Arsenic Exposure, Blood DNA Methylation, and Cardiovascular Disease.

BACKGROUND: Epigenetic dysregulation has been proposed as a key mechanism for arsenic-related cardiovascular disease (CVD). We evaluated differentially methylated positions (DMPs) as potential mediators on the association between arsenic and CVD. METHODS: Blood DNA methylation was measured in 2321 participants (mean age 56.2, 58.6% women) of the Strong Heart Study, a prospective cohort of American Indians. Urinary arsenic species were measured using high-performance liquid chromatography coupled to inductively coupled plasma mass spectrometry. We identified DMPs that are potential mediators between arsenic and CVD. In a cross-species analysis, we compared those DMPs with differential liver DNA methylation following early-life arsenic exposure in the apoE knockout (apoE-/-) mouse model of atherosclerosis. RESULTS: A total of 20 and 13 DMPs were potential mediators for CVD incidence and mortality, respectively, several of them annotated to genes related to diabetes. Eleven of these DMPs were similarly associated with incident CVD in 3 diverse prospective cohorts (Framingham Heart Study, Women's Health Initiative, and Multi-Ethnic Study of Atherosclerosis). In the mouse model, differentially methylated regions in 20 of those genes and DMPs in 10 genes were associated with arsenic. CONCLUSIONS: Differential DNA methylation might be part of the biological link between arsenic and CVD. The gene functions suggest that diabetes might represent a relevant mechanism for arsenic-related cardiovascular risk in populations with a high burden of diabetes.

Addressing dispersion in mis-measured multivariate binomial outcomes: A novel statistical approach for detecting differentially methylated regions in bisulfite sequencing data.

Motivated by a DNA methylation application, this article addresses the problem of fitting and inferring a multivariate binomial regression model for outcomes that are contaminated by errors and exhibit extra-parametric variations, also known as dispersion. While dispersion in univariate binomial regression has been extensively studied, addressing dispersion in the context of multivariate outcomes remains a complex and relatively unexplored task. The complexity arises from a noteworthy data characteristic observed in our motivating dataset: non-constant yet correlated dispersion across outcomes. To address this challenge and account for possible measurement error, we propose a novel hierarchical quasi-binomial varying coefficient mixed model, which enables flexible dispersion patterns through a combination of additive and multiplicative dispersion components. To maximize the Laplace-approximated quasi-likelihood of our model, we further develop a specialized two-stage expectation-maximization (EM) algorithm, where a plug-in estimate for the multiplicative scale parameter enhances the speed and stability of the EM iterations. Simulations demonstrated that our approach yields accurate inference for smooth covariate effects and exhibits excellent power in detecting non-zero effects. Additionally, we applied our proposed method to investigate the association between DNA methylation, measured across the genome through targeted custom capture sequencing of whole blood, and levels of anti-citrullinated protein antibodies (ACPA), a preclinical marker for rheumatoid arthritis (RA) risk. Our analysis revealed 23 significant genes that potentially contribute to ACPA-related differential methylation, highlighting the relevance of cell signaling and collagen metabolism in RA. We implemented our method in the R Bioconductor package called "SOMNiBUS."

SHLD2/FAM35A co-operates with REV7 to coordinate DNA double-strand break repair pathway choice.

DNA double-strand breaks (DSBs) can be repaired by two major pathways: non-homologous end-joining (NHEJ) and homologous recombination (HR). DNA repair pathway choice is governed by the opposing activities of 53BP1, in complex with its effectors RIF1 and REV7, and BRCA1. However, it remains unknown how the 53BP1/RIF1/REV7 complex stimulates NHEJ and restricts HR to the S/G2 phases of the cell cycle. Using a mass spectrometry (MS)-based approach, we identify 11 high-confidence REV7 interactors and elucidate the role of SHLD2 (previously annotated as FAM35A and RINN2) as an effector of REV7 in the NHEJ pathway. FAM35A depletion impairs NHEJ-mediated DNA repair and compromises antibody diversification by class switch recombination (CSR) in B cells. FAM35A accumulates at DSBs in a 53BP1-, RIF1-, and REV7-dependent manner and antagonizes HR by limiting DNA end resection. In fact, FAM35A is part of a larger complex composed of REV7 and SHLD1 (previously annotated as C20orf196 and RINN3), which promotes NHEJ and limits HR Together, these results establish SHLD2 as a novel effector of REV7 in controlling the decision-making process during DSB repair.

