Correcting for variable age of onset in the estimation of familial relative risk when there is a secular trend in incidence of disease

We discuss the effects that a secular trend in incidence would have on estimation of familial relative risk (ratio of observed to expected cumulative incidence among relatives of index cases). For example, when age-specific incidence rates of a condition have increased during the lifetimes of relatives among whom relative risk is to be estimated, familial relative risk will be biased downward if cross-sectional, age-specific incidence data are used to estimate expected cumulative incidence among relatives. The stronger the trend and the older the ages of unaffected relatives, the greater the bias will be. Incorporating different age-specific incidence curves for different birth cohorts into the analysis is an approach we suggest for correcting the bias.     

Cancer of the Breast in Ontario

The breast is the leading cancer site among women; it accounts for 20% of all female cancer deaths. The lifetime probability of death from breast cancer for a female born in Ontario is 3.3%; that is, one in every 30 women will die of breast cancer. The risk of developing breast cancer is almost twice this figure.The medical certificate of death yields a reliable estimate of the number of persons who die of breast cancer, and the level of the age-specific breast cancer death rates has not changed over the past 30 years. Cohort analysis yields an indistinguishable mortality pattern for succeeding cohorts of women from 1871. Available information indicates no change in incidence.The stable mortality and incidence rates suggest that there has been little or no change in the survival rate, despite emphasis on early diagnosis and improvement in therapeutic skills and in technical facilities.     

Negative selection on BRCA1 susceptibility alleles sheds light on the population genetics of late-onset diseases and aging theory

The magnitude of negative selection on alleles involved in age-specific mortality decreases with age. This is the foundation of the evolutionary theory of senescence. Because of this decrease in negative selection with age, and because of the absence of reproduction after menopause, alleles involved in women's late-onset diseases are generally considered evolutionarily neutral. Recently, genetic and epidemiological data on alleles involved in late onset-diseases have become available. It is therefore timely to estimate selection on these alleles. Here, we estimate selection on BRCA1 alleles leading to susceptibility to late-onset breast and ovarian cancer. For this, we integrate estimates of the risk of developing a cancer for BRCA1-carriers into population genetics frameworks, and calculate selection coefficients on BRCA1 alleles for different demographic scenarios varying across the extent of human demography. We then explore the magnitude of negative selection on alleles leading to a diverse range of risk patterns, to capture a variety of late-onset diseases. We show that BRCA1 alleles may have been under significant negative selection during human history. Although the mean age of onset of the disease is long after menopause, variance in age of onset means that there are always enough cases occurring before the end of reproductive life to compromise the selective value of women carrying a susceptibility allele in BRCA1. This seems to be the case for an extended range of risk of onset functions varying both in mean and variance. This finding may explain the distribution of BRCA1 alleles' frequency, and also why alleles for many late-onset diseases, like certain familial forms of cancer, coronary artery diseases and Alzheimer dementia, are typically recent and rare. Finally, we discuss why the two most popular evolutionary theories of aging, mutation accumulation and antagonistic pleiotropy, may underestimate the effect of selection on survival at old ages.     

Using Lifetime Risk Estimates in Personal Genomic Profiles: Estimation of Uncertainty

Personal genome tests are now offered direct-to-consumer (DTC) via genetic variants identified by genome-wide association studies (GWAS) for common diseases. Tests report risk estimates (age-specific and lifetime) for various diseases based on genotypes at multiple loci. However, uncertainty surrounding such risk estimates has not been systematically investigated. With breast cancer as an example, we examined the combined effect of uncertainties in population incidence rates, genotype frequency, effect sizes, and models of joint effects among genetic variants on lifetime risk estimates. We performed simulations to estimate lifetime breast cancer risk for carriers and noncarriers of genetic variants. We derived population-based cancer incidence rates from Surveillance, Epidemiology, and End Results (SEER) Program and comparative international data. We used data for non-Hispanic white women from 2003 to 2005. We derived genotype frequencies and effect sizes from published GWAS and meta-analyses. For a single genetic variant in FGFR2 gene (rs2981582), combination of uncertainty in these parameters produced risk estimates where upper and lower 95% simulation intervals differed by more than 3-fold. Difference in population incidence rates was the largest contributor to variation in risk estimates. For a panel of five genetic variants, estimated lifetime risk of developing breast cancer before age 80 for a woman that carried all risk variants ranged from 6.1% to 21%, depending on assumptions of additive or multiplicative joint effects and breast cancer incidence rates. Epidemiologic parameters involved in computation of disease risk have substantial uncertainty, and cumulative uncertainty should be properly recognized. Reliance on point estimates alone could be seriously misleading.     

