Disease Status Of Individuals Research Articles

Genetic analysis of infectious diseases: estimating gene effects for susceptibility and infectivity.

BackgroundGenetic selection of livestock against infectious diseases can complement existing interventions to control infectious diseases. Most genetic approaches that aim at reducing disease prevalence assume that individual disease status (infected/not-infected) is solely a function of its susceptibility to a particular pathogen. However, individual infectivity also affects the risk and prevalence of an infection in a population. Variation in susceptibility and infectivity between hosts affects transmission of an infection in the population, which is usually measured by the value of the basic reproduction ratio R0. R0 is an important epidemiological parameter that determines the risk and prevalence of infectious diseases. An individual’s breeding value for R0 is a function of its genes that influence both susceptibility and infectivity. Thus, to estimate the effects of genes on R0, we need to estimate the effects of genes on individual susceptibility and infectivity. To that end, we developed a generalized linear model (GLM) to estimate relative effects of genes for susceptibility and infectivity. A simulation was performed to investigate bias and precision of the estimates, the effect of R0, the size of the effects of genes for susceptibility and infectivity, and relatedness among group mates on bias and precision. We considered two bi-allelic loci that affect, respectively, the individuals’ susceptibility only and individuals’ infectivity only.ResultsA GLM with complementary log–log link function can be used to estimate the relative effects of genes on the individual’s susceptibility and infectivity. The model was developed from an equation that describes the probability of an individual to become infected as a function of its own susceptibility genotype and infectivity genotypes of all its infected group mates. Results show that bias is smaller when R0 ranges approximately from 1.8 to 3.1 and relatedness among group mates is higher. With larger effects, both absolute and relative standard deviations become clearly smaller, but the relative bias remains the same.ConclusionsWe developed a GLM to estimate the relative effect of genes that affect individual susceptibility and infectivity. This model can be used in genome-wide association studies that aim at identifying genes that influence the prevalence of infectious diseases.

Metagenomic Analysis of Crohn's Disease Patients Identifies Changes in the Virome and Microbiome Related to Disease Status and Therapy, and Detects Potential Interactions and Biomarkers.

The aim of this study was to survey the bacterial and viral communities in different types of samples from patients with Crohn's disease (CD) at different stages of the disease to relate their distribution with the origin and progression of this disorder. A total of 42 fecal samples and 15 biopsies from 20 patients with CD and 20 healthy control individuals were collected for bacterial 16S rRNA gene profiling and DNA/RNA virome metagenomic analysis through 454 pyrosequencing. Their composition, abundance, and diversity were analyzed, and comparisons of disease status, patient status, and sample origin were used to determine statistical differences between the groups. Bacterial composition and relative abundance in new-onset patients with CD differed markedly from control individuals. Individual variability and sample origin had a stronger impact on viral communities than the disease, contrary to what was observed for bacterial populations although increased numbers of overrepresented viruses were observed in feces from patients with CD. Correlation-based networks were constructed to show potential relations between bacteria and between those and viruses. The bacterial community reflects the disease status of individuals more accurately than their viral counterparts. However, numerous viral biomarkers specifically associated with CD disease were identified. Because viruses can modulate bacterial communities, the correlation networks between both communities constitute a step forward in unraveling their interactions under normal and CD disease conditions.

RETRACTED ARTICLE: Occurrence, spread and control measures of Bothriocephalus acheilognathi (Bothriocephalidae: Cestoda)

Fish infections can be readily detected at autopsy, with the recovery of entire tapeworms followed by microscopic examination of the scolex. The examination of faecal material may reveal detached segments from adult tapeworms or eggs, which possess an operculum at the apex. The presence of Bothriocephalus acheilognathi may also be achieved by a squash plate method. Glass slides or plates are used to flatten the intestinal tract and the worms are detected by reflected light and low-power microscopy. Although mature parasites may be conspicuous within the intestine of infected fish, the detection of light infections or presence of juvenile parasites and plerocercoids requires microscopic examination. While these infections may hold little importance to the disease status of individuals, detection may be critical for effective disease control and limiting the spread of infected fish. The pathology may be divided generally into: (i) damage caused by scolex attachment; and (ii) damage caused by the presence of strobila within the intestine lumen. Other organs may exhibit signs of pathological change. For example, infected fish can show signs of nutritional deficiency, with atrophy of hepatocytes within the liver. In severe cases, these changes are consistent with starvation. B. acheilognathi causes a number of physiological changes in juvenile fish. These include: (i) protein depletion; (ii) altered digestive enzyme activity; (iii) elevated muscle fatigue in heavily infected hosts; and (iv) mortality of young fishes. B. acheilognathi can have pronounced detrimental effects on fish. These include severe damage to the intestinal tract, physiological disturbance, reduced growth, condition loss and death. Records of 100 % mortality in hatchery reared common carp (Cyprinus carpio) highlight the pathogenic potential of this parasite.