A novel statistical method for modeling covariate effects in bisulfite sequencing derived measures of DNA methylation.

Identifying disease-associated changes in DNA methylation can help us gain a better understanding of disease etiology. Bisulfite sequencing allows the generation of high-throughput methylation profiles at single-base resolution of DNA. However, optimally modeling and analyzing these sparse and discrete sequencing data is still very challenging due to variable read depth, missing data patterns, long-range correlations, data errors, and confounding from cell type mixtures. We propose a regression-based hierarchical model that allows covariate effects to vary smoothly along genomic positions and we have built a specialized EM algorithm, which explicitly allows for experimental errors and cell type mixtures, to make inference about smooth covariate effects in the model. Simulations show that the proposed method provides accurate estimates of covariate effects and captures the major underlying methylation patterns with excellent power. We also apply our method to analyze data from rheumatoid arthritis patients and controls. The method has been implemented in R package SOMNiBUS.

Risk tolerance in adjuvant and metastatic melanoma settings: a patient perspective study using the threshold technique.

Background: Adverse events (e.g., pyrexia) may affect treatment patterns and adherence. This study explored pyrexia risk tolerance among melanoma patients when treatment benefit is unknown versus known. Materials & methods: US respondents with stage III (n = 100) or stage III unresectable/stage IV melanoma (n = 125) chose between hypothetical melanoma treatments, defined by reoccurrence/progression-free survival and pyrexia risk, one resembling standard-of-care and one resembling dabrafenib + trametinib. Respondents chose first when efficacy was unknown and then when efficacy was known; pyrexia risk was varied systematically to define maximum acceptable risk. Results: Maximum acceptable risk of pyrexia was statistically significantly higher when efficacy was known versus unknown in stage III patients (85 vs 34%) and stage III unresectable/stage IV patients (66 vs 57%). Conclusion: Patients accepted higher levels of pyrexia risk when they understood treatment benefit.

Distinct homologous recombination gene expression profiles after neoadjuvant chemotherapy associated with clinical outcome in patients with ovarian cancer.

OBJECTIVE: The expression of homologous recombination (HR) genes in high grade ovarian cancer (HGOC) samples from debulking surgeries were correlated to outcomes in patients selected for chemotherapy treatment regimens. STUDY DESIGN: RNA was extracted from 96 fresh frozen tumor samples from debulking surgeries from chemotherapy naïve patients with HGOC (primary derived surgeries (PDS), n = 55) or following neoadjuvant chemotherapy treatment (NACT), n = 41). The samples were selected for high tumor content by a gynecological pathologist, and cancer cell content was further confirmed using a percent tumor content covariate, and mutation score covariate analysis. Gene expression analysis was performed using a tailored NanoString-based Pancancer Pathway Panel. Cox proportional hazard regression models were used to assess the associations between the expression of 19 HR genes and survival. RESULTS: In the PDS group, over-expression of six HR genes (C11orf30, NBN, FANCF, FANCC, FANCB, RAD50) was associated with improved outcome, in contrast to the NACT group where four HR genes (BRCA2, TP53, FANCB, RAD51) were associated with worse outcome. With the adding extent of debulking as a covariate, three HR genes (NBN, FANCF, RAD50), and only one HR gene (RAD51) remained significantly associated with survival in PDS and NACT groups, respectively. CONCLUSION: Distinct HR expression profiles define subgroups associated with overall outcome in patients that are exposed to neoadjuvant chemotherapy and not only chemotherapy-naïve patients.

Gene networks show associations with seed region connectivity.

