Multiple Related Diseases Research Articles

Crosstalk between the gut microbiota and innate lymphoid cells in intestinal mucosal immunity.

The human gastrointestinal mucosa is colonized by thousands of microorganisms, which participate in a variety of physiological functions. Intestinal dysbiosis is closely associated with the pathogenesis of several human diseases. Innate lymphoid cells (ILCs), which include NK cells, ILC1s, ILC2s, ILC3s and LTi cells, are a type of innate immune cells. They are enriched in the mucosal tissues of the body, and have recently received extensive attention. The gut microbiota and its metabolites play important roles in various intestinal mucosal diseases, such as inflammatory bowel disease (IBD), allergic disease, and cancer. Therefore, studies on ILCs and their interaction with the gut microbiota have great clinical significance owing to their potential for identifying pharmacotherapy targets for multiple related diseases. This review expounds on the progress in research on ILCs differentiation and development, the biological functions of the intestinal microbiota, and its interaction with ILCs in disease conditions in order to provide novel ideas for disease treatment in the future.

Leber's hereditary optic neuropathy companied with multiple-related diseases.

To elucidate the clinical, radiologic characteristics of Leber's hereditary optic neuropathy (LHON) associated with the other diseases. Clinical data were retrospectively collected from hospitalized patients with LHON associated with the other diseases at the Neuro-Ophthalmology Department at the Chinese People's Liberation Army General Hospital (PLAGH) from December 2014 to October 2018. A total of 13 patients, 24 eyes (10 men and 3 women; mean age, 30.69 ± 12.76 years) with LHON mitochondrial DNA (mtDNA) mutations, were included in the cohort. 14502(5)11778(4)11778 &11696(1)12811(1)11696(1)3460(1). One patient was positive for aquaporin-4 antibody (AQP4-Ab), and two were positive for myelin oligodendrocyte glycoprotein antibody (MOG-Ab). Three patients were associated with idiopathic optic neuritis (ON). Two patients were with compression optic neuropathy. Three patients were with the central nervous system (CNS) diseases. One patient was with proliferative diabetic retinopathy (PDR) and one with idiopathic orbital inflammatory syndrome (IOIS). At the onset, visual acuity (VA) in eighteen eyes was below 0.1, one eye was 0.5, five eyes were above 0.5, while VA in sixteen eyes was below a 0.1 outcome, three eyes experienced moderate vision loss. MRI images showed T2 lesions and enhancement in nine patients who received corticosteroids treatment; additional immune modulators treatment was performed on two patients. None of the patients had relapse during the follow-up time. Leber's hereditary optic neuropathy can be accompanied with multiple-related diseases, especially different subtypes of ON, which were also exhibited with IOIS and compression optic neuropathy for the first time in this cohort. This condition may be a distinct entity with an unusual clinical and therapeutic profile.

Bayesian hierarchical modeling of joint spatiotemporal risk patterns for Human Immunodeficiency Virus (HIV) and Tuberculosis (TB) in Kenya.

The simultaneous spatiotemporal modeling of multiple related diseases strengthens inferences by borrowing information between related diseases. Numerous research contributions to spatiotemporal modeling approaches exhibit their strengths differently with increasing complexity. However, contributions that combine spatiotemporal approaches to modeling of multiple diseases simultaneously are not so common. We present a full Bayesian hierarchical spatio-temporal approach to the joint modeling of Human Immunodeficiency Virus and Tuberculosis incidences in Kenya. Using case notification data for the period 2012-2017, we estimated the model parameters and determined the joint spatial patterns and temporal variations. Our model included specific and shared spatial and temporal effects. The specific random effects allowed for departures from the shared patterns for the different diseases. The space-time interaction term characterized the underlying spatial patterns with every temporal fluctuation. We assumed the shared random effects to be the structured effects and the disease-specific random effects to be unstructured effects. We detected the spatial similarity in the distribution of Tuberculosis and Human Immunodeficiency Virus in approximately 29 counties around the western, central and southern regions of Kenya. The distribution of the shared relative risks had minimal difference with the Human Immunodeficiency Virus disease-specific relative risk whereas that of Tuberculosis presented many more counties as high-risk areas. The flexibility and informative outputs of Bayesian Hierarchical Models enabled us to identify the similarities and differences in the distribution of the relative risks associated with each disease. Estimating the Human Immunodeficiency Virus and Tuberculosis shared relative risks provide additional insights towards collaborative monitoring of the diseases and control efforts.

