<p>Immune-mediated thrombotic thrombocytopenic purpura (iTTP) is characterized by recurring episodes of thrombotic microangiopathy causing ischemic organ impairment. Blacks are overrepresented in iTTP cohorts in the United States but racial disparities in iTTP outcomes and response to therapy have not been studied. Using the United States Thrombotic Microangiopathy (USTMA) Consortium iTTP Registry, we evaluated the impact of race on mortality and relapse-free survival (RFS) in confirmed iTTP in the US from 1995 to 2020. We separately examined the impact of rituximab therapy and presentation with newly diagnosed (de novo) or relapsed iTTP on RFS by race. 645 participants with 1308 iTTP episodes were available for analysis. Acute iTTP mortality did not differ by race. When all episodes of iTTP were included, Black race was associated with shorter RFS [hazard ratio (HR) of 1.60 (95% CI 1.16-2.21)]; the addition of rituximab to corticosteroids improved RFS in White [HR 0.37 (95% CI 0.18-0.73)] but not Black patients [HR 0.96 (95% CI 0.71-1.31)]. In de novo iTTP, rituximab delayed relapse, but Blacks had shorter RFS than Whites regardless of treatment. In relapsed iTTP, rituximab significantly improved RFS in White but not Black patients. Race affects overall relapse risk and response to rituximab in iTTP. Black patients may require closer monitoring, earlier retreatment, and alternative immunosuppression following rituximab treatment. How race, racism, and the social determinants of health contribute to the disparity in relapse risk in iTTP deserve further study.</p>

Tobacco and alcohol use are heritable behaviours associated with 15% and 5.3% of worldwide deaths, respectively, due largely to broad increased risk for disease and injury1–4. These substances are used across the globe, yet genome-wide association studies have focused largely on individuals of European ancestries5. Here we leveraged global genetic diversity across 3.4 million individuals from four major clines of global ancestry (approximately 21% non-European) to power the discovery and fine-mapping of genomic loci associated with tobacco and alcohol use, to inform function of these loci via ancestry-aware transcriptome-wide association studies, and to evaluate the genetic architecture and predictive power of polygenic risk within and across populations. We found that increases in sample size and genetic diversity improved locus identification and fine-mapping resolution, and that a large majority of the 3,823 associated variants (from 2,143 loci) showed consistent effect sizes across ancestry dimensions. However, polygenic risk scores developed in one ancestry performed poorly in others, highlighting the continued need to increase sample sizes of diverse ancestries to realize any potential benefit of polygenic prediction. A multi-ancestry meta-regression study analyses diverse genome-wide association studies and genome loci associated with tobacco and alcohol use.

Introduction: Obstructive sleep apnea (OSA) is a common disorder associated with increased risk for cardiovascular disease, diabetes, and premature mortality. There is strong clinical and epi-demio...

Investigations of networked public spheres often examine the structures of online platforms by studying users' interactions. These works suggest that users' interactions can lead to cyberbalkani-zation when interlocutors form homophilous communities that typically have few connections to others with opposing ideologies. Yet, rather than assuming communities are isolated, this study examines community-level interactions to reveal how communities in online discourses are more interdependent than previously theorized. Specifically, we examine how such interactions influence the evolution of topics overtime in source and target communities. Our analysis found that (a) the size of a source community (the community that initiates interactions) and a target community (the community that receives interactions), (b) the stability of the source community, and (c) the volume of mentions from a source community to a target community predicts the level of influence one community has on another's discussion topics. We argue this has significant theoretical and practical implications. Lay Summary Political discussions online, especially those in the United States, seem to range between harmonious discussions of likeminded people and heated debates that end with few, if any, who have changed their minds. Researchers have often examined these balkanized/polarized situations by studying online communities as isolated echo chambers of opinion. Our study focuses on the interactions between online communities who have different worldviews. We examine communities engaged in the global refugee crisis. We consider how the inter-community interactions influence the agenda of the respective communities. Our longitudinal analysis on the one hand

