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Genome-wide association studies (GWAS) have identified many single nucleotide polymorphisms (SNPs) that play important roles in the genetic heritability of traits and diseases. With most of these SNPs located on the non-coding part of the genome, it is currently assumed that these SNPs influence the expression of nearby genes on the genome. However, identifying which genes are targeted by these disease-associated SNPs remains challenging. In the past, protein knowledge graphs have often been used to identify genes that are associated with disease, also referred to as “disease genes”. Here, we explore whether protein knowledge graphs can be used to identify genes that are targeted by disease-associated non-coding SNPs by testing and comparing the performance of six existing methods for a protein knowledge graph, four of which were developed for disease gene identification. We compare our performance against two baselines: (1) an existing state-of-the-art method that is based on guilt-by-association, and (2) the leading assumption that SNPs target the nearest gene on the genome. We test these methods with four reference sets, three of which were obtained by different means. Furthermore, we combine methods to investigate whether their combination improves performance. We find that protein knowledge graphs that include predicate information perform comparable to the current state of the art, achieving an area under the receiver operating characteristic curve (AUC) of 79.6% on average across all four reference sets. Protein knowledge graphs that lack predicate information perform comparable to our other baseline (genetic distance) which achieved an AUC of 75.7% across all four reference sets. Combining multiple methods improved performance to 84.9% AUC. We conclude that methods for a protein knowledge graph can be used to identify which genes are targeted by disease-associated non-coding SNPs.

Objective: After stratifying for age, sex and multimorbidity at baseline, our aim is to analyse time trends in incident multimorbidity and polypharmacy in the 15-year clinical trajectories of individual patients in a family medicine setting. Methods: This study was carried out using data from the Registration Network Family Medicine in the South of the Netherlands. The clinical trajectories of 10037 subjects during the 15-year period (2000–2014) were analyzed in a repeated measurement of using a generalized estimating equations model as well as a multilevel random intercept model with repeated measurements to determine patterns of incident multimorbidity and polypharmacy. Hierarchical age-period-cohort models were used to generate age and cohort trajectories for comparison with prevalence trends in multimorbidity literature. Results: Multimorbidity was more common in females than in males throughout the duration of the 15-year trajectory (females: 39.6%; males: 33.5%). With respective ratios of 11.7 and 5.9 between the end and the beginning of the 15-year period, the youngest female and male groups showed a substantial increase in multimorbidity prevalence. Ratios in the oldest female and male groups were 2.2 and 1.9 respectively. Females had higher levels of multimorbidity than males in the 0-24-year and 25-44-year age groups, but the levels converged to a prevalence of 92.2% in the oldest male and 90.7% in the oldest female group. Similar, albeit, moderate differences were found in polypharmacy patterns. Conclusions: We sought to specify the progression of multimorbidity from an early age. As a result, our study adds to the multimorbidity literature by specifying changes in chronic disease accumulation with relation to polypharmacy, and by tracking differences in patient trajectories according to age and sex. Multimorbidity and polypharmacy are common and their prevalence is accelerating, with a relatively rapid increase in younger groups. From the point of view o

The application of genetic technology seems to threaten what is considered natural in elite sport. This paper explores the role of genetic technology in elite sport and questions the significance of dichotomizing between the natural and artificial element. How do shifts in technology affect the attribution of the human element in athletic performance? To explore the attribution of human agency we compare the genetic enhancement of athletes with two other shifts of the ‘natural performance’ in sport: first, the introduction of a revolutionary high jump technique, the Fosbury flop, in 1968; and, second, the introduction of the klapskate in speed skating in 1997. The three cases show that artificiality as such can hardly be a criterion for evaluating processes of innovation. The context of elite sport is itself highly artificial. Boundaries between the natural body and the enhanced body are the outcome of institutional processes of boundary work. When discussing new technologies in sport it is better to ask if athletes are still playing the same game and whether or not there are equal opportunities and an equal distribution of means for playing the game. The introduction of gene technology may result in inequalities, with great impact on the outcome of the game. This outcome may be considered irrelevant for the inequalities that the game is supposed to produce and measure. Genetic enhancement may also threaten the public view of athletes as moral agents and possibly change the appreciation for human performance.

Background: The prevalence of multimorbidity is increasing in recent years, and patients with multimorbidity often have a decrease in quality of life and require more health care. The aim of this study was to explore the evolution of multimorbidity taking the sequence of diseases into consideration. Methods: We used a Belgian database collected by extracting coded parameters and more than 100 chronic conditions from the Electronic Health Records of general practitioners to study patients older than 40 years with multiple diagnoses between 1991 and 2015 (N = 65 939). We applied Markov chains to estimate the probability of developing another condition in the next state after a diagnosis. The results of Weighted Association Rule Mining (WARM) allow us to show strong associations among multiple conditions. Results: About 66.9% of the selected patients had multimorbidity. Conditions with high prevalence, such as hypertension and depressive disorder, were likely to occur after the diagnosis of most conditions. Patterns in several disease groups were apparent based on the results of both Markov chain and WARM, such as musculoskeletal diseases and psychological diseases. Psychological diseases were frequently followed by irritable bowel syndrome. Conclusions: Our study used Markov chains and WARM for the first time to provide a comprehensive view of the relations among 103 chronic conditions, taking sequential chronology into consideration. Some strong associations among specific conditions were detected and the results were consistent with current knowledge in literature, meaning the approaches were valid to be used on larger data sets, such as National Health care Systems or private insurers.