Primary patterns in adult brain connectivity are established during development by coordinated networks of transiently expressed genes; however, neural networks remain malleable throughout life. The present study hypothesizes that structural connectivity from key seed regions may induce effects on their connected targets, which are reflected in gene expression at those targeted regions. To test this hypothesis, analyses were performed on data from two brains from the Allen Human Brain Atlas, for which both gene expression and DW-MRI were available. Structural connectivity was estimated from the DW-MRI data and an approach motivated by network topology, that is, weighted gene coexpression network analysis (WGCNA), was used to cluster genes with similar patterns of expression across the brain. Group exponential lasso models were then used to predict gene cluster expression summaries as a function of seed region structural connectivity patterns. In several gene clusters, brain regions located in the brain stem, diencephalon, and hippocampal formation were identified that have significant predictive power for these expression summaries. These connectivity-associated clusters are enriched in genes associated with synaptic signaling and brain plasticity. Furthermore, using seed region based connectivity provides a novel perspective in understanding relationships between gene expression and connectivity. Hum Brain Mapp 38:3126-3140, 2017. © 2017 Wiley Periodicals, Inc.

funtooNorm: an R package for normalization of DNA methylation data when there are multiple cell or tissue types.

MOTIVATION: DNA methylation patterns are well known to vary substantially across cell types or tissues. Hence, existing normalization methods may not be optimal if they do not take this into account. We therefore present a new R package for normalization of data from the Illumina Infinium Human Methylation450 BeadChip (Illumina 450 K) built on the concepts in the recently published funNorm method, and introducing cell-type or tissue-type flexibility. RESULTS: funtooNorm is relevant for data sets containing samples from two or more cell or tissue types. A visual display of cross-validated errors informs the choice of the optimal number of components in the normalization. Benefits of cell (tissue)-specific normalization are demonstrated in three data sets. Improvement can be substantial; it is strikingly better on chromosome X, where methylation patterns have unique inter-tissue variability. AVAILABILITY AND IMPLEMENTATION: An R package is available at https://github.com/GreenwoodLab/funtooNorm, and has been submitted to Bioconductor at http://bioconductor.org.

Chromosome-breakage genomic instability and chromothripsis in breast cancer.

BACKGROUND: Chromosomal breakage followed by faulty DNA repair leads to gene amplifications and deletions in cancers. However, the mere assessment of the extent of genomic changes, amplifications and deletions may reduce the complexity of genomic data observed by array comparative genomic hybridization (array CGH). We present here a novel approach to array CGH data analysis, which focuses on putative breakpoints responsible for rearrangements within the genome. RESULTS: We performed array comparative genomic hybridization in 29 primary tumors from high risk patients with breast cancer. The specimens were flow sorted according to ploidy to increase tumor cell purity prior to array CGH. We describe the number of chromosomal breaks as well as the patterns of breaks on individual chromosomes in each tumor. There were differences in chromosomal breakage patterns between the 3 clinical subtypes of breast cancers, although the highest density of breaks occurred at chromosome 17 in all subtypes, suggesting a particular proclivity of this chromosome for breaks. We also observed chromothripsis affecting various chromosomes in 41% of high risk breast cancers. CONCLUSIONS: Our results provide a new insight into the genomic complexity of breast cancer. Genomic instability dependent on chromosomal breakage events is not stochastic, targeting some chromosomes clearly more than others. We report a much higher percentage of chromothripsis than described previously in other cancers and this suggests that massive genomic rearrangements occurring in a single catastrophic event may shape many breast cancer genomes.

Cohort profile: Greifswald approach to individualized medicine (GANI_MED).

BACKGROUND: Individualized Medicine aims at providing optimal treatment for an individual patient at a given time based on his specific genetic and molecular characteristics. This requires excellent clinical stratification of patients as well as the availability of genomic data and biomarkers as prerequisites for the development of novel diagnostic tools and therapeutic strategies. The University Medicine Greifswald, Germany, has launched the "Greifswald Approach to Individualized Medicine" (GANI_MED) project to address major challenges of Individualized Medicine. Herein, we describe the implementation of the scientific and clinical infrastructure that allows future translation of findings relevant to Individualized Medicine into clinical practice. METHODS/DESIGN: Clinical patient cohorts (N > 5,000) with an emphasis on metabolic and cardiovascular diseases are being established following a standardized protocol for the assessment of medical history, laboratory biomarkers, and the collection of various biosamples for bio-banking purposes. A multi-omics based biomarker assessment including genome-wide genotyping, transcriptome, metabolome, and proteome analyses complements the multi-level approach of GANI_MED. Comparisons with the general background population as characterized by our Study of Health in Pomerania (SHIP) are performed. A central data management structure has been implemented to capture and integrate all relevant clinical data for research purposes. Ethical research projects on informed consent procedures, reporting of incidental findings, and economic evaluations were launched in parallel.

