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In patients with type 2 diabetes, sleep-disordered breathing is a widespread cause of deteriorated quality of life. However, robust prevalence estimates for sleep-disordered breathing in patients with type 2 diabetes are limited due to scarce data. We investigated sex differences in sleep-disordered breathing prevalence and its modulators in the DIACORE SDB substudy, a sample of outpatient type 2 diabetes. 721 participants were tested for sleep-disordered breathing using a two-channel sleep apnoea monitoring device. Patients were stratified according to the severity of sleep-disordered breathing, defined as an apnoea-hypopnoea index

Age-related macular degeneration (AMD) is the main cause of blindness in industrialized societies. Population-based epidemiological investigations generate important data on prevalence, incidence, risk factors, and future trends. This review summarizes the most important epidemiological studies on AMD with a focus on their transferability to Germany including existing evidence for the main risk factors for AMD development and progression. Future tasks, such as the standardization of grading systems and the use of recent retinal imaging technology in epidemiological studies are discussed. In Germany, epidemiological data on AMD are scarce. However, the need for epidemiological research in ophthalmology is currently being addressed by several recently started population-based studies.

Obesity is a heritable trait and a risk factor for many common diseases such as type 2 diabetes, heart disease, and hypertension. We used a dense whole-genome scan of DNA samples from the Framingham Heart Study participants to identify a common genetic variant near the INSIG2 gene associated with obesity. We have replicated the finding in four separate samples composed of individuals of Western European ancestry, African Americans, and children. The obesity-predisposing genotype is present in 10% of individuals. Our study suggests that common genetic polymorphisms are important determinants of obesity.

In the KORA surveys, numerous candidate genes in the context of type 2 diabetes, myocardial infarction, atherosclerosis or obesity are under investigation. Current focus is on genotyping single nucleotide polymorphism (SNPs). Haplotypes are also of increasing interest: haplotypes are combinations of alleles within a certain section of one chromosome. Analysing haplotypes in genetic association studies is often more efficient than studying the SNPs separately. A statistical problem in this context is the reconstruction of the phase: genotyping the SNPs determines the alleles of an individual at one particular locus of the DNA, but does not reveal which allele is located on which one of the two chromosomes. This information is required when talking about haplotypes. There are statistical approaches to identify the most likely two haplotypes of an individual given the genotypes. However, a certain error in prognosis is unavoidable. There are also errors in the genotypes. These errors are assumed to be small for one SNP but can accumulate over the SNPs involved in one haplotype and thus can induce further uncertainty in the haplotype. It is therefore the aim of our project to quantify the uncertainties in the haplotypes particularly for genes investigated in the KORA surveys. We conduct computer simulations based on the haplotypes and their frequencies observed in the KORA individuals and compare the results with simulations based on mathematical modelling of the evolutionary process ("coalescent models"). The uncertainties in the haplotypes have an impact on the search for association between genes and disease: an association may not be detected as the haplotype uncertainty obscures the haplotype frequency differences between cases and controls. It is a further aim of our project to elucidate the extent of this problem and to develop strategies for reducing it.

It is well established that odds ratios estimated by logistic regression are subject to bias if exposure is measured with error. The dependence of this bias on exposure parameter values, particularly for multiplicative measurement error, and its implications in epidemiology are not, however, as fully acknowledged. We have been motivated by a German West case-control study on lung cancer and residential radon, where restriction to a subgroup exhibiting larger mean and variance of exposure than the entire group has shown higher odds ratio estimates as compared to the full analysis. By means of correction formulae and simulations, we show that bias from additive classical type error depends on the exposure variance, not on the exposure mean, and that bias from multiplicative classical type error depends on the geometric standard deviation (in other words on the coefficient of variation of exposure), but not on the geometric mean of exposure. Bias from additive or multiplicative Berkson type error is independent of exposure distribution parameters. This indicates that there is a potential of differential bias between groups where these parameters vary. Such groups are commonly compared in epidemiology: for example when the results of subgroup analyses are contrasted or meta-analyses are performed. For the German West radon study, we show that the difference of measurement error bias between the subgroup and the entire group exhibits the same direction but not the same dimension as the observed results. Regarding meta-analysis of five European radon studies, we find that a study such as this German study will necessarily result in smaller odds ratio estimates than other studies due to the smaller exposure variance and coefficient of variation of exposure. Therefore, disregard of measurement error can not only lead to biased estimates, but also to inconsistent results and wrongly concluded effect differences between groups.

Item does not contain fulltext The male-to-female sex ratio at birth is constant across world populations with an average of 1.06 (106 male to 100 female live births) for populations of European descent. The sex ratio is considered to be affected by numerous biological and environmental factors and to have a heritable component. The aim of this study was to investigate the presence of common allele modest effects at autosomal and chromosome X variants that could explain the observed sex ratio at birth. We conducted a large-scale genome-wide association scan (GWAS) meta-analysis across 51 studies, comprising overall 114 863 individuals (61 094 women and 53 769 men) of European ancestry and 2 623 828 common (minor allele frequency >0.05) single-nucleotide polymorphisms (SNPs). Allele frequencies were compared between men and women for directly-typed and imputed variants within each study. Forward-time simulations for unlinked, neutral, autosomal, common loci were performed under the demographic model for European populations with a fixed sex ratio and a random mating scheme to assess the probability of detecting significant allele frequency differences. We do not detect any genome-wide significant (P < 5 x 10(-8)) common SNP differences between men and women in this well-powered meta-analysis. The simulated data provided results entirely consistent with these findings. This large-scale investigation across approximately 115 000 individuals shows no detectable contribution from common genetic variants to the observed skew in the sex ratio. The absence of sex-specific differences is useful in guiding genetic association study design, for example when using mixed controls for sex-biased traits.