The killifish germline regulates longevity and somatic repair in a sex-specific manner.

Classical evolutionary theories propose tradeoffs among reproduction, damage repair and lifespan. However, the specific role of the germline in shaping vertebrate aging remains largely unknown. In this study, we used the turquoise killifish (Nothobranchius furzeri) to genetically arrest germline development at discrete stages and examine how different modes of infertility impact life history. We first constructed a comprehensive single-cell gonadal atlas, providing cell-type-specific markers for downstream phenotypic analysis. We show here that germline depletion-but not arresting germline differentiation-enhances damage repair in female killifish. Conversely, germline-depleted males instead showed an extension in lifespan and rejuvenated metabolic functions. Through further transcriptomic analysis, we highlight enrichment of pro-longevity pathways and genes in germline-depleted male killifish and demonstrate functional conservation of how these factors may regulate longevity in germline-depleted Caenorhabditis elegans. Our results, therefore, demonstrate that different germline manipulation paradigms can yield pronounced sexually dimorphic phenotypes, implying alternative responses to classical evolutionary tradeoffs.

The killifish germline regulates longevity and somatic repair in a sex-specific manner.

Classical evolutionary theories propose tradeoffs between reproduction, damage repair, and lifespan. However, the specific role of the germline in shaping vertebrate aging remains largely unknown. Here, we use the turquoise killifish ( N. furzeri ) to genetically arrest germline development at discrete stages, and examine how different modes of infertility impact life-history. We first construct a comprehensive single-cell gonadal atlas, providing cell-type-specific markers for downstream phenotypic analysis. Next, we show that germline depletion - but not arresting germline differentiation - enhances damage repair in female killifish. Conversely, germline-depleted males instead showed an extension in lifespan and rejuvenated metabolic functions. Through further transcriptomic analysis, we highlight enrichment of pro-longevity pathways and genes in germline-depleted male killifish and demonstrate functional conservation of how these factors may regulate longevity in germline-depleted C. elegans . Our results therefore demonstrate that different germline manipulation paradigms can yield pronounced sexually dimorphic phenotypes, implying alternative responses to classical evolutionary tradeoffs.

Power of testing for exposure effects under incomplete mediation.

Mediation analysis studies situations where an exposure may affect an outcome both directly and indirectly through intervening variables called mediators. It is frequently of interest to test for the effect of the exposure on the outcome, and the standard approach is simply to regress the latter on the former. However, it seems plausible that a more powerful test statistic could be achieved by also incorporating the mediators. This would be useful in cases where the exposure effect size might be small, which for example is common in genomics applications. Previous work has shown that this is indeed possible under complete mediation, where there is no direct effect. In most applications, however, the direct effect is likely nonzero. In this paper we study linear mediation models and find that under certain conditions, power gain is still possible under this incomplete mediation setting for testing the null hypothesis that there is neither a direct nor an indirect effect. We study a class of procedures that can achieve this performance and develop their application to both low- and high-dimensional mediators. We then illustrate their performances in simulations as well as in an analysis using DNA methylation mediators to study the effect of cigarette smoking on gene expression.

Tobacco Smoke Exposure According to Location of Home Smoking in Israel: Findings from the Project Zero Exposure Study.

Young children are particularly vulnerable to harms from tobacco smoke exposure (TSE). This study aimed to compare TSE: (1) between children who live in smoking families and those who do not; and (2) among children who live in smoking households with varying smoking locations. The data came from two studies that were conducted concurrently in Israel (2016-2018). Study 1: a randomized controlled trial of smoking families (n = 159); Study 2: a cohort study of TSE among children in non-smoking families (n = 20). Hair samples were collected from one child in each household. Baseline hair nicotine data were analyzed for 141 children in Study 1 and 17 children in Study 2. Using a logistic regression analysis (exposed vs. not exposed as per laboratory determination) and a linear regression (log hair nicotine), we compared TSE between: (1) children in Study 1 vs. Study 2; (2) children in families with different smoking locations in Study 1: balcony; garden, yard, or other place outside of the home; or inside the home (designated smoking areas within the home (DSAs) or anywhere). A higher proportion of children living in smoking households were measurably exposed to tobacco smoke (68.8%) compared to children living in non-smoking households (35.3%, p = 0.006). Among children from smoking families, 75.0% of those whose parents smoked in the house were exposed, while 61.8% of children whose parents restricted smoking to the porch (n = 55) were exposed, and 71.4% of those whose parents smoked outside the home (including gardens and yards) (n = 42) were exposed. In univariable and multivariable models, smoking location was not significantly associated with exposure. The majority of children in smoking families were measurably exposed to TSE, even if smoking was restricted to designated areas in the home, balconies, orgarden/yard/other outdoor areas. Reducing population smoking rates, particularly among parents, restricting smoking to at least 10 meters from homes and children, and denormalizing smoking around others are recommended to reduce population-level child TSE and tobacco-attributable disease and death.