STAT6 mutations enriched at diffuse large B-cell lymphoma relapse reshape the tumor microenvironment.

Diffuse large B-cell lymphoma (DLBCL) relapses in approximately 40% of patients following frontline therapy. We reported that STAT6D419 mutations are enriched in relapsed/refractory DLBCL (rrDLBCL) samples, suggesting that JAK/STAT signaling plays a role in therapeutic resistance. We hypothesized that STAT6D419 mutations can improve DLBCL cell survival by reprogramming the microenvironment to sustain STAT6 activation. Thus, we investigated the role of STAT6D419 mutations on DLBCL cell growth and its microenvironment. We found that phospho-STAT6D419N was retained in the nucleus longer than phospho-STAT6WT following IL-4 stimulation, and STAT6D419N recognized a more restricted DNA-consensus sequence than STAT6WT. Upon IL-4 induction, STAT6D419N expression led to a higher magnitude of gene expression changes, but in a more selective list of gene targets compared with STATWT. The most significantly expressed genes induced by STAT6D419N were those implicated in survival, proliferation, migration, and chemotaxis, in particular CCL17. This chemokine, also known as TARC, attracts helper T-cells to the tumor microenvironment, especially in Hodgkin's lymphoma. To this end, in DLBCL, phospho-STAT6+ rrDLBCL cells had a greater proportion of infiltrating CD4+ T-cells than phospho-STAT6- tumors. Our findings suggest that STAT6D419 mutations in DLBCL lead to cell autonomous changes, enhanced signaling, and altered composition of the tumor microenvironment.

A model for successful use of student response systems.

AIM: This article presents a model developed to assist teachers in selecting, implementing, and assessing student response system (SRS) use in the classroom. BACKGROUND: Research indicates that SRS technology is effective in achieving desired outcomes in higher education settings. Studies indicate that effective SRS use promotes greater achievement of learning outcomes, increased student attention, improved class participation, and active engagement. METHOD: The model offered in this article is based on best practices described in the literature and several years of SRS use in a traditional higher education classroom setting. RESULTS: Student feedback indicates increased class participation and engagement with SRS technology. Teacher feedback indicates opportunities for contingent teaching. CONCLUSIONS: The model described in this article provides a process to assist teachers in the successful selection, implementation, and assessment of SRS technology in the classroom.

Patient preferences for features of HER2-targeted treatment of advanced or metastatic breast cancer: a discrete-choice experiment study.

BACKGROUND: We aimed to quantify patients' benefit-risk preferences for attributes associated with human epidermal growth factor receptor 2 (HER2)-targeted breast cancer treatments and estimate minimum acceptable benefits (MABs), denominated in additional months of progression-free survival (PFS), for given treatment-related adverse events (AEs). METHODS: We conducted an online discrete-choice experiment (DCE) among patients with self-reported advanced/metastatic breast cancer in the United States, United Kingdom, and Japan (N = 302). In a series of nine DCE questions, respondents chose between two hypothetical treatment profiles created by an experimental design. Profiles were defined by six attributes with varying levels: PFS, nausea/vomiting, diarrhea, liver function problems, risk of heart failure, and risk of serious lung damage and infections. Data were analyzed using an error component random-parameters logit model. RESULTS: Among the attributes, patients placed the most importance on a change in PFS from 5 to 26 months; change from no diarrhea to severe diarrhea was the least important. Avoiding a 15% risk of heart failure had the largest MAB (5.8 additional months of PFS), followed by avoiding a 15% risk of serious lung damage and infections (4.6 months), possible severe liver function problems (4.2 months), severe nausea/vomiting (3.7 months), and severe diarrhea (2.3 months) compared with having none of the AEs. The relative importance of 21 additional months of PFS (increasing from 5 to 26 months) increased for women with HER2-negative disease and those with children. CONCLUSIONS: Patients valued PFS gain higher than the potential risk of AEs when deciding between hypothetical breast cancer treatments.

