Effects Of Key Risk Factors Research Articles

Cardiovascular risk factors: The effects of ageing and smoking on the immune system, an observational clinical study.

Currently immunomodulatory compounds are under investigation for use in patients with cardiovascular disease, caused by atherosclerosis. These trials, using recurrent cardiovascular events as endpoint, require enrollment of large patient groups. We investigated the effect of key risk factors for atherosclerosis development, ageing and smoking, on the immune system, with the objective to identify biomarkers differentiating between human populations, and potentially serving as endpoints for future phase 1B trials with immunomodulatory compounds. Blood was collected from young healthy volunteers (aged 18-25 years, n=30), young smokers (18-25 years, n=20), elderly healthy volunteers (>60 years, n=20), heavy smokers (>45 years, 15 packyears, n=11) and patients with stable coronary artery disease (CAD) (>60 years, n=27). Circulating immune cell subsets were characterized by flow cytometry, and collected plasma was evaluated by proteomics (Olink). Clear ageing effects were observed, mostly illustrated by a lower level in CD8+ and naïve CD4+ and CD8+ T cells, with an increase in CD4+ and CD8+ effector memory T cells in elderly healthy volunteers compared to young healthy volunteers. Heavy smokers showed a more inflammatory cellular phenotype, especially a shift in Th1/Th2 ratio: higher Th1 and lower Th2 percentages compared to young healthy volunteers. A significant decrease in circulating atheroprotective oxLDL-specific IgM was found in patients with CAD compared to young healthy volunteers. Elevated pro-inflammatory and chemotactic proteins TREM1 and CCL11 were observed in elderly volunteers compared to young volunteers. In addition, heavy smokers had an increase in pro-inflammatory cytokine IL-6 and lysosomal protein LAMP3. These data show that ageing and smoking are associated with an inflammatory immunophenotype, and that heavy smokers or aged individuals may serve as potential populations for future clinical trials investigating immunomodulatory drugs targeted for cardiovascular disease.

Depression, anxiety and PTSD symptoms before and during the COVID-19 pandemic in the UK.

The impact of the coronavirus disease 2019 (COVID-19) pandemic on mental health is still being unravelled. It is important to identify which individuals are at greatest risk of worsening symptoms. This study aimed to examine changes in depression, anxiety and post-traumatic stress disorder (PTSD) symptoms using prospective and retrospective symptom change assessments, and to find and examine the effect of key risk factors. Online questionnaires were administered to 34 465 individuals (aged 16 years or above) in April/May 2020 in the UK, recruited from existing cohorts or via social media. Around one-third (n = 12 718) of included participants had prior diagnoses of depression or anxiety and had completed pre-pandemic mental health assessments (between September 2018 and February 2020), allowing prospective investigation of symptom change. Prospective symptom analyses showed small decreases in depression (PHQ-9: -0.43 points) and anxiety [generalised anxiety disorder scale - 7 items (GAD)-7: -0.33 points] and increases in PTSD (PCL-6: 0.22 points). Conversely, retrospective symptom analyses demonstrated significant large increases (PHQ-9: 2.40; GAD-7 = 1.97), with 55% reported worsening mental health since the beginning of the pandemic on a global change rating. Across both prospective and retrospective measures of symptom change, worsening depression, anxiety and PTSD symptoms were associated with prior mental health diagnoses, female gender, young age and unemployed/student status. We highlight the effect of prior mental health diagnoses on worsening mental health during the pandemic and confirm previously reported sociodemographic risk factors. Discrepancies between prospective and retrospective measures of changes in mental health may be related to recall bias-related underestimation of prior symptom severity.

Modeling of Farmers' Vegetable Safety Production Based on Identification of Key Risk Factors From Beijing, China.

Food safety emphasizes risk control in the production process, and has attracted much attention from food regulators and consumers in recent years. The objectives of this study were to conduct early key risk factors identification and risk modeling for vegetable safety production. To achieve these objectives, this article quantitatively identified the key direct and indirect risk factors in vegetable safety production through questionnaire surveys and a multivariate linear model, and modeled the effects of key risk factors affecting vegetable safety production based on the catastrophe progression method. Based on 973 valid farmers' questionnaires from Beijing, China, the results showed that key direct risk factors are production violation, farmland biological control, pesticide and fertilizer use criteria, and agricultural consumable handling; key indirect risk factors included cooperative participation, planting years, prohibited pesticide knowledge, production recording, and product type. Through the empirical analysis, it can be seen that there are regional differences in the production risk of vegetable farmers in Beijing. The production risks of Changping, Huairou, and Shunyi are the most serious; from a city-wide perspective, the risk of farmland biological control is greatest, followed by risk aversion ability. The findings of this research have important implications for safe vegetable production and farmers' production risk control.

Quantifying and comparing the effects of key risk factors on various types of roadway segment crashes with LightGBM and SHAP

Understanding and quantifying the effects of risk factors on crash frequency is of great importance for developing cost-effective safety countermeasures. In this paper, the effects of key crash contributing factors on total crashes and crashes of different collision types are analyzed separately and compared. A novel Machine Learning (ML) method, Light Gradient Boosting Machine (LightGBM), is introduced to model a Texas dataset consisting of vehicle crashes occurred from 2015 to 2017. Compared with other commonly used ML methods such as eXtreme Gradient Boosting (XGBoost), LightGBM performs significantly better in terms of mean absolute error (MAE) and root mean squared error (RMSE). In addition, the SHapley Additive explanation (SHAP) approach is employed to interpret the LightGBM outputs. Significant risk factors are identified, including speed limits, area type, number of lanes, roadway functional class, shoulder width and shoulder type. With the SHAP method, the importance, total effects, and main and interaction effects of risk factors are quantified. The results suggest that the importance of risk factors vary across collision types. Speed limit is a more important risk factor than right/left shoulder width, lane width, and median width for Rear-End (RE) crashes, while the opposite relationship is found for Run-Off-Road (ROR) crashes. Also, it is found that narrow lanes (8ft to 11ft) increase the risk for all types of crashes (i.e., Total, ROR, and RE) in this study. For road segments with 5 or 6 lanes in both directions combined, a lane width greater than or equal to 12ft may help reduce the risk of all types of crashes. These results have important implications for developing accurate crash modification factors and cost-effective safety countermeasures.

Risk mechanisms of large group emergency decision-making based on multi-agent simulation

There are many critical sources of risk in large group emergency decision-making. In this paper, we systematically identify individual and group sources of risk in large group emergency decision-making and explore the relationship between each risk factor and two group effects, namely cognitive conflict and relationship conflict. Based on this analysis, we model a large group emergency decision-making risk-causing system. The model allows examination of the effect of key risk factors. These variables include individual acceptance, group structure, interaction mode, decision strategy, and decision-making environment, and variable values are set. A multi-agent simulation model of large group emergency decision-making risk-causing is developed using the Netlogo tool based on opinion dynamics. Through case simulation, we obtain the generalization rule of the causal mechanisms for each risk factor. The simulation shows that controlling the proportion of highly accepted decision-making subjects, increasing the interaction between clusters, and taking necessary anticipatory measures can noticeably reduce decision-making risks and help large teams function in highly dynamic decision-making environments. By highlighting the composition and collective influence of risk factors in large group emergency decision-making, this research offers reference and guidance for strategy selection in emergency decision-making.

Examining correlations between motorcyclist’s conspicuity, apparel related factors and injury severity score: Evidence from new motorcycle crash causation study

Motorcyclists are vulnerable road users at a particularly high risk of serious injury or death when involved in a crash. In order to evaluate key risk factors in motorcycle crashes, this study quantifies how different “policy-sensitive” factors correlate with injury severity, while controlling for rider and crash specific factors as well as other observed/unobserved factors. The study analyzes data from 321 motorcycle injury crashes from a comprehensive US DOT FHWA’s Motorcycle Crash Causation Study (MCCS). These were all non-fatal injury crashes that are representative of the vast majority (82%) of motorcycle crