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bioRxiv2017

Cadmium Exposure Increases The Risk Of Juvenile Obesity: A Human And Zebrafish Comparative Study

OBJECTIVEHuman obesity is a complex metabolic disorder disproportionately affecting people of lower socioeconomic strata, and ethnic minorities, especially African Americans and Hispanics. Although genetic predisposition and a positive energy balance are implicated in obesity, these factors alone do not account for the excess prevalence of obesity in lower socioeconomic populations. Therefore, environmental factors, including exposure to pesticides, heavy metals, and other contaminants, are agents widely suspected to have obesogenic activity, and they also are spatially correlated with lower socioeconomic status. Our study investigates the causal relationship between exposure to the heavy metal, cadmium (Cd), and obesity in a cohort of children and a zebrafish model of adipogenesis.\n\nDESIGNAn extensive collection of first trimester maternal blood samples obtained as part of the Newborn Epigenetics Study (NEST) were analyzed for the presence Cd, and these results were cross analyzed with the weight-gain trajectory of the children through age five years. Next, the role of Cd as a potential obesogen was analyzed in an in vivo zebrafish model.\n\nRESULTSOur analysis indicates that the presence of Cd in maternal blood during pregnancy is associated with increased risk of juvenile obesity in the offspring, independent of other variables, including lead (Pb) and smoking status. Our results are recapitulated in a zebrafish model, in which exposure to Cd at levels approximating those observed in the NEST study is associated with increased adiposity.\n\nCONCLUSIONOur findings identify Cd as potential human obesogen. Moreover, these observations are recapitulated in a zebrafish model, suggesting that the underlying mechanisms may be evolutionarily conserved, and that zebrafish may be a valuable model for uncovering pathways leading to Cd-mediated obesity in human populations.

epidemiology↗

bioRxiv2017

Stacked Generalization: An Introduction to Super Learning

Stacked generalization is an ensemble method that allows researchers to combine several different prediction algorithms into one. Since its introduction in the early 1990s, the method has evolved several times into what is now known as \"Super Learner\". Super Learner uses V -fold cross-validation to build the optimal weighted combination of predictions from a library of candidate algorithms. Optimality is defined by a user-specified objective function, such as minimizing mean squared error or maximizing the area under the receiver operating characteristic curve. Although relatively simple in nature, use of the Super Learner by epidemiologists has been hampered by limitations in understanding conceptual and technical details. We work step-by-step through two examples to illustrate concepts and address common concerns.

epidemiology↗

bioRxiv2017

A New Mechanism for Cost Savings in NHS Prescribing: Minimising "Price-Per-Unit"

BackgroundMinimising prescription costs while maintaining quality is a core element of delivering high value healthcare. There are various strategies to achieve savings, but almost no research to date on determining the most effective approach. We describe a new method of identifying potential savings due to large national variations in drug cost, including variation in generic drug cost; and compare these with potential savings from an established method (generic prescribing).\n\nMethodsWe used English NHS Digital prescribing data, from October 2015 to September 2016. Potential cost savings were calculated by determining the price-per-unit (e.g. pill, ml) for each drug and dose within each general practice. This was compared against the same cost for the practice at the lowest cost decile, to determine achievable savings. We compared these price-per-unit savings to the savings possible from generic switching; and determined the chemicals with the highest savings nationally. A senior pharmacist manually assessed whether a random sample of savings were practically achievable.\n\nResultsWe identified a theoretical maximum of {pound}410M of savings over 12 months. {pound}273M of these savings were for individual prescribing changes worth over {pound}50 per practice per month; this compares favorably with generic switching, where only {pound}35M of achievable savings were identified. The biggest savings nationally were on glucose blood testing reagents ({pound}12M), fluticasone propionate ({pound}9M) and venlafaxine ({pound}8M). Approximately half of all savings were deemed practically achievable.\n\nDiscussionWe have developed a new method to identify and enable large potential cost savings within NHS community prescribing. Given the current pressures on the NHS, it is vital that these potential savings are realised. Our tool enabling doctors to achieve these savings is now launched in pilot form. However savings could potentially be achieved more simply through national policy change.\n\nAbbreviations

epidemiology↗

bioRxiv2017

Identifying factors that may improve mechanistic forecasting models for influenza