Sperm DNA methylome abnormalities occur both pre- and post-treatment in men with Hodgkin disease and testicular cancer.

BACKGROUND: Combination chemotherapy has contributed to increased survival from Hodgkin disease (HD) and testicular cancer (TC). However, questions concerning the quality of spermatozoa after treatment have arisen. While studies have shown evidence of DNA damage and aneuploidy in spermatozoa years following anticancer treatment, the sperm epigenome has received little attention. Our objectives here were to determine the impact of HD and TC, as well as their treatments, on sperm DNA methylation. Semen samples were collected from community controls (CC) and from men undergoing treatment for HD or TC, both before initiation of chemotherapy and at multiple times post-treatment. Sperm DNA methylation was assessed using genome-wide and locus-specific approaches. RESULTS: Imprinted gene methylation was not affected in the sperm of HD or TC men, before or after treatment. Prior to treatment, using Illumina HumanMethylation450 BeadChip (450 K) arrays, a subset of 500 probes was able to distinguish sperm samples from TC, HD and CC subjects; differences between groups persisted post-treatment. Comparing altered sperm methylation between HD or TC patients versus CC men, twice as many sites were affected in TC versus HD men; for both groups, the most affected CpGs were hypomethylated. For TC patients, the promoter region of GDF2 contained the largest region of differential methylation. To assess alterations in DNA methylation over time/post-chemotherapy, serial samples from individual patients were compared. With restriction landmark genome scanning and 450 K array analyses, some patients who underwent chemotherapy showed increased alterations in DNA methylation, up to 2 to 3 years post-treatment, when compared to the CC cohort. Similarly, a higher-resolution human sperm-specific assay that includes assessment of environmentally sensitive regions, or "dynamic sites," also demonstrated persistently altered sperm DNA methylation in cancer patients post-treatment and suggested preferential susceptibility of "dynamic" CpG sites. CONCLUSIONS: Distinct sperm DNA methylation signatures were present pre-treatment in men with HD and TC and may help explain increases in birth defects reported in recent clinical studies. Epigenetic defects in spermatozoa of some cancer survivors were evident even up to 2 years post-treatment. Abnormalities in the sperm epigenome both pre- and post-chemotherapy may contribute to detrimental effects on future reproductive health.

Novel insights into systemic sclerosis using a sensitive computational method to analyze whole-genome bisulfite sequencing data.

BACKGROUND: Abnormal DNA methylation is thought to contribute to the onset and progression of systemic sclerosis. Currently, the most comprehensive assay for profiling DNA methylation is whole-genome bisulfite sequencing (WGBS), but its precision depends on read depth and it may be subject to sequencing errors. SOMNiBUS, a method for regional analysis, attempts to overcome some of these limitations. Using SOMNiBUS, we re-analyzed WGBS data previously analyzed using bumphunter, an approach that initially fits single CpG associations, to contrast DNA methylation estimates by both methods. METHODS: Purified CD4+ T lymphocytes of 9 SSc and 4 control females were sequenced using WGBS. We separated the resulting sequencing data into regions with dense CpG data, and differentially methylated regions (DMRs) were inferred with the SOMNiBUS region-level test, adjusted for age. Pathway enrichment analysis was performed with ingenuity pathway analysis (IPA). We compared the results obtained by SOMNiBUS and bumphunter. RESULTS: Of 8268 CpG regions of ≥ 60 CpGs eligible for analysis with SOMNiBUS, we identified 131 DMRs and 125 differentially methylated genes (DMGs; p-values less than Bonferroni-corrected threshold of 6.05-06 controlling family-wise error rate at 0.05; 1.6% of the regions). In comparison, bumphunter identified 821,929 CpG regions, 599 DMRs (of which none had ≥ 60 CpGs) and 340 DMGs (q-value of 0.05; 0.04% of all regions). The top ranked gene identified by SOMNiBUS was FLT4, a lymphangiogenic orchestrator, and the top ranked gene on chromosome X was CHST7, known to catalyze the sulfation of glycosaminoglycans in the extracellular matrix. The top networks identified by IPA included connective tissue disorders. CONCLUSIONS: SOMNiBUS is a complementary method of analyzing WGBS data that enhances biological insights into SSc and provides novel avenues of investigation into its pathogenesis.