Parental Perceptions of Children's Exposure to Tobacco Smoke and Parental Smoking Behaviour.

Around 40% of children are exposed to tobacco smoke, increasing their risk of poor health. Previous research has demonstrated misunderstanding among smoking parents regarding children's exposure. The parental perceptions of exposure (PPE) measure uses visual and textual vignettes to assess awareness of exposure to smoke. The study aimed to determine whether PPE is related to biochemical and reported measures of exposure in children with smoking parents. Families with at least one smoking parent and a child ≤ age 8 were recruited. In total, 82 parents completed the PPE questionnaire, which was assessed on a scale of 1-7 with higher scores denoting a broader perception of exposure. Parents provided a sample of their child's hair and a self-report of parental smoking habits. Parents who reported smoking away from home had higher PPE ratings than parents who smoke in and around the home (p = 0.026), constituting a medium effect size. PPE corresponded with home smoking frequency, with rare or no home exposure associated with higher PPE scores compared to daily or weekly exposure (p < 0.001). PPE was not significantly related to hair nicotine but was a significant explanatory factor for home smoking location. PPE was significantly associated with parental smoking behaviour, including location and frequency. High PPE was associated with lower exposure according to parental report. This implies that parental understanding of exposure affects protective behaviour and constitutes a potential target for intervention to help protect children.

Changing Exposure Perceptions: A Randomized Controlled Trial of an Intervention with Smoking Parents.

Children who live with smokers are at risk of poor health, and of becoming smokers themselves. Misperceptions of the nature of tobacco smoke exposure have been demonstrated among parents, resulting in continued smoking in their children's environment. This study aimed to change parents' perceptions of exposure by providing information on second- and third-hand exposure and personalised information on children's exposure [NIH registry (NCT02867241)]. One hundred and fifty-nine families with a child < 8 years and at least one smoking parent were randomized into intervention (69), control (70), and enhanced control (20) groups. Reported exposure, parental smoking details, and a child hair sample were obtained at the start of the study and 6-8 months later. Parental perceptions of exposure (PPE) were assessed via a questionnaire. The intervention consisted of motivational interviews, feedback of home air quality and child's hair nicotine level, and information brochures. PPE were significantly higher at the study end (94.6 ± 17.6) compared to study beginning (86.5 ± 19.3) in intervention and enhanced control groups (t(72) = -3.950; p < 0.001). PPE at study end were significantly higher in the intervention group compared to the regular control group (p = 0.020). There was no significant interaction between time and group. Parallel changes in parental smoking behaviour were found. Parental perceptions of exposure were increased significantly post intervention, indicating that they can be altered. By making parents more aware of exposure and the circumstances in which it occurs, we can help parents change their smoking behaviour and better protect their children.

Estimation in the Cox survival regression model with covariate measurement error and a changepoint.

The Cox regression model is a popular model for analyzing the relationship between a covariate vector and a survival endpoint. The standard Cox model assumes a constant covariate effect across the entire covariate domain. However, in many epidemiological and other applications, the covariate of main interest is subject to a threshold effect: a change in the slope at a certain point within the covariate domain. Often, the covariate of interest is subject to some degree of measurement error. In this paper, we study measurement error correction in the case where the threshold is known. Several bias correction methods are examined: two versions of regression calibration (RC1 and RC2, the latter of which is new), two methods based on the induced relative risk under a rare event assumption (RR1 and RR2, the latter of which is new), a maximum pseudo-partial likelihood estimator (MPPLE), and simulation-extrapolation (SIMEX). We develop the theory, present simulations comparing the methods, and illustrate their use on data concerning the relationship between chronic air pollution exposure to particulate matter PM10 and fatal myocardial infarction (Nurses Health Study (NHS)), and on data concerning the effect of a subject's long-term underlying systolic blood pressure level on the risk of cardiovascular disease death (Framingham Heart Study (FHS)). The simulations indicate that the best methods are RR2 and MPPLE.