Partner status and survival after cancer: A competing risks analysis

ObjectiveThe survival benefits of having a partner for all cancers combined is well recognized, however its prognostic importance for individual cancer types, including competing mortality causes, is less clear. This study was undertaken to quantify the impact of partner status on survival due to cancer-specific and competing mortality causes.MethodsData were obtained from the population-based Queensland Cancer Registry on 176,050 incident cases of ten leading cancers diagnosed in Queensland (Australia) from 1996 to 2012. Flexible parametric competing-risks models were used to estimate cause-specific hazards and cumulative probabilities of death, adjusting for age, stage (breast, colorectal and melanoma only) and stratifying by sex.ResultsBoth unpartnered males and females had higher total cumulative probability of death than their partnered counterparts for each site. For example, the survival disadvantage for unpartnered males ranged from 3% to 30% with higher mortality burden from both the primary cancer and competing mortality causes. The cause-specific age-adjusted hazard ratios were also consistent with patients without a partner having increased mortality risk although the specific effect varied by site, sex and cause of death. For all combined sites, unpartnered males had a 46%, 18% and 44% higher risk of cancer-specific, other cancer and non-cancer mortality respectively with similar patterns for females. The higher mortality risk persisted after adjustment for stage.ConclusionsIt is important to better understand the mechanisms by which having a partner is beneficial following a cancer diagnosis, so that this can inform improvements in cancer management for all people with cancer.     

Nonparametric association analysis of exchangeable clustered competing risks data

Summary .  The work is motivated by the Cache County Study of Aging, a population-based study in Utah, in which sibship associations in dementia onset are of interest. Complications arise because only a fraction of the population ever develops dementia, with the majority dying without dementia. The application of standard dependence analyses for independently right-censored data may not be appropriate with such multivariate competing risks data, where death may violate the independent censoring assumption. Nonparametric estimators of the bivariate cumulative hazard function and the bivariate cumulative incidence function are adapted from the simple nonexchangeable bivariate setup to exchangeable clustered data, as needed with the large sibships in the Cache County Study. Time-dependent association measures are evaluated using these estimators. Large sample inferences are studied rigorously using empirical process techniques. The practical utility of the methodology is demonstrated with realistic samples both via simulations and via an application to the Cache County Study, where dementia onset clustering among siblings varies strongly by age.     

Lexis Diagram and Illness-Death Model: Simulating Populations in Chronic Disease Epidemiology

Chronic diseases impose a tremendous global health problem of the 21st century. Epidemiological and public health models help to gain insight into the distribution and burden of chronic diseases. Moreover, the models may help to plan appropriate interventions against risk factors. To provide accurate results, models often need to take into account three different time-scales: calendar time, age, and duration since the onset of the disease. Incidence and mortality often change with age and calendar time. In many diseases such as, for example, diabetes and dementia, the mortality of the diseased persons additionally depends on the duration of the disease. The aim of this work is to describe an algorithm and a flexible software framework for the simulation of populations moving in an illness-death model that describes the epidemiology of a chronic disease in the face of the different times-scales. We set up a discrete event simulation in continuous time involving competing risks using the freely available statistical software R. Relevant events are birth, the onset (or diagnosis) of the disease and death with or without the disease. The Lexis diagram keeps track of the different time-scales. Input data are birth rates, incidence and mortality rates, which can be given as numerical values on a grid. The algorithm manages the complex interplay between the rates and the different time-scales. As a result, for each subject in the simulated population, the algorithm provides the calendar time of birth, the age of onset of the disease (if the subject contracts the disease) and the age at death. By this means, the impact of interventions may be estimated and compared.     

A review on coronary artery disease,its risk factors,and therapeutics

Coronary artery disease (CAD) is one of the major cardiovascular diseases affecting the global human population. This disease has been proved to be the major cause of death in both the developed and developing countries. Lifestyle, environmental factors, and genetic factors pose as risk factors for the development of cardiovascular disease. The prevalence of risk factors among healthy individuals elucidates the probable occurrence of CAD in near future. Genome-wide association studies have suggested the association of chromosome 9p21.3 in the premature onset of CAD. The risk factors of CAD include diabetes mellitus, hypertension, smoking, hyperlipidemia, obesity, homocystinuria, and psychosocial stress. The eradication and management of CAD has been established through extensive studies and trials. Antiplatelet agents, nitrates, β-blockers, calcium antagonists, and ranolazine are some of the few therapeutic agents used for the relief of symptomatic angina associated with CAD.     

