DNA methylation at the suppressor of cytokine signaling 3 (SOCS3) gene influences height in childhood

Human height is strongly influenced by genetics but the contribution of modifiable epigenetic factors is under-explored, particularly in low and middle-income countries (LMIC). We investigate links between blood DNA methylation and child height in four LMIC cohorts (n = 1927) and identify a robust association at three CpGs in the suppressor of cytokine signaling 3 (SOCS3) gene which replicates in a high-income country cohort (n = 879). SOCS3 methylation (SOCS3m)—height associations are independent of genetic effects. Mendelian randomization analysis confirms a causal effect of SOCS3m on height. In longitudinal analysis, SOCS3m explains a maximum 9.5% of height variance in mid-childhood while the variance explained by height polygenic risk score increases from birth to 21 years. Children’s SOCS3m is associated with prenatal maternal folate and socio-economic status. In-vitro characterization confirms a regulatory effect of SOCS3m on gene expression. Our findings suggest epigenetic modifications may play an important role in driving child height in LMIC.</p

DNA methylation signatures associated with cardiometabolic risk factors in children from India and The Gambia: results from the EMPHASIS study.

BACKGROUND: The prevalence of cardiometabolic disease (CMD) is rising globally, with environmentally induced epigenetic changes suggested to play a role. Few studies have investigated epigenetic associations with CMD risk factors in children from low- and middle-income countries. We sought to identify associations between DNA methylation (DNAm) and CMD risk factors in children from India and The Gambia. RESULTS: Using the Illumina Infinium HumanMethylation 850 K Beadchip array, we interrogated DNAm in 293 Gambian (7-9 years) and 698 Indian (5-7 years) children. We identified differentially methylated CpGs (dmCpGs) associated with systolic blood pressure, fasting insulin, triglycerides and LDL-Cholesterol in the Gambian children; and with insulin sensitivity, insulinogenic index and HDL-Cholesterol in the Indian children. There was no overlap of the dmCpGs between the cohorts. Meta-analysis identified dmCpGs associated with insulin secretion and pulse pressure that were different from cohort-specific dmCpGs. Several differentially methylated regions were associated with diastolic blood pressure, insulin sensitivity and fasting glucose, but these did not overlap with the dmCpGs. We identified significant cis-methQTLs at three LDL-Cholesterol-associated dmCpGs in Gambians; however, methylation did not mediate genotype effects on the CMD outcomes. CONCLUSION: This study identified cardiometabolic biomarkers associated with differential DNAm in Indian and Gambian children. Most associations were cohort specific, potentially reflecting environmental and ethnic differences

Protocol for the EMPHASIS study; epigenetic mechanisms linking maternal pre-conceptional nutrition and children's health in India and Sub-Saharan Africa.

BACKGROUND: Animal studies have shown that nutritional exposures during pregnancy can modify epigenetic marks regulating fetal development and susceptibility to later disease, providing a plausible mechanism to explain the developmental origins of health and disease. Human observational studies have shown that maternal peri-conceptional diet predicts DNA methylation in offspring. However, a causal pathway from maternal diet, through changes in DNA methylation, to later health outcomes has yet to be established. The EMPHASIS study (Epigenetic Mechanisms linking Pre-conceptional nutrition and Health Assessed in India and Sub-Saharan Africa, ISRCTN14266771) will investigate epigenetically mediated links between peri-conceptional nutrition and health-related outcomes in children whose mothers participated in two randomized controlled trials of micronutrient supplementation before and during pregnancy. METHODS: The original trials were the Mumbai Maternal Nutrition Project (MMNP, ISRCTN62811278) in which Indian women were offered a daily snack made from micronutrient-rich foods or low-micronutrient foods (controls), and the Peri-conceptional Multiple Micronutrient Supplementation Trial (PMMST, ISRCTN13687662) in rural Gambia, in which women were offered a daily multiple micronutrient (UNIMMAP) tablet or placebo. In the EMPHASIS study, DNA methylation will be analysed in the children of these women (~1,100 children aged 5-7 y in MMNP and 298 children aged 7-9 y in PMMST). Cohort-specific and cross-cohort effects will be explored. Differences in DNA methylation between allocation groups will be identified using the Illumina Infinium MethylationEPIC array, and by pyrosequencing top hits and selected candidate loci. Associations will be analysed between DNA methylation and health-related phenotypic outcomes, including size at birth, and children's post-natal growth, body composition, skeletal development, cardio-metabolic risk markers (blood pressure, serum lipids, plasma glucose and insulin) and cognitive function. Pathways analysis will be used to test for enrichment of nutrition-sensitive loci in biological pathways. Causal mechanisms for nutrition-methylation-phenotype associations will be explored using Mendelian Randomization. Associations between methylation unrelated to supplementation and phenotypes will also be analysed. CONCLUSION: The study will increase understanding of the epigenetic mechanisms underpinning the long-term impact of maternal nutrition on offspring health. It will potentially lead to better nutritional interventions for mothers preparing for pregnancy, and to identification of early life biomarkers of later disease risk

