MATERIALS AND METHODS: Cardiovascular risk factors (CRFs) were estimated using the 30-year Framingham Risk Score in 73 childhood leukemia survivors (median age: 25; median years from diagnosis: 19) and 78 healthy controls (median age: 23). Radial arterial stiffness was measured using pulse wave analyzer, while endothelial activation markers were measured by soluble intercellular adhesion molecule 1 (sICAM-1) and soluble vascular cell adhesion molecule 1 (sVCAM-1). Retinal fundus images were analyzed for central retinal artery/vein equivalents (CRAE/CRVE) and arteriolar-venular ratio (AVR).
RESULTS: cALL survivors had higher CRF (P<0.0001), arterial stiffness (P=0.001), and sVCAM-1 (P=0.007) compared with controls. Survivors also had significantly higher CRVE (P=0.021) while AVR was significantly lower (P=0.026) in survivors compared with controls, compatible with endothelial dysfunction. In cALL survivors with intermediate risk for CVD, CRAE, and AVR are significantly lower, while sVCAM-1 and sICAM-1 are significantly higher when compared with survivors with low CVD risk after adjusting with covariates (age, sex, and smoking status).
CONCLUSIONS: cALL survivors have an increased risk of CVD compared with age-matched peers. The survivors demonstrated microvasculopathy, as measured by retinal vascular analysis, in addition to physical and biochemical evidence of endothelial dysfunction. These changes predate other measures of CVD. Retinal vessel analysis may be utilized as a robust screening tool for identifying survivors at increased risk for developing CVD.
OBJECTIVE: To assess, by diffusion tensor imaging, microstructural integrity of white matter in paediatric patients with acute lymphoblastic leukaemia (ALL) following intrathecal and intravenous chemotherapy.
MATERIALS AND METHODS: Eleven children diagnosed with de novo ALL underwent MRI scans of the brain with diffusion tensor imaging (DTI) prior to commencement of chemotherapy and at 12 months after diagnosis, using a 3-tesla (T) MRI scanner. We investigated the changes in DTI parameters in white matter tracts before and after chemotherapy using tract-based spatial statistics overlaid on the International Consortium of Brain Mapping DTI-81 atlas. All of the children underwent formal neurodevelopmental assessment at the two study time points.
RESULTS: Whole-brain DTI analysis showed significant changes between the two time points, affecting several white matter tracts. The tracts demonstrated longitudinal changes of decreasing mean and radial diffusivity. The neurodevelopment of the children was near compatible for age at the end of ALL treatment.
CONCLUSION: The quantification of white matter tracts changes in children undergoing chemotherapy showed improving longitudinal values in DTI metrics (stable fractional anisotropy, decreasing mean and radial diffusivity), which are incompatible with deterioration of microstructural integrity in these children.
MATERIALS AND METHODS: We evaluated simple statistics and published model-based approaches. Multiplex-qPCR was conducted to determine the expression of 24 candidate RG in AMLs (N=9). Singleplex-qPCR was carried out on selected RG (SRP14, B2M and ATP5B) and genes of interest in AML (N=15) and healthy controls, HC (N=12).
RESULTS: RG expression levels in AML samples were highly variable and coefficient of variance (CV) ranged from 0.37% to 10.17%. Analysis using GeNorm and Normfinder listed different orders of most stable genes but the top seven (ACTB, UBE2D2, B2M, NF45, RPL37A, GK, QARS) were the same. In singleplex-qPCR, SRP14 maintained the lowest CV in AML samples. B2M, one of most stable reference genes in AML, was expressed near significantly different in AML and HC. GeNorm selected ATP5B+SRP14 while Normfinder chose SRP14+B2M as the best two RG in combination. The median expressions of combined RG genes in AML compared to HC were less significantly different than individually implying smaller expression variation after combination. Genes of interest normalised with RG in combination or individually, displayed significantly different expression patterns.
CONCLUSIONS: The selection of best reference gene in qPCR must consider all sample sets. Model-based approaches are important in large candidate gene analysis. This study showed combination of RG SRP14+B2M was the most suitable normalisation factor for qPCR analysis of AML and healthy individuals.