The study aimed at assessing the potential use of lower total and HMW adiponectin levels for predicting cardiovascular risk in patients with type 2 diabetes mellitus (T2DM). Concentrations of total adiponectin or high molecular weight (HMW) adiponectin decrease in association with the development of metabolic dysfunction such as obesity, insulin resistance, or T2DM. Increased adiponectin levels are associated with a lower risk for coronary heart disease. A total of 551 individuals were assessed. The first group comprised metabolically healthy participants (143 females, and 126 males) and the second group were T2DM patients (164 females, and 118 males). Both total adiponectin and HMW adiponectin in diabetic patients were significantly lower when compared with the group of metabolically healthy individuals. There was a weak monotonic correlation between HMW adiponectin levels and triglycerides levels. Binary logistic regression analysis, gender adjusted, showed a higher cardiovascular risk in diabetic persons when both total adiponectin (OR = 1.700) and HMW adiponectin (OR = 2.785) levels were decreased. A decrease in total adiponectin levels as well as a decrease in its HMW adiponectin is associated with a higher cardiovascular risk in individuals with T2DM. This association suggests that adiponectin levels may be potentially used as an epidemiological marker for cardiovascular risk in diabetic patients.
Eligibility to anti-HER2 therapy for breast tumors strictly depends on demonstrating HER2 overexpression (by immunohistochemistry) or HER2 gene amplification by in situ hybridization (ISH), usually defined by the ratio of HER2 gene to chromosome 17 centromere (CEP17) copies. However, the CEP17 copy number increase (CNI) has been proven responsible for misleading HER2 FISH results and recent small cohort studies suggest that chromosome 17 polysomy is actually very rare. Here we investigated by FISH the frequency of true chromosome 17 polysomy in a consecutive cohort of 5,477 invasive breast cancer patients. We evaluated and selected the LSI 17p11.2 probe for chromosome 17 enumeration on a training cohort of 67 breast cancer samples (CEP17 ≥ 2.5). LSI 17p11.2 was used in the 297/5,477 patients from the validation cohort displaying CEP17 CNI (CEP17 ≥ 3.0). Using HER2/17p11.2 scoring criteria, 37.3%/1.5% patients initially classified as equivocal/non-amplified were reclassified as amplified. For a more accurate assessment of chromosome 17 and ploidy in the samples, we tested six markers located on chromosome 17 and centromeric regions of chromosome 8 (CEP8) and 11 (CEP11) in 67 patients with CEP17 and LSI 17p11.2 CNI. True polysomy (hyperdiploidy) according to these markers was found in 0.48% of cases (24/5,020). CEP8 and CEP11 CNI (≥3.0) was more frequent in the hyperdiploid than CEP17 non-polysomic group (55.6% vs. 6.1% and 25% vs. 2.3%, respectively). Our results suggest that chromosome 17 polysomy is a rare event found in <1% breast cancer cases and that polysomy of other chromosomes frequently occurs with chromosome 17 polysomy.
Regression analysis with compositional response, observations carrying relative information, is an appropriate tool for statistical modelling in many scientific areas (e.g. medicine, geochemistry, geology, economics). Even though this technique has been recently intensively studied, there are still some practical aspects that deserve to be further analysed. Here we discuss the issue related to the coordinate representation of compositional data. It is shown that linear relation between particular orthonormal coordinates and centred log-ratio coordinates can be utilized to simplify the computation concerning regression parameters estimation and hypothesis testing. To enhance interpretation of regression parameters, the orthogonal coordinates and their relation with orthonormal and centred log-ratio coordinates are presented. Further we discuss the quality of prediction in different coordinate system. It is shown that the mean squared error (MSE) for orthonormal coordinates is less or equal to the MSE for log-transformed data. Finally, an illustrative real-world example from geology is presented.
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