Joint Modeling for Cognitive Trajectory and Risk of Dementia in the Presence of Death

Summary .  Dementia is characterized by accelerated cognitive decline before and after diagnosis as compared to normal aging. It has been known that cognitive impairment occurs long before the diagnosis of dementia. For individuals who develop dementia, it is important to determine the time when the rate of cognitive decline begins to accelerate and the subsequent gap time to dementia diagnosis. For normal aging individuals, it is also useful to understand the trajectory of cognitive function until their death. A Bayesian change-point model is proposed to fit the trajectory of cognitive function for individuals who develop dementia. In real life, people in older ages are subject to two competing risks, e.g., dementia and dementia-free death. Because the majority of people do not develop dementia, a mixture model is used for survival data with competing risks, which consists of dementia onset time after the change point of cognitive function decline for demented individuals and death time for nondemented individuals. The cognitive trajectories and the survival process are modeled jointly and the parameters are estimated using the Markov chain Monte Carlo method. Using data from the Honolulu Asia Aging Study, we show the trajectories of cognitive function and the effect of education, apolipoprotein E 4 genotype, and hypertension on cognitive decline and the risk of dementia.     

An ecosystem analysis of spider mite outbreaks: physiological stimulation or natural enemy suppression

A simulation model was used to assess the role of several mechanisms proposed to be responsible for spider mite outbreaks on cotton that are typically observed following applications of insecticides. Simulation results were compared to an outbreak that occurred after two pyrethroid applications on cotton in a controlled experiment in the San Joaquin Valley of California. In the model, physiological effects were simulated by increasing spider mite fecundity and decreasing developmental duration, whereas loss of natural enemies was simulated by increasing spider mite age-specific survival. At the levels simulated, survival had the greatest impact on maximum spider mite density, degree days (^oD) to maximum density, and cumulative spider mite-^oD, whereas fecundity had the least, and developmental duration had an intermediate effect. There were substantial two-way interactions among all three life history parameters, with age-specific survival having the most influence. Survival had the greatest effect on spider mite population dynamics when in combination with short developmental duration. The influence of developmental duration on maximum spider mite density was greater than comparable percentage changes in fecundity, an effect that was more pronounced at high than at low survival. Changing fecundity, developmental duration, or age-specific survival individually did not result in a spider mite outbreak of the magnitude observed in the field. However, changing these three parameters simultaneously, resulted in a simulated maximum density of 8,000/m², which represents a 12-fold increase over the untreated control, and closely mimicked the previously observed field outbreak. It is proposed that spider mite outbreaks on cotton following insecticide applications are not solely the result of physiological stimulation, but are rather due to several life history parameters being affected simultaneously, with natural enemy-mediated survival having the greatest individual impact. Implications of chemically-induced phenomena affecting spider mite management on cotton are discussed. A copy of the crop and herbivore simulation models can be obtained by sending an IBM compatible disk to L. T. Wilson.     

A penalized likelihood approach for an illness-death model with interval-censored data: application to age-specific incidence of dementia

We consider the problem of estimating the intensity functions for a continuous time 'illness-death' model with intermittently observed data. In such a case, it may happen that a subject becomes diseased between two visits and dies without being observed. Consequently, there is an uncertainty about the precise number of transitions. Estimating the intensity of transition from health to illness by survival analysis (treating death as censoring) is biased downwards. Furthermore, the dates of transitions between states are not known exactly. We propose to estimate the intensity functions by maximizing a penalized likelihood. The method yields smooth estimates without parametric assumptions. This is illustrated using data from a large cohort study on cerebral ageing. The age-specific incidence of dementia is estimated using an illness-death approach and a survival approach.     

Nutritional contributions to dementia prevention: main issues on antioxidant micronutrients

There is an impressing body of evidence supporting the beneficial role of balanced nutrition in lowering the risk of dementia and its commonest form, Alzheimer’s disease. Nevertheless, and despite worldwide dementia epidemic, there is much unfounded skepticism and lack of information among physicians. As a result, the diagnosis of cognitive impairment occurs still far too late, at best symptomatic drugs keep being prescribed and patients and caregivers are left with little concrete support in the hands of the natural history of the disease. This review summarizes knowledge about the impact of nutrition as part of a healthy lifestyle and of micronutrients in particular on delaying and avoiding dementia onset.     