Influenza causes substantial morbidity and mortality and places strain on healthcare systems, some of which could be mitigated by accurate forecasting. Specific humidity and school vacations have both been shown independently to affect the transmission dynamics of influenza at large spatial scales. Here, we compare the ability of five compartmental transmission models, which include these two processes, to explain influenza-like-illness (ILI) incidence data for five United States counties for which school vacations and specific humidity data were available over a span of four seasons. We used the models in two different ways. First we fitted all available data at the same time and assessed model performance using standard measures of parsimony and goodness-of-fit. Then we conducted a retrospective forecasting study in which we attempted to predict incidence beyond a given week by fitting to data available up to that week. In general, when fitting the data using the whole season, we found that either specific humidity, school closures, or a combined model incorporating both effects captured the variability in incidence better than a fully constrained SIR-like model. Moreover, where these factors play a role, the timing of the variations suggests a causal relationship. When school vacations and specific humidity were important, the model-estimated parameters were broadly consistent. Retrospective forecasting simulations were consistent with the explanatory use of the models, with both specific humidity and school vacations giving more accurate forecasts than a simple SIR-like model in some populations and for some seasons. Our results suggest that influenza forecast models should test for the importance of different factors such as school vacations and specific humidity on a population-by-population and year-by-year basis.\n\nAuthor summaryUnderstanding the underlying factors that contribute to the transmission of influenza is crucial for developing models with predictive capabilities. In this study, we address two key effects: humidity and school vacations. We show that both can play an important role, depending on the location of the population as well as the timing of the school vacations. We then demonstrate how such mechanistic models can be used to forecast an influenza season as it unfolds, including estimates of the uncertainty of the predictions at each week of the forecast. To make better influenza forecasts, models need to test whether factors such as school vacations and specific humidity are important for a given season and population.

epidemiology↗

bioRxiv2017

On the relative role of different age groups during epidemics associated with the respiratory syncytial virus

BackgroundWhile RSV circulation results in high burden of hospitalization, particularly among infants, young children and the elderly, little is known about the role of different age groups in propagating annual RSV epidemics in the community.\n\nMethodsDuring a communicable disease outbreak, some subpopulations may play a disproportionate role during the outbreak's ascent due to increased susceptibility and/or contact rates. Such subpopulations can be identified by considering the proportion that cases in a subpopulation represent among all cases in the population occurring before (Bp) and after the epidemic peak (Ap) to calculate the subpopulation's relative risk, RR=Bp/Ap. We estimated RR for several age groups using data on RSV hospitalizations in the US between 2001-2012 from the Healthcare Cost and Utilization Project (HCUP).\n\nResultsChildren aged 3-4y and 5-6y each had the highest RR estimate for 5/11 seasons in the data, with RSV hospitalization rates in infants being generally higher during seasons when children aged 5-6y had the highest RR estimates. Children aged 2y had the highest RR estimate during one season. RR estimates in infants and individuals aged 11y and older were mostly lower than in children aged 1-10y.\n\nConclusionsThe RR estimates suggest that preschool and young school-age children have the leading relative roles during RSV epidemics. We hope that those results will aid in the design of RSV vaccination policies.

epidemiology↗

bioRxiv2017

Predictors of intestinal inflammation in asymptomatic first-degree relatives of patients with Crohn’s disease

