Estimating the Continuous-Time Dynamics of Energy and Fat Metabolism in Mice

The mouse has become the most popular organism for investigating molecular mechanisms of body weight regulation. But understanding the physiological context by which a molecule exerts its effect on body weight requires knowledge of energy intake, energy expenditure, and fuel selection. Furthermore, measurements of these variables made at an isolated time point cannot explain why body weight has its present value since body weight is determined by the past history of energy and macronutrient imbalance. While food intake and body weight changes can be frequently measured over several weeks (the relevant time scale for mice), correspondingly frequent measurements of energy expenditure and fuel selection are not currently feasible. To address this issue, we developed a mathematical method based on the law of energy conservation that uses the measured time course of body weight and food intake to estimate the underlying continuous-time dynamics of energy output and net fat oxidation. We applied our methodology to male C57BL/6 mice consuming various ad libitum diets during weight gain and loss over several weeks and present the first continuous-time estimates of energy output and net fat oxidation rates underlying the observed body composition changes. We show that transient energy and fat imbalances in the first several days following a diet switch can account for a significant fraction of the total body weight change. We also discovered a time-invariant curve relating body fat and fat-free masses in male C57BL/6 mice, and the shape of this curve determines how diet, fuel selection, and body composition are interrelated

Non-Hodgkin's lymphoma, obesity and energy homeostasis polymorphisms

A population-based case–control study of lymphomas in England collected height and weight details from 699 non-Hodgkin's lymphoma (NHL) cases and 914 controls. Obesity, defined as a body mass index (BMI) over 30 kg m−2 at five years before diagnosis,, was associated with an increased risk of NHL (OR=1.5, 95% CI 1.1–2.1). The excess was most pronounced for diffuse large B-cell lymphoma (OR=1.9, 95% CI 1.3–2.8). Genetic variants in the leptin (LEP 19G>A, LEP −2548G>A) and leptin receptor genes (LEPR 223Q>R), previously shown to modulate NHL risk, as well as a polymorphism in the energy regulatory gene adiponectin (APM1 276G>T), were investigated. Findings varied with leptin genotype, the risks being decreased with LEP 19AA (OR=0.7, 95% CI 0.5–1.0) and increased with LEP −2548GA (OR=1.3, 95% CI 1.0–1.7) and −2548AA (OR=1.4, 95% CI 1.0–1.9), particularly for follicular lymphoma. These genetic findings, which were independent of BMI, were stronger for men than women

Peer Support Workers in Health:A Qualitative Metasynthesis of Their Experiences

Peer support models, where an individual has a specific illness or lifestyle experience and supports others experiencing similar challenges, have frequently been used in different fields of healthcare to successfully engage hard-to-reach groups. Despite recognition of their value, the impact of these roles on the peer has not been systematically assessed. By synthesising the qualitative literature we sought to review such an impact, providing a foundation for designing future clinical peer models.Systematic review and qualitative metasynthesis of studies found in Medline, CINAHL or Scopus documenting peer worker experiences.1,528 papers were found, with 34 meeting the criteria of this study. Findings were synthesised to reveal core constructs of reframing identity through reciprocal relations and the therapeutic use of self, enhancing responsibility.The ability of the Peer Support Worker to actively engage with other marginalised or excluded individuals based on their unique insight into their own experience supports a therapeutic model of care based on appropriately sharing their story. Our findings have key implications for maximising the effectiveness of Peer Support Workers and in contributing their perspective to the development of a therapeutic model of care

Regulation of gene expression in ovarian cancer cells by luteinizing hormone receptor expression and activation

<p>Abstract</p> <p>Background</p> <p>Since a substantial percentage of ovarian cancers express gonadotropin receptors and are responsive to the relatively high concentrations of pituitary gonadotropins during the postmenopausal years, it has been suggested that receptor activation may contribute to the etiology and/or progression of the neoplasm. The goal of the present study was to develop a cell model to determine the impact of luteinizing hormone (LH) receptor (LHR) expression and LH-mediated LHR activation on gene expression and thus obtain insights into the mechanism of gonadotropin action on ovarian surface epithelial (OSE) carcinoma cells.</p> <p>Methods</p> <p>The human ovarian cancer cell line, SKOV-3, was stably transfected to express functional LHR and incubated with LH for various periods of time (0-20 hours). Transcriptomic profiling was performed on these cells to identify LHR expression/activation-dependent changes in gene expression levels and pathways by microarray and qRT-PCR analyses.</p> <p>Results</p> <p>Through comparative analysis on the LHR-transfected SKOV-3 cells exposed to LH, we observed the differential expression of 1,783 genes in response to LH treatment, among which five significant families were enriched, including those of growth factors, translation regulators, transporters, G-protein coupled receptors, and ligand-dependent nuclear receptors. The most highly induced early and intermediate responses were found to occupy a network impacting transcriptional regulation, cell growth, apoptosis, and multiple signaling transductions, giving indications of LH-induced apoptosis and cell growth inhibition through the significant changes in, for example, tumor necrosis factor, Jun and many others, supportive of the observed cell growth reduction in <it>in vitro </it>assays. However, other observations, e.g. the substantial up-regulation of the genes encoding the endothelin-1 subtype A receptor, stromal cell-derived factor 1, and insulin-like growth factor II, all of which are potential therapeutic targets, may reflect a positive mediation of ovarian cancer growth.</p> <p>Conclusion</p> <p>Overall, the present study elucidates the extensive transcriptomic changes of ovarian cancer cells in response to LH receptor activation, which provides a comprehensive and objective assessment for determining new cancer therapies and potential serum markers, of which over 100 are suggested.</p

Wine and other alcohol consumption and risk of ovarian cancer in the California Teachers Study cohort

OBJECTIVE: Whether alcohol consumption influences ovarian cancer risk is unclear. Therefore, we investigated the association between alcohol intake at various ages and risk of ovarian cancer. METHODS: Among 90,371 eligible members of the California Teachers Study cohort who completed a baseline alcohol assessment in 1995–1996, 253 women were diagnosed with epithelial ovarian cancer by the end of 2003. Multivariate Cox proportional hazards regression analysis was performed to estimate relative risks (RRs) and 95% confidence intervals (CIs). RESULTS: Consumption of total alcohol, beer, or liquor in the year prior to baseline, at ages 30–35 years, or at ages 18–22 years was not associated with risk of ovarian cancer. Consumption of at least one glass per day of wine, compared to no wine, in the year before baseline was associated with increased risk of developing ovarian cancer: RR = 1.57 (95% CI 1.11–2.22), P(trend) = 0.01. The association with wine intake at baseline was particularly strong among peri-/post-menopausal women who used estrogen-only hormone therapy and women of high socioeconomic status. CONCLUSIONS: Alcohol intake does not appear to affect ovarian cancer risk. Constituents of wine other than alcohol or, more likely, unmeasured determinants of wine drinking were associated with increased risk of ovarian cancer

Genetic linkage analysis in the age of whole-genome sequencing

For many years, linkage analysis was the primary tool used for the genetic mapping of Mendelian and complex traits with familial aggregation. Linkage analysis was largely supplanted by the wide adoption of genome-wide association studies (GWASs). However, with the recent increased use of whole-genome sequencing (WGS), linkage analysis is again emerging as an important and powerful analysis method for the identification of genes involved in disease aetiology, often in conjunction with WGS filtering approaches. Here, we review the principles of linkage analysis and provide practical guidelines for carrying out linkage studies using WGS data