A methylation study of long-term depression risk.

Recurrent and chronic major depressive disorder (MDD) accounts for a substantial part of the disease burden because this course is most prevalent and typically requires long-term treatment. We associated blood DNA methylation profiles from 581 MDD patients at baseline with MDD status 6 years later. A resampling approach showed a highly significant association between methylation profiles in blood at baseline and future disease status (P = 2.0 × 10-16). Top MWAS results were enriched specific pathways, overlapped with genes found in GWAS of MDD disease status, autoimmune disease and inflammation, and co-localized with eQTLS and (genic enhancers of) of transcription sites in brain and blood. Many of these findings remained significant after correction for multiple testing. The major themes emerging were cellular responses to stress and signaling mechanisms linked to immune cell migration and inflammation. This suggests that an immune signature of treatment-resistant depression is already present at baseline. We also created a methylation risk score (MRS) to predict MDD status 6 years later. The AUC of our MRS was 0.724 and higher than risk scores created using a set of five putative MDD biomarkers, genome-wide SNP data, and 27 clinical, demographic and lifestyle variables. Although further studies are needed to examine the generalizability to different patient populations, these results suggest that methylation profiles in blood may present a promising avenue to support clinical decision making by providing empirical information about the likelihood MDD is chronic or will recur in the future.

Diagnostic Discordance in Pediatric Critical Care Transport: A Single-Center Experience.

OBJECTIVES: The aims of the study were to describe diagnostic discordance rates at our pediatric tertiary care center between the reason for transfer of critically ill/injured children (determined by the referring institution) and the inpatient admission diagnosis (determined by our accepting institution), to identify potential factors associated with discordance, and to determine its impact on patient outcomes. METHODS: We conducted a retrospective chart review of all critically ill/injured children transferred to the Johns Hopkins Children's Center between July 1, 2017, and June 30, 2018. All patients whose initial inpatient disposition was the pediatric intensive care unit were included. RESULTS: Six hundred forty-three children (median age, 51 months) from 57 institutions (median pediatric capability level: 3) met inclusion criteria: 46.8% were transported during nighttime, 86.5% by ground, and 21.2% accompanied by a physician. Nearly half (43.4%) had respiratory admission diagnoses. The rest included surgical/neurosurgical (14.2%), neurologic (11.2%), cardiovascular/shock (8.7%), endocrine (8.2%), infectious disease (6.8%), poisoning (3.1%), hematology-oncology (2.2%), gastrointestinal/metabolic (1.9%), and renal (0.3%). Forty-six (7.2%) had referral-to-admission diagnostic discordance: 25 of 46 had discordance across different diagnostic groups and 21 of 46 had clinically significant discordance within the same diagnostic group. The discordant group had higher need for respiratory support titration in transport (43.9% vs 27.9%, p = 0.02); more invasive procedures and vasopressor needs during the day of admission (26.1% vs 11.6%, P = 0.008; 19.6% vs 7%, P = 0.006); and longer intensive care unit (ICU) and hospital stays (5 vs 2 days; 11 vs 3 days, P < 0.001). When compared with respiratory admission diagnoses, patients with cardiovascular/shock and neurologic diagnoses were more likely to have discordant diagnoses (odds ratio [95% confidence interval], 13.24 [5.41-35.05]; 6.47 [2.48-17.75], P < 0.001). CONCLUSIONS: Seven percent of our critically ill/injured pediatric cohort had clinically significant referral-to-admission diagnostic discordance. Patients with cardiovascular/shock and neurologic diagnoses were particularly at risk. Those with discordant diagnoses had more in-transit events; a higher need for ICU interventions postadmission; and significantly longer ICU stays and hospitalizations, deserving further investigation.

Rates of increase of antibiotic resistance and ambient temperature in Europe: a cross-national analysis of 28 countries between 2000 and 2016.