A modified partial likelihood score method for Cox regression with covariate error under the internal validation design.

We develop a new method for covariate error correction in the Cox survival regression model, given a modest sample of internal validation data. Unlike most previous methods for this setting, our method can handle covariate error of arbitrary form. Asymptotic properties of the estimator are derived. In a simulation study, the method was found to perform very well in terms of bias reduction and confidence interval coverage. The method is applied to data from the Health Professionals Follow-Up Study (HPFS) on the effect of diet on incidence of Type II diabetes.

On Estimation of the Hazard Function from Population-based Case-Control Studies.

The population-based case-control study design has been widely used for studying the etiology of chronic diseases. It is well established that the Cox proportional hazards model can be adapted to the case-control study and hazard ratios can be estimated by (conditional) logistic regression model with time as either a matched set or a covariate (Prentice and Breslow, 1978). However, the baseline hazard function, a critical component in absolute risk assessment, is unidentifiable, because the ratio of cases and controls is controlled by the investigators and does not reflect the true disease incidence rate in the population. In this paper we propose a simple and innovative approach, which makes use of routinely collected family history information, to estimate the baseline hazard function for any logistic regression model that is fit to the risk factor data collected on cases and controls. We establish that the proposed baseline hazard function estimator is consistent and asymptotically normal and show via simulation that it performs well in finite samples. We illustrate the proposed method by a population-based case-control study of prostate cancer where the association of various risk factors is assessed and the family history information is used to estimate the baseline hazard function.

Parental Perceptions and Misconceptions of Child Tobacco Smoke Exposure.

Introduction: Forty percent of young children worldwide are exposed to the harmful effects of tobacco smoke, predominantly by parental smoking. Little is known about why parents regularly expose their children to these risks; perhaps parents underestimate the degree of exposure. Qualitative methods were used to investigate parental perceptions of tobacco smoke exposure. Methods: Sixty-five in-depth interviews were conducted with parents of young children in smoking families in central Israel. Parents were asked to explain what "exposure to smoking" meant. Thematic analysis was performed, a conceptual model of perceptions was built, and misconceptions were identified. Results: Parents reported that exposure occurs when smoke or smokers are visible, when smoke can be smelled, felt, or inhaled, or when it "reaches" an individual. Conversely, some believed that exposure does not occur in the absence of odor, visible smoke, or smokers or if smoking occurs outdoors or in indoor ventilated environments. Proximity in space and time affected perceptions of exposure; some parents believed that smoke does not spread far but dissipates rapidly. There was some uncertainty regarding whether or not exposure was occurring. Conclusions: Awareness of child exposure to tobacco smoke among parents in this study was based on sensory perceptions in the context of the physical environment. The limited capacity of humans to perceive tobacco smoke can lead to misconceptions about exposure. In order to protect children, parents must be convinced that exposure can occur even in situations where they are unable to sense it. Implications: Parents use sensory perceptions (sight, smell, and feel) in the context of the physical environment to assess whether or not their children are exposed to tobacco smoke. Because 85% of smoke is invisible and the sense of smell is unreliable, assessments based on sensory perceptions cannot provide accurate information about the presence of tobacco smoke. In order to protect children, parents must be convinced that exposure can occur even in situations where they are unable to sense it. The scientific information summarized here about exposure in common situations should be useful in persuading parents to protect their children. Clinical Trial Registration: This study is registered as a Phase I study which is part of a larger research endeavor entitled: A program to protect young children from tobacco smoke exposure. Registration number: NCT01335178.

Instrumental variables estimation of exposure effects on a time-to-event endpoint using structural cumulative survival models.

