Bachelors, Divorcees, and Widowers: Does Marriage Protect Men from Type 2 Diabetes?

While research has suggested that being married may confer a health advantage, few studies to date have investigated the role of marital status in the development of type 2 diabetes. We examined whether men who are not married have increased risk of incident type 2 diabetes in the Health Professionals Follow-up Study. Men (n = 41,378) who were free of T2D in 1986, were followed for ≤22 years with biennial reports of T2D, marital status and covariates. Cox proportional hazard models were used to compare risk of incident T2D by marital status (married vs unmarried and married vs never married, divorced/separated, or widowed). There were 2,952 cases of incident T2D. Compared to married men, unmarried men had a 16% higher risk of developing T2D (95%CI:1.04,1.30), adjusting for age, family history of diabetes, ethnicity, lifestyle and body mass index (BMI). Relative risks (RR) for developing T2D differed for divorced/separated (1.09 [95%CI: 0.94,1.27]), widowed (1.29 [95%CI:1.06,1.57]), and never married (1.17 [95%CI:0.91,1.52]) after adjusting for age, family history of diabetes and ethnicity. Adjusting for lifestyle and BMI, the RR for T2D associated with widowhood was no longer significant (RR:1.16 [95%CI:0.95,1.41]). When allowing for a 2-year lag period between marital status and disease, RRs of T2D for widowers were augmented and borderline significant (RR:1.24 [95%CI:1.00,1.54]) after full adjustment. In conclusion, not being married, and more specifically, widowhood was more consistently associated with an increased risk of type 2 diabetes in men and this may be mediated, in part, through unfavorable changes in lifestyle, diet and adiposity

Semiparametric theory and empirical processes in causal inference

In this paper we review important aspects of semiparametric theory and empirical processes that arise in causal inference problems. We begin with a brief introduction to the general problem of causal inference, and go on to discuss estimation and inference for causal effects under semiparametric models, which allow parts of the data-generating process to be unrestricted if they are not of particular interest (i.e., nuisance functions). These models are very useful in causal problems because the outcome process is often complex and difficult to model, and there may only be information available about the treatment process (at best). Semiparametric theory gives a framework for benchmarking efficiency and constructing estimators in such settings. In the second part of the paper we discuss empirical process theory, which provides powerful tools for understanding the asymptotic behavior of semiparametric estimators that depend on flexible nonparametric estimators of nuisance functions. These tools are crucial for incorporating machine learning and other modern methods into causal inference analyses. We conclude by examining related extensions and future directions for work in semiparametric causal inference

Prostate involvement during sexually transmitted infections as measured by prostate-specific antigen concentration

Background:We investigated prostate involvement during sexually transmitted infections by measuring serum prostate-specific antigen (PSA) as a marker of prostate infection, inflammation, and/or cell damage in young, male US military members.Methods:We measured PSA before and during infection for 299 chlamydia, 112 gonorrhoea, and 59 non-chlamydial, non-gonococcal urethritis (NCNGU) cases, and 256 controls.Results:Chlamydia and gonorrhoea, but not NCNGU, cases were more likely to have a large rise (⩾40%) in PSA than controls (33.6%, 19.1%, and 8.2% vs 8.8%, P<0.0001, 0.021, and 0.92, respectively).Conclusion:Chlamydia and gonorrhoea may infect the prostate of some infected men

Proceedings of the second international molecular pathological epidemiology (MPE) meeting

Disease classification system increasingly incorporates information on pathogenic mechanisms to predict clinical outcomes and response to therapy and intervention. Technological advancements to interrogate omics (genomics, epigenomics, transcriptomics, proteomics, metabolomics, metagenomics, interactomics, etc.) provide widely-open opportunities in population-based research. Molecular pathological epidemiology (MPE) represents integrative science of molecular pathology and epidemiology. This unified paradigm requires multidisciplinary collaboration between pathology, epidemiology, biostatistics, bioinformatics, and computational biology. Integration of these fields enables better understanding of etiologic heterogeneity, disease continuum, causal inference, and the impact of environment, diet, lifestyle, host factors (including genetics and immunity), and their interactions on disease evolution. Hence, the Second International MPE Meeting was held in Boston in December 2014, with aims to: (1) develop conceptual and practical frameworks; (2) cultivate and expand opportunities; (3) address challenges; and (4) initiate the effort of specifying guidelines for MPE. The meeting mainly consisted of presentations of method developments and recent data in various malignant neoplasms and tumors (breast, prostate, ovarian and colorectal cancers, renal cell carcinoma, lymphoma, and leukemia), followed by open discussion sessions on challenges and future plans. In particular, we recognized need for efforts to further develop statistical methodologies. This meeting provided an unprecedented opportunity for interdisciplinary collaboration, consistent with the purposes of the BD2K (Big Data to Knowledge), GAME-ON (Genetic Associations and Mechanisms in Oncology), and Precision Medicine Initiatives of the U.S.A. National Institute of Health. The MPE Meeting Series can help advance transdisciplinary population science, and optimize training and education systems for 21st century medicine and public health