GWAS of five gynecologic diseases and cross-trait analysis in Japanese

We performed genome-wide association studies of five gynecologic diseases using data of 46,837 subjects (5236 uterine fibroid, 645 endometriosis, 647 ovarian cancer (OC), 909 uterine endometrial cancer (UEC), and 538 uterine cervical cancer (UCC) cases allowing overlaps, and 39,556 shared female controls) from Biobank Japan Project. We used the population-specific imputation reference panel (n = 3541), yielding 7,645,193 imputed variants. Analyses performed under logistic model, linear mixed model, and model incorporating correlations identified nine significant associations with three gynecologic diseases including four novel findings (rs79219469:C > T, LINC02183, P = 3.3 × 10−8 and rs567534295:C > T, BRCA1, P = 3.1 × 10−8 with OC, rs150806792:C > T, INS-IGF2, P = 4.9 × 10−8 and rs140991990:A > G, SOX9, P = 3.3 × 10−8 with UCC). Random-effect meta-analysis of the five GWASs correcting for the overlapping subjects suggested one novel shared risk locus (rs937380553:A > G, LOC730100, P = 2.0 × 10−8). Reverse regression analysis identified three additional novel associations (rs73494486:C > T, GABBR2, P = 4.8 × 10−8, rs145152209:A > G, SH3GL3/BNC1, P = 3.3 × 10−8, and rs147427629:G > A, LOC107985484, P = 3.8 × 10−8). Estimated heritability ranged from 0.026 for OC to 0.220 for endometriosis. Genetic correlations were relatively strong between OC and UEC, endometriosis and OC, and uterine fibroid and OC (rg > 0.79) compared with relatively weak correlations between UCC and the other four (rg = −0.08 ~ 0.25). We successfully identified genetic associations with gynecologic diseases in the Japanese population. Shared genetic effects among multiple related diseases may help understanding the pathophysiology.

Spatiotemporal multivariate mixture models for Bayesian model selection in disease mapping

It is often the case that researchers wish to simultaneously explore the behavior of and estimate overall risk for multiple, related diseases with varying rarity while accounting for potential spatial and/or temporal correlation. In this paper, we propose a flexible class of multivariate spatio-temporal mixture models to fill this role. Further, these models offer flexibility with the potential for model selection as well as the ability to accommodate lifestyle, socio-economic, and physical environmental variables with spatial, temporal, or both structures. Here, we explore the capability of this approach via a large scale simulation study and examine a motivating data example involving three cancers in South Carolina. The results which are focused on four model variants suggest that all models possess the ability to recover simulation ground truth and display improved model fit over two baseline Knorr-Held spatio-temporal interaction model variants in a real data application.

A functional U-statistic method for association analysis of sequencing data.