On the definition and utilization of heritable variation among hosts in reproduction ratio R0 for infectious diseases.

Infectious diseases have a major role in evolution by natural selection and pose a worldwide concern in livestock. Understanding quantitative genetics of infectious diseases, therefore, is essential both for understanding the consequences of natural selection and for designing artificial selection schemes in agriculture. The basic reproduction ratio, R0, is the key parameter determining risk and severity of infectious diseases. Genetic improvement for control of infectious diseases in host populations should therefore aim at reducing R0. This requires definitions of breeding value and heritable variation for R0, and understanding of mechanisms determining response to selection. This is challenging, as R0 is an emergent trait arising from interactions among individuals in the population. Here we show how to define breeding value and heritable variation for R0 for genetically heterogeneous host populations. Furthermore, we identify mechanisms determining utilization of heritable variation for R0. Using indirect genetic effects, next-generation matrices and a SIR (Susceptible, Infected and Recovered) model, we show that an individual's breeding value for R0 is a function of its own allele frequencies for susceptibility and infectivity and of population average susceptibility and infectivity. When interacting individuals are unrelated, selection for individual disease status captures heritable variation in susceptibility only, yielding limited response in R0. With related individuals, however, there is a secondary selection process, which also captures heritable variation in infectivity and additional variation in susceptibility, yielding substantially greater response. This shows that genetic variation in susceptibility represents an indirect genetic effect. As a consequence, response in R0 increased substantially when interacting individuals were genetically related.

Fitting and interpreting continuous‐time latent Markov models for panel data

Multistate models characterize disease processes within an individual. Clinical studies often observe the disease status of individuals at discrete time points, making exact times of transitions between disease states unknown. Such panel data pose considerable modeling challenges. Assuming the disease process progresses accordingly, a standard continuous-time Markov chain (CTMC) yields tractable likelihoods, but the assumption of exponential sojourn time distributions is typically unrealistic. More flexible semi-Markov models permit generic sojourn distributions yet yield intractable likelihoods for panel data in the presence of reversible transitions. One attractive alternative is to assume that the disease process is characterized by an underlying latent CTMC, with multiple latent states mapping to each disease state. These models retain analytic tractability due to the CTMC framework but allow for flexible, duration-dependent disease state sojourn distributions. We have developed a robust and efficient expectation-maximization algorithm in this context. Our complete data state space consists of the observed data and the underlying latent trajectory, yielding computationally efficient expectation and maximization steps. Our algorithm outperforms alternative methods measured in terms of time to convergence and robustness. We also examine the frequentist performance of latent CTMC point and interval estimates of disease process functionals based on simulated data. The performance of estimates depends on time, functional, and data-generating scenario. Finally, we illustrate the interpretive power of latent CTMC models for describing disease processes on a dataset of lung transplant patients. We hope our work will encourage wider use of these models in the biomedical setting.

OP0188 Tocilizumab and Rituximab, but Not Adalimumab, Display Highly Concordant Molecular Effects in the Rheumatoid Arthritis Synovium

ObjectivesThe clinical effects of biologics in patients with rheumatoid arthritis (RA) are well known. By contrast, little is known about their molecular effects in the RA synovium, and how administration...

What difference does dependent comorbidity make in burden of disease studies? A survey analysis and simulation

BackgroundIn the absence of comprehensive individual disease status data (for all diseases for all individuals in a population), adjustments in estimates of years lived with disability (YLDs) to account for disease comorbidity are problematic. Under the Mather model commonly used to adjust disability weights for the presence of multiple diseases, simulation techniques work well if diseases are assumed to be independent, but estimating the effects on YLDs in the presence of correlated diseases is a challenge because these correlations are generally not known. MethodsUsing large population health surveys from New Zealand and Canada that asked individuals about their disease status, we were able to estimate dependent comorbidity for major chronic diseases. These served as a starting point for extensive computer simulations to estimate the effect of comorbidity on YLD estimates. FindingsComorbidity estimates for the New Zealand and Canadian population were generally consistent and there was no evidence those comorbidity estimates differed by age. Simulations using these estimates suggested that reductions in overall YLDs for dependent comorbidity beyond that required for adjustment due to independent comorbidity were small, although they did increase slightly with age. InterpretationAt least for countries in the developed world, YLD estimates for comorbidity adjusted for independent comorbidity will be sufficiently accurate that additional adjustments for dependent comorbidity may not be required. FundingUniversity of Alberta and the New Zealand Government's Ministry of Health.

