Forecasting of non-accidental, cardiovascular, and respiratory mortality with environmental exposures adopting machine learning approaches.
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ABSTRACT:
SUBMITTER: Lee W
PROVIDER: S-EPMC9281380 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Epigenetic modifications are heritable alterations of the genome, which can govern gene expression without altering the DNA sequence. The purpose of this review is to render an overview of the possible mechanisms of epigenetic regulation of gene expression in response to environmental pollutants leading to cardiovascular diseases (CVD).An era of cataloging epigenetic marks of the various diseased states has recently commenced, including those within the genes responsible for atherosclerosis, ischemia, hypertension and heart failure. From varied study approaches directed either toward the general understanding of the key pathway regulatory genes, or sampling population cohorts for global and gene-specific changes, it has been possible to identify several epigenetic signatures of environmental exposure relevant to CVD. Signatures of epigenetic dysregulation can be detected in peripheral blood samples, even within a few hours of environmental exposure. However, the field now faces the demand for thorough, systematic, rationalized approaches to establish the relation of exposure-driven epigenetic changes to clinical outcomes, by using sophisticated and reliable research designs and tools.An understanding of chromatin remodelling in response to environmental stimuli conducive to CVD is emerging, with the promise of novel diagnostic and therapeutic candidates.
| S-EPMC3742092 | biostudies-literature
Project description:BackgroundDespite the fact that Iran has been exposed to severe dust storms during the past 2 decades, few studies have investigated the health effects of these events in Iran. This study was conducted to assess the association between dust storms and daily non-accidental, cardiovascular, and respiratory mortality in Dezful City (Khuzestan Province, Iran) during 2014 to 2019.MethodsIn this study, mortality, meteorological, and climatological data were obtained from the Dezful University of Medical Sciences, Iranian Meteorological Organization, and Department of Environment in Khuzestan Province, respectively. Days of dust storm were identified based on the daily concentration threshold of particulate matter with an aerodynamic diameter of less than 10 μm (PM10) according to Hoffmanns҆ definition, and then an ecological time-series was used to estimate the short-term effects of dust storms on daily mortality. Statistical analysis was performed using a distributed lag linear model (DLM) and a distributed lag non-linear model (DLNM) packages by R software and the study results were reported as excess mortality.ResultsDuring the study period, 15 223 deaths were recorded, and 139 dust storms occurred in Dezful city. In addition, there was statistically significant excess risk of mortality due to dust storms in Dezful City (mortality in the group under 15 years of age, lag4: 34.17% and 15-64 years of age groups, lag5: 32.19%, lag6: 3.28%), also dust storms had statistically significant effects on respiratory mortality (lag6: 5.49%).ConclusionThe findings of the current study indicate that dust storms increase the risk of mortality with some lags. An evidence-based early warning system may be able to aware the people of the health effects of dust storms.
| S-EPMC8606970 | biostudies-literature
Project description:BackgroundA critical challenge in genomic medicine is identifying the genetic and environmental risk factors for disease. Currently, the available data links a majority of known coding human genes to phenotypes, but the environmental component of human disease is extremely underrepresented in these linked data sets. Without environmental exposure information, our ability to realize precision health is limited, even with the promise of modern genomics. Achieving integration of gene, phenotype, and environment will require extensive translation of data into a standard, computable form and the extension of the existing gene/phenotype data model. The data standards and models needed to achieve this integration do not currently exist.ObjectivesOur objective is to foster development of community-driven data-reporting standards and a computational model that will facilitate the inclusion of exposure data in computational analysis of human disease. To this end, we present a preliminary semantic data model and use cases and competency questions for further community-driven model development and refinement.DiscussionThere is a real desire by the exposure science, epidemiology, and toxicology communities to use informatics approaches to improve their research workflow, gain new insights, and increase data reuse. Critical to success is the development of a community-driven data model for describing environmental exposures and linking them to existing models of human disease. https://doi.org/10.1289/EHP7215.
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Project description:Background: China is at its most important stage of air pollution control. Research on the association between air pollutants and human health is very important and necessary. The purpose of this study was to evaluate the association between PM2.5 concentrations and residents' mortality and to compare the effect of PM2.5 on the different diseases, accidental deaths, sex or age of residents from high polluted areas with less polluted areas. Methods: The semi-parametric generalized additive model (GAM) with Poisson distribution of time series analysis was used. The excess risk (ER) of mortality with the incremental increase of 10 µg/m³ in PM2.5 concentration was calculated. Concentration-response relationship curves and autocorrelation between different lags of PM2.5 were also evaluated. Results: PM2.5 exposure was significantly associated with the mortality of residents. The strongest ERs per 10 µg/m³ increase in PM2.5 were 0.74% (95% CI: 0.11?1.38%) for all-cause, 0.67% (95% CI: 0.01?1.33%) for non-accidental, 1.81% (95% CI: 0.22?3.42%) for accidental, 3.04% (95% CI: 0.60?5.55%) for total respiratory disease, 6.38% (95% CI: 2.78?10.11%) for chronic lower respiratory disease (CLRD), 8.24% (95% CI: 3.53?13.17%) for chronic obstructive pulmonary disease (COPD), 1.04% (95% CI: 0.25?1.84%) for male and 1.32% (95% CI: 0.46?2.19%) for elderly. Furthermore, important information on the concentration-response relationship curves was provided. Conclusions: PM2.5 can increase the risk of residents' mortality, even in places with less air pollution and developed economy in China.