Although sequencing studies hold great promise for uncovering novel variants predisposing to human diseases, the high dimensionality of the sequencing data brings tremendous challenges to data analysis. Moreover, for many complex diseases (e.g., psychiatric disorders) multiple related phenotypes are collected. These phenotypes can be different measurements of an underlying disease, or measurements characterizing multiple related diseases for studying common genetic mechanism. Although jointly analyzing these phenotypes could potentially increase the power of identifying disease-associated genes, the different types of phenotypes pose challenges for association analysis. To address these challenges, we propose a nonparametric method, functional U-statistic method (FU), for multivariate analysis of sequencing data. It first constructs smooth functions from individuals' sequencing data, and then tests the association of these functions with multiple phenotypes by using a U-statistic. The method provides a general framework for analyzing various types of phenotypes (e.g., binary and continuous phenotypes) with unknown distributions. Fitting the genetic variants within a gene using a smoothing function also allows us to capture complexities of gene structure (e.g., linkage disequilibrium, LD), which could potentially increase the power of association analysis. Through simulations, we compared our method to the multivariate outcome score test (MOST), and found that our test attained better performance than MOST. In a real data application, we apply our method to the sequencing data from Minnesota Twin Study (MTS) and found potential associations of several nicotine receptor subunit (CHRN) genes, including CHRNB3, associated with nicotine dependence and/or alcohol dependence.

Major Problems to the Clinical Reassessment of Celiac Disease in Elderly

Old age seems to be the greatest risk factor for celiac disease. Strict adherence to a gluten-free diet is difficult for these patients. We present the clinical reassessment in the case of a third age patient, with celiac disease under gluten-free diet and multiple related diseases.

The Links Between Insulin Resistance, Diabetes, and Cancer

The growing epidemic of obesity has resulted in a large increase in multiple related diseases. Recent evidence has strengthened the proposed synergistic relationship between obesity-related insulin resistance (IR) and/or diabetes mellitus (DM) and cancer. Within the past year, many studies have examined this relationship. Although the precise mechanisms and pathways are uncertain, it is becoming clear that hyperinsulinemia and possibly sustained hyperglycemia are important regulators of not only the development of cancer but also of treatment outcome. Further, clinical decision-making regarding the treatment of choice for DM will likely be impacted as we learn more about the non-metabolic effects of the available hyperglycemic agents. In our review, we endeavored to synthesize the recent literature and provide a concise view of the journey from macro-level clinical associations to specific mechanistic relationships being elucidated in cell lines and animal models.

MULTIVARIATE ANALYSIS OF SPATIAL AND TEMPORAL VARIATION IN CANCER MORTALITY IN GREECE

Disease mapping is a classical epidemiological approach for studying spatial, and to a lesser degree, spatiotemporal variations in the underlying disease-specific risk surfaces (Elliott et al. (2000); Bermardinelli et al. (1995); Knorr-Held and Besag (1998)). The purpose is both to describe such variations and to generate hypotheses about the possible ‘causes’ which could explain them. In recent years there has been particular interest in the joint analysis of area-specific rates of several potentially related diseases with a view to detecting common patterns of geographical or temporal variation in the underlying disease rates. This may provide more convincing evidence of real clustering than would be available from the analysis of a single disease. It may also be possible to use existing knowledge about the aetiology of one disease to generate hypotheses regarding the aetiology of others that exhibit the same pattern of risk. However, most of the developed models have considered identification of a single pattern of risk shared by all diseases. Most diseases are multi-factorial and so it may be more plausible that there will be various different patterns of risk common to different subsets of diseases. Following an earlier attempt by Yanai et al. (1978), we use the factor analysis model with one or more common latent factors to study multiple potentially related diseases simultaneously, in space and over time. We present results of simulation studies and real data based on agestandardised mortality rates of 19 male and 22 female cancer sites across the 51 administrative districts in Greece during the period from 1980 to 1999. Preliminary results indicate strong temporal trends for lung and colorectal neoplasms in both males and females, prostatic and pancreatic neoplasms in males and breast and ovarian neoplasms in females. Furthermore, substantial differences within the country exist and marked north to south geographical trends (north: high; south: low) are observed for specific cancer sites, namely stomach and colorectal cancers for both sexes and lung cancer in males. Findings based on the simulation studies indicate that the method can reliably identify two or more major factors which correlate with underlying risk factors and are shared by a number of diseases. In summary and on the basis of our findings, factor analysis approach may be a useful exploratory tool for studying multiple related diseases simultaneously.