ObjectiveRelatives of individuals with Crohns disease (CD) carry an increased number of CD-associated genetic variants and are at increased risk of developing the disease. Multiple environmental and genetic factors contribute to this increased risk. We aimed to estimate the utility of genotype, smoking, family history, and a panel of biomarkers to predict risk in asymptomatic first-degree relatives (FDRs) of CD patients.\n\nDesignWe calculated a combined genotype (72 CD-associated genetic markers) and smoking relative risk score in 454 FDRs, and performed capsule endoscopy and collected 22 biomarkers in individuals from the highest and lowest risk quartiles. We then predicted small intestinal inflammation using genetic risk score, smoking status, number of relatives with CD, capsule transit time, and the panel of biomarkers in 124 individuals with complete data. Our principal analysis was to calculate the predictive utility from two machine learning classifiers: an elastic net and a random forest.\n\nResultsBoth classifiers successfully predicted FDRs with intestinal inflammation: elastic net (AUC=0.80, 95% CI: 0.62-0.98), random forest (AUC=0.87, 95% CI: 0.75-1.00). The elastic net selected a 3-predictor solution: CD family history (OR=1.31), genetic risk score (OR=1.14), and faecal calprotectin (OR=1.04). The same 3 variables were among the top 5 most important predictors as ranked by the random forest.\n\nConclusionA readily collectable panel of genetic risk variants, added to family history and faecal calprotectin, predicts those at greatest risk for developing CD with a good degree of accuracy.

epidemiology↗

bioRxiv2017

Analysis of multistage in vitro fertilization data with mixed multilevel outcomes using joint modelling approaches

In vitro fertilization comprises a sequence of interventions concerned with the creation and culture of embryos which are then transferred to the patients uterus. While the clinically important endpoint is birth, the responses to each stage of treatment contain additional information about the reasons for success or failure. Joint analysis of the sequential responses is complicated by mixed outcome types defined at two levels (patient and embryo). We develop three methods for multistage analysis based on joining submodels for the different responses using latent variables and entering outcome variables as covariates for downstream responses. An application to routinely collected data is presented, and the strengths and limitations of each method are discussed.

epidemiology↗

bioRxiv2017

Automating Mendelian randomization through machine learning to construct a putative causal map of the human phenome

A major application for genome-wide association studies (GWAS) has been the emerging field of causal inference using Mendelian randomization (MR), where the causal effect between a pair of traits can be estimated using only summary level data. MR depends on SNPs exhibiting vertical pleiotropy, where the SNP influences an outcome phenotype only through an exposure phenotype. Issues arise when this assumption is violated due to SNPs exhibiting horizontal pleiotropy. We demonstrate that across a range of pleiotropy models, instrument selection will be increasingly liable to selecting invalid instruments as GWAS sample sizes continue to grow. Methods have been developed in an attempt to protect MR from different patterns of horizontal pleiotropy, and here we have designed a mixture-of-experts machine learning framework (MR-MoE 1.0) that predicts the most appropriate model to use for any specific causal analysis, improving on both power and false discovery rates. Using the approach, we systematically estimated the causal effects amongst 2407 phenotypes. Almost 90% of causal estimates indicated some level of horizontal pleiotropy. The causal estimates are organised into a publicly available graph database (http://eve.mrbase.org), and we use it here to highlight the numerous challenges that remain in automated causal inference.

epidemiology↗

bioRxiv2017

Multiscale Model Within-host and Between-host for Viral Infectious Diseases

Multiscale models possess the potential to uncover new insights into infectious diseases. Here, a rigorous stability analysis of a multiscale model within-host and between-host is presented. The within-host model describes virus replication and the respective immune response while disease transmission is represented by a simple susceptible-infected (SI) model.\n\nThe bridge of within-to between-host is by considering transmission as a function of the viral load of the within-host level. Consequently, stability and bifurcation analyses were developed coupling the two basic reproduction numbers [Formula] and [Formula]for the within- and the between-host subsystems, respectively. Local stability results for each subsystem, such as a unique stable equilibrium point, recapitulate classical approaches to infection and epidemic control.\n\nUsing a Lyapunov function, global stability of the between-host system was obtained. A main result was the derivation of the [Formula] as a general increasing function of [Formula]. Numerical analyses reveal that a Michaelis-Menten form based on the virus is more likely to recapitulate the behavior between the scales than a form directly proportional to the virus. Our work contributes basic understandings of the two models and casts light on the potential effects of the coupling function on linking the two scales.

epidemiology↗

bioRxiv2017

Use of whole genome sequencing to investigate an outbreak of gonorrhoea among females in urban New South Wales, Australia, 2012 to 2014.