Salivary diagnostics

The ability to monitor health status, disease onset and progression, and treatment outcome through non-invasive means is a most desirable goal in the health care promotion and delivery. There are three prerequisites to materialize this goal: specific biomarkers associated with a health or disease state; a non-invasive approach to detect and monitor the biomarkers; and the technologies to discriminate the biomarkers. A national initiative catalyzed by the National Institute of Dental & Craniofacial Research (NIDCR) has created a roadmap to achieve these goals through the use of oral fluids as the diagnostic medium to scrutinize the health and/or disease status of individuals. Progress has shown this is an ideal opportunity to bridge state of the art saliva-based biosensors, optimized to disease discriminatory salivary biomarkers, for diagnostic applications. Oral fluid being the 'mirror of body' is a perfect medium to be explored for health and disease surveillance. The translational applications and opportunities are enormous.

Efficiency of two-phase methods with focus on a planned population-based case-control study on air pollution and stroke

BackgroundWe plan to conduct a case-control study to investigate whether exposure to nitrogen dioxide (NO2) increases the risk of stroke. In case-control studies, selective participation can lead to bias and loss of efficiency. A two-phase design can reduce bias and improve efficiency by combining information on the non-participating subjects with information from the participating subjects. In our planned study, we will have access to individual disease status and data on NO2 exposure on group (area) level for a large population sample of Scania, southern Sweden. A smaller sub-sample will be selected to the second phase for individual-level assessment on exposure and covariables. In this paper, we simulate a case-control study based on our planned study. We develop a two-phase method for this study and compare the performance of our method with the performance of other two-phase methods.MethodsA two-phase case-control study was simulated with a varying number of first- and second-phase subjects. Estimation methods: Method 1: Effect estimation with second-phase data only. Method 2: Effect estimation by adjusting the first-phase estimate with the difference between the adjusted and unadjusted second-phase estimate. The first-phase estimate is based on individual disease status and residential address for all study subjects that are linked to register data on NO2-exposure for each geographical area. Method 3: Effect estimation by using the expectation-maximization (EM) algorithm without taking area-level register data on exposure into account. Method 4: Effect estimation by using the EM algorithm and incorporating group-level register data on NO2-exposure.ResultsThe simulated scenarios were such that, unbiased or marginally biased (< 7%) odds ratio (OR) estimates were obtained with all methods. The efficiencies of method 4, are generally higher than those of methods 1 and 2. The standard errors in method 4 decreased further when the case/control ratio is above one in the second phase. For all methods, the standard errors do not become substantially reduced when the number of first-phase controls is increased.ConclusionIn the setting described here, method 4 had the best performance in order to improve efficiency, while adjusting for varying participation rates across areas.

The Oral Fluid MEMS/NEMS Chip (OFMNC): Diagnostic &amp; Translational Applications

The ability to monitor health status, disease onset and progression, and treatment outcome through non-invasive means is a most desirable goal in health-care promotion and delivery. There are three prerequisites for this goal to be realized: specific biomarkers associated with a health or disease state, a non-invasive approach to detect and monitor the biomarkers, and the technologies to discriminate between and among the biomarkers. We present a roadmap to achieve these goals using oral fluids as the diagnostic medium to scrutinize the health and/or disease status of individuals. This is an ideal opportunity to bridge state-of-the-art micro-/nano-electromechanical system (MEMS/NEMS) sensors to oral fluid for diagnostic applications. As the "mirror of body", oral fluid is a perfect medium to be explored for health and disease surveillance. The translational applications and opportunities are enormous.

A multi-species epidemic model with spatial dynamics

A model is formulated that describes the spatial propagation of a disease that can be transmitted between multiple species. The spatial component consists, for each species, of a certain number of patches that make up the vertices of a digraph, the arcs of which represent the movement of the various species between the patches. In each of the patches and for each species, a susceptible-exposed-infectious-recovered (SEIR) epidemic model describes the evolution of the disease status of individuals. Also in each patch, there is transmission of the disease from species to species. An analysis of the system is given, beginning with results on the mobility component. A formula is derived for the computation of the basic reproduction number R(0) for sspecies and npatches, which then determines the global stability properties of the disease free equilibrium. Simulations for the spread of a disease in one species and two patches are presented.