The use of instrumental variables for estimating the effect of an exposure on an outcome is popular in econometrics, and increasingly so in epidemiology. This increasing popularity may be attributed to the natural occurrence of instrumental variables in observational studies that incorporate elements of randomization, either by design or by nature (e.g., random inheritance of genes). Instrumental variables estimation of exposure effects is well established for continuous outcomes and to some extent for binary outcomes. It is, however, largely lacking for time-to-event outcomes because of complications due to censoring and survivorship bias. In this article, we make a novel proposal under a class of structural cumulative survival models which parameterize time-varying effects of a point exposure directly on the scale of the survival function; these models are essentially equivalent with a semi-parametric variant of the instrumental variables additive hazards model. We propose a class of recursive instrumental variable estimators for these exposure effects, and derive their large sample properties along with inferential tools. We examine the performance of the proposed method in simulation studies and illustrate it in a Mendelian randomization study to evaluate the effect of diabetes on mortality using data from the Health and Retirement Study. We further use the proposed method to investigate potential benefit from breast cancer screening on subsequent breast cancer mortality based on the HIP-study.

Development and Validation of Diagnostic Criteria for IBD Subtypes Including IBD-unclassified in Children: a Multicentre Study From the Pediatric IBD Porto Group of ESPGHAN.

BACKGROUND: The revised Porto criteria identify subtypes of paediatric inflammatory bowel diseases: ulcerative colitis [UC], atypical UC, inflammatory bowel disease unclassified [IBDU], and Crohn's disease [CD]. Others have proposed another subclassifiction of Crohn's colitis. In continuation of the Porto criteria, we aimed to derive and validate criteria, termed "PIBD-classes," for standardising the classification of the different IBD subtypes. METHODS: This was a multicentre retrospective longitudinal study from 23 centres affiliated with the Port -group of ESPGHAN. Both a hypothesis-driven judgmental approach and mathematical classification and regression tree [CART] modelling were used for creating a diagnostic algorithm. Since small bowel inflammation is easily recognised as CD, we focused here primarily on the phenotype of colitis. RESULTS: In all, 749 IBD children were enrolled: 236 [32%] Crohn's colitis, 272 [36%] UC and 241 [32%] IBDU [age 10.9 ± 3.6 years] with a median follow-up of 2.8 years (interquartile range [IQR] 1.7-4.3). A total of 23 features were clustered in three classes according to their prevalence in UC: six class-1 features [0% prevalence in UC], 12 class-2 features [< 5% prevalence], and five class-3 features [5-10% prevalence]. According to the algorithm, the disease should be classified as UC if no features exist in any of the classes. When at least one feature exists, different combinations classify the disease into atypical UC, IBDU or CD. The algorithm differentiated UC from CD and IBDU with 80% sensitivity (95% confidence interval [CI] 71-88%) and 84% specificity [77-89%], and CD from IBDU and UC with 78% sensitivity [67-87%] and 94% specificity [89-97%]. CONCLUSIONS: The validated PIBD-classes algorithm can adequately classify children with IBD into small bowel CD, colonic CD, IBDU, atypical UC, and UC.

Accounting for measurement error in biomarker data and misclassification of subtypes in the analysis of tumor data.

A common paradigm in dealing with heterogeneity across tumors in cancer analysis is to cluster the tumors into subtypes using marker data on the tumor, and then to analyze each of the clusters separately. A more specific target is to investigate the association between risk factors and specific subtypes and to use the results for personalized preventive treatment. This task is usually carried out in two steps-clustering and risk factor assessment. However, two sources of measurement error arise in these problems. The first is the measurement error in the biomarker values. The second is the misclassification error when assigning observations to clusters. We consider the case with a specified set of relevant markers and propose a unified single-likelihood approach for normally distributed biomarkers. As an alternative, we consider a two-step procedure with the tumor type misclassification error taken into account in the second-step risk factor analysis. We describe our method for binary data and also for survival analysis data using a modified version of the Cox model. We present asymptotic theory for the proposed estimators. Simulation results indicate that our methods significantly lower the bias with a small price being paid in terms of variance. We present an analysis of breast cancer data from the Nurses' Health Study to demonstrate the utility of our method. Copyright © 2016 John Wiley & Sons, Ltd.

Parental receptivity to child biomarker testing for tobacco smoke exposure: A qualitative study.

