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John, C.

Publications and source records attributed to John, C..

3 recordsLinked to original sources

Macrophages protect against loss of adipose tissue during cancer cachexia

Cancer cachexia represents a central obstacle in medical oncology as it is associated with poor therapy response and reduced overall survival. Systemic inflammation is considered to be a key driver of cancer cachexia, however, clinical studies with anti-inflammatory drugs failed to show a robust cachexia-inhibiting effect. To address this contradiction, we investigated the functional importance of innate immune cells for hepatocellular carcinoma (HCC)-associated cachexia. To this end, we used a transgenic HCC mouse model intercrossed with mice harboring a defect in myeloid cell-mediated inflammation. We identified robust cachexia in the HCC mouse model as evidenced by a marked loss of visceral fat and lean mass. Computed tomography-based analyses demonstrated that a subgroup of human HCC patients displays reduced visceral fat mass, complementing the murine data. While the myeloid cell-mediated inflammation defect resulted in reduced expression of pro-inflammatory cytokines in the serum of HCC-bearing mice, this unexpectedly did not translate into diminished, but rather enhanced cachexia-associated fat loss. Defective myeloid cell-mediated inflammation was associated with decreased macrophage abundance in visceral adipose tissue, suggesting a role for local macrophages in the regulation of cancer-induced fat loss. Taken together, myeloid cell-mediated inflammation displays a rather unexpected beneficial function in a murine HCC model. These results demonstrate that immune cells are capable of protecting the host against cancer-induced tissue wasting, adding a further layer of complexity to the pathogenesis of cachexia and providing a potential explanation for the contradictory results of clinical studies with anti-inflammatory drugs.

cancer biology

Cohort Profile: Extended Cohort for E-health, Environment and DNA (EXCEED)

EXCEED is a longitudinal population-based cohort which facilitates investigation of genetic, environmental and lifestyle-related determinants of a broad range of diseases and of multiple morbidity through data collected at baseline and via electronic healthcare record linkage. Recruitment has taken place in Leicester, Leicestershire and Rutland since 2013 and is ongoing, with 10 156 participants aged 30-69 to date. The population of Leicester is diverse and additional recruitment from the local South Asian community is ongoing. Participants have consented to follow-up for up to 25 years through electronic health records (EHR). Data available includes baseline demographics, anthropometry, spirometry, lifestyle factors (smoking and alcohol use) and longitudinal health information from primary care records, with additional linkage to other EHR datasets planned. Patients have consented to be contacted for recall-by-genotype and recall-by-phenotype sub-studies, providing an important resource for precision medicine research. We welcome requests for collaboration and data access by contacting the study management team via exceed@le.ac.uk.

epidemiology

Causal Analyses, Statistical Efficiency And Phenotypic Precision Through Recall-By-Genotype Study Design

Genome-wide association studies have been useful in identifying common genetic variants related to a variety of complex traits and diseases; however, they are often limited in their ability to inform about underlying biology. Whilst bioinformatics analyses, studies of cells, animal models and applied genetic epidemiology have provided some understanding of genetic associations or causal pathways, there is a need for new genetic studies that elucidate causal relationships and mechanisms in a cost-effective, precise and statistically efficient fashion. We discuss the motivation for and the characteristics of the Recall-by-Genotype (RbG) study design, an approach that enables genotype-directed deep-phenotyping and improvement in drawing causal inferences. Specifically, we present RbG designs using single and multiple variants and discuss the inferential properties, analytical approaches and applications of both. We consider the efficiency of the RbG approach, the likely value of RbG studies for the causal investigation of disease aetiology and the practicalities of incorporating genotypic data into population studies in the context of the RbG study design. Finally, we provide a catalogue of the UK-based resources for such studies, an online tool to aid the design of new RbG studies and discuss future developments of this approach.

genetics