GD2 and GD3 gangliosides as diagnostic biomarkers for all stages and subtypes of epithelial ovarian cancer.

Background: Ovarian cancer (OC) is the deadliest gynecological cancer, often diagnosed at advanced stages. A fast and accurate diagnostic method for early-stage OC is needed. The tumor marker gangliosides, GD2 and GD3, exhibit properties that make them ideal potential diagnostic biomarkers, but they have never before been quantified in OC. We investigated the diagnostic utility of GD2 and GD3 for diagnosis of all subtypes and stages of OC. Methods: This retrospective study evaluated GD2 and GD3 expression in biobanked tissue and serum samples from patients with invasive epithelial OC, healthy donors, non-malignant gynecological conditions, and other cancers. GD2 and GD3 levels were evaluated in tissue samples by immunohistochemistry (n=299) and in two cohorts of serum samples by quantitative ELISA. A discovery cohort (n=379) showed feasibility of GD2 and GD3 quantitative ELISA for diagnosing OC, and a subsequent model cohort (n=200) was used to train and cross-validate a diagnostic model. Results: GD2 and GD3 were expressed in tissues of all OC subtypes and FIGO stages but not in surrounding healthy tissue or other controls. In serum, GD2 and GD3 were elevated in patients with OC. A diagnostic model that included serum levels of GD2+GD3+age was superior to the standard of care (CA125, p<0.001) in diagnosing OC and early-stage (I/II) OC. Conclusion: GD2 and GD3 expression was associated with high rates of selectivity and specificity for OC. A diagnostic model combining GD2 and GD3 quantification in serum had diagnostic power for all subtypes and all stages of OC, including early stage. Further research exploring the utility of GD2 and GD3 for diagnosis of OC is warranted.

Impact of clinical and demographic characteristics on patient preferences for psoriasis treatment features: Results from a discrete-choice experiment in a multicountry study.

OBJECTIVES: This study aimed to elicit preferences for psoriasis treatment features and to test for preference heterogeneity across groups of respondents. MATERIALS AND METHODS: A discrete-choice experiment was employed to elicit preferences of patients with plaque psoriasis in multiple countries. The survey instrument included a series of choice questions between three hypothetical treatments, each characterized by varying levels of six attributes (namely, lesion reduction, risk of impairing side effects, time to reach results, mode and frequency of administration, itching reduction, and side effects). Random parameters logit was used to model the data. Results were compared across a total of 18 subgroup sets. RESULTS: The data analysis from 1,123 respondents showed that, on average, respondents receive more utility gain from higher levels of lesion reduction and lower risks of impairing side effects than changes in other attributes included in the study. Systematic differences were detected for 13 sets; the most pronounced differences were observed based on disease severity, nail psoriasis, biologic experience, and quality-of-life scores. CONCLUSION: These many sources of preference heterogeneity identified by our analysis suggest that to improve patient satisfaction and, probably, adherence and persistence, clinicians should discuss options with patients when prescribing their treatment.

Exploring patient preference heterogeneity for pharmacological treatments for chronic pain: A latent class analysis.