| S-EPMC6388241 | biostudies-literature
Project description:Staphylococcus aureus is a major human and animal pathogen, colonizing diverse ecological niches within its hosts. Predicting whether an isolate will infect a specific host and its subsequent clinical fate remains unknown. In this study, we investigated the S. aureus pangenome using a curated set of 356 strains, spanning a wide range of hosts, origins, and clinical display and antibiotic resistance profiles. We used genome-wide association study (GWAS) and random forest (RF) algorithms to discriminate strains based on their origins and clinical sources. Here, we show that the presence of sak and scn can discriminate strains based on their host specificity, while other genes such as mecA are often associated with virulent outcomes. Both GWAS and RF indicated the importance of intergenic regions (IGRs) and coding DNA sequence (CDS) but not sRNAs in forecasting an outcome. Additional transcriptomic analyses performed on the most prevalent clonal complex 8 (CC8) clonal types, in media mimicking nasal colonization or bacteremia, indicated three RNAs as potential RNA markers to forecast infection, followed by 30 others that could serve as infection severity predictors. Our report shows that genetic association and transcriptomics are complementary approaches that will be combined in a single analytical framework to improve our understanding of bacterial pathogenesis and ultimately identify potential predictive molecular markers. IMPORTANCE Predicting the outcome of bacterial colonization and infections, based on extensive genomic and transcriptomic data from a given pathogen, would be of substantial help for clinicians in treating and curing patients. In this report, genome-wide association studies and random forest algorithms have defined gene combinations that differentiate human from animal strains, colonization from diseases, and nonsevere from severe diseases, while it revealed the importance of IGRs and CDS, but not small RNAs (sRNAs), in anticipating an outcome. In addition, transcriptomic analyses performed on the most prevalent clonal types, in media mimicking either nasal colonization or bacteremia, revealed significant differences and therefore potent RNA markers. Overall, the use of both genomic and transcriptomic data in a single analytical framework can enhance our understanding of bacterial pathogenesis.
| S-EPMC9426533 | biostudies-literature
Project description:We compared the methylation profile between ARDS survivor v.s. non survivor
2022-12-31 | GSE176529 | GEO
Project description:In the last decade, more than half of U.S. children were born to working mothers and 65% of working men and women were of reproductive age. In 2004 more than 28 million women age 18-44 were employed full time. This implies the need for clinicians to possess an awareness about the impact of work on the health of their patients and their future offspring. Most chemicals in the workplace have not been evaluated for reproductive toxicity, and where exposure limits do exist, they were generally not designed to mitigate reproductive risk. Therefore, many toxicants with unambiguous reproductive and developmental effects are still in regular commercial or therapeutic use and thus present exposure potential to workers. Examples of these include heavy metals, (lead, cadmium), organic solvents (glycol ethers, percholoroethylene), pesticides and herbicides (ethylene dibromide) and sterilants, anesthetic gases and anti-cancer drugs used in healthcare. Surprisingly, many of these reproductive toxicants are well represented in traditional employment sectors of women, such as healthcare and cosmetology. Environmental exposures also figure prominently in evaluating a woman's health risk and that to a pregnancy. Food and water quality and pesticide and solvent usage are increasingly topics raised by women and men contemplating pregnancy. The microenvironment of a woman, such as her choices of hobbies and leisure time activities also come into play. Caregivers must be aware of their patients' potential environmental and workplace exposures and weigh any risk of exposure in the context of the time-dependent window of reproductive susceptibility. This will allow informed decision-making about the need for changes in behavior, diet, hobbies or the need for added protections on the job or alternative duty assignment. Examples of such environmental and occupational history elements will be presented together with counseling strategies for the clinician.