Prevalence, associated factors and outcomes of pressure injuries in adult intensive care unit patients: the DecubICUs study

Funder: European Society of Intensive Care Medicine; doi: http://dx.doi.org/10.13039/501100013347Funder: Flemish Society for Critical Care NursesAbstract: Purpose: Intensive care unit (ICU) patients are particularly susceptible to developing pressure injuries. Epidemiologic data is however unavailable. We aimed to provide an international picture of the extent of pressure injuries and factors associated with ICU-acquired pressure injuries in adult ICU patients. Methods: International 1-day point-prevalence study; follow-up for outcome assessment until hospital discharge (maximum 12 weeks). Factors associated with ICU-acquired pressure injury and hospital mortality were assessed by generalised linear mixed-effects regression analysis. Results: Data from 13,254 patients in 1117 ICUs (90 countries) revealed 6747 pressure injuries; 3997 (59.2%) were ICU-acquired. Overall prevalence was 26.6% (95% confidence interval [CI] 25.9–27.3). ICU-acquired prevalence was 16.2% (95% CI 15.6–16.8). Sacrum (37%) and heels (19.5%) were most affected. Factors independently associated with ICU-acquired pressure injuries were older age, male sex, being underweight, emergency surgery, higher Simplified Acute Physiology Score II, Braden score 3 days, comorbidities (chronic obstructive pulmonary disease, immunodeficiency), organ support (renal replacement, mechanical ventilation on ICU admission), and being in a low or lower-middle income-economy. Gradually increasing associations with mortality were identified for increasing severity of pressure injury: stage I (odds ratio [OR] 1.5; 95% CI 1.2–1.8), stage II (OR 1.6; 95% CI 1.4–1.9), and stage III or worse (OR 2.8; 95% CI 2.3–3.3). Conclusion: Pressure injuries are common in adult ICU patients. ICU-acquired pressure injuries are associated with mainly intrinsic factors and mortality. Optimal care standards, increased awareness, appropriate resource allocation, and further research into optimal prevention are pivotal to tackle this important patient safety threat

Correction to: Prevalence, associated factors and outcomes of pressure injuries in adult intensive care unit patients: the DecubICUs study

The original version of this article unfortunately contained a mistake

Design and Analysis Methods for Modern Biomedical Studies with Longitudinal Outcomes

Electronic health records, existing cohort studies and clinical trials provide easily accessible data on outcome and covariates (e.g., disease status, vital signs) for most or all study subjects. However, many scientific questions require additional collection of costly variables that are the primary exposures of interest (e.g., DNA genotype, gut microbiome). When the exposure of interest is expensive to collect, resource constraints will limit the sample size. In these settings, two-phase outcome dependent sampling (ODS) studies are pragmatic solutions that allow researcher to identify the most informative subjects for expensive exposure ascertainment. In this dissertation, we aim introduce novel two-phase ODS designs and inference procedures for settings where the outcome is collected longitudinally, and an expensive exposure needs to be ascertained retrospectively. In particular, we want to answer the questions on who we should sample for expensive exposure ascertainment, and how we can analyze the data after exposure collection. First, we focus on settings with multivariate longitudinal continuous outcomes and introduce two designs and inference procedures that use different amounts of information to identify the most informative individuals and to estimate the parameters of interest. Importantly, we show how our approaches allow us to perform secondary analysis of data that have been previously collected in a two-phase ODS study. We demonstrate the advantages of the proposed methods through extensive simulations and an application to the Lung Health Study. Second, we focus on longitudinal binary outcomes and introduce a class of residual based designs that identifies informative individuals based on the available outcome and covariates data. We show how these designs can increase efficiency compared to existing two-phase ODS designs and introduce a semi-parametric approach to estimate the parameters of interest. Additionally, we introduce an R package that creates a framework for the design and analysis of two-phase ODS studies with longitudinal binary outcomes. Finally, we consider longitudinal ordinal outcomes and introduce and compare different analysis strategies to study the association between an expensive exposure and mortality in settings where patients who died were oversampled

Instrumental variables selection : a comparison between regularization and post-regularization methods

Instrumental variables are commonly used in statistics, econometrics, and epidemiology to obtain consistent parameter estimates in regression models when some of the predictors are correlated with the error term. However, the properties of these estimators are sensitive to the choice of valid instruments. Since in many applications, valid instruments come in a bigger set that includes also weak and possibly irrelevant instruments, the researcher needs to select a smaller subset of variables that are relevant and strongly correlated with the predictors in the model. This thesis reviews part of the instrumental variables literature, examines the problems caused by having many potential instruments, and uses different variables selection methods in order to identify the relevant instruments. Specifically, the performance of different techniques is compared by looking at the number of relevant variables correctly detected, and at the root mean square error of the regression coefficients’ estimate. Simulation studies are conducted to evaluate the performance of the described methods.Science, Faculty ofStatistics, Department ofGraduat

