Both gender and BCR-ABL transcript, but not age, were significantly associated with the molecular response. Men with b2a2 represent a less favorable group in their response to imatinib treatment and may need alternative therapy regimen and closer monitoring.
ObjectivesAllogeneic stem cell transplantation is a curative therapy for patients with haematological malignancies and nonmalignant haematological disorders. Measuring the bone marrow engraftment via donor chimerism following transplantation is often used as a predictor for the overall survival. However, prognostic factors associated with bone marrow engraftment are poorly understood. The objective of this study is to evaluate the effect of age, gender and the recipient-donor relationship on the extent of bone marrow engraftment following transplantation and thereby provide more precise counselling to patients.MethodsThe bone marrow engraftment was routinely monitored using the multiplex fluorescent short tandem repeat analysis. Complete engraftment is defined as ≥95% of both donor-derived white blood cells and T cells. Data from a cohort of 194 patients with various conditions and up to 10 years of follow up was retrospectively analysed. Logistic regression and survival analyses were performed using the R statistic software.Results64.9% of patients reached complete engraftment, ˜70% out of which achieved this milestone within 2 years post-transplantation; but 6.3% of these patients relapsed. Gender and recipient-donor relationship (related or not, same sex or not) did not have significant effect on the probability and the time for reaching complete engraftment. Age was inversely related to the probability of complete engraftment. The older the patient was, the less likely he/she achieved complete engraftment (p value 0.0015), and the longer time took for reaching complete engraftment (p value 0.0009).ConclusionAge is significantly associated with the likelihood and time for reaching complete engraftment.
Causal inference is a critical step in improving our understanding of biological processes and Mendelian randomisation (MR) has emerged as one of the foremost methods to efficiently interrogate diverse hypotheses using large-scale, observational data from biobanks. Although many extensions have been developed to address the three core assumptions of MR-based causal inference (relevance, exclusion restriction, and exchangeability), most approaches implicitly assume that any putative causal effect is linear. Here we propose PolyMR, an MR-based method which provides a polynomial approximation of an (arbitrary) causal function between an exposure and an outcome. We show that this method provides accurate inference of the shape and magnitude of causal functions with greater accuracy than existing methods. We applied this method to data from the UK Biobank, testing for effects between anthropometric traits and continuous health-related phenotypes and found most of these (84%) to have causal effects which deviate significantly from linear. These deviations ranged from slight attenuation at the extremes of the exposure distribution, to large changes in the magnitude of the effect across the range of the exposure (e.g. a 1 kg/m2 change in BMI having stronger effects on glucose levels if the initial BMI was higher), to non-monotonic causal relationships (e.g. the effects of BMI on cholesterol forming an inverted U shape). Finally, we show that the linearity assumption of the causal effect may lead to the misinterpretation of health risks at the individual level or heterogeneous effect estimates when using cohorts with differing average exposure levels.
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