, George Rust, and Charles H. Hennekens. Altitude and variable effects on infant mortality in the United States. High Alt Med Biol. 19:265-271, 2018. AIMS: To explore whether altitude has different effects on infant mortality from newborn respiratory distress, nontraumatic intracranial hemorrhage, and necrotizing enterocolitis. RESULTS: Infants born in the US Mountain Census Division (AR, CO, ID, NV, NM, UT, and WY) had lower mortality from newborn respiratory distress (p < 0.001, mortality rate ratios [MRR] = 0.5 for non-Hispanic blacks and non-Hispanic whites and 0.6 for Hispanic whites) relative to infants born elsewhere in the United States, while Mountain Division non-Hispanic white infants had significantly higher mortality from nontraumatic intracranial hemorrhage (MRR = 1.3 [1.1, 1.6] p < 0.001). After adjustment for state average birth weight, gestational age, and income inequality, a statistically significant, inverse association remained between state average altitude and non-Hispanic white infant mortality from newborn respiratory distress. County altitude (3058 counties in 9 categories from ≤0 to ≥7000 feet) was negatively correlated with newborn respiratory distress (r = -0.91, p < 0.001) and necrotizing enterocolitis (r = -0.81, p = 0.006) at ≤0 to ≥7000 feet and positively correlated with nontraumatic intracranial hemorrhage at ≤0 to 6000-6999 feet (r = 0.78, p = 0.02). CONCLUSIONS: These data show variable cause-specific effects of altitude on infant mortality. Analytic epidemiologic research is needed to confirm or refute the hypotheses generated by these descriptive data.

Graph Theoretical Analysis of Genome-Scale Data: Examination of Gene Activation Occurring in the Setting of Community-Acquired Pneumonia.

INTRODUCTION: We have previously reported evidence that Black individuals appear to have a significantly higher incidence of infection-related hospitalizations compared with White individuals. It is possible that the host immune response is responsible for this vital difference. In support of such a hypothesis, the aim of this study was to determine whether Black and White individuals exhibit differential whole blood gene network activation. METHODS: We examined whole blood network activation in a subset of patients (n = 22 pairs, propensity score matched (1:1) Black and White patients) with community-acquired pneumonia (CAP) from the Genetic and Inflammatory Markers of Sepsis study. We employed day one whole blood transcriptomic data generated from this cohort and constructed co-expression graphs for each racial group. Pearson correlation coefficients were used to weight edges. Spectral thresholding was applied to ascribe significance. Innovative graph theoretical methods were then invoked to detect densely connected gene networks and provide differential structural analysis. RESULTS: Propensity matching was employed to reduce potential bias due to confounding variables. Although Black and White patients had similar socio- and clinical demographics, we identified novel differences in molecular network activation-dense subgraphs known as paracliques that displayed complete gene connection for both White (three paracliques) and Black patients (one paraclique). Specifically, the genes that comprised the paracliques in the White patients include circadian loop, cell adhesion, mobility, proliferation, tumor suppression, NFκB, and chemokine signaling. However, the genes that comprised the paracliques in the Black patients include DNA and messenger RNA processes, and apoptosis signaling. We investigated the distribution of Black paracliques across White paracliques. Black patients had five paracliques (with almost complete connection) comprised of genes that are critical for host immune response widely distributed across 22 parcliques in the White population. Anchoring the analysis on two critical inflammatory mediators, interleukin (IL)-6 and IL-10 identified further differential network activation among the White and Black patient populations. CONCLUSIONS: These results demonstrate that, at the molecular level, Black and White individuals may experience different activation patterns with CAP. Further validation of the gene networks we have identified may help pinpoint genetic factors that increase host susceptibility to community-acquired pneumonia, and may lay the groundwork for personalized management of CAP.

A novel approach to analyzing lung cancer mortality disparities: Using the exposome and a graph-theoretical toolchain.

OBJECTIVES: The aim is to identify exposures associated with lung cancer mortality and mortality disparities by race and gender using an exposome database coupled to a graph theoretical toolchain. METHODS: Graph theoretical algorithms were employed to extract paracliques from correlation graphs using associations between 2162 environmental exposures and lung cancer mortality rates in 2067 counties, with clique doubling applied to compute an absolute threshold of significance. Factor analysis and multiple linear regressions then were used to analyze differences in exposures associated with lung cancer mortality and mortality disparities by race and gender. RESULTS: While cigarette consumption was highly correlated with rates of lung cancer mortality for both white men and women, previously unidentified novel exposures were more closely associated with lung cancer mortality and mortality disparities for blacks, particularly black women. CONCLUSIONS: Exposures beyond smoking moderate lung cancer mortality and mortality disparities by race and gender. POLICY IMPLICATIONS: An exposome approach and database coupled with scalable combinatorial analytics provides a powerful new approach for analyzing relationships between multiple environmental exposures, pathways and health outcomes. An assessment of multiple exposures is needed to appropriately translate research findings into environmental public health practice and policy.

