Jenny Häggström

Association between Cannabinoid CB1 Receptor Expression and Akt Signalling in Prostate Cancer

Background In prostate cancer, tumour expression of cannabinoid CB1 receptors is associated with a poor prognosis. One explanation for this association comes from experiments with transfected astrocytoma cells, where a high CB receptor expression recruits the Akt signalling survival pathway. In the present study, we have investigated the association between CB1 receptor expression and the Akt pathway in a well-characterised prostate cancer tissue microarray. Methodology/Principal Findings Phosphorylated Akt immunoreactivity (pAkt-IR) scores were available in the database. CB1 receptor immunoreactivity (CB1IR) was rescored from previously published data using the same scale as pAkt-IR. There was a highly significant correlation between CB1IR and pAkt-IR. Further, cases with high expression levels of both biomarkers were much more likely to have a more severe form of the disease at diagnosis than those with low expression levels. The two biomarkers had additive effects, rather than an interaction, upon disease-specific survival. Conclusions/Significance The present study provides data that is consistent with the hypothesis that at a high CB1 receptor expression, the Akt signalling pathway becomes operative.

Potential upstream regulators of cannabinoid receptor 1 signaling in prostate cancer: a Bayesian network analysis of data from a tissue microarray.

The endocannabinoid system regulates cancer cell proliferation, and in prostate cancer a high cannabinoid CB1 receptor expression is associated with a poor prognosis. Down‐stream mediators of CB1 receptor signaling in prostate cancer are known, but information on potential upstream regulators is lacking.

Untangling the role of one-carbon metabolism in colorectal cancer risk: a comprehensive Bayesian network analysis

The role of one-carbon metabolism (1CM), particularly folate, in colorectal cancer (CRC) development has been extensively studied, but with inconclusive results. Given the complexity of 1CM, the conventional approach, investigating components individually, may be insufficient. We used a machine learning-based Bayesian network approach to study, simultaneously, 14 circulating one-carbon metabolites, 17 related single nucleotide polymorphisms (SNPs), and several environmental factors in relation to CRC risk in 613 cases and 1190 controls from the prospective Northern Sweden Health and Disease Study. The estimated networks corresponded largely to known biochemical relationships. Plasma concentrations of folate (direct), vitamin B6 (pyridoxal 5-phosphate) (inverse), and vitamin B2 (riboflavin) (inverse) had the strongest independent associations with CRC risk. Our study demonstrates the importance of incorporating B-vitamins in future studies of 1CM and CRC development, and the usefulness of Bayesian network learning for investigating complex biological systems in relation to disease.

Components of One-carbon Metabolism Other than Folate and Colorectal Cancer Risk.

Background: Despite extensive study, the role of folate in colorectal cancer remains unclear. Research has therefore begun to address the role of other elements of the folate-methionine metabolic cycles. This study investigated factors other than folate involved in one-carbon metabolism, i.e., choline, betaine, dimethylglycine, sarcosine, and methionine and relevant polymorphisms, in relation to the risk of colorectal cancer in a population with low intakes and circulating levels of folate. Methods: This was a prospective case–control study of 613 case subjects and 1,190 matched control subjects nested within the population-based Northern Sweden Health and Disease Study. We estimated odds ratios (OR) by conditional logistic regression, and marginal risk differences with weighted maximum likelihood estimation using incidence data from the study cohort. Results: Higher plasma concentrations of methionine and betaine were associated with modest colorectal cancer risk reductions (OR [95% confidence interval {CI}] for highest versus lowest tertile: 0.76 [0.57, 0.99] and 0.72 [0.55, 0.94], respectively). Estimated marginal risk differences corresponded to approximately 200 fewer colorectal cancer cases per 100,000 individuals on average. We observed no clear associations between choline, dimethylglycine, or sarcosine and colorectal cancer risk. The inverse association of methionine was modified by plasma folate concentrations (OR [95% CI] for highest/lowest versus lowest/lowest tertile of plasma methionine/folate concentrations 0.39 [0.24, 0.64], Pinteraction = 0.06). Conclusions: In this population-based, nested case–control study with a long follow-up time from baseline to diagnosis (median: 8.2 years), higher plasma concentrations of methionine and betaine were associated with lower colorectal cancer risk. See Video Abstract at http://links.lww.com/EDE/B83.

