Sahar Hassani

Fish Oil Supplementation Alters the Plasma Lipidomic Profile and Increases Long-Chain PUFAs of Phospholipids and Triglycerides in Healthy Subjects

Background While beneficial health effects of fish and fish oil consumption are well documented, the incorporation of n-3 polyunsaturated fatty acids in plasma lipid classes is not completely understood. The aim of this study was to investigate the effect of fish oil supplementation on the plasma lipidomic profile in healthy subjects. Methodology/Principal Findings In a double-blinded randomized controlled parallel-group study, healthy subjects received capsules containing either 8 g/d of fish oil (FO) (1.6 g/d EPA+DHA) (n = 16) or 8 g/d of high oleic sunflower oil (HOSO) (n = 17) for seven weeks. During the first three weeks of intervention, the subjects completed a fully controlled diet period. BMI and total serum triglycerides, total-, LDL- and HDL-cholesterol were unchanged during the intervention period. Lipidomic analyses were performed using Ultra Performance Liquid Chromatography (UPLC) coupled to electrospray ionization quadrupole time-of-flight mass spectrometry (QTOFMS), where 568 lipids were detected and 260 identified. Both t-tests and Multi-Block Partial Least Square Regression (MBPLSR) analysis were performed for analysing differences between the intervention groups. The intervention groups were well separated by the lipidomic data after three weeks of intervention. Several lipid classes such as phosphatidylcholine, phosphatidylethanolamine, lysophosphatidylcholine, sphingomyelin, phosphatidylserine, phosphatidylglycerol, and triglycerides contributed strongly to this separation. Twenty-three lipids were significantly decreased (FDR<0.05) in the FO group after three weeks compared with the HOSO group, whereas fifty-one were increased including selected phospholipids and triglycerides of long-chain polyunsaturated fatty acids. After seven weeks of intervention the two intervention groups showed similar grouping. Conclusions/Significance In healthy subjects, fish oil supplementation alters lipid metabolism and increases the proportion of phospholipids and triglycerides containing long-chain polyunsaturated fatty acids. Whether the beneficial effects of fish oil supplementation may be explained by a remodeling of the plasma lipids into phospholipids and triglycerides of long-chain polyunsaturated fatty acids needs to be further investigated. Trial Registration ClinicalTrials.gov NCT01034423

Characterising protein, salt and water interactions with combined vibrational spectroscopic techniques

In this paper a combination of NIR spectroscopy and FTIR and Raman microspectroscopy was used to elucidate the effects of different salts (NaCl, KCl and MgSO(4)) on structural proteins and their hydration in muscle tissue. Multivariate multi-block technique Consensus Principal Component Analysis enabled integration of different vibrational spectroscopic techniques: macroscopic information obtained by NIR spectroscopy is directly related to microscopic information obtained by FTIR and Raman microspectroscopy. Changes in protein secondary structure observed at different concentrations of salts were linked to changes in protein hydration affinity. The evidence for this was given by connecting the underlying FTIR bands of the amide I region (1700-1600 cm(-1)) and the water region (3500-3000 cm(-1)) with water vibrations obtained by NIR spectroscopy. In addition, Raman microspectroscopy demonstrated that different cations affected structures of aromatic amino acid residues differently, which indicates that cation-π interactions play an important role in determination of the final structure of protein molecules.

30-Day Survival Probabilities as a Quality Indicator for Norwegian Hospitals: Data Management and Analysis.

Background The Norwegian Knowledge Centre for the Health Services (NOKC) reports 30-day survival as a quality indicator for Norwegian hospitals. The indicators have been published annually since 2011 on the website of the Norwegian Directorate of Health (www.helsenorge.no), as part of the Norwegian Quality Indicator System authorized by the Ministry of Health. Openness regarding calculation of quality indicators is important, as it provides the opportunity to critically review and discuss the method. The purpose of this article is to describe the data collection, data pre-processing, and data analyses, as carried out by NOKC, for the calculation of 30-day risk-adjusted survival probability as a quality indicator. Methods and Findings Three diagnosis-specific 30-day survival indicators (first time acute myocardial infarction (AMI), stroke and hip fracture) are estimated based on all-cause deaths, occurring in-hospital or out-of-hospital, within 30 days counting from the first day of hospitalization. Furthermore, a hospital-wide (i.e. overall) 30-day survival indicator is calculated. Patient administrative data from all Norwegian hospitals and information from the Norwegian Population Register are retrieved annually, and linked to datasets for previous years. The outcome (alive/death within 30 days) is attributed to every hospital by the fraction of time spent in each hospital. A logistic regression followed by a hierarchical Bayesian analysis is used for the estimation of risk-adjusted survival probabilities. A multiple testing procedure with a false discovery rate of 5% is used to identify hospitals, hospital trusts and regional health authorities with significantly higher/lower survival than the reference. In addition, estimated risk-adjusted survival probabilities are published per hospital, hospital trust and regional health authority. The variation in risk-adjusted survival probabilities across hospitals for AMI shows a decreasing trend over time: estimated survival probabilities for AMI in 2011 varied from 80.6% (in the hospital with lowest estimated survival) to 91.7% (in the hospital with highest estimated survival), whereas it ranged from 83.8% to 91.2% in 2013. Conclusions Since 2011, several hospitals and hospital trusts have initiated quality improvement projects, and some of the hospitals have improved the survival over these years. Public reporting of survival/mortality indicators are increasingly being used as quality measures of health care systems. Openness regarding the methods used to calculate the indicators are important, as it provides the opportunity of critically reviewing and discussing the methods in the literature. In this way, the methods employed for establishing the indicators may be improved.

Probing the Association between Early Evolutionary Markers and Schizophrenia

Schizophrenia is suggested to be a by-product of the evolution in humans, a compromise for our language, creative thinking and cognitive abilities, and thus, essentially, a human disorder. The time of its origin during the course of human evolution remains unclear. Here we investigate several markers of early human evolution and their relationship to the genetic risk of schizophrenia. We tested the schizophrenia evolutionary hypothesis by analyzing genome-wide association studies of schizophrenia and other human phenotypes in a statistical framework suited for polygenic architectures. We analyzed evolutionary proxy measures: human accelerated regions, segmental duplications, and ohnologs, representing various time periods of human evolution for overlap with the human genomic loci associated with schizophrenia. Polygenic enrichment plots suggest a higher prevalence of schizophrenia associations in human accelerated regions, segmental duplications and ohnologs. However, the enrichment is mostly accounted for by linkage disequilibrium, especially with functional elements like introns and untranslated regions. Our results did not provide clear evidence that markers of early human evolution are more likely associated with schizophrenia. While SNPs associated with schizophrenia are enriched in HAR, Ohno and SD regions, the enrichment seems to be mediated by affiliation to known genomic enrichment categories. Taken together with previous results, these findings suggest that schizophrenia risk may have mainly developed more recently in human evolution.