Parent-child similarity is a function of genetic and environmental transmission. In addition, genetic effects not transmitted to offspring may drive parental behavior, thereby affecting the rearing environment of the child. Measuring genetic proclivity directly, through polygenic risk scores (PRSs), provides a way to test for the effect of nontransmitted parental genotype, on offspring outcome, termed a genetic nurture effect-in other words, if and how parental genomes might affect their children through the environment. The current study used polygenic risk scores for smoking initiation, cigarettes per day, and drinks per week to predict substance use in a sample of 3,008 twins, assessed prospectively from age 17-29, and their parents, from the Minnesota Center for Twin and Family Research. Mixed-effects models were used to test for a genetic nurture effect whereby parental PRSs predict offspring tobacco and alcohol use after statistically adjusting for offspring's own PRS. Parental smoking initiation PRS predicted offspring cigarettes per day at age 24 (β = .103, 95% CI [.03, .17]) and alcohol use at age 17 (β = .091, 95% CI [.04, .14]) independent of shared genetics. There was also a suggestive independent association between the parent PRS and offspring smoking at age 17 (β = .096; 95% CI [.02, .17]). Mediation analyses provided some evidence for environmental effects of parental smoking, alcohol use, and family socioeconomic status. These findings, and more broadly the molecular genetic method used, have implications on the identification of environmental effects on developmental outcomes such as substance use. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Although many loci have been associated with height in European ancestry populations, very few have been identified in African ancestry individuals. Furthermore, many of the known loci have yet to be generalized to and fine-mapped within a large-scale African ancestry sample. We performed sex-combined and sex-stratified meta-analyses in up to 52,764 individuals with height and genome-wide genotyping data from the African Ancestry Anthropometry Genetics Consortium (AAAGC). We additionally combined our African ancestry meta-analysis results with published European genome-wide association study (GWAS) data. In the African ancestry analyses, we identified three novel loci (SLC4A3, NCOA2, ECD/FAM149B1) in sex-combined results and two loci (CRB1, KLF6) in women only. In the African plus European sex-combined GWAS, we identified an additional three novel loci (RCCD1, G6PC3, CEP95) which were equally driven by AAAGC and European results. Among 39 genome-wide significant signals at known loci, conditioning index SNPs from European studies identified 20 secondary signals. Two of the 20 new secondary signals and none of the 8 novel loci had minor allele frequencies (MAF) < 5%. Of 802 known European height signals, 643 displayed directionally consistent associations with height, of which 205 were nominally significant (p < 0.05) in the African ancestry sex-combined sample. Furthermore, 148 of 241 loci contained ≤20 variants in the credible sets that jointly account for 99% of the posterior probability of driving the associations. In summary, trans-ethnic meta-analyses revealed novel signals and further improved fine-mapping of putative causal variants in loci shared between African and European ancestry populations.

Objective: : Developing clinical natural language processing systems often requires access to many clinical documents, which are not widely available to the public due to privacy and security concerns. To address this challenge, we propose to develop methods to generate synthetic clinical notes and evaluate their utility in real clinical natural language processing tasks. Materials and Methods: : We implemented 4 state-of-the-art text generation models, namely CharRNN, Seg-GAN, GPT-2, and CTRL, to generate clinical text for the History and Present Illness section. We then manually annotated clinical entities for randomly selected 500 History and Present Illness notes generated from the best-performing algorithm. To compare the utility of natural and synthetic corpora, we trained named entity recognition (NER) models from all 3 corpora and evaluated their performance on 2 independent natural corpora. Results: : Our evaluation shows GPT-2 achieved the best BLEU (bilingual evaluation understudy) score (with a BLEU-2 of 0.92). NER models trained on synthetic corpus generated by GPT-2 showed slightly better performance on 2 independent corpora: strict F1 scores of 0.709 and 0.748, respectively, when compared with the NER models trained on natural corpus (F1 scores of 0.706 and 0.737, respectively), indicating the good utility of synthetic corpora in clinical NER model development. In addition, we also demonstrated that an augmented method that combines both natural and synthetic corpora achieved better performance than that uses the natural corpus only. Conclusions: : Recent advances in text generation have made it possible to generate synthetic clinical notes that could be useful for training NER models for information extraction from natural clinical notes, thus lowering the privacy concern and increasing data availability. Further investigation is needed to apply this technology to practice.

Background: Body mass index (BMI), a well-known risk factor for poor cardiovascular outcomes, is associated with differential DNA methylation (DNAm). Similarly, metabolic health has also been associated with changes in DNAm. It is unclear how overall metabolic health outside of BMI may modify the relationship between BMI and methylation profiles, and what consequences this may have on downstream cardiovascular disease. The purpose of this study was to identify cytosine-phosphate-guanine (CpG) sites at which the association between BMI and DNAm could be modified by overall metabolic health. Results: The discovery study population was derived from three Women’s Health Initiative (WHI) ancillary studies (n = 3977) and two Atherosclerosis Risk in Communities (ARIC) ancillary studies (n = 3520). Findings were validated in the Multi-Ethnic Study of Atherosclerosis (MESA) cohort (n = 1200). Generalized linear models regressed methylation β values on the interaction between BMI and metabolic health Z score (BMI × MHZ) adjusted for BMI, MHZ, cell composition, chip number and location, study characteristics, top three ancestry principal components, smoking, age, ethnicity (WHI), and sex (ARIC). Among the 429,566 sites examined, differential associations between BMI × MHZ and DNAm were identified at 22 CpG sites (FDR q < 0.05), with one site replicated in MESA (cg18989722, in the TRAPPC9 gene). Three of the 22 sites were associated with incident coronary heart disease (CHD) in WHI. For each 0.01 unit increase in DNAm β value, the risk of incident CHD increased by 9% in one site and decreased by 6–10% in two sites over 25 years. Conclusions: Differential associations between DNAm and BMI by MHZ were identified at 22 sites, one of which was validated (cg18989722) and three of which were predictive of incident CHD. These sites are located in several genes related to NF-kappa-B signaling, suggesting a potential role for inflammation between DNA methylation and BMI-associated metabolic health.

