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While age is an important risk factor, there are some disadvantages to including it in a stroke risk model: age can dominate the risk score and lead to over- or under-predictions in some age groups. There is evidence to suggest that some of these disadvantages are due to the non-proportionality of other risk factors with age, e.g., risk factors contribute differently to stroke risk based on an individual’s age. In this paper, we present a framework to test if risk factors are proportional with age. We then apply the framework to a set of risk factors using Framingham heart study data from the NHLBI Biologic Specimen and Data Repository Information Coordinating Center to determine if we can find evidence of non-proportionality. Using our framework, we find that a number of risk factors (diastolic blood pressure, total cholesterol, BMI, sex, high blood pressure treatment) may be non-proportional to age. This suggests that testing for the proportionality of risk factors with age should be something that is considered in stroke risk prediction modelling and traditional modelling methods may need to be adjusted to capture this non-proportionality.

Agent-based models can be used to better understand the impacts of lifting restrictions or implementing interventions during a pandemic. However, agent-based models are computationally expensive, and running a model of a large population can result in a simulation taking too long to run for the model to be a useful analysis tool during a public health crisis. To reduce computing time and power while running a detailed agent-based model for the spread of COVID-19 in the Republic of Ireland, we introduce a scaling factor that equates 1 agent to 100 people in the population. We present the results from model validation and show that the scaling factor increases the variability in the model output, but the average model results are similar in scaled and un-scaled models of the same population, and the scaled model is able to accurately simulate the number of cases per day in Ireland during the autumn of 2020. We then test the usability of the model by using the model to explore the likely impacts of increasing community mixing when schools reopen after summer holidays.

The dynamics that lead to the spread of an infectious disease through a population can be characterized as a complex system. One way to model such a system, in order to improve preparedness, and learn more about how an infectious disease, such as COVID-19, might spread through a population, is agent-based epidemiological modelling. When a pandemic is caused by an emerging disease, it takes time to develop a completely new model that captures the complexity of the system. In this paper, we discuss adapting an existing agent-based model for the spread of measles in Ireland to simulate the spread of COVID-19. The model already captures the population structure and commuting patterns of the Irish population, and therefore, once adapted to COVID-19, it can provide important insight on the pandemic, specifically in Ireland. We first investigate the different disease parameters that need to be adjusted to simulate the spread of COVID-19 instead of measles and then run a set of experiments initially comparing the model output for our original measles model with that from the adjusted COVID-19 model. We then report on experiments on how the different values of the basic reproductive number, R0, influence the simulated outbreaks, and find that our model behaves as expected: the higher the R0, the more agents are infected. Then, we demonstrate how different intervention strategies, such as vaccinations and school closures, influence the spread of measles and COVID-19 and how we can simulate real pandemic timings and interventions in our model. We show that with the same society, environment and transportation components among the different disease components lead to very different results for the two diseases, and that our COVID-19 model, when run for Leitrim County, Ireland, predicts a similar outbreak length to a real outbreak in Leitrim County, Ireland, but the model results in a higher number of infected agents compared to the real outbreak. This difference in cases is most likely due to identifying all cases of COVID-19 in the model opposed to only those tested. Once an agent-based model is created to simulate a specific complex system or society, the disease component can be adapted to simulate different infectious disease outbreaks. This makes agent-based models a powerful tool that can be used to help understand the spread of new and emerging infectious diseases.

Short-chain fatty acids (SCFAs) are the major microbial metabolites produced from the fermentation of dietary fiber in the gut. They are recognised as secretagogues of the glucagon-like peptides, GLP-1 and GLP-2, likely mediated by the activation of free fatty acid receptors 2 and 3 (FFAR2 and 3) expressed on enteroendocrine L-cells. Fiber-deficient diets are associated with decreased intestinal function and decreased colonic GLP-1 and GLP-2 content. Here, we speculated that the lowered colonic GLP-1 observed following a fiber-free diet was a consequence of decreased SCFA production and a subsequent decrease in FFAR2/3 activation. Furthermore, we explored the consequences of a fiber-free diet followed by intestinal injury, and we mechanistically explored the SCFA-FFAR2/3-GLP-1 pathway to explain the increased severity. Colonic luminal content from mice fed either a fiber-free or chow diet were analysed for SCFA content by LC–MS. FFAR2/3 receptor contributions to SCFA-mediated colonic GLP-1 secretion were assessed in isolated perfused preparations of the colon from FFAR2/3 double knockout (KO) and wild-type (WT) mice. Colitis was induced by the delivery of 3% dextran sulfate sodium (DSS) for 4 days in the drinking water of mice exposed to a fiber-free diet for 21 days. Colitis was induced by the delivery of 3% DSS for 7 days in FFAR2/3 KO mice. The removal of dietary fiber significantly decreased SCFA concentrations in the luminal contents of fiber-free fed mice compared to chow-fed mice. In the perfused colon, luminal SCFAs significantly increased colonic GLP-1 secretion in WT mice but not in FFAR2/3 KO mice. In the DSS-induced colitis model, the removal of dietary fiber increased the severity and prevented the recovery from intestinal injury. Additionally, colitis severity was similar in FFAR2/3 KO and WT mice after DSS application. In conclusion, the results confirm that the removal of dietary fiber is sufficient to decrease the colonic concentrations of SCFAs. Additionally, we show that a fiber-free diet predisposes the colon to increased intestinal injury, but this effect is independent of FFAR2 and FFAR3 signalling; therefore, it is unlikely that a fiber-free diet induces a decrease in luminal SCFAs and sensitivity to intestinal disease involves the SCFA-FFAR2/3-GLP-1 pathway.

Accumulating evidence implicates glucagon-like peptide-1 (GLP-1) to have, beyond glucose maintenance, a beneficial role in the gastrointestinal tract. Here, we review emerging data investigating GLP-1 as a novel treatment for intestinal diseases, including inflammatory bowel diseases, short-bowel syndrome, intestinal toxicities and coeliac disease. Possible beneficial mechanisms for these diseases include GLP-1′s influence on gastric emptying, its anti-inflammatory properties and its intestinotrophic effect. The current knowledge basis derives from the available GLP-1 agonist treatments in experimental animals and small clinical trials. However, new novel strategies including dual GLP-1/GLP-2 agonists are also in development for the treatment of intestinal diseases.

Intestinal adaptation is an important response and a natural repair mechanism in acute intestinal injury and is critical for recovery. Glucagon-like peptide 2 (GLP-2) has been demonstrated to enhance mucosal repair following intestinal damage. In this study, we aimed to investigate the role of GLP-2 receptor activation on intestinal protection and adaptation upon chemotherapy-induced intestinal injury. The injury was induced with a single injection of 5-fluorouracil in female GLP-2 receptor knockout (GLP-2R(-/-)) mice and their wild type (WT) littermates. The mice were euthanized in the acute or the recovery phase of the injury; the small intestines were analysed for weight changes, morphology, histology, inflammation, apoptosis and proliferation. In the acute phase, only inflammation was slightly increased in the GLP-2R(-/-) mice compared to WT. In the recovery phase, we observed the natural compensatory response with an increase in small intestinal weight, crypt depth and villus height in WT mice, and this was absent in the GLP-2R(-/-) mice. Both genotypes responded with hyperproliferation. From this, we concluded that GLP-2R signalling does not have a major impact on acute intestinal injury but is pivotal for the adaptive response in the small intestine.