Design and Analysis Methods for Modern Biomedical Studies with Longitudinal Outcomes

Electronic health records, existing cohort studies and clinical trials provide easily accessible data on outcome and covariates (e.g., disease status, vital signs) for most or all study subjects. However, many scientific questions require additional collection of costly variables that are the primary exposures of interest (e.g., DNA genotype, gut microbiome). When the exposure of interest is expensive to collect, resource constraints will limit the sample size. In these settings, two-phase outcome dependent sampling (ODS) studies are pragmatic solutions that allow researcher to identify the most informative subjects for expensive exposure ascertainment. In this dissertation, we aim introduce novel two-phase ODS designs and inference procedures for settings where the outcome is collected longitudinally, and an expensive exposure needs to be ascertained retrospectively. In particular, we want to answer the questions on who we should sample for expensive exposure ascertainment, and how we can analyze the data after exposure collection. First, we focus on settings with multivariate longitudinal continuous outcomes and introduce two designs and inference procedures that use different amounts of information to identify the most informative individuals and to estimate the parameters of interest. Importantly, we show how our approaches allow us to perform secondary analysis of data that have been previously collected in a two-phase ODS study. We demonstrate the advantages of the proposed methods through extensive simulations and an application to the Lung Health Study. Second, we focus on longitudinal binary outcomes and introduce a class of residual based designs that identifies informative individuals based on the available outcome and covariates data. We show how these designs can increase efficiency compared to existing two-phase ODS designs and introduce a semi-parametric approach to estimate the parameters of interest. Additionally, we introduce an R package that creates a framework for the design and analysis of two-phase ODS studies with longitudinal binary outcomes. Finally, we consider longitudinal ordinal outcomes and introduce and compare different analysis strategies to study the association between an expensive exposure and mortality in settings where patients who died were oversampled

Should We Implement Geographic or Patient-Reported Social Determinants of Health Measures in Cardiovascular Patients?

Objectives: To compare patient-reported social determinants of health (SDOH) to the Brokamp Area Deprivation Index (ADI), and evaluate the association of patient-reported SDOH and ADI with mortality in patients with cardiovascular disease (CVD).Design: Prospective cohort.Setting: Academic medical center.Participants: Adults with acute coronary syndrome (ACS) and/or acute exacerbation of heart failure (HF) hospitalized between 2011 and 2015.Methods: Patient-reported SDOH included: income range, education, health insurance, and household size. ADI was calculated using census tract level variables of poverty, median income, high school completion, lack of health insurance, assisted income, and vacant housing.Primary outcome: All-cause mortality, up to 5 years follow-up.Results: The sample was 60% male, 84% White, and 93% insured; mean patient-reported household income was $48,000 (SD$34,000). ADI components were significantly associated with corresponding patient-report­ed variables. In age, sex, and race adjusted Cox regression models, ADI was associated with mortality for ACS (HR 1.23, 95% CI 1.06, 1.42), but not HF (HR 1.09, 95% CI .99, 1.21). Mortality models for ACS improved with consideration of social determinants data (C-statistics: base demographic model=.612; ADI added=.644; patient-reported SDOH added=.675; both ADI and patient-reported SDOH added=.689). HF mortality models improved only slightly (C-statistics: .600, .602, .617, .620, respectively).Conclusions: The Brokamp ADI is associated with mortality in hospitalized patients with CVD. In the absence of available patient-reported data, hospitals could implement the Brokamp ADI as an approximation for patient-reported data to enhance risk stratification of patients with CVD. Ethn Dis. 2021;31(1):9-22; doi:10.18865/ed.31.1.9</jats:p

Getting started with tables

BACKGROUND: Tables are often overlooked by many readers of papers who tend to focus on the text. Good tables tell much of the story of a paper and give a richer insight into the details of the study participants and the main research findings. Being confident in reading tables and constructing clear tables are important skills for researchers to master. METHOD: Common forms of tables were considered, along with the standard statistics used in them. Papers in the Archives of Public Health published during 2015 and 2016 were hand-searched for examples to illustrate the points being made. Presentation of graphs and figures were not considered as they are outside the scope of the paper. RESULTS: Basic statistical concepts are outlined to aid understanding of each of the tables presented. The first table in many papers gives an overview of the study population and its characteristics, usually giving numbers and percentages of the study population in different categories (e.g. by sex, educational attainment, smoking status) and summaries of measured characteristics (continuous variables) of the participants (e.g. age, height, body mass index). Tables giving the results of the analyses follow; these often include summaries of characteristics in different groups of participants, as well as relationships between the outcome under study and the exposure of interest. For continuous outcome data, results are often expressed as differences between means, or regression or correlation coefficients. Ratio/relative measures (e.g. relative risks, odds ratios) are usually used for binary outcome measures that take one of two values for each study participants (e.g. dead versus alive, obese versus non-obese). Tables come in many forms, but various standard types are described here. CONCLUSION: Clear tables provide much of the important detail in a paper and researchers are encouraged to read and construct them with care