Objectives Differentiation between normal and abnormal features of vascular ageing is crucial, as the latter is associated with adverse outcomes. The normal aortic ageing process is accompanied by gradual luminal dilatation and reduction of vessel compliance. However, the influence of age on longitudinal aortic dimensions and geometry has not been well studied. This study aims to describe the normal evolution of aortic length and shape throughout life.Methods A total of 210 consecutive patients were prospectively enrolled in this cross-sectional single-centre study. All subjects underwent CT on a third-generation dual-source CT scanner. Morphometric measurements, including measurements of segmental length and tortuosity, were performed on three-dimensional models of the thoracic aorta.Results The length of the thoracic aorta was significantly related to age (r=0.54) and increased by 59 mm (males) or 66 mm (females) between the ages of 20 and 80 years. Elongation was most pronounced in the proximal descending aorta, which showed an almost 2.5-fold length increase during life. The lengthening of the thoracic aorta was accompanied by a marked change of its geometry: whereas the aortic apex was located between the branch vessels in younger patients, it shifted to a more distalward position in the elderly.Conclusions The normal ageing process is accompanied by gradual aortic elongation and a notable change of aortic geometry. Part II of this two-part article investigates the hypothesis that excessive elongation could play a role in the occurrence of acute aortic dissection.

Objectives Prophylactic surgery for prevention of acute type A aortic dissection (ATAAD) is reserved for patients with an ascending aortic aneurysm =55 mm. Identification of additional risk predictors is warranted since over 70% of patients presenting with ATAAD have a non-dilated aorta or an aneurysm that would not have met the diameter criterion for preventative surgery. Aim of the study was to evaluate ascending aortic elongation as a risk factor for ATAAD and to compare aortic lengths between ATAAD patients and healthy controls.Methods Aortic lengths and diameters of ATAAD patients were measured on three-dimensional modelled computed tomography and adjusted to predissection dimensions in this cross-sectional single-centre study. Logistic regression was used to evaluate the relation between ATAAD and aortic dimensions. Lengths of different aortic segments were compared with a healthy control group using propensity score matching.Results Two-hundred and fifty patients were included in the study (ATAAD, n=40; controls, n=210). Ascending aortic length and diameter proved to be independent predictors for ATAAD (OR=5.3, CI 2.5 to 11.4, pConclusions Ascending aortic length could serve as an independent predictor for ATAA D. Future studies addressing indications for prophylactic surgery should also investigate aortic length.

Little is known about whether and how two chronic diseases interact with each other in modifying the risk of physical inactivity. The aim of the present study is to identify chronic disease pairs that are associated with compliance or noncompliance with the Dutch PA guideline recommendation and to study whether specific chronic disease pairs indicate an extra effect on top of the effects of the diseases individually. Cross-sectional data from 3,386 participants of cohort study SMILE were used and logistic regression analysis was performed to study the joint effect of the two diseases of each chronic disease pair for compliance with the Dutch PA guideline. For six chronic disease pairs, patients suffering from both diseases belonging to these disease pairs in question show a higher probability of noncompliance to the Dutch PA guideline, compared to what one would expect based on the effects of each of the two diseases alone. These six chronic disease pairs were chronic respiratory disease and severe back problems; migraine and inflammatory joint disease; chronic respiratory disease and severe kidney disease; chronic respiratory disease and inflammatory joint disease; inflammatory joint disease and rheumatoid arthritis; and rheumatoid arthritis and osteoarthritis of the knees, hips, and hands.

Little is known about whether and how two chronic diseases interact with each other in modifying the risk of physical inactivity. The aim of the present study is to identify chronic disease pairs that are associated with compliance or noncompliance with the Dutch PA guideline recommendation and to study whether specific chronic disease pairs indicate an extra effect on top of the effects of the diseases individually. Cross-sectional data from 3,386 participants of cohort study SMILE were used and logistic regression analysis was performed to study the joint effect of the two diseases of each chronic disease pair for compliance with the Dutch PA guideline. For six chronic disease pairs, patients suffering from both diseases belonging to these disease pairs in question show a higher probability of noncompliance to the Dutch PA guideline, compared to what one would expect based on the effects of each of the two diseases alone. These six chronic disease pairs were chronic respiratory disease and severe back problems; migraine and inflammatory joint disease; chronic respiratory disease and severe kidney disease; chronic respiratory disease and inflammatory joint disease; inflammatory joint disease and rheumatoid arthritis; and rheumatoid arthritis and osteoarthritis of the knees, hips, and hands.