OBJECTIVE: Widespread tobacco smoke exposure (TSE) of children suggests that parents may be unaware of their children's exposure. Biomarkers demonstrate exposure and may motivate behavior change, but their acceptability is not well understood. METHODS: Sixty-five in-depth interviews were conducted with parents of young children, in smoking families in central Israel. Data were analyzed using thematic analysis. RESULTS: Consent to testing was associated with desire for information, for reassurance or to motivate change, and with concerns for long-term health, taking responsibility for one's child, and trust in research. Opposition to testing was associated with preference to avoid knowledge, reluctance to cause short-term discomfort, perceived powerlessness, and mistrust of research. Most parents expressed willingness to allow measurement by urine (83%), hair (88%), or saliva (93%), but not blood samples (43%); and believed that test results could motivate behavior change. CONCLUSIONS: Parents were receptive to non-invasive child biomarker testing. Biomarker information could help persuade parents who smoke that their children need protection. PRACTICE IMPLICATIONS: Biomarker testing of children in smoking families is an acceptable and promising tool for education, counseling, and motivation of parents to protect their children from TSE. Additionally, biomarker testing allows objective assessment of population-level child TSE.

Calibrated predictions for multivariate competing risks models.

Prediction models for time-to-event data play a prominent role in assessing the individual risk of a disease, such as cancer. Accurate disease prediction models provide an efficient tool for identifying individuals at high risk, and provide the groundwork for estimating the population burden and cost of disease and for developing patient care guidelines. We focus on risk prediction of a disease in which family history is an important risk factor that reflects inherited genetic susceptibility, shared environment, and common behavior patterns. In this work family history is accommodated using frailty models, with the main novel feature being allowing for competing risks, such as other diseases or mortality. We show through a simulation study that naively treating competing risks as independent right censoring events results in non-calibrated predictions, with the expected number of events overestimated. Discrimination performance is not affected by ignoring competing risks. Our proposed prediction methodologies correctly account for competing events, are very well calibrated, and easy to implement.

A regularization corrected score method for nonlinear regression models with covariate error.

Many regression analyses involve explanatory variables that are measured with error, and failing to account for this error is well known to lead to biased point and interval estimates of the regression coefficients. We present here a new general method for adjusting for covariate error. Our method consists of an approximate version of the Stefanski-Nakamura corrected score approach, using the method of regularization to obtain an approximate solution of the relevant integral equation. We develop the theory in the setting of classical likelihood models; this setting covers, for example, linear regression, nonlinear regression, logistic regression, and Poisson regression. The method is extremely general in terms of the types of measurement error models covered, and is a functional method in the sense of not involving assumptions on the distribution of the true covariate. We discuss the theoretical properties of the method and present simulation results in the logistic regression setting (univariate and multivariate). For illustration, we apply the method to data from the Harvard Nurses' Health Study concerning the relationship between physical activity and breast cancer mortality in the period following a diagnosis of breast cancer.

Power comparison of summary measure, mixed model, and survival analysis methods for analysis of repeated-measures trials.

In two-group repeated-measures studies, a traditional statistical approach is to base analysis directly on the observed continuous measurements, using either summary measures or a mixed linear model. In some medical studies, however, an alternate approach has been taken: Declare the occurrence of an "event" when the sequence of measurements crosses a prespecified threshold, and compare the groups with respect to time to event using the log-rank test. This approach is appealing to clinicians, but clearly involves a loss of information and therefore statistical efficiency. The aim of this article is to quantify the degree of power loss in the context of the random line model. We also compare the summary measures approach to the random line approach. In regard to the efficiency loss with the survival analysis approach, we find that the loss ranges, depending on the location of the threshold, from moderate to dramatic. Using an optimally weighted log-rank test in place of the standard log-rank test leads to minimal gain in efficiency. In regard to analysis based on the original continuous measurements, for testing the slope a weighted summary measure appears to be the best overall choice, whereas for testing the intercept the maximum likelihood (ML) approach is typically much more efficient than the summary measures approach, although the efficiency of the ML approach can be compromised in studies with a small number of observation timepoints. These results have obvious implications for the choice of study design and analysis.

Development, design, and conceptual issues of project zero exposure: A program to protect young children from tobacco smoke exposure.