Robust Bayesian prediction of subject disease status and population prevalence using several similar diagnostic tests.

Sometimes several diagnostic tests are performed on the same population of subjects with the aim of assessing disease status of individuals and the prevalence of the disease in the population, but no test is a reference test. Although the diagnostic tests may have the same biological underpinnings, test results may disagree for some specific animals. In that case, it may be difficult to determine disease status for individual subjects, and consequently population prevalence estimation becomes difficult. In this paper, we propose a robust method of estimating disease status and prevalence that uses heavy-tailed sampling distributions in a hierarchical model to protect against the influence of conflicting observations on inferences. If a subject has a test outcome that is discordant with the other test results then it is downweighted in diagnosing a subject's disease status, and for estimating disease prevalence. The amount of downweighting depends on the degree of conflict among the test results for the subject.

A third locus (GLC1D) for adult-onset primary open-angle glaucoma maps to the 8q23 region

Purpose: Two genes for adult-onset primary open-angle glaucoma have been mapped to chromosomes 2cen-q13 and 3q21-q24. We studied a family with adult-onset primary open-angle glaucoma in which the disease did not map to these two chromosomal regions. Methods: We ascertained a four-generation family with adult-onset primary open-angle glaucoma in which the disease status of individuals was objectively assigned using defined criteria. Complete ophthalmologic examinations, visual field testing, optic nerve head photographs, and venous blood samples were obtained. Family members were genotyped using polymerase chain reaction amplification of microsatellite polymorphic markers. Linkage analysis was performed and lod scores were calculated. Haplotype transmission data were analyzed. Results: A total of 20 subjects in three successive generations agreed to participate in the study. This included samples from eight affected subjects, one glaucoma suspect, one normal individual, and two spouses in generations II and III, and an additional eight individuals in generation IV. The phenotype in this family appears to be variable, with onset of visual field loss in middle age, followed by modest elevation of intraocular pressure and progression of the disease in older individuals. Linkage was established with a group of DNA markers located in the 8q23 region. A lod score value of 3.61 was obtained using marker D8S1471. Three other markers from the same region gave lod score values of over 3.0. Haplotype transmission data identified two recombination events that placed the gene in a 6.3-cM region flanked by D8S1830 and D8S592. The disease-bearing haplotype was inherited by eight affected subjects and three glaucoma suspects. Conclusion: We present evidence for a third adult-onset primary open-angle glaucoma locus (GLC1D) on chromosome 8q23. The genetic heterogeneity of adult-onset glaucoma is evident from the multiplicity of chromosomal loci associated with this disease.

Determining the size of a cross‐sectional sample to estimate the age‐specific incidence of an irreversible disease

The design of a cross-sectional survey to estimate the age-specific incidence of an irreversible disease is considered, where the incidence rate is not changing over time and the risk of mortality is not affected by the onset of disease. The sample is assumed to give information on the current age and disease status of individuals, but not on age at onset of the disease. We consider the problem of determining overall sample size in this context, as well as how best to choose the distribution of sampling across various age groups. These issues are considered with the aim of obtaining incidence estimates achieving acceptable levels of precision. The proposed methods are illustrated by measles incidence estimation.

Assessing the effect of time-varying covariates in cross-sectional studies

In cross-sectional studies exposure and disease status of individuals are assessed at the same point in time, but sometimes information on prior exposure status is also gathered. Under such circumstances one approach to assessing the relationship between disease and exposure is using linear or logistic regression analysis, adjusting for exposure status at different points in time. It is shown that estimates for the effect of exposure at a certain point in time, adjusted for exposure at another point in time, are obtained from comparisons between groups with different patterns of exposure. Careful interpretation of the resulting estimates is necessary, taking into account a detailed consideration of possible exposure patterns. In addition, if changes in exposure are caused by the occurrence of the disease, then adjusting for multiple measurements of exposure can give misleading results. A regression analysis on dummy variables describing possible patterns of changes in exposure is proposed as an alternative approach. This approach facilitates interpretation of the resulting estimates. Furthermore, it can serve as a diagnostic tool to check for disease related changes in exposure. For this case transferring exposure change rates of healthy subjects to diseased subjects is suggested as an ad hoc method for assessing the hypothetical current relationship between exposure and disease.