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Project description:BackgroundAccidental hypothermia is a critical condition with high risks of fatal arrhythmia, multiple organ failure, and mortality; however, there is no established model to predict the mortality. The present study aimed to develop and validate machine learning-based models for predicting in-hospital mortality using easily available data at hospital admission among the patients with accidental hypothermia.MethodThis study was secondary analysis of multi-center retrospective cohort study (J-point registry) including patients with accidental hypothermia. Adult patients with body temperature 35.0 °C or less at emergency department were included. Prediction models for in-hospital mortality using machine learning (lasso, random forest, and gradient boosting tree) were made in development cohort from six hospitals, and the predictive performance were assessed in validation cohort from other six hospitals. As a reference, we compared the SOFA score and 5A score.ResultsWe included total 532 patients in the development cohort [N = 288, six hospitals, in-hospital mortality: 22.0% (64/288)], and the validation cohort [N = 244, six hospitals, in-hospital mortality 27.0% (66/244)]. The C-statistics [95% CI] of the models in validation cohorts were as follows: lasso 0.784 [0.717-0.851] , random forest 0.794[0.735-0.853], gradient boosting tree 0.780 [0.714-0.847], SOFA 0.787 [0.722-0.851], and 5A score 0.750[0.681-0.820]. The calibration plot showed that these models were well calibrated to observed in-hospital mortality. Decision curve analysis indicated that these models obtained clinical net-benefit.ConclusionThis multi-center retrospective cohort study indicated that machine learning-based prediction models could accurately predict in-hospital mortality in validation cohort among the accidental hypothermia patients. These models might be able to support physicians and patient's decision-making. However, the applicability to clinical settings, and the actual clinical utility is still unclear; thus, further prospective study is warranted to evaluate the clinical usefulness.
| S-EPMC7797142 | biostudies-literature
Project description:Most approaches to machine learning from electronic health data can only predict a single endpoint. The ability to simultaneously simulate dozens of patient characteristics is a crucial step towards personalized medicine for Alzheimer's Disease. Here, we use an unsupervised machine learning model called a Conditional Restricted Boltzmann Machine (CRBM) to simulate detailed patient trajectories. We use data comprising 18-month trajectories of 44 clinical variables from 1909 patients with Mild Cognitive Impairment or Alzheimer's Disease to train a model for personalized forecasting of disease progression. We simulate synthetic patient data including the evolution of each sub-component of cognitive exams, laboratory tests, and their associations with baseline clinical characteristics. Synthetic patient data generated by the CRBM accurately reflect the means, standard deviations, and correlations of each variable over time to the extent that synthetic data cannot be distinguished from actual data by a logistic regression. Moreover, our unsupervised model predicts changes in total ADAS-Cog scores with the same accuracy as specifically trained supervised models, additionally capturing the correlation structure in the components of ADAS-Cog, and identifies sub-components associated with word recall as predictive of progression.
| S-EPMC6754403 | biostudies-other
Project description:Importance:Machine learning algorithms could identify patients with cancer who are at risk of short-term mortality. However, it is unclear how different machine learning algorithms compare and whether they could prompt clinicians to have timely conversations about treatment and end-of-life preferences. Objectives:To develop, validate, and compare machine learning algorithms that use structured electronic health record data before a clinic visit to predict mortality among patients with cancer. Design, Setting, and Participants:Cohort study of 26 525 adult patients who had outpatient oncology or hematology/oncology encounters at a large academic cancer center and 10 affiliated community practices between February 1, 2016, and July 1, 2016. Patients were not required to receive cancer-directed treatment. Patients were observed for up to 500 days after the encounter. Data analysis took place between October 1, 2018, and September 1, 2019. Exposures:Logistic regression, gradient boosting, and random forest algorithms. Main Outcomes and Measures:Primary outcome was 180-day mortality from the index encounter; secondary outcome was 500-day mortality from the index encounter. Results:Among 26 525 patients in the analysis, 1065 (4.0%) died within 180 days of the index encounter. Among those who died, the mean age was 67.3 (95% CI, 66.5-68.0) years, and 500 (47.0%) were women. Among those who were alive at 180 days, the mean age was 61.3 (95% CI, 61.1-61.5) years, and 15 922 (62.5%) were women. The population was randomly partitioned into training (18 567 [70.0%]) and validation (7958 [30.0%]) cohorts at the patient level, and a randomly selected encounter was included in either the training or validation set. At a prespecified alert rate of 0.02, positive predictive values were higher for the random forest (51.3%) and gradient boosting (49.4%) algorithms compared with the logistic regression algorithm (44.7%). There was no significant difference in discrimination among the random forest (area under the receiver operating characteristic curve [AUC], 0.88; 95% CI, 0.86-0.89), gradient boosting (AUC, 0.87; 95% CI, 0.85-0.89), and logistic regression (AUC, 0.86; 95% CI, 0.84-0.88) models (P for comparison = .02). In the random forest model, observed 180-day mortality was 51.3% (95% CI, 43.6%-58.8%) in the high-risk group vs 3.4% (95% CI, 3.0%-3.8%) in the low-risk group; at 500 days, observed mortality was 64.4% (95% CI, 56.7%-71.4%) in the high-risk group and 7.6% (7.0%-8.2%) in the low-risk group. In a survey of 15 oncology clinicians with a 52.1% response rate, 100 of 171 patients (58.8%) who had been flagged as having high risk by the gradient boosting algorithm were deemed appropriate for a conversation about treatment and end-of-life preferences in the upcoming week. Conclusions and Relevance:In this cohort study, machine learning algorithms based on structured electronic health record data accurately identified patients with cancer at risk of short-term mortality. When the gradient boosting algorithm was applied in real time, clinicians believed that most patients who had been identified as having high risk were appropriate for a timely conversation about treatment and end-of-life preferences.
| S-EPMC6822091 | biostudies-literature