BackgroundThe rapid increase of bacterial antibiotic resistance could soon render our most effective method to address infections obsolete. Factors influencing pathogen resistance prevalence in human populations remain poorly described, though temperature is known to contribute to mechanisms of spread.AimTo quantify the role of temperature, spatially and temporally, as a mechanistic modulator of transmission of antibiotic resistant microbes.MethodsAn ecologic analysis was performed on country-level antibiotic resistance prevalence in three common bacterial pathogens across 28 European countries, collectively representing over 4 million tested isolates. Associations of minimum temperature and other predictors with change in antibiotic resistance rates over 17 years (2000-2016) were evaluated with multivariable models. The effects of predictors on the antibiotic resistance rate change across geographies were quantified.ResultsDuring 2000-2016, for Escherichia coli and Klebsiella pneumoniae, European countries with 10°C warmer ambient minimum temperatures compared to others, experienced more rapid resistance increases across all antibiotic classes. Increases ranged between 0.33%/year (95% CI: 0.2 to 0.5) and 1.2%/year (95% CI: 0.4 to 1.9), even after accounting for recognised resistance drivers including antibiotic consumption and population density. For Staphylococcus aureus a decreasing relationship of -0.4%/year (95% CI: -0.7 to 0.0) was found for meticillin resistance, reflecting widespread declines in meticillin-resistant S. aureus across Europe over the study period.ConclusionWe found evidence of a long-term effect of ambient minimum temperature on antibiotic resistance rate increases in Europe. Ambient temperature might considerably influence antibiotic resistance growth rates, and explain geographic differences observed in cross-sectional studies. Rising temperatures globally may hasten resistance spread, complicating mitigation efforts.

RaMWAS: fast methylome-wide association study pipeline for enrichment platforms.

Motivation: Enrichment-based technologies can provide measurements of DNA methylation at tens of millions of CpGs for thousands of samples. Existing tools for methylome-wide association studies cannot analyze datasets of this size and lack important features like principal component analysis, combined analysis with SNP data and outcome predictions that are based on all informative methylation sites. Results: We present a Bioconductor R package called RaMWAS with a full set of tools for large-scale methylome-wide association studies. It is free, cross-platform, open source, memory efficient and fast. Availability and implementation: Release version and vignettes with small case study at bioconductor.org/packages/ramwas Development version at github.com/andreyshabalin/ramwas. Supplementary information: Supplementary data are available at Bioinformatics online.

Lessons learned from managing a prospective, private practice joint replacement registry: a 25-year experience.

BACKGROUND: In 1984, we developed a private practice joint replacement registry (JRR) to prospectively follow patients undergoing THA and TKA to assess clinical and radiographic outcomes, complications, and implant survival. Little has been reported in the literature regarding management of this type of database, and it is unclear whether and how the information can be useful for addressing longer-term questions. QUESTIONS/PURPOSES: We answered the following questions: (1) What is the rate of followup for THA and TKA in our JRR? (2) What factors affect followup? (3) How successful is this JRR model in capturing data and what areas of improvement are identified? And (4) what costs are associated with maintaining this JRR? METHODS: We collected clinical data on all 12,047 patients having primary THA and TKA since 1984. Clinical and radiographic data were collected at routine followup intervals and entered into a prospective database. We searched this database to assess the rate of successful followup and data collection and to compare the effect of patient variables on followup. Costs related to database management were evaluated. RESULTS: Followup was poor at every time interval after surgery, with a tendency for worsening over time. Patients with a complication and those younger than 70 years tended to followup with greater frequency. There were difficulties with data capture and substantial expenses related to managing the database. CONCLUSIONS: Our findings highlight the difficulties in managing a JRR. Followup is poor and data collection is often incomplete. Newer technologies that allow easier tracking of patients and facilitate data capture may streamline this process and control costs.

Improved state-level influenza nowcasting in the United States leveraging Internet-based data and network approaches.

In the presence of health threats, precision public health approaches aim to provide targeted, timely, and population-specific interventions. Accurate surveillance methodologies that can estimate infectious disease activity ahead of official healthcare-based reports, at relevant spatial resolutions, are important for achieving this goal. Here we introduce a methodological framework which dynamically combines two distinct influenza tracking techniques, using an ensemble machine learning approach, to achieve improved state-level influenza activity estimates in the United States. The two predictive techniques behind the ensemble utilize (1) a self-correcting statistical method combining influenza-related Google search frequencies, information from electronic health records, and historical flu trends within each state, and (2) a network-based approach leveraging spatio-temporal synchronicities observed in historical influenza activity across states. The ensemble considerably outperforms each component method in addition to previously proposed state-specific methods for influenza tracking, with higher correlations and lower prediction errors.