The anti-inflammatory compound palmitoylethanolamide inhibits prostaglandin and hydroxyeicosatetraenoic acid production by a macrophage cell line

The anti‐inflammatory agent palmitoylethanolamide (PEA) reduces cyclooxygenase (COX) activity in vivo in a model of inflammatory pain. It is not known whether the compound reduces prostaglandin production in RAW264.7 cells, whether such an action is affected by compounds preventing the breakdown of endogenous PEA, whether other oxylipins are affected, or whether PEA produces direct effects upon the COX‐2 enzyme. RAW264.7 cells were treated with lipopolysaccharide and interferon‐γ to induce COX‐2. At the level of mRNA, COX‐2 was induced >1000‐fold following 24 h of the treatment. Coincubation with PEA (10 μmol/L) did not affect the levels of COX‐2, but reduced the levels of prostaglandins D2 and E2 as well as 11‐ and 15‐hydroxyeicosatetraenoic acid, which can also be synthesised by a COX‐2 pathway in macrophages. These effects were retained when hydrolysis of PEA to palmitic acid was blocked. Linoleic acid‐derived oxylipin levels were not affected by PEA. No direct effects of PEA upon the oxygenation of either arachidonic acid or 2‐arachidonoylglycerol by COX‐2 were found. It is concluded that in lipopolysaccharide and interferon‐γ‐stimulated RAW264.7 cells, PEA reduces the production of COX‐2‐derived oxylipins in a manner that is retained when its metabolism to palmitic acid is inhibited.

Regional inequalities in pre-pregnancy overweight and obesity in Sweden, 1992, 2000, and 2010

Aims: To investigate regional differences and time trends in women’s overweight and obesity in Sweden. Methods: Using data from the Swedish Medical Birth Register (women aged ⩾18 years, first pregnancy only) and the Total Population Register accessed through the Umeå SIMSAM Lab, age-standardized prevalence of pre-pregnancy overweight/obesity (BMI ⩾ 25 kg/m2) and obesity (BMI ⩾ 30 kg/m2) were estimated by county for the years 1992, 2000, and 2010. Maps were created using ArcMap v10.2.2 to display regional variations over time and logistic regression analyses were used to assess if the observed trends were significant. Results: The prevalence of pre-pregnancy overweight/obesity and obesity increased significantly in all Swedish counties between 1992, and 2010. In 2010, Södermanland and Gotland exhibited the highest age-standardized overweight/obesity (39.7%) and obesity (15.1%) prevalence, respectively. The sharpest increases between 1992 and 2010 were observed in Västerbotten for overweight/obesity (75% increase) and in Gotland for obesity (233% increase). Across the years, Stockholm had the lowest prevalence of overweight/obesity (26.3% in 2010) and obesity (7.3% in 2010) and one of the least steep increases in prevalence of both between 1992 and 2010. Conclusions: Substantial regional differences in pre-pregnancy overweight and obesity prevalence are apparent in Sweden. Further research should elucidate the mechanisms causing these differences.

Targeted smoothing parameter selection for estimating average causal effects

The non-parametric estimation of average causal effects in observational studies often relies on controlling for confounding covariates through smoothing regression methods such as kernel, splines or local polynomial regression. Such regression methods are tuned via smoothing parameters which regulates the amount of degrees of freedom used in the fit. In this paper we propose data-driven methods for selecting smoothing parameters when the targeted parameter is an average causal effect. For this purpose, we propose to estimate the exact expression of the mean squared error of the estimators. Asymptotic approximations indicate that the smoothing parameters minimizing this mean squared error converges to zero faster than the optimal smoothing parameter for the estimation of the regression functions. In a simulation study we show that the proposed data-driven methods for selecting the smoothing parameters yield lower empirical mean squared error than other methods available such as, e.g., cross-validation.