Atherosclerosis in rheumatoid arthritis and systemic lupus erythematosus

PURPOSE OF REVIEW: Our aim was to review recent studies that address the increased risk of atherosclerosis and coronary heart disease in patients with rheumatoid arthritis and systemic lupus erythematosus. We examine the strength of this association, how inflammation mediates this increased risk and what impact therapies may have. RECENT FINDINGS: Atherosclerosis is more prevalent and accelerated in both conditions. Indeed the process may actually precede the onset of clinical inflammatory disease. Metabolic alterations include insulin resistance and the generation of proinflammatory HDL. In addition, inflammatory mechanisms central to both rheumatoid arthritis and systemic lupus erythematosus such as macrophage activation, interferon-1 and complement deficiency may contribute to atherogenesis. There is still no consensus as to the value of primary preventive strategies in these conditions. However, drugs such as hydroxychloroquine seem to modify coronary heart disease risk and may improve survival. The recently developed antitumour necrosis factor drugs may also reduce coronary heart disease risk but biomarker studies to date have been inconclusive. SUMMARY: There is an urgent need for clinical trials to examine both the lipid-lowering and inflammatory hypotheses of atherosclerosis in rheumatoid arthritis and systemic lupus erythematosus. Novel targeted therapies in development may also have a major impact on future coronary heart disease risk in these conditions.     

Humanin and the Receptors for Humanin

Alzheimer’s disease (AD) is a prevalent dementia-causing neurodegenerative disease. Neuronal death is closely linked to the progression of AD-associated dementia. Accumulating evidence has established that a 24-amino-acid bioactive peptide, Humanin, protects neurons from AD-related neuronal death. A series of studies using various murine AD models including familial AD gene-expressing transgenic mice have shown that Humanin is effective against AD-related neuronal dysfunction in vivo. Most recently, it has been shown that Humanin inhibits neuronal cell death and dysfunction by binding to a novel IL-6-receptor-related receptor(s) on the cell surface involving CNTFRα, WSX-1, and gp130. These findings suggest that endogenous Humanin [or a Humanin-like substance(s)] may suppress the onset of AD-related dementia by inhibiting both AD-related neuronal cell death and dysfunction.     

Bayesian inference on age-specific survival for censored and truncated data

1. Traditional estimation of age-specific survival and mortality rates in vertebrates is limited to individuals with known age. Although this subject has been studied extensively using effective capture-recapture and capture-recovery models, inference remains challenging because of large numbers of incomplete records (i.e. unknown age of many individuals) and because of the inadequate duration of the studies. 2. Here, we present a hierarchical model for capture-recapture/recovery (CRR) data sets with large proportions of unknown times of birth and death. The model uses a Bayesian framework to draw inference on population-level age-specific demographic rates using parametric survival functions and applies this information to reconstruct times of birth and death for individuals with unknown age. 3. We simulated a set of CRR data sets with varying study span and proportions of individuals with known age, and varying recapture and recovery probabilities. We used these data sets to compare our method to a traditional CRR model, which requires knowledge of individual ages. Subsequently, we applied our method to a subset of a long-term CRR data set on Soay sheep. 4. Our results show that this method performs better than the common CRR model when sample sizes are low. Still, our model is sensitive to the choice of priors with low recapture probability and short studies. In such cases, priors that overestimate survival perform better than those that underestimate it. Also, the model was able to estimate accurately ages at death for Soay sheep, with an average error of 0.94 years and to identify differences in mortality rate between sexes. 5. Although many of the problems in the estimation of age-specific survival can be reduced through more efficient sampling schemes, most ecological data sets are still sparse and with a large proportion of missing records. Thus, improved sampling needs still to be combined with statistical models capable of overcoming the unavoidable limitations of any fieldwork. We show that our approach provides reliable estimates of parameters and unknown times of birth and death even with the most incomplete data sets while being flexible enough to accommodate multiple recapture probabilities and covariates.     

Causes of death in men with prostate cancer: Results from the Danish Prostate Cancer Registry (DAPROCAdata)

BackgroundCurrent knowledge of the validity of registry data on prostate cancer-specific death is limited. We aimed to determine the underlying cause of death among Danish men with prostate cancer, to estimate the level of misattribution of prostate cancer death, and to examine the risk of death from prostate cancer when accounting for competing risk of death.Material and methodsWe investigated a nationwide cohort of 15,878 prostate cancer patients diagnosed in 2010–2014; with 3343 deaths occurring through 2016. Blinded medical chart review was carried out for 670 deaths and compared to the national cause of death registry. Five death categories were defined: 1) prostate cancer-specific death, 2) other unspecified urological cancer death, 3) other cancer death 4) cardiovascular disease death, and 5) other causes of death. Competing risk analyses compared Cox cause-specific and Fine-Gray regression models.ResultsChart review attributed 51.2% of deaths to prostate cancer, 17.0% to cardiovascular disease, and 16.7% to other causes. The Danish Register of Causes of Death attributed 71.7% of deaths to prostate cancer when including all registered contributing causes of death, and 57.0% of deaths when including only the primary registered cause of death. The probability of death by prostate cancer was 10% at 2-year survival.ConclusionsMore than half of the deceased men in our study cohort died of their prostate cancer disease within a mean of 2.4 years of follow up. Data from the death registry is prone to misclassification, potentially overestimating the proportion of deaths from prostate cancer.     