Meta-omics analysis of elite athletes identifies a performance-enhancing microbe that functions via lactate metabolism.

The human gut microbiome is linked to many states of human health and disease1. The metabolic repertoire of the gut microbiome is vast, but the health implications of these bacterial pathways are poorly understood. In this study, we identify a link between members of the genus Veillonella and exercise performance. We observed an increase in Veillonella relative abundance in marathon runners postmarathon and isolated a strain of Veillonella atypica from stool samples. Inoculation of this strain into mice significantly increased exhaustive treadmill run time. Veillonella utilize lactate as their sole carbon source, which prompted us to perform a shotgun metagenomic analysis in a cohort of elite athletes, finding that every gene in a major pathway metabolizing lactate to propionate is at higher relative abundance postexercise. Using 13C3-labeled lactate in mice, we demonstrate that serum lactate crosses the epithelial barrier into the lumen of the gut. We also show that intrarectal instillation of propionate is sufficient to reproduce the increased treadmill run time performance observed with V. atypica gavage. Taken together, these studies reveal that V. atypica improves run time via its metabolic conversion of exercise-induced lactate into propionate, thereby identifying a natural, microbiome-encoded enzymatic process that enhances athletic performance.

Epigenetic Aging in Major Depressive Disorder.

OBJECTIVE: Major depressive disorder is associated with an increased risk of mortality and aging-related diseases. The authors examined whether major depression is associated with higher epigenetic aging in blood as measured by DNA methylation (DNAm) patterns, whether clinical characteristics of major depression have a further impact on these patterns, and whether the findings replicate in brain tissue. METHOD: DNAm age was estimated using all methylation sites in blood of 811 depressed patients and 319 control subjects with no lifetime psychiatric disorders and low depressive symptoms from the Netherlands Study of Depression and Anxiety. The residuals of the DNAm age estimates regressed on chronological age were calculated to indicate epigenetic aging. Major depression diagnosis and clinical characteristics were assessed with questionnaires and psychiatric interviews. Analyses were adjusted for sociodemographic characteristics, lifestyle, and health status. Postmortem brain samples of 74 depressed patients and 64 control subjects were used for replication. Pathway enrichment analysis was conducted using ConsensusPathDB to gain insight into the biological processes underlying epigenetic aging in blood and brain. RESULTS: Significantly higher epigenetic aging was observed in patients with major depression compared with control subjects (Cohen's d=0.18), with a significant dose effect with increasing symptom severity in the overall sample. In the depression group, epigenetic aging was positively and significantly associated with childhood trauma score. The case-control difference was replicated in an independent data set of postmortem brain samples. The top significantly enriched Gene Ontology terms included neuronal processes. CONCLUSIONS: As compared with control subjects, patients with major depression exhibited higher epigenetic aging in blood and brain tissue, suggesting that they are biologically older than their corresponding chronological age. This effect was even more profound in the presence of childhood trauma.

Correcting for cell-type effects in DNA methylation studies: reference-based method outperforms latent variable approaches in empirical studies.

Based on an extensive simulation study, McGregor and colleagues recently recommended the use of surrogate variable analysis (SVA) to control for the confounding effects of cell-type heterogeneity in DNA methylation association studies in scenarios where no cell-type proportions are available. As their recommendation was mainly based on simulated data, we sought to replicate findings in two large-scale empirical studies. In our empirical data, SVA did not fully correct for cell-type effects, its performance was somewhat unstable, and it carried a risk of missing true signals caused by removing variation that might be linked to actual disease processes. By contrast, a reference-based correction method performed well and did not show these limitations. A disadvantage of this approach is that if reference methylomes are not (publicly) available, they will need to be generated once for a small set of samples. However, given the notable risk we observed for cell-type confounding, we argue that, to avoid introducing false-positive findings into the literature, it could be well worth making this investment.Please see related Correspondence article: https://genomebiology.biomedcentral.com/articles/10/1186/s13059-017-1149-7 and related Research article: https://genomebiology.biomedcentral.com/articles/10.1186/s13059-016-0935-y.