Increasing rates of gonorrhoea have been observed among urban heterosexuals within the Australian state of New South Wales (NSW). Here, we applied whole genome sequencing (WGS) to better understand transmission dynamics. Ninety-four isolates of a particular N. gonorrhoeae genotype (G122) associated with female patients (years 2012 to 2014) underwent phylogenetic analysis using core single nucleotide polymorphisms (SNPs). Context for genetic variation was provided by including an unbiased selection of 1,870 N. gonorrhoeae genomes from a recent United Kingdom (UK) study. NSW genomes formed a single clade, with the majority of isolates belonging to one of five clusters, and comprised patients of varying age groups. Intra-patient variability was less than 7 core SNPs. Several patients had indistinguishable core SNPs, suggesting a common infection source. These data have provided an enhanced understanding of transmission of N. gonorrhoeae among urban heterosexuals in NSW, Australia, and highlight the value of using WGS in N. gonorrhoeae outbreak investigations.

epidemiology↗

bioRxiv2017

Assessing the performance of real-time epidemic forecasts

Real-time forecasts based on mathematical models can inform critical decision-making during infectious disease outbreaks. Yet, epidemic forecasts are rarely evaluated during or after the event, and there is little guidance on the best metrics for assessment. Here, we propose an evaluation approach that disentangles different components of forecasting ability using metrics that separately assess the calibration, sharpness and unbiasedness of forecasts. This makes it possible to assess not just how close a forecast was to reality but also how well uncertainty has been quantified. We used this approach to analyse the performance of weekly forecasts we generated in real time in Western Area, Sierra Leone, during the 2013-16 Ebola epidemic in West Africa. We investigated a range of forecast model variants based on the model fits generated at the time with a semi-mechanistic model, and found that good probabilistic calibration was achievable at short time horizons of one or two weeks ahead but models were increasingly inaccurate at longer forecasting horizons. This suggests that forecasts may have been of good enough quality to inform decision making requiring predictions a few weeks ahead of time but not longer, reflecting the high level of uncertainty in the processes driving the trajectory of the epidemic. Comparing forecasts based on the semi-mechanistic model to simpler null models showed that the best semi-mechanistic model variant performed better than the null models with respect to probabilistic calibration, and that this would have been identified from the earliest stages of the outbreak. As forecasts become a routine part of the toolkit in public health, standards for evaluation of performance will be important for assessing quality and improving credibility of mathematical models, and for elucidating difficulties and trade-offs when aiming to make the most useful and reliable forecasts.

epidemiology↗

bioRxiv2017

Recurring infection with ecologically distinct human papillomavirus (HPV) types explains high prevalence and diversity

The high prevalence of human papillomavirus (HPV), the most common sexually transmitted infection, arises from the coexistence of over 200 genetically distinct types. Accurately predicting the impact of vaccines that target multiple types requires understanding the factors that determine HPV diversity. The diversity of many pathogens is driven by type-specific or \"homologous\" immunity, which promotes the spread of variants to which hosts have little immunity. To test for homologous immunity and to identify mechanisms determining HPV transmission, we fitted nonlinear mechanistic models to longitudinal data on genital infections in unvaccinated men. Our results provide no evidence for homologous immunity, instead showing that infection with one HPV type strongly increases the risk of infection with that type for years afterwards. For HPV16, the type responsible for most HPV-related cancers, an initial infection increases the one-year probability of reinfection by 20-fold, and the probability of reinfection remains 14-fold higher two years later. This increased risk occurs in both sexually active and celibate men, suggesting that it arises from auto-inoculation, episodic reactivation of latent virus, or both. Overall our results show that high HPV prevalence and diversity can be explained by a combination of a lack of homologous immunity, frequent reinfections, weak competition between types, and variation in type fitness between host subpopulations. Due to the high risk of reinfection, vaccinating boys that have not yet been exposed may be crucial to reduce prevalence, but our results suggest that there may also be large benefits from vaccinating previously infected individuals.