Subjective and objective risk of breast cancer in Ashkenazi Jewish individuals at risk for BRCA1/2 mutations

The aims of the study were to (1) examine the differences between subjective and objective estimates of the risk of breast cancer in those being tested for BRCA1/2 mutations, (2) explore new ways to conceptualize risk, and (3) examine the change in subjective risk of developing breast cancer throughout the process of genetic counseling and testing. Participants were 86 Ashkenazi Jewish women with a family or personal history indicating risk for BRCA1/2 mutations. Surveys to assess subjective risk of breast cancer (percentage risk, projected age of onset, and survival time) were administered before counseling, after counseling, and after receipt of test results. Subjective percentage risk of breast cancer was compared to estimated objective risk to determine accuracy. Those with no personal history of cancer receiving positive results became more accurate from post-counseling to post-result. Those receiving positive results increased their estimate of their percentage risk, and those receiving uninformative negative results decreased their estimate of their percentage risk from post-counseling to post-result. Those without a personal history of cancer decreased in perceived risk from post-counseling to post-result. No change in projected age of onset of breast cancer or survival time with breast cancer was seen from pre- to post-counseling or from post-counseling to post-result, and no change in accuracy or in percentage risk of breast cancer was seen from pre- to post-counseling. Individuals use information from genetic counseling to form estimates of percentage risk following receipt of test results; however, projected age of onset and survival time with breast cancer, areas not targeted by genetic counseling that may be more closely linked to health behavior, do not change.     

Coronary heart disease mortality and radon exposure in the Newfoundland fluorspar miners’ cohort, 1950–2001

Kreuzer and coworkers recently reported no association between cumulative exposure to radiation and death from cardiovascular disease in a cohort of German uranium miners. Here, we report on the relationship between cumulative exposure to radon progeny and coronary heart disease among Newfoundland fluorspar miners. Previous analyses in this cohort found elevated death rates from coronary heart disease among those with higher cumulative radon exposure. However, this finding was based on a relatively small number of deaths and was not statistically significant. Since then, the follow-up of this cohort has been extended by 10 years until the end of 2001. Among the 2,070 miners in our study, 267 died from coronary heart disease. There was no trend evident between cumulative exposure to radon and the relative risk of death from coronary heart disease (P = 0.63). This finding was unchanged after adjusting for the lifetime smoking status that was available for approximately 54% of the cohort. Similarly, the cumulative radon exposure was found to be unrelated to deaths of the circulatory system, acute myocardial infarction, and cerebrovascular disease. These findings are consistent with those recently reported by Kreuzer and colleagues. We share their view that uncontrolled confounding for other coronary heart disease risk factors hinders the interpretation of the risk estimates.     

Model fitting and hypothesis testing for age-specific mortality data

Demographic studies focusing on age-specific mortality rates are becoming increasingly common throughout the fields of life-history evolution, ecology and biogerontology. Well-defined statistical techniques for quantifying patterns of mortality within a cohort and identifying differences in age-specific mortality among cohorts are needed. Here I discuss using maximum likelihood (ML) statistical methods to estimate the parameters of mathematical models, which are used to describe the change in mortality with age. ML provides a convenient and powerful framework for choosing an adequate mortality model, estimating model parameters and testing hypotheses about differences in parameters among experimental or ecological treatments. Simulations suggest that experiments designed to estimate age-specific mortality should involve at least 100-500 individuals per cohort per treatment. Significant bias in the estimation of model parameters is introduced when the mortality model is misspecified and samples are too small to detect the true mortality pattern. Furthermore, the lack of simple and efficient procedures for comparing different mortality models has forced the use of the Gompertz model, which specifies an exponentially increasing mortality with age, and which may not apply to the majority of experimental systems.     

Papillomaviruses: death-defying acts in skin cancer

Ultraviolet (UV) radiation is an environmental agent that has a major impact on humans, and cumulative exposure poses a serious risk in terms of developing skin cancer. Acute doses of UV induce apoptotic cell death in the skin via signalling pathways that are, in part, dependent on the p53 tumour suppressor protein. However, p53-independent mechanisms have also been described. Recent findings show that a high proportion of non-melanoma skin cancers contain human papillomavirus. The viral E6 protein effectively blocks the epidermal apoptotic response to UV and might play a key role in promoting tumour development in cooperation with the mutagenic effects of UV.