Infant Deaths and Mortality from Gun Violence: Causal or Casual?

OBJECTIVE: Describe trends in non-Hispanic black infant mortality (IM) in the New York City (NYC) counties of Bronx, Kings, Queens, and Manhattan and correlations with gun-related assault mortality. METHODS: Linked Birth/Infant Death data (1999-2013) and Compressed Mortality data at ages 1 to ≥85 years (1999-2013). NYC and United States (US) Census data for income inequality and poverty. Pearson coefficients were used to describe correlations of IM with gun-related assault mortality and other causes of death. RESULTS: In NYC, the risk of non-Hispanic black IM in 2013 was 49% lower than in 1995 (rate ratio: 0.51; 95% CI: 0.43, 0.61). Yearly declines between 1999 and 2013 were significantly correlated with declines in gun-related assault mortality (correlation coefficient (r) = 0.70, p = 0.004), drug-related mortality (r = 0.59, p = 0.020), major heart disease and stroke (r = 0.85, p < 0.001), malignant neoplasms (r = 0.57, p = 0.026), diabetes mellitus (r = 0.63, p = 0.011), and pneumonia and influenza (r = 0.78, p < 0.001). There were no significant correlations of IM with chronic lower respiratory or liver disease, non-drug-related accidental deaths, and non-gun-related assault. Yearly IM (1995-2012) was inversely correlated with income share of the top 1% of the population (r = -0.66, p = 0.007). CONCLUSIONS: In NYC, non-Hispanic black IM declined significantly despite increasing income inequality and was strongly correlated with gun-related assault mortality and other major causes of death. These data are compatible with the hypothesis that activities related to overall population health, including those pertaining to gun-related homicide, may provide clues to reducing IM. Analytic epidemiological studies are needed to test these and other hypotheses formulated from these descriptive data.

Genome-wide DNA methylation analysis reveals loci that distinguish different types of adipose tissue in obese individuals.

BACKGROUND: Epigenetic mechanisms provide an interface between environmental factors and the genome and are known to play a role in complex diseases such as obesity. These mechanisms, including DNA methylation, influence the regulation of development, differentiation and the establishment of cellular identity. Here we employ two approaches to identify differential methylation between two white adipose tissue depots in obese individuals before and after gastric bypass and significant weight loss. We analyse genome-wide DNA methylation data using (a) traditional paired t tests to identify significantly differentially methylated loci (Bonferroni-adjusted P ≤ 1 × 10-7) and (b) novel combinatorial algorithms to identify loci that differentiate between tissue types. RESULTS: Significant differential methylation was observed for 3239 and 7722 CpG sites, including 784 and 1129 extended regions, between adipose tissue types before and after significant weight loss, respectively. The vast majority of these extended differentially methylated regions (702) were consistent across both time points and enriched for genes with a role in transcriptional regulation and/or development (e.g. homeobox genes). Other differentially methylated loci were only observed at one time point and thus potentially highlight genes important to adipose tissue dysfunction observed in obesity. Strong correlations (r > 0.75, P ≤ 0.001) were observed between changes in DNA methylation (subcutaneous adipose vs omentum) and changes in clinical trait, in particular for CpG sites within PITX2 and fasting glucose and four CpG sites within ISL2 and HDL. A single CpG site (cg00838040, ATP2C2) gave strong tissue separation, with validation in independent subcutaneous (n = 681) and omental (n = 33) adipose samples. CONCLUSIONS: This is the first study to report a genome-wide DNA methylome comparison of subcutaneous abdominal and omental adipose before and after weight loss. The combinatorial approach we utilised is a powerful tool for the identification of methylation loci that strongly differentiate between these tissues. This study provides a solid basis for future research focused on the development of adipose tissue and its potential dysfunction in obesity, as well as the role DNA methylation plays in these processes.

A multifactorial obesity model developed from nationwide public health exposome data and modern computational analyses.

STATEMENT OF THE PROBLEM: Obesity is both multifactorial and multimodal, making it difficult to identify, unravel and distinguish causative and contributing factors. The lack of a clear model of aetiology hampers the design and evaluation of interventions to prevent and reduce obesity. METHODS: Using modern graph-theoretical algorithms, we are able to coalesce and analyse thousands of inter-dependent variables and interpret their putative relationships to obesity. Our modelling is different from traditional approaches; we make no a priori assumptions about the population, and model instead based on the actual characteristics of a population. Paracliques, noise-resistant collections of highly-correlated variables, are differentially distilled from data taken over counties associated with low versus high obesity rates. Factor analysis is then applied and a model is developed. RESULTS AND CONCLUSIONS: Latent variables concentrated around social deprivation, community infrastructure and climate, and especially heat stress were connected to obesity. Infrastructure, environment and community organisation differed in counties with low versus high obesity rates. Clear connections of community infrastructure with obesity in our results lead us to conclude that community level interventions are critical. This effort suggests that it might be useful to study and plan interventions around community organisation and structure, rather than just the individual, to combat the nation's obesity epidemic.