epidemiology↗

bioRxiv2018

Response to Therapeutic Sleep Deprivation: A Naturalistic Study of Clinical and Genetic Factors and Post-Treatment Depressive Symptom Trajectory

Research has shown that therapeutic sleep deprivation (SD) has rapid antidepressant effects in the majority of depressed patients. Investigation of factors preceding and accompanying these effects may facilitate the identification of the underlying biological mechanisms. This exploratory study aimed to examine clinical and genetic factors predicting response to SD and determine the impact of SD on illness course. Mood and tiredness during SD were also assessed via visual analogue scales (VAS). Depressed inpatients (n = 78) and healthy controls (n = 15) underwent ~36hrs of SD. Response to SD was defined as a score of [≤]2 on the Clinical Global Impression Scale for Global Improvement. Depressive symptom trajectories were evaluated for up to a month using self/expert ratings. Impact of genetic burden was calculated using polygenic risk scores for major depressive disorder. 72% of patients responded to SD. Responders and nonresponders did not differ in baseline self/expert depression symptom ratings, but mood subjectively measured by VAS scale differed. Response was associated with lower age (p = 0.007) and later age at life-time disease onset (p = 0.003). Higher genetic burden of depression was observed in non-responders than healthy controls. Up to a month post-SD, depressive symptoms decreased in both patients groups, but more in responders, in whom effects were sustained. The present findings suggest that re-examining SD with a greater focus on biological mechanisms will lead to better understanding of mechanisms of depression.

epidemiology↗

bioRxiv2017

Meta-analysis and Commentary: Preemptive Correction of Arteriovenous Access Stenosis

BackgroundA recent meta-analysis by Ravani and colleagues (Ravani, P., et al., Am J Kidney Dis, 2016. 67(3): p. 446-60.) studied the effect of pre-emptive correction of arterio-venous dialysis vascular access versus deferred care, based on data from 11 trials. The authors reported a non-significant protective treatment effect of pre-emptive correction on access loss, while showing a significant protective effect on thrombosis rates conferred by pre-emptive correction. We revisit this analysis, including data extraction and effects of a heterogenous study population.\n\nMethodsWe repeated data extraction from all referenced publications in the meta-analysis by Ravani et al. and corrected event counts where applicable. We repeated the meta-analyses with access loss as the outcome for studies that recruited patients with arterio-venous fistulae (AVF) and grafts (AVG), respectively, using a random effects model with relative risk (RR) and risk difference (RD) of access loss as the outcomes of interest. We repeated data extraction from all referenced publications, and corrected event counts where applicable.\n\nResultsOur conclusions differ from the original findings in two ways. First, after some amendment of the event counts extracted from Mayer et al. (Vascular and Endovascular Surgery 1993), we find a significant overall positive effect of pre-emptive correction on arterio-venous access loss in the overall study population [RR 0.80 (95% CI 0.64 to 0.99), RD -0.07 (95% CI -0.12 to -0.02); Figure 1]. Secondly, we highlight the impact of heterogeneous study populations on the meta-analysis. Whereas the data do not conclusively show a benefit of pre-emptive correction for arteriovenous grafts (AVG; RR = 0.87, 95% CI: 0.69 - 1.11), they show a strong protective effect for arteriovenous fistulae (AVF; RR = 0.5, 95% CI: 0.29 to 0.86).\n\nO_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=128 SRC=\"FIGDIR/small/179580_fig1.gif\" ALT=\"Figure 1\">\nView larger version (41K):\norg.highwire.dtl.DTLVardef@af3c13org.highwire.dtl.DTLVardef@911a50org.highwire.dtl.DTLVardef@568cf1org.highwire.dtl.DTLVardef@3f3c08_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOFigure 1:C_FLOATNO Meta-analysis of access loss, overall and by access type using risk ratio (RR) as the measure of association.\n\nC_FIG\n\nDiscussion and ConclusionThese findings corroborate clinical arguments such as superior long-term patency of AVF and the nature of AVG failure that often involve infectious causes. The available data indicate mild or no benefit of pre-emptive correction for AVG, but strongly support tight monitoring of dialysis accesses and preemptive intervention and correction upon the slightest suspicion of access stenosis for AVF.