Infectious disease epidemics are plaguing the world and a lot of research is focused on the development of models to reproduce disease dynamics for eco-environmental and biological investigation, and disease management. Leptospirosis is an example of a neglected zoonosis strongly mediated by ecohydrological dynamics with emerging endemic and epidemic patterns worldwide in both animal and human populations. By accounting for large heterogeneities of affected areas we show how exponential endemics and scale-free epidemics are largely predictable and linked to common socio-environmental features via scaling laws with different exponents that inform about vulnerability factors. This led to the development of a novel pattern-oriented integrated model that can be used as an early-warning signal (EWS) tool for endemic-epidemic regime classification, risk determinant attribution, and near real-time forecast of outbreaks. Forecasts are grounded on expected outbreak recurrence time dependent on exceedance probabilities and statistical EWS that sense outbreak onset. A stochastic spatially-explicit model is shown to comprehensively predict outbreak dynamics (early sensing, timing, magnitude, decay, and eco-environmental determinants) and derive a spreading factor characterizing endemics and epidemics, where average over maximum rainfall is the critical factor characterizing disease transitions. Dynamically, case cross-correlation considering neighboring communities senses 2-weeks in advance outbreaks. Eco-environmental scaling relationships highlight how predicted host suitability and topographic index can be used as epidemiological footprints to effectively distinguish and control Leptospirosis regimes and areas dependent on hydro-climatological dynamics as the main trigger. The spatio-temporal scale-invariance of epidemics – underpinning persistent criticality and neutrality or independence among areas – is emphasized by the high accuracy in reproducing sequence and magnitude of cases via reliable surveillance. Further investigations of robustness and universality of eco-environmental determinants are required; nonetheless a comprehensive and computationally simple EWS method for the full characterization of Leptospirosis is provided. The tool is extendable to other climate-sensitive zoonoses to define vulnerability factors and predict outbreaks useful for optimal disease risk prevention and control.

Background: DNA methylation patterns associated with habitual diet have not been well studied. Methods: Diet quality was characterized using a Mediterranean-style diet score and the Alternative Healthy Eating Index score. We conducted ethnicity-specific and trans-ethnic epigenome-wide association analyses for diet quality and leukocyte-derived DNA methylation at over 400 000 CpGs (cytosine-guanine dinucleotides) in 5 population-based cohorts including 6662 European ancestry, 2702 African ancestry, and 360 Hispanic ancestry participants. For diet-associated CpGs identified in epigenome-wide analyses, we conducted Mendelian randomization (MR) analysis to examine their relations to cardiovascular disease risk factors and examined their longitudinal associations with all-cause mortality. Results: We identified 30 CpGs associated with either Mediterranean-style diet score or Alternative Healthy Eating Index, or both, in European ancestry participants. Among these CpGs, 12 CpGs were significantly associated with all-cause mortality (Bonferroni corrected P<1.6×10-3). Hypermethylation of cg18181703 (SOCS3) was associated with higher scores of both Mediterranean-style diet score and Alternative Healthy Eating Index and lower risk for all-cause mortality (P=5.7×10-15). Ten additional diet-associated CpGs were nominally associated with all-cause mortality (P<0.05). MR analysis revealed 8 putatively causal associations for 6 CpGs with 4 cardiovascular disease risk factors (body mass index, triglycerides, high-density lipoprotein cholesterol concentrations, and type 2 diabetes mellitus; Bonferroni corrected MR P<4.5×10-4). For example, hypermethylation of cg11250194 (FADS2) was associated with lower triglyceride concentrations (MR, P=1.5×10-14).and hypermethylation of cg02079413 (SNORA54; NAP1L4) was associated with body mass index (corrected MR, P=1×10-6). Conclusions: Habitual diet quality was associated with differential peripheral leukocyte DNA methylation levels of 30 CpGs, most of which were also associated with multiple health outcomes, in European ancestry individuals. These findings demonstrate that integrative genomic analysis of dietary information may reveal molecular targets for disease prevention and treatment.