BACKGROUND: Tobacco smoke exposure (TSE) is a serious threat to child health. Roughly 40% of children worldwide are exposed to tobacco smoke, and the very young are often "captive smokers" in homes in which others smoke.The goal of this research project is to develop and evaluate an intervention to reduce young child tobacco smoke exposure. The objective of this paper is to document our approach to building the intervention, to describe the planned intervention, and to explore the conceptual issues regarding the intervention and its evaluation. METHODS/DESIGN: This project is being developed using an iterative approach. We are currently in the middle of Stage 1. In this first stage, Intervention Development, we have already conducted a comprehensive search of the professional literature and internet resources, consulted with experts in the field, and conducted several Design Workshops. The planned intervention consists of parental group support therapy, a website to allow use of an "online/offline" approach, involvement of pediatricians, use of a video simulation game ("Dr. Cruz") to teach parents about child TSE, and personalized biochemical feedback on exposure levels. As part of this stage we will draw on a social marketing approach. We plan to use in-depth interviews and focus groups in order to identify barriers for behavior change, and to test the acceptability of program components.In Stage II, we plan to pilot the planned intervention with 5-10 groups of 10 parents each.In Stage III, we plan to implement and evaluate the intervention using a cluster randomized controlled trial with an estimated 540 participants. DISCUSSION: The major challenges in this research are twofold: building an effective intervention and measuring the effects of the intervention. Creation of an effective intervention to protect children from TSE is a challenging but sorely needed public health endeavor. We hope that our approach will contribute to building a stronger evidence base for control of child exposure to tobacco smoke.

Survival analysis with error-prone time-varying covariates: a risk set calibration approach.

Occupational, environmental, and nutritional epidemiologists are often interested in estimating the prospective effect of time-varying exposure variables such as cumulative exposure or cumulative updated average exposure, in relation to chronic disease endpoints such as cancer incidence and mortality. From exposure validation studies, it is apparent that many of the variables of interest are measured with moderate to substantial error. Although the ordinary regression calibration (ORC) approach is approximately valid and efficient for measurement error correction of relative risk estimates from the Cox model with time-independent point exposures when the disease is rare, it is not adaptable for use with time-varying exposures. By recalibrating the measurement error model within each risk set, a risk set regression calibration (RRC) method is proposed for this setting. An algorithm for a bias-corrected point estimate of the relative risk using an RRC approach is presented, followed by the derivation of an estimate of its variance, resulting in a sandwich estimator. Emphasis is on methods applicable to the main study/external validation study design, which arises in important applications. Simulation studies under several assumptions about the error model were carried out, which demonstrated the validity and efficiency of the method in finite samples. The method was applied to a study of diet and cancer from Harvard's Health Professionals Follow-up Study (HPFS).

Second-hand smoke levels in Israeli bars, pubs and cafes before and after implementation of smoke-free legislation.

BACKGROUND: In 2007, Israel passed a law to extend existing restrictions on smoking in public places and to strengthen enforcement. Responsibility for ensuring smoke-free indoor public places was placed on establishment owners. Bars and pubs were included in the law for the first time. This study aimed to assess changes in air quality in popular Israeli bars, pubs and cafes after the implementation of law, and to examine changes in patron numbers, percentage of smoking patrons and venue-seating sections. METHODS: Air quality was determined by measuring respirable suspended particles (PM(2.5) µg(-3)) in 33 randomly selected venues in Jerusalem and Tel Aviv, including bars, pubs and cafes, before and after law implementation. Numbers of patrons and smoking patrons were recorded. RESULTS: Average respirable small particles (RSP) level was 245 µg(-3) prior to implementation and 161 µg(-3) following implementation of the law, representing a decline of 34% (P = 0.0043). RSP levels decreased in bars and pubs and in cafes. Percentage of smoking patrons declined from 19% to 9% (P = 0.0036). The magnitude of the effect decreased over time (P = 0.0039). Non-smoking establishments were more common following the legislation (P = 0.0047). CONCLUSION: Indoor air pollution from second-hand smoke in Israeli bars, pubs and cafes in Jerusalem and Tel Aviv declined following the implementation of law. This demonstrates that a law to extend existing restrictions and enforcement policies may help protect workers and patrons from tobacco smoke. However, RSP levels in Israeli bars and pubs, especially in Tel Aviv, remain unacceptably high. Enforced, 100% smoke-free laws are essential for complete protection.