epidemiology↗

bioRxiv2017

Prevalence of Mycoplasma genitalium in different population groups: systematic review and meta-analysis

BackgroundMycoplasma genitalium is a common cause of non-gonococcal non-chlamydial urethritis and cervicitis. Testing of asymptomatic populations has been proposed, but prevalence rates in asymptomatic populations are not well established. We aimed to estimate the prevalence of M. genitalium in adults in the general population, in clinic-based samples, pregnant women, men who have sex with men (MSM) and female sex workers (FSW).\n\nMethodsWe searched Embase, Medline, IndMED, AIM and LILACS from 1 January 1991 to 12 July 2016 without language restrictions. We included studies with 500 participants or more. We screened and selected studies and extracted data in duplicate. We examined eligible studies in forest plots and conducted random effects meta-analysis to estimate prevalence, if appropriate. Between study heterogeneity was examined using the I2 statistic and meta-regression.\n\nResultsOf 3,316 screened records, 63 were included. In randomly selected samples from the general population, the summary prevalence estimate was 1.3% (95% confidence intervals, CI 1.0 to 1.8%, I2 41.5%, 3 studies) in countries with higher levels of development and 3.9% (95% CI 2.2 to 6.7, I2 89.2%, 3 studies) in countries with lower levels. Prevalence estimates were similar in women and men (p=0.47). In clinic-based samples prevalence estimates were higher, except in asymptomatic patients (0.8%, 95% CI 0.4 to 1.4, I2 0.0%, 3 studies). Summary prevalence estimates were: pregnant women 0.9% (95% CI 0.6 to 1.4%, I2 0%, 4 studies); MSM in the community 3.2% (95% CI 2.1 to 5.1, I2 78.3%, 5 studies); FSW in the community 15.9% (95% CI 13.5 to 18.9, I2 =79.9%, 4 studies).\n\nDiscussionThis systematic review can inform testing guidelines for M. genitalium infection. The low estimated prevalence of M. genitalium in the general population, pregnant women and asymptomatic attenders at clinics does not support expansion of testing to asymptomatic people in these groups.\n\nRegistration Numbers\n\nPROSPERO: CRD42015020420

epidemiology↗

bioRxiv2017

Cord blood buffy coat DNA methylation is comparable to whole cord blood methylation

BackgroundCord blood DNA methylation is associated with numerous health outcomes and environmental exposures. Whole cord blood DNA reflects all nucleated blood cell types, while centrifuging whole blood separates red blood cells by generating a white blood cell buffy coat. Both sample types are used in DNA methylation studies. Cell types have unique methylation patterns and processing can impact cell distributions, which may influence comparability.\n\nObjectivesTo evaluate differences in cell composition and DNA methylation between buffy coat and whole cord blood samples.\n\nMethodsCord blood DNA methylation was measured with the Infinium EPIC BeadChip (Illumina) in 8 individuals, each contributing buffy coat and whole blood samples. We analyzed principal components (PC) of methylation, performed hierarchical clustering, and computed correlations of mean-centered methylation between pairs. We conducted moderated t-tests on single sites and estimated cell composition.\n\nResultsDNA methylation PCs were associated with individual (PPC1=1.4x10-9; PPC2=2.9x10-5; PPC3=3.8x10-5; PPC4=4.2x10-6; PPC5=9.9x10-13), and not with sample type (PPC1-5>0.7). Samples hierarchically clustered by individual. Pearson correlations of mean-centered methylation between paired individual samples ranged from r=0.66 to r=0.87. No individual site significantly differed between buffy coat and whole cord blood when adjusting for multiple comparisons (5 sites had unadjusted P<10-5). Estimated cell type proportions did not differ by sample type (P=0.86), and estimated cell counts were highly correlated between paired samples (r=0.99).\n\nConclusionsDifferences in methylation and cell composition between buffy coat and whole cord blood are much lower than inter-individual variation, demonstrating that both sample preparation types can be analytically combined and compared.

