Multimorbidity patterns in primary care: interactions among chronic diseases using factor analysis

Objectives

The primary objective of this study was to identify the existence of chronic disease multimorbidity patterns in the primary care population, describing their clinical components and analysing how these patterns change and evolve over time both in women and men. The secondary objective of this study was to generate evidence regarding the pathophysiological processes underlying multimorbidity and to understand the interactions and synergies among the various diseases.

Methods

This observational, retrospective, multicentre study utilised information from the electronic medical records of 19 primary care centres from 2008. To identify multimorbidity patterns, an exploratory factor analysis was carried out based on the tetra-choric correlations between the diagnostic information of 275,682 patients who were over 14 years of age. The analysis was stratified by age group and sex.

Results

Multimorbidity was found in all age groups, and its prevalence ranged from 13% in the 15 to 44 year age group to 67% in those 65 years of age or older. Goodness-of-fit indicators revealed sample values between 0.50 and 0.71. We identified five patterns of multimorbidity: cardio-metabolic, psychiatric-substance abuse, mechanical-obesity-thyroidal, psychogeriatric and depressive. Some of these patterns were found to evolve with age, and there were differences between men and women.

Conclusions

Non-random associations between chronic diseases result in clinically consistent multimorbidity patterns affecting a significant proportion of the population. Underlying pathophysiological phenomena were observed upon which action can be taken both from a clinical, individual-level perspective and from a public health or population-level perspective.     

Normalization of sphingomyelin levels by 2-hydroxyoleic acid induces autophagic cell death of SF767 cancer cells

The very high mortality rate of gliomas reflects the unmet therapeutic need associated with this type of brain tumor. We have discovered that the plasma membrane fulfills a critical role in the propagation of tumorigenic signals, whereby changes in membrane lipid content can either activate or silence relevant pathways. We have designed a synthetic fatty acid, 2-hydroxyoleic acid (2OHOA), that specifically activates sphingomyelin synthase (SGMS), thereby modifying the lipid content of cancer cell membranes and restoring lipid levels to those found in normal cells. In reverting, the structure of the membrane by activating SGMS, 2OHOA inhibits the RAS-MAPK pathway, which in turn fails to activate the CCND (Cyclin D)-CDK4/CDK6 and PI3K-AKT1 pathways. The overall result in SF767 cancer cells, a line that is resistant to apoptosis, is the sequential induction of cell cycle arrest, cell differentiation and autophagy. Such effects are not observed in normal cells (MRC-5) and thus, this specific activation of programmed cell death infers greater efficacy and lower toxicity to 2OHOA than that associated with temozolomide (TMZ), the reference drug for the treatment of glioma.     

A quinoxaline 1,4-di-N-oxide derivative induces DNA oxidative damage not attenuated by vitamin C and E treatment

Some anticancer compounds are pro-drugs which give rise to toxic species through enzymatic reduction. The quinoxaline-di-N-oxide derivative Q-85 HCl (7-chloro-3-[[(N,N-dimethylamino)propyl]amino]-2-quinoxalinecarbonitrile 1,4-di-N-oxide hydrochloride) is a bioreductive compound selectively toxic in hypoxia. Due to the possibility of secondary tumors the study of the genotoxic capability of antitumoral drugs is very important. The aim of this study was to assess the ability of Q-85 HCl to produce reactive oxygen species (ROS) and oxidative DNA damage in Caco-2 cells, both in hypoxia and in well-oxygenated conditions. Secondly, we attempted to evaluate the effect of vitamins C and E under hypoxic and normoxic conditions, in order to determine if these antioxidant substances modify Q-85 HCl effect in hypoxic cells or possibly exert a protective action in normal cells. Caco-2 cells were treated with Q-85 HCl for 2h, at high concentrations in normoxia (0.1-5 microM) and at low concentrations in hypoxia (0.002-0.1 microM). In normoxia, a dose-related significant increase in intracellular ROS level was evident; in hypoxia all the concentrations produced very high level of ROS. Just after the treatment and 24h later, oxidative DNA damage was evaluated by the modified comet assay after post-digestion of the cells with formamidopyrimidine-DNA glycosylase (FPG) and endonuclease III (Endo III). Q-85 HCl treatment evoked a significant dose-dependent increase in the total comet score of the cells both in hypoxia and normoxia, indicating that this compound or some metabolite is able to oxidize purine and pyrimidine bases. After 24h DNA damage caused by the compound was completely repaired with only one exception: cells treated with the highest concentration of Q-85 HCl in hypoxia and post-digested with FPG. Vitamin C (5-100 microM) and vitamin E (500-400 microM) did not have a pro-oxidant effect in Caco-2 cells. Treatment of cells with vitamin C (10 microM) or vitamin E (100 microM) did not significantly reduce oxidative DNA damage in hypoxia and normoxia. In conclusion, the use of these vitamins would not hinder toxicity against hypoxic cells, but a protective effect in normoxic cells was not evident.     

Machine learning application in soccer: a systematic review

Due to the chaotic nature of soccer, the predictive statistical models have become in a current challenge to decision-making based on scientific evidence. The aim of the present study was to systematically identify original studies that applied machine learning (ML) to soccer data, highlighting current possibilities in ML and future applications. A systematic review of PubMed, SPORTDiscus, and FECYT (Web of Sciences, CCC, DIIDW, KJD, MEDLINE, RSCI, and SCIELO) was performed according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. From the 145 studies initially identified, 32 were fully reviewed, and their outcome measures were extracted and analyzed. In summary, all articles were clustered into three groups: injury (n = 7); performance (n = 21), which was classified in match/league outcomes forecasting, physical/physiological forecasting, and technical/tactical forecasting; and the last group was about talent forecasting (n = 5). The development of technology, and subsequently the large amount of data available, has become ML in an important strategy to help team staff members in decision-making predicting dose-response relationship reducing the chaotic nature of this team sport. However, since ML models depend upon the amount of dataset, further studies should analyze the amount of data input needed make to a relevant predictive attempt which makes accurate predicting available.     

Cubic regression-based degree of correction predicts the performance of whole bisulfitome amplified DNA methylation analysis

Epigenetic mechanisms, including DNA methylation, are important determinants in development and disease. There is a need for technologies capable of detecting small variations in methylation levels in an accurate and reproducible manner, even if only limited amounts of DNA are available (which is the case in many studies in humans). Quantitative methylation analysis of minute DNA amounts after whole bisulfitome amplification (qMAMBA) has been proposed as an alternative, but this technique has not been adequately standardized and no comparative study against conventional methods has been performed, that includes a wide range of methylation percentages and different target assays. We designed an experiment to compare the performance of qMAMBA and bisulfite-treated genomic (non-amplified) DNA pyrosequencing. Reactions were performed in duplicate for each technique in eight different target genes, using nine artificially constructed DNA samples with methylation levels ranging between 0% and 100% with intervals of 12.5%. Cubic polynomial curves were plotted from the experimental results and the real methylation values and the resulting equation was used to estimate new corrected data points. The use of the cubic regression-based correction benefits the accuracy and the power of discrimination in methylation studies. Additionally, dispersion of the new estimated data around a y = x line (R²) served to fix a cutoff that can discriminate, with a single 9-point curve experiment, whether whole bisulfitome amplification and subsequent qMAMBA can produce accurate methylation results. Finally, even with an optimized reagent kit, DNA samples subjected to whole bisulfitome amplification enhance the preferential amplification of unmethylated alleles, and subtle changes in methylation levels cannot be detected confidently.