An IPW estimator for mediation effects in hazard models: with an application to schooling, cognitive ability and mortality

Large differences in mortality rates across those with different levels of education are a well-established fact. Cognitive ability may be affected by education so that it becomes a mediating factor in the causal chain. In this paper, we estimate the impact of education on mortality using inverse-probability-weighted (IPW) estimators. We develop an IPW estimator to analyse the mediating effect in the context of survival models. Our estimates are based on administrative data, on men born between 1944 and 1947 who were examined for military service in the Netherlands between 1961 and 1965, linked to national death records. For these men, we distinguish four education levels and we make pairwise comparisons. The results show that levels of education have hardly any impact on the mortality rate. Using the mediation method, we only find a significant effect of education on mortality running through cognitive ability, for the lowest education group that amounts to a 15% reduction in the mortality rate. For the highest education group, we find a significant effect of education on mortality through other pathways of 12%

Diagnostic, prognostic and predictive value of cell-free miRNAs in prostate cancer : A systematic review

Publisher Copyright: © 2016 Endzeliņš et al.Prostate cancer, the second most frequently diagnosed cancer in males worldwide, is estimated to be diagnosed in 1.1 million men per year. Introduction of PSA testing substantially improved early detection of prostate cancer, however it also led to overdiagnosis and subsequent overtreatment of patients with an indolent disease. Treatment outcome and management of prostate cancer could be improved by the development of non-invasive biomarker assays that aid in increasing the sensitivity and specificity of prostate cancer screening, help to distinguish aggressive from indolent disease and guide therapeutic decisions. Prostate cancer cells release miRNAs into the bloodstream, where they exist incorporated into ribonucleoprotein complexes or extracellular vesicles. Later, cell-free miRNAs have been found in various other biofluids. The initial RNA sequencing studies suggested that most of the circulating cell-free miRNAs in healthy individuals are derived from blood cells, while specific disease-associated miRNA signatures may appear in the circulation of patients affected with various diseases, including cancer. This raised a hope that cell-free miRNAs may serve as non-invasive biomarkers for prostate cancer. Indeed, a number of cell-free miRNAs that potentially may serve as diagnostic, prognostic or predictive biomarkers have been discovered in blood or other biofluids of prostate cancer patients and need to be validated in appropriately designed longitudinal studies and clinical trials. In this review, we systematically summarise studies investigating cell-free miRNAs in biofluids of prostate cancer patients and discuss the utility of the identified biomarkers in various clinical scenarios. Furthermore, we discuss the possible mechanisms of miRNA release into biofluids and outline the biological questions and technical challenges that have arisen from these studies.publishersversionPeer reviewe

Testing Gene-Gene Interactions in the Case-Parents Design

The case-parents design has been widely used to detect genetic associations as it can prevent spurious association that could occur in population-based designs. When examining the effect of an individual genetic locus on a disease, logistic regressions developed by conditioning on parental genotypes provide complete protection from spurious association caused by population stratification. However, when testing gene-gene interactions, it is unknown whether conditional logistic regressions are still robust. Here we evaluate the robustness and efficiency of several gene-gene interaction tests that are derived from conditional logistic regressions. We found that in the presence of SNP genotype correlation due to population stratification or linkage disequilibrium, tests with incorrectly specified main-genetic-effect models can lead to inflated type I error rates. We also found that a test with fully flexible main genetic effects always maintains correct test size and its robustness can be achieved with negligible sacrifice of its power. When testing gene-gene interactions is the focus, the test allowing fully flexible main effects is recommended to be used