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Purpose: We investigated longitudinal associations of sedentary behavior, light-intensity physical activity (LPA) and moderate-to-vigorous physical activity (MVPA) with body composition in colorectal cancer (CRC) survivors, between 6 week and 24 months post-treatment. In addition, we explored whether body composition mediated associations of sedentary behavior and MVPA with fatigue. Method: A prospective cohort study was conducted in 459 stage I-III CRC patients recruited at diagnosis. Measurements were performed of accelerometer-assessed sedentary time (hours/day), self-reported LPA and MVPA (hours/week), anthropometric assessment of body mass index (BMI), waist circumference and fat percentage (measures of adiposity), and muscle circumference and handgrip strength (measures of muscle mass/function) repeated at 6 weeks, and 6, 12 and 24 months post-treatment. Longitudinal associations of sedentary time and physical activity with body composition were analyzed using confounder-adjusted linear mixed models. Mediation analyses were performed to explore the role of body mass index (BMI) and handgrip strength as mediators in associations of sedentary time and MVPA with fatigue. Results: Less sedentary time and LPA were, independent of MVPA, longitudinally associated with increased handgrip strength, but not with measures of adiposity. More MVPA was associated with increased adiposity and increased handgrip strength. Higher BMI partly mediated associations between higher sedentary time and more fatigue. Conclusion: Within the first two years after CRC treatment, changes in sedentary behavior, physical activity and body composition are interrelated and associated with fatigue. Intervention studies are warranted to investigate causality.

Purpose: The importance of genetic and epigenetic alterations maybe in their aggregate role in altering core pathways in tumorigenesis. Experimental Design: Merging genome-wide genomic and epigenomic alterations, we identify key genes and pathways altered in colorectal cancers (CRC). DNA methylation analysis was tested for predicting survival in CRC patients using Cox proportional hazard model. Results: We identified 29 low frequency-mutated genes that are also inactivated by epigenetic mechanisms in CRC. Pathway analysis showed the extracellular matrix (ECM) remodeling pathway is silenced in CRC. Six ECM pathway genes were tested for their prognostic potential in large CRC cohorts (n = 777). DNA methylation of IGFBP3 and EVL predicted for poor survival (IGFBP3: HR = 2.58, 95% CI: 1.37–4.87, P = 0.004; EVL: HR = 2.48, 95% CI: 1.07–5.74, P = 0.034) and simultaneous methylation of multiple genes predicted significantly worse survival (HR = 8.61, 95% CI: 2.16–34.36, P < 0.001 for methylation of IGFBP3, EVL, CD109, and FLNC). DNA methylation of IGFBP3 and EVL was validated as a prognostic marker in an independent contemporary-matched cohort (IGFBP3 HR = 2.06, 95% CI: 1.04–4.09, P = 0.038; EVL HR = 2.23, 95% CI: 1.00–5.0, P = 0.05) and EVL DNA methylation remained significant in a secondary historical validation cohort (HR = 1.41, 95% CI: 1.05–1.89, P = 0.022). Moreover, DNA methylation of selected ECM genes helps to stratify the high-risk stage 2 colon cancers patients who would benefit from adjuvant chemotherapy (HR: 5.85, 95% CI: 2.03–16.83, P = 0.001 for simultaneous methylation of IGFBP3, EVL, and CD109). Conclusions: CRC that have silenced genes in ECM pathway components show worse survival suggesting that our finding provides novel prognostic biomarkers for CRC and reflects the high importance of integrative analyses linking genetic and epigenetic abnormalities with pathway disruption in cancer. Clin Cancer Res; 17(6); 1535–45. ©2011 AACR.

International audience; Background: Screening policies to identify candidates for applying Health Impact Assessment (HIA) is generally a qualitative process. Our aim is to show how the use of multivariate exploratory statistical methods such as cluster analysis may help the screening step of HIA in getting a shortlist of policies as HIA candidates quickly and reliably. Methods: A panel of 7 international renowned public health experts from Nova University in 2011 rated 76 policies proposed by the Technical Group planning the Portuguese hospital reform on a 10-point scale (1-Very low to 10-Very high) regarding Potential Impact, Ease of implementation and Implementation costs. Hierarchical cluster analysis is used to identify groups of similar policies regarding these 3 dimensions and prioritize those more pertinent of being considered for HIA according to chosen priority criteria, given different scenarios. Results: The analysis identified 7 groups of policies and showed that Ease of implementation was not as relevant to differentiate these groups as were Potential impact and Implementation costs: 3 groups present higher mean levels of potential impact (one with also higher mean levels of implementation costs, another with intermediate costs and a last one with lower costs): 2 groups present intermediate mean levels of potential impact (one with higher mean costs and another with lower ones): 2 groups present lower mean levels of potential impact (one with higher mean costs and another with lower ones). The groups of policies were then re-ranked according to different priority criteria, but Group 1 lead all possible scenarios being the group of most obvious candidates for HIA. Conclusion: Although multivariate statistical methodologies may be complex, decision-makers may thus obtain more evidence-based knowledge and not necessarily in a harder way to grasp. Applying multivariate statistical methods such as hierarchical cluster analysis as early as the screening step of HIA may be very helpful towards obtaining evidence based, reliable, participatory, flexible and more efficient ways of selecting policies for HIA.