RESUMO
Hypothyroidism is associated with an increased risk for cardiovascular disease, which can not be fully explained by the atherogenic lipid profile, particularly total cholesterol and LDL-C, and other pathogenic factors may be involved. Plasma total homocysteine (tHcy) is an independent risk factor for cardiovascular disease and accelerated atherosclerosis. The aim of this study was to investigate the serum total homocysteine (tHcy) levels and its relation to total cholesterol, creatinine and thyroid hormones fT3, fT4 and TSH levels in overt hypothyroid patients compared to control subjects. In this study thirty recently diagnosed, non-treated overt hypothyroid patients (f=27, m=3) and twenty normal volunteers control (f=18, m=2) were included and subjected to determination of serum tHcy by enzyme immunoassay (EIA) technique, fT3, fT4 and TSH by Elecsys cobas e 601 analyzer, total cholesterol by enzymatic method and creatinine by kinetic method. The data was statistically analysed by SPSS-10 and p values less than 0.05 were considered significant.Our results showed that there were a significant increase of tHcy, TSH, T.cholesterol and creatinine levels by 113%, 12-folds, 58% and 54%, respectively, and a significant decrease of fT4 and fT3 levels by 49.6% and 56.4% , respectively, in hypothyroid patients than in control group. For tHcy (Mean±SD, 24.45±5.50 µmol/l vs 11.48±3.03 µmol/l, respectively; P < 0.001). tHcy was significantly positively correlated with TSH, creatinine and age and negatively correlated with free thyroxine (fT4) and no significant correlations with fT3 and T.cholesterol. In conclusion, our study confirmed the observation of elevated serum tHcy, T.cholesterol and creatinine in overt hypothyroidism and the presence of an inverse relation between tHcy with fT4 and a positive relation with TSH.
RESUMO
Whole-genome studies of genetic variation are now performed routinely and have accelerated the identification of disease-associated allelic variants, positive selection, recombination, and structural variation. However, these studies are sensitive to the presence of outlier data from individuals of different ancestry than the rest of the sample. Currently, the most common method of excluding outlier individuals is to collect a population sample and exclude outliers after genome-wide data have been collected. Here we show that a small collection of 20-27 polymorphic Alu insertions, selected using a principal component-based method with genetic ancestry estimates, may be used to easily assign Africans, East Asians, and Europeans to their population of origin. In addition, we show that samples from a geographically and genetically intermediate population (in our study, samples from India) can be identified within the original sample of Africans, East Asians, and Europeans. Finally, we show that outlier individuals from neighboring geographic regions (in our study, Yemen and sub-Saharan Africa) can be identified. These results will be of value in preselection of samples for more in-depth analysis as well as customized identification of maximally informative polymorphic markers for regional studies.