epidemiology↗

bioRxiv2017

Patterns of sedentary and active time accumulation are associated with mortality in US adults: The NHANES study

PurposeSedentary behavior has become a public health pandemic and has been associated with a variety of comorbidities including cardiovascular disease, type 2 diabetes, and some cancers. Previous studies have also shown that excessive amount of sedentary behavior is associated with all-cause mortality. However, no studies investigated whether patterns of sedentary and active time accumulation are associated with mortality independently of total sedentary and total active times. This study addresses this question by i) comparing several analytical ways to quantify patterns of both sedentary and active time accumulation through metrics of fragmentation of objectively-measured physical activity and ii) exploring the association of these metrics with all-cause mortality in a nationally representative US sample of elderly adults.\n\nMethodsThe accelerometry data of 3400 participants aged 50 to 84 in the National Health and Nutrition Examination Survey 2003-2006 cohorts were analyzed. Ten fragmentation metrics were calculated to quantify the duration of sedentary and active bouts: average bout duration, Gini index, average hazard, between-state transition probability, and the parameter of power law distribution. The association of these fragmentation metrics with all-cause mortality followed through December 31, 2011 was assessed with survey-weighted Cox proportional hazard models.\n\nResultsIn models adjusted for age, sex, race/ethnicity, education, body mass index, common comorbidities, and total sedentary/active time, four fragmentation metrics were associated with lower mortality risk: average active bout duration (HR=0.72 for 1SD increase, 95% CI = 0.590.88), Gini index for active bouts (HR = 0.75, 95% CI = 0.64-0.86), the parameter of power law distribution for sedentary bouts (HR = 0.75, 95% CI = 0.63-0.90), and sedentary-to-active transition probability (HR = 0.77, 95% CI = 0.61-0.96), and four fragmentation metrics were associated with higher mortality risk: the active-to-sedentary transition probability (HR = 1.40, 95% CI=1.23-1.58), the parameter of power law distribution for active bouts (HR = 1.33, 95% CI = 1.16-1.52), average hazard for durations of active bouts (HR = 1.32, 95% CI = 1.18-1.48), and average sedentary bout duration (HR =1.07, 95% CI = 1.01-1.13). After sensitivity analysis, average sedentary bout duration and sedentary-to-active transition probability became insignificant.\n\nConclusionLonger average duration of active bouts, a lower probability of transitioning from active to sedentary behavior, and a higher normalized variability of active bout durations were strongly negatively associated with all-cause mortality independently of total active time. A larger proportion of longer sedentary bouts were positively associated with all-cause mortality independently of total sedentary time. The results also suggested a nonlinear association of average active bout duration with mortality that corresponded to the largest risk increase in subjects with average active bout duration less than 3 minutes.

epidemiology↗

bioRxiv2017

Timescales of influenza A/H3N2 antibody dynamics

Human immunity influences the evolution and impact of novel influenza strains. Because individuals are infected with multiple influenza strains during their lifetime and each virus can generate a cross-reactive antibody response, it is challenging to quantify the processes that shape observed immune responses, or to reliably detect recent infection from serological samples. Using a Bayesian model of antibody dynamics at multiple timescales, we explain complex cross-reactive antibody landscapes by inferring participants histories of infection with serological data from cross-sectional and longitudinal studies of influenza A/H3N2 in southern China and Vietnam. We show an individuals influenza antibody profile can be explained by a short-lived, broadly cross-reactive response that decays within a year to leave a smaller long-term response acting against a narrower range of strains. We also demonstrate that accounting for dynamic immune responses can provide a more accurate alternative to traditional definitions seroconversion for the estimation of infection attack rates. Our work provides a general model for explaining mechanisms of influenza immunity acting at multiple timescales based on contemporary serological data, and suggests a two-armed immune response to influenza infection consistent with competitive dynamics between B cell populations. This approach to analysing multiple timescales for antigenic responses could also be applied to other multi-strain pathogens such as dengue and related flaviviruses.

epidemiology↗