Bandwidth selection for backfitting estimation of semiparametric additive models: A simulation study

This thesis is a contribution to the research area concerned with selection of smoothing parameters in the framework of nonparametric and semiparametric regression. Selection of smoothing parameters is one of the most important issues in this framework and the choice can heavily influence subsequent results. A nonparametric or semiparametric approach is often desirable when large datasets are available since this allow us to make fewer and weaker assumptions as opposed to what is needed in a parametric approach. In the first paper we consider smoothing parameter selection in nonparametric regression when the purpose is to accurately predict future or unobserved data. We study the use of accumulated prediction errors and make comparisons to leave-one-out cross-validation which is widely used by practitioners. In the second paper a general semiparametric additive model is considered and the focus is on selection of smoothing parameters when optimal estimation of some specific parameter is of interest. We introduce a double smoothing estimator of a mean squared error and propose to select smoothing parameters by minimizing this estimator. Our approach is compared with existing methods.The third paper is concerned with the selection of smoothing parameters optimal for estimating average treatment effects defined within the potential outcome framework. For this estimation problem we propose double smoothing methods similar to the method proposed in the second paper. Theoretical properties of the proposed methods are derived and comparisons with existing methods are made by simulations.In the last paper we apply our results from the third paper by using a double smoothing method for selecting smoothing parameters when estimating average treatment effects on the treated. We estimate the effect on BMI of divorcing in middle age. Rich data on socioeconomic conditions, health and lifestyle from Swedish longitudinal registers is used.

Levels of oxylipins, endocannabinoids and related lipids in plasma before and after low-level exposure to acrolein in healthy individuals and individuals with chemical intolerance

Oxylipins and endocannabinoids play important biological roles, including effects upon inflammation. It is not known whether the circulating levels of these lipids are affected by inhalation of the environmental pollutant acrolein. In the present study, we have investigated the consequences of low-level exposure to acrolein on oxylipin, endocannabinoid and related lipid levels in the plasma of healthy individuals and individuals with chemical intolerance (CI), an affliction with a suggested inflammatory origin. Participants were exposed twice (60min) to heptane and a mixture of heptane and acrolein. Blood samples were collected before exposure, after and 24h post-exposure. There were no overt effects of acrolein exposure on the oxylipin lipidome or endocannibinoids detectable in the bloodstream at the time points investigated. No relationship between basal levels or levels after exposure to acrolein and CI could be identified. This implicates a minor role of inflammatory mediators on the systemic level in CI.

Data-driven confounder selection via Markov and Bayesian networks

To unbiasedly estimate a causal effect on an outcome unconfoundedness is often assumed. If there is sufficient knowledge on the underlying causal structure then existing confounder selection criteria can be used to select subsets of the observed pretreatment covariates, X, sufficient for unconfoundedness, if such subsets exist. Here, estimation of these target subsets is considered when the underlying causal structure is unknown. The proposed method is to model the causal structure by a probabilistic graphical model, for example, a Markov or Bayesian network, estimate this graph from observed data and select the target subsets given the estimated graph. The approach is evaluated by simulation both in a high-dimensional setting where unconfoundedness holds given X and in a setting where unconfoundedness only holds given subsets of X. Several common target subsets are investigated and the selected subsets are compared with respect to accuracy in estimating the average causal effect. The proposed method is implemented with existing software that can easily handle high-dimensional data, in terms of large samples and large number of covariates. The results from the simulation study show that, if unconfoundedness holds given X, this approach is very successful in selecting the target subsets, outperforming alternative approaches based on random forests and LASSO, and that the subset estimating the target subset containing all causes of outcome yields smallest MSE in the average causal effect estimation.