BACKGROUND: Several pharmaceutical treatments for chronic pain caused by osteoarthritis (OA) and chronic low back pain (CLBP) are available or currently under development, each associated with different adverse events (AEs) and efficacy profiles. It is therefore important to understand what trade-offs patients are willing to make when choosing between treatments. METHODS: A discrete-choice experiment (DCE) was conducted with 437 adults with chronic pain caused by OA and/or CLBP. Respondents were presented with a series of scenarios and asked to choose between pairs of hypothetical treatments, each defined by six attributes: level of symptom control; risks of heart attack, rapidly progressive osteoarthritis and dependency; frequency and mode of administration and cost. Attributes were based on known profiles of oral nonsteroidal anti-inflammatory drugs, opioids and injected nerve growth factor inhibitors, the last of which were under clinical development at the time of the study. Data were analysed using a latent class (LC) model to explore preference heterogeneity. RESULTS: Overall, respondents considered improving symptom control and reducing risk of physical dependency to be the most important attributes. The LC analysis identified four participant classes: an 'efficacy-focused' class (33.7%), a 'cost-averse' class (29.4%), a 'physical-dependence-averse' class (19.6%) and a 'needle-averse' class (17.3%). Subgroup membership was incompletely predicted by participant age and their responses to comprehension questions. CONCLUSIONS: Preference heterogeneity across respondents indicates a need for a personalized approach to offering treatment options. Symptom improvement, cost, physical dependence and route of administration might be important to different patients. SIGNIFICANCE: Multiple treatment options that differ substantially in terms of efficacy and adverse events are available for the management of chronic pain. With a growing emphasis on a patient-centred care model that incorporates patients' priorities and values into treatment decisions, there is a need to understand how individuals with chronic musculoskeletal pain balance the benefits and risks of treatment and how treatment priorities vary among individuals. This study was designed to identify patient preferences for different characteristics of treatments for the management of chronic pain and to investigate how preferences differ among respondents.

High Systolic Blood Pressure at Hospital Admission Is an Important Risk Factor in Models Predicting Outcome of COVID-19 Patients.

BACKGROUND: The risk that coronavirus disease 2019 (COVID-19) patients develop critical illness that can be fatal depends on their age and immune status and may also be affected by comorbidities like hypertension. The goal of this study was to develop models that predict outcome using parameters collected at admission to the hospital. METHODS AND RESULTS: This is a retrospective single-center cohort study of COVID-19 patients at the Seventh Hospital of Wuhan City, China. Forty-three demographic, clinical, and laboratory parameters collected at admission plus discharge/death status, days from COVID-19 symptoms onset, and days of hospitalization were analyzed. From 157 patients, 120 were discharged and 37 died. Pearson correlations showed that hypertension and systolic blood pressure (SBP) were associated with death and respiratory distress parameters. A penalized logistic regression model efficiently predicts the probability of death with 13 of 43 variables. A regularized Cox regression model predicts the probability of survival with 7 of above 13 variables. SBP but not hypertension was a covariate in both mortality and survival prediction models. SBP was elevated in deceased compared with discharged COVID-19 patients. CONCLUSIONS: Using an unbiased approach, we developed models predicting outcome of COVID-19 patients based on data available at hospital admission. This can contribute to evidence-based risk prediction and appropriate decision-making at hospital triage to provide the most appropriate care and ensure the best patient outcome. High SBP, a cause of end-organ damage and an important comorbid factor, was identified as a covariate in both mortality and survival prediction models.

Thousands of CpGs Show DNA Methylation Differences in ACPA-Positive Individuals.

High levels of anti-citrullinated protein antibodies (ACPA) are often observed prior to a diagnosis of rheumatoid arthritis (RA). We undertook a replication study to confirm CpG sites showing evidence of differential methylation in subjects positive vs. negative for ACPA, in a new subset of 112 individuals sampled from the population cohort and biobank CARTaGENE in Quebec, Canada. Targeted custom capture bisulfite sequencing was conducted at approximately 5.3 million CpGs located in regulatory or hypomethylated regions from whole blood; library and protocol improvements had been instituted between the original and this replication study, enabling better coverage and additional identification of differentially methylated regions (DMRs). Using binomial regression models, we identified 19,472 ACPA-associated differentially methylated cytosines (DMCs), of which 430 overlapped with the 1909 DMCs reported by the original study; 814 DMRs of relevance were clustered by grouping adjacent DMCs into regions. Furthermore, we performed an additional integrative analysis by looking at the DMRs that overlap with RA related loci published in the GWAS Catalog, and protein-coding genes associated with these DMRs were enriched in the biological process of cell adhesion and involved in immune-related pathways.