Stroke and coronary heart disease in relation to hyperglycemia gender and age

This study was carried out to compare the fasting plasma glucose (FPG) and 2-h plasma glucose (2-h PG) criteria for diabetes with regard to their relation to stroke mortality and the incidence of ischemic and hemorrhagic stroke. In addition, the age-and gender difference in the incidence of coronary heart disease (CHD) and stroke and their relation with known cardiovascular disease risk factors and diabetes mellitus was examined. The study was a sub-data analysis of the Diabetes Epidemiology: Collaborative analysis Of Diagnostic criteria in Europe (DECODE) study including 25 181 individuals, 11 844 (47%) men and 13 345 (53%) women aged 25 to 90 years, from 14 European cohorts. In individuals without a history of diabetes elevated 2-h post-challenge glucose was a better predictor of stroke mortality than elevated fasting glucose in men, whereas the latter was better than the former in women. Elevated FPG and 2-h PG levels were associated with an increased risk of ischemic stroke incidence. 2-h PG contributed to the risk more strongly than FPG. No relationship between hyperglycemia and the risk of hemorrhagic stroke was found. The risk of CHD and ischemic stroke incidence increased with age in both genders, but was higher in all age groups in men than in women. The gender difference was, however, more marked for CHD than for ischemic stroke. Age, smoking and diabetes contributed to the development of both CHD and ischemic stroke. Elevated cholesterol levels predicted CHD only, whereas elevated blood pressure was a risk predictor for the incidence of ischemic stroke. The CHD and ischemic stroke risk was higher in men than in women with and without diabetes, however, the gender difference diminished for CHD but enlarged for ischemic stroke in diabetic individuals. The known risk factors including diabetes contributed differently to the risk of CHD and ischemic stroke in women and in men. Hyperglycemia defined by FPG or 2-h PG increases the risk of ischemic stroke in individuals without diabetes. FPG better predicts stroke mortality in women and 2-h PG in men. The risk of acute CHD and ischemic stroke is higher in men than in women in all ages, but such gender difference is more marked for CHD than for ischemic stroke. CHD risk is higher in men than in women, but the difference is reduced in diabetic population. Diabetes, however, increases stroke risk more in men than in women in all ages.

Human genetic variation in the Baltic Sea region: Features of population history and natural selection

In this thesis, the genetic variation of human populations from the Baltic Sea region was studied in order to elucidate population history as well as evolutionary adaptation in this region. The study provided novel understanding of how the complex population level processes of migration, genetic drift, and natural selection have shaped genetic variation in North European populations. Results from genome-wide, mitochondrial DNA and Y-chromosomal analyses suggested that the genetic background of the populations of the Baltic Sea region lies predominantly in Continental Europe, which is consistent with earlier studies and archaeological evidence. The late settlement of Fennoscandia after the Ice Age and the subsequent small population size have led to pronounced genetic drift, especially in Finland and Karelia but also in Sweden, evident especially in genome-wide and Y-chromosomal analyses. Consequently, these populations show striking genetic differentiation, as opposed to much more homogeneous pattern of variation in Central European populations. Additionally, the eastern side of the Baltic Sea was observed to have experienced eastern influence in the genome-wide data as well as in mitochondrial DNA and Y-chromosomal variation – consistent with linguistic connections. However, Slavic influence in the Baltic Sea populations appears minor on genetic level. While the genetic diversity of the Finnish population overall was low, genome-wide and Y-chromosomal results showed pronounced regional differences. The genetic distance between Western and Eastern Finland was larger than for many geographically distant population pairs, and provinces also showed genetic differences. This is probably mainly due to the late settlement of Eastern Finland and local isolation, although differences in ancestral migration waves may contribute to this, too. In contrast, mitochondrial DNA and Y-chromosomal analyses of the contemporary Swedish population revealed a much less pronounced population structure and a fusion of the traces of ancient admixture, genetic drift, and recent immigration. Genome-wide datasets also provide a resource for studying the adaptive evolution of human populations. This study revealed tens of loci with strong signs of recent positive selection in Northern Europe. These results provide interesting targets for future research on evolutionary adaptation, and may be important for understanding the background of disease-causing variants in human populations.

Studies on Binary Time Series Models with Applications to Empirical Macroeconomics and Finance

This thesis studies binary time series models and their applications in empirical macroeconomics and finance. In addition to previously suggested models, new dynamic extensions are proposed to the static probit model commonly used in the previous literature. In particular, we are interested in probit models with an autoregressive model structure. In Chapter 2, the main objective is to compare the predictive performance of the static and dynamic probit models in forecasting the U.S. and German business cycle recession periods. Financial variables, such as interest rates and stock market returns, are used as predictive variables. The empirical results suggest that the recession periods are predictable and dynamic probit models, especially models with the autoregressive structure, outperform the static model. Chapter 3 proposes a Lagrange Multiplier (LM) test for the usefulness of the autoregressive structure of the probit model. The finite sample properties of the LM test are considered with simulation experiments. Results indicate that the two alternative LM test statistics have reasonable size and power in large samples. In small samples, a parametric bootstrap method is suggested to obtain approximately correct size. In Chapter 4, the predictive power of dynamic probit models in predicting the direction of stock market returns are examined. The novel idea is to use recession forecast (see Chapter 2) as a predictor of the stock return sign. The evidence suggests that the signs of the U.S. excess stock returns over the risk-free return are predictable both in and out of sample. The new "error correction" probit model yields the best forecasts and it also outperforms other predictive models, such as ARMAX models, in terms of statistical and economic goodness-of-fit measures. Chapter 5 generalizes the analysis of univariate models considered in Chapters 2 4 to the case of a bivariate model. A new bivariate autoregressive probit model is applied to predict the current state of the U.S. business cycle and growth rate cycle periods. Evidence of predictability of both cycle indicators is obtained and the bivariate model is found to outperform the univariate models in terms of predictive power.