Search

Colorectal cancer (CRC) affects approximately 2 million people worldwide. Obesity is the major risk factor for CRC. In addition, obesity contributes to a chronic inflammatory stage that enhances tumor progression through the secretion of proinflammatory cytokines. In addition to an increased inflammatory response, obesity-associated cancer presents accrued molecular factors related to cancer characteristics, such as genome instability, sustained cell proliferation, telomere dysfunctions, angiogenesis, and microbial alteration, among others. Despite the evidence accumulated over the last few years, the treatments for obesity-associated CRC do not differ from the CRC treatments in normal-weight individuals. In this review, we summarize the current knowledge on obesity-associated cancer, including its epidemiology, risk factors, molecular factors, and current treatments. Finally, we enumerate possible new therapeutic targets that may improve the conditions of obese CRC patients. Obesity is key for the development of CRC, and treatments resulting in the reversal of obesity should be considered as a strategy for improving antineoplastic CRC therapies. [ABSTRACT FROM AUTHOR]

Altered cholesterol, oxysterol, sphingolipid, and fatty acid concentrations are reported in blood, cerebrospinal fluid, and brain tissue of people with relapsing-remitting multiple sclerosis (RRMS) and are linked to disease progression and treatment responses. CD4 + T cells are pathogenic in RRMS, and defective T-cell function could be mediated in part by liver X receptors (LXRs)—nuclear receptors that regulate lipid homeostasis and immunity. RNA-sequencing and pathway analysis identified that genes within the 'lipid metabolism' and 'signalling of nuclear receptors' pathways were dysregulated in CD4 + T cells isolated from RRMS patients compared with healthy donors. While LXRB and genes associated with cholesterol metabolism were upregulated, other T-cell LXR-target genes, including genes involved in cellular lipid uptake (inducible degrader of the LDL receptor, IDOL), and the rate-limiting enzyme for glycosphingolipid biosynthesis (UDP-glucosylceramide synthase, UGCG) were downregulated in T cells from patients with RRMS compared to healthy donors. Correspondingly, plasma membrane glycosphingolipids were reduced, and cholesterol levels increased in RRMS CD4 + T cells, an effect partially recapitulated in healthy T cells by in vitro culture with T-cell receptor stimulation in the presence of serum from RRMS patients. Notably, stimulation with LXR-agonist GW3965 normalized membrane cholesterol levels, and reduced proliferation and IL17 cytokine production in RRMS CD4 + T-cells. Thus, LXR-mediated lipid metabolism pathways were dysregulated in T cells from patients with RRMS and could contribute to RRMS pathogenesis. Therapies that modify lipid metabolism could help restore immune cell function. Lipid metabolism was dysregulated in CD4 + T cells and serum from patients with relapsing-remitting multiple sclerosis compared to healthy controls, characterized by differential regulation of genes involved in cellular lipid uptake and lipid biosynthesis, altered T-cell plasma membrane lipid profiles and changes in serum lipoprotein expression. Alterations in T-cell plasma membrane lipids (lipid rafts) could be partially recapitulated in healthy T cells by in vitro culture with T-cell receptor stimulation in the presence of serum from RRMS patients. Conversely, co-stimulation of RRMS T cells with a liver X receptor (LXR)-agonist (a nuclear receptor important for lipid homeostasis), normalized membrane cholesterol levels, and reduced proliferation and IL17 cytokine production, suggesting that defects in LXR-mediated lipid metabolism pathways could contribute to RRMS pathogenesis. Graphical Abstract [ABSTRACT FROM AUTHOR]

Context: Long-term multi-environment trials (METs) could improve genomic prediction models for plant breeding programs by better representing the target population of environments (TPE). However, METs are generally highly unbalanced because genotypes are routinely dropped from trials after a few years. Furthermore, in the presence of genotype × environment interaction (GEI), selection of the environments to include in a prediction set becomes critical to represent specific TPEs. Aims: The goals of this study were to compare strategies for modelling GEI in genomic prediction, using large METs from oat (Avena sativa L.) breeding programs in the Midwest United States, and to develop a variety decision tool for farmers and plant breeders. Methods: The performance of genotypes in TPEs was predicted by using different strategies for handling GEI in genomic prediction models including systematic and/or random GEI components. These strategies were also used to build the variety decision tool for farmers. Key results: Genomic prediction for unknown genotypes, locations and years within TPEs had moderate to high predictive ability, accuracy and reliability. Modelling GEI was beneficial in small, but not in large, mega-environments. The latest 3 years were highly predictive of performance in an upcoming year for most years but not for years with unusual weather patterns. High predictive ability, accuracy and reliability were obtained when large datasets were used in TPEs. Conclusions: Deployment of historical datasets can be accomplished through meaningful delineation and prediction for TPEs. Implications: We have shown the performance of a simple modelling strategy for handling prediction for TPEs when deploying large historical datasets. Rankings of genotypic performance may change from one environment to the next, creating genotype × environment interaction (GEI), which can impact genomic prediction and complicate identification of superior genotypes. We compared strategies for predicting the performance of individuals in the presence of GEI. The results provide useful tools for the optimisation of training populations and testing environments for the prediction of individuals in the targeted populations of environments. [ABSTRACT FROM AUTHOR]

Objectives: The aim of this study is to evaluate risk factors for severe coronavirus disease 2019 (COVID-19) in patients with immune-mediated rheumatic diseases, stratified by systemic autoimmune conditions and chronic inflammatory arthritis. Methods: An observational, cross-sectional multicentre study was performed. Patients from 10 rheumatology departments in Madrid who presented with severe acute respiratory syndrome coronavirus-2 infection between February 2020 and May 2021 were included. The main outcome was COVID-19 severity (hospital admission or mortality). Risk factors for severity were estimated, adjusting for covariates (socio-demographic, clinical, and treatments), using logistic regression analyses. Results: In total, 523 patients with COVID-19 were included, among whom 192 (35.6%) patients required hospital admission and 38 (7.3%) died. Male gender, older age, and comorbidities such as diabetes mellitus, hypertension, and obesity were associated with severe COVID-19. Corticosteroid doses >10 mg/day, rituximab, sulfasalazine, and mycophenolate use, were independently associated with worse outcomes. COVID-19 severity decreased over the different pandemic waves. Mortality was higher in the systemic autoimmune conditions (univariate analysis, P < .001), although there were no differences in the overall severity in the multivariate analysis. Conclusions: This study confirms and provides new insights regarding the harmful effects of corticosteroids, rituximab, and other therapies (mycophenolate and sulfasalazine) in COVID-19. Methotrexate and anti-tumour necrosis factor therapy were not associated with worse outcomes. [ABSTRACT FROM AUTHOR]