Does psychological status influence clinical outcomes in patients with inflammatory bowel disease (IBD) and other chronic gastroenterological diseases: An observational cohort prospective study

Background: Whether there is a temporal relationship between psychological problems and clinical outcomes in patients with diseases of the digestive tract has not been widely researched. Thus, our aims were 1) To observe and compare prospectively clinical outcomes in relation to psychological co-morbidity in patients with inflammatory bowel disease (IBD), irritable bowel syndrome (IBS) and chronic hepatitis C (HCV) and, 2) To test the hypothesis that patients with psychological co-morbidities are less likely to have a satisfactory response to standard treatment at 12 months. Methods: Overall, 139 patients were enrolled in this observational cohort prospective study. Over the ensuing year, physical and psychological measures were made at baseline and after 12 months (HADS, SCL90, SF-12 and disease activity measures). A logistic regression was conducted to observe any relationship between baseline characteristics and patients' clinical outcomes after 12 months. Results: Overall, there was no relationship between psychological status and quality of life at baseline and relapse at 12 months (p > 0.05). However, patients with inactive disease at baseline were at lower risk of relapse after 12 months (OR = 0.046, CI: 0.012–0.178). No significant relationship was found between psychological problems such as depression/anxiety and a total number of relapses in the IBD group. However, interestingly, patients with an active disease at baseline tended to have a greater number of relapses (OR = 3.07, CI: 1.650–5.738) and CD participants were found at lower risk of relapse than UC participants (OR = 0.382, CI: 0.198–0.736). Conclusion: In contrast to previous investigations, this study suggests that there is no temporal relationship between psychological problems at baseline and clinical outcomes over time. Longer and larger prospective studies are needed to better understand this result.Antonina A Mikocka-Walus, Deborah A Turnbull, Nicole T Moulding, Ian G Wilson, Gerald J Holtmann and Jane M Andrew

The use of diffuse reflectance mid-infrared spectroscopy for the prediction of the concentration of chemical elements estimated by X-ray fluorescence in agricultural and grazing European soils

The aim of this study was to develop partial least-squares (PLS) regression models using diffuse reflectance Fourier transform mid-infrared (MIR) spectroscopy for the prediction of the concentration of elements in soil determined by X-ray fluorescence (XRF). A total of 4130 soils from the GEMAS European soil sampling program (geochemical mapping of agricultural soils and grazing land of Europe) were used for the development of models to predict concentrations of Al, As, Ba, Ca, Ce, Co, Cr, Cs, Cu, Fe, Ga, Hf, K, La, Mg, Mn, Na, Nb, Ni, P, Pb, Rb, Sc, Si, Sr, Th, Ti, V, Y, Zn and Zr in soil using MIR spectroscopy. The results were compared with those obtained where MIR models were developed with the same soils but using the concentration of elements extracted with aqua regia (AR). The PLS models were cross-validated against the experimental log-transformed XRF values of all the elements. The calibration models were derived from a set of 1000 randomly selected calibration samples. The rest of the samples (3130) were used as an independent validation set. According to the residual predictive deviation (RPD), predictions were classified as follows: ‘‘Good quality’’, Ca (2.9), Mg (2.5), Al (2.3), Fe (2.2), Ga (2.2), Si (2.1), Na (2.0); ‘‘Indicator quality’’, V (1.9), Ni (1.9), Sc (1.9), K (1.8), Ti (1.8), Rb (1.8), Zn (1.7), Co (1.7), Zr (1.6), Cr (1.6), Sr (1.6), Y (1.6), Nb (1.6), Ba (1.5), Mn (1.5), As (1.5), Ce (1.5); ‘‘Poor quality’’, Cs (1.4), Th (1.4), P (1.4), Cu (1.4), Pb (1.3), La (1.2), Hf (1.1). Good agreement was observed between the RPD values obtained for the elements analysed in this study and those from the AR study. Despite the different elemental concentrations determined by the XRF method compared to the AR method, MIR spectroscopy was still capable of predicting elemental concentrations

Lessons from Model Organisms: Phenotypic Robustness and Missing Heritability in Complex Disease

Genetically tractable model organisms from phages to mice have taught us invaluable lessons about fundamental biological processes and disease-causing mutations. Owing to technological and computational advances, human biology and the causes of human diseases have become accessible as never before. Progress in identifying genetic determinants for human diseases has been most remarkable for Mendelian traits. In contrast, identifying genetic determinants for complex diseases such as diabetes, cancer, and cardiovascular and neurological diseases has remained challenging, despite the fact that these diseases cluster in families. Hundreds of variants associated with complex diseases have been found in genome-wide association studies (GWAS), yet most of these variants explain only a modest amount of the observed heritability, a phenomenon known as “missing heritability.” The missing heritability has been attributed to many factors, mainly inadequacies in genotyping and phenotyping. We argue that lessons learned about complex traits in model organisms offer an alternative explanation for missing heritability in humans. In diverse model organisms, phenotypic robustness differs among individuals, and those with decreased robustness show increased penetrance of mutations and express previously cryptic genetic variation. We propose that phenotypic robustness also differs among humans and that individuals with lower robustness will be more responsive to genetic and environmental perturbations and hence susceptible to disease. Phenotypic robustness is a quantitative trait that can be accurately measured in model organisms, but not as yet in humans. We propose feasible approaches to measure robustness in large human populations, proof-of-principle experiments for robustness markers in model organisms, and a new GWAS design that takes differences in robustness into account

GEMAS: establishing geochemical background and threshold for 53 chemical elements in European agricultural soil

The GEMAS (geochemical mapping of agricultural soil) project collected 2108 Ap horizon soil samples from regularly ploughed fields in 33 European countries, covering 5.6 million km2. The <2 mm fraction of these samples was analysed for 53 elements by ICP-MS and ICP-AES, following a HNO3/HCl/H2O (modified aqua regia) digestion. Results are used here to establish the geochemical background variation and threshold values, derived statistically from the data set, in order to identify unusually high element concentrations for these elements in the Ap samples. Potentially toxic elements (PTEs), namely Ag, B, As, Ba, Bi, Cd, Co, Cr, Cu, Hg, Mn, Mo, Ni, Pb, Sb, Se, Sn, U, V and Zn, and emerging ‘high-tech’ critical elements (HTCEs), i.e., lanthanides (e.g., Ce, La), Be, Ga, Ge, In, Li and Tl, are of particular interest. For the latter, neither geochemical background nor threshold at the European scale has been established before. Large differences in the spatial distribution of many elements are observed between northern and southern Europe. It was thus necessary to establish three different sets of geochemical threshold values, one for the whole of Europe, a second for northern and a third for southern Europe. These values were then compared to existing soil guideline values for (eco)toxicological effects of these elements, as defined by various European authorities. The regional sample distribution with concentrations above the threshold values is studied, based on the GEMAS data set, following different methods of determination. Occasionally local contamination sources (e.g., cities, metal smelters, power plants, agriculture) can be identified. No indications could be detected at the continental scale for a significant impact of diffuse contamination on the regional distribution of element concentrations in the European agricultural soil samples. At this European scale, the variation in the natural background concentration of all investigated elements in the agricultural soil samples is much larger than any anthropogenic impact