Aim: We conducted a methylome-wide association study to examine associations between DNA methylation in whole blood and central adiposity and body fat distribution, measured as waist circumference, waist-to-hip ratio and waist-to-height ratio adjusted for body mass index, in 2684 African-American adults in the Atherosclerosis Risk in Communities study. Materials & methods: We validated significantly associated cytosine-phosphate-guanine methylation sites (CpGs) among adults using the Women's Health Initiative and Framingham Heart Study participants (combined n = 5743) and generalized associations in adolescents from The Raine Study (n = 820). Results & conclusion: We identified 11 CpGs that were robustly associated with one or more central adiposity trait in adults and two in adolescents, including CpG site associations near TXNIP, ADCY7, SREBF1 and RAP1GAP2 that had not previously been associated with obesity-related traits.

We investigated the heritability of educational attainment and how it differed between birth cohorts and cultural–geographic regions. A classical twin design was applied to pooled data from 28 cohorts representing 16 countries and including 193,518 twins with information on educational attainment at 25 years of age or older. Genetic factors explained the major part of individual differences in educational attainment (heritability: a2 = 0.43; 0.41–0.44), but also environmental variation shared by co-twins was substantial (c2 = 0.31; 0.30–0.33). The proportions of educational variation explained by genetic and shared environmental factors did not differ between Europe, North America and Australia, and East Asia. When restricted to twins 30 years or older to confirm finalized education, the heritability was higher in the older cohorts born in 1900–1949 (a2 = 0.44; 0.41–0.46) than in the later cohorts born in 1950–1989 (a2 = 0.38; 0.36–0.40), with a corresponding lower influence of common environmental factors (c2 = 0.31; 0.29–0.33 and c2 = 0.34; 0.32–0.36, respectively). In conclusion, both genetic and environmental factors shared by co-twins have an important influence on individual differences in educational attainment. The effect of genetic factors on educational attainment has decreased from the cohorts born before to those born after the 1950s.

Since 2013, the Centers for Disease Control and Prevention (CDC) has hosted an annual influenza season forecasting challenge. The 2015–2016 challenge consisted of weekly probabilistic forecasts of multiple targets, including fourteen models submitted by eleven teams. Forecast skill was evaluated using a modified logarithmic score. We averaged submitted forecasts into a mean ensemble model and compared them against predictions based on historical trends. Forecast skill was highest for seasonal peak intensity and short-term forecasts, while forecast skill for timing of season onset and peak week was generally low. Higher forecast skill was associated with team participation in previous influenza forecasting challenges and utilization of ensemble forecasting techniques. The mean ensemble consistently performed well and outperformed historical trend predictions. CDC and contributing teams will continue to advance influenza forecasting and work to improve the accuracy and reliability of forecasts to facilitate increased incorporation into public health response efforts.

Suboptimal ambient temperature exposure significantly affects public health. Previous studies have primarily focused on risk assessment, with few examining the health outcomes from an economic perspective. To inform environmental health policies, we estimated the economic costs of health outcomes associated with suboptimal temperature in the Minneapolis/St. Paul Twin Cities Metropolitan Area. We used a distributed lag nonlinear model to estimate attributable fractions/cases for mortality, emergency department visits, and emergency hospitalizations at various suboptimal temperature levels. The analyses were stratified by age group (i.e., youth (0–19 years), adult (20–64 years), and senior (65+ years)). We considered both direct medical costs and loss of productivity during economic cost assessment. Results show that youth have a large number of temperature-related emergency department visits, while seniors have large numbers of temperature-related mortality and emergency hospitalizations. Exposures to extremely low and high temperatures lead to $2.70 billion [95% empirical confidence interval (eCI): $1.91 billion, $3.48 billion](costs are all based on 2016 USD value)economic costs annually. Moderately and extremely low and high temperature leads to $9.40 billion [eCI: $6.05 billion, $12.57 billion]economic costs. The majority of the economic costs are consistently attributed to cold (>75%), rather than heat exposures and to mortality (>95%), rather than morbidity. Our findings support prioritizing temperature-related health interventions designed to minimize the economic costs by targeting seniors and to reduce attributable cases by targeting youth.

A wide range of research has promised new tools for forecasting infectious disease dynamics, but little of that research is currently being applied in practice, because tools do not address key public health needs, do not produce probabilistic forecasts, have not been evaluated on external data, or do not provide sufficient forecast skill to be useful. We developed an open collaborative forecasting challenge to assess probabilistic forecasts for seasonal epidemics of dengue, a major global public health problem. Sixteen teams used a variety of methods and data to generate forecasts for 3 epidemiological targets (peak incidence, the week of the peak, and total incidence) over 8 dengue seasons in Iquitos, Peru and San Juan, Puerto Rico. Forecast skill was highly variable across teams and targets. While numerous forecasts showed high skill for midseason situational awareness, early season skill was low, and skill was generally lowest for high incidence seasons, those for which forecasts would be most valuable. A comparison of modeling approaches revealed that average forecast skill was lower for models including biologically meaningful data and mechanisms and that both multimodel and multiteam ensemble forecasts consistently outperformed individual model forecasts. Leveraging these insights, data, and the forecasting framework will be critical to improve forecast skill and the application of forecasts in real time for epidemic preparedness and response. Moreover, key components of this project-integration with public health needs, a common forecasting framework, shared and standardized data, and open participation-can help advance infectious disease forecasting beyond dengue.

Correction for “An open challenge to advance probabilistic forecasting for dengue epidemics,” by Michael A. Johansson, Karyn M. Apfeldorf, Scott Dobson, Jason Devita, Anna L. Buczak, Benjamin Baugher, Linda J. Moniz, Thomas Bagley, Steven M. Babin, Erhan Guven, Teresa K. Yamana, Jeffrey Shaman, Terry Moschou, Nick Lothian, Aaron Lane, Grant Osborne, Gao Jiang, Logan C. Brooks, David C. Farrow, Sangwon Hyun, Ryan J. Tibshirani, Roni Rosenfeld, Justin Lessler, Nicholas G. Reich, Derek A. T. Cummings, Stephen A. Lauer, Sean M. Moore, Hannah E. Clapham, Rachel Lowe, Trevor C. Bailey, Markel García-Díez, Marilia Sá Carvalho, Xavier Rodó, Tridip Sardar, Richard Paul, Evan L. Ray, Krzysztof Sakrejda, Alexandria C. Brown, Xi Meng, Osonde Osoba, Raffaele Vardavas, David Manheim, Melinda Moore, Dhananjai M. Rao, Travis C. Porco, Sarah Ackley, Fengchen Liu, Lee Worden, Matteo Convertino, Yang Liu, Abraham Reddy, Eloy Ortiz, Jorge Rivero, Humberto Brito, Alicia Juarrero, Leah R. Johnson, Robert B. Gramacy, Jeremy M. Cohen, Erin A. Mordecai, Courtney C. Murdock, Jason R. Rohr, Sadie J. Ryan, Anna M. Stewart-Ibarra, Daniel P. Weikel, Antarpreet Jutla, Rakibul Khan, Marissa Poultney, Rita R. Colwell, Brenda Rivera-García, Christopher M. Barker, Jesse E. Bell, Matthew Biggerstaff, David Swerdlow, Luis Mier-y-Teran-Romero, Brett M. Forshey, Juli Trtanj, Jason Asher, Matt Clay, Harold S. Margolis, Andrew M. Hebbeler, Dylan George, and Jean-Paul Chretien, which was first published November 11, 2019; 10.1073/pnas.1909865116. The authors note that the affiliation for Xavier Rodó should instead appear as Catalan Institution for Research and Advanced Studies (ICREA) and Climate and Health Program, Barcelona Institute for Global Health (ISGlobal). The corrected author and affiliation lines appear below. The online version has been corrected.

Fathers are more than social accidents. Research has demonstrated that fathers matter to children's development. Despite noted progress, challenges remain on how best to conceptualize and assess fathering and father–child relationships. The current monograph is the result of an SRCD-sponsored meeting of fatherhood scholars brought together to discuss these challenges and make recommendations for best practices for incorporating fathers in studies on parenting and children's development. The first aim of this monograph was to provide a brief update on the current state of research on fathering and to lay out a developmental ecological systems perspective as a conceptual framework for understanding the different spaces fathers inhabit in their children's lives. Because there is wide variability in fathers’ roles, the ecological systems perspective situates fathers, mothers, children, and other caregivers within an evolving network of interrelated social relationships in which children and their parents change over time and space (e.g., residence). The second aim was to present examples of empirical studies conducted by members of the international working group that highlighted different methods, data collection, and statistical analyses used to capture the variability in father–child relationships. The monograph ends with a commentary that elaborates on the ecological systems framework with a discussion of the broader macrosystem and social-contextual influences that impinge on fathers and their children. The collection of articles contributes to research on father–child relationships by advancing theory and presenting varied methods and analysis strategies that assist in understanding the father–child relationship and its impact on child development.