Age at menopause in women with type 1 diabetes is not lower than that in the general population in Finland. The only statistically significant factors independently associated with earlier menopause in our study were microvascular complications, that is, end-stage renal disease and proliferative retinopathy.

Aims/hypothesis To assess the number of live births in a population-based, retrospective cohort of women and men with childhood-onset type 1 diabetes, and matched controls. Methods The reproductive histories of people in a Finnish cohort of 2,307 women and 2,819 men with type 1 diabetes and two matched controls (for each case) were obtained from National Population Register data. All persons with diabetes were diagnosed with the disease in 1965-1979 at the age of 17 or under. A proportional hazards model was used to model the association between the rate of live births as a function of the age of an individual and the observed covariates (sex and age at onset of diabetes).Results Both women and men with diabetes had a smaller number of live births than the controls; the HR of having a first child for diabetic women compared with controls was 0.66 (95% CI 0.62, 0.71) and for men was 0.77 (95% CI 0.72, 0.83). In women, a birth cohort effect was detected; in more recent birth cohorts, the difference between diabetic women and controls as regards having children was significantly smaller than in earlier cohorts. Later age at onset of diabetes was associated with a higher rate of having a first child among men (p00.04) and having a second live birth among women (p00.002). Conclusions/interpretation Type 1 diabetes affects the number of live births in both women and men. The age at onset of diabetes is associated with the pattern of reproduction in both diabetic women and men.

Bayesian spatial modeling has become important in disease mapping and has also been suggested as a useful tool in genetic fine mapping. We have implemented the Potts model and applied it to the Genetic Analysis Workshop 14 (GAW14) simulated data. Because the "answers" were known we have analyzed latent phenotype P1-related observed phenotypes affection status (genetically determined) and i (random) in the Danacaa population replicate 2. Analysis of the microsatellite/ single-nucleotide polymorphism-based haplotypes at chromosomes 1 and 3 failed to identify multiple clusters of haplotype effects. However, the analysis of separately simulated data with postulated differences in the effects of the two clusters has yielded clear estimated division into the two clusters, demonstrating the correctness of the algorithm. Although we could not clearly identify the disease-related and the non-associated groups of haplotypes, results of both GAW14 and our own simulation encourage us to improve the efficiency and sensitivity of the estimation algorithm and to further compare the proposed method with more traditional methods.

A shared and additive genetic variance component-long-term survivor (LTS) model for familial aggregation studies of complex diseases with variable age-at-onset phenotype and non-susceptible subjects in the study cohort is proposed. LTS has been used from the early 1970s, especially in epidemiological studies of cancer. The LTS model utilizes information on the age at onset (survival) distribution to make inference on partially latent susceptibility. Bayesian modeling with uninformative priors is used and estimates of the posterior distribution of age at onset and susceptibility parameters of interest have been obtained using Bayesian Markov chain Monte Carlo (MCMC) methods with OpenBugs program. A simulation study confirms that we obtain posterior estimates of the model parameters on shared and genetic variance components of age at onset and susceptibility with good coverage rates. Further, we analyze familial aggregation of diabetic nephropathy (DN) in large Finnish cohort of 528 sibships with type 1 diabetes (T1D). According to the variance components estimated a substantial familial variation in the susceptibility to DN exist among families, while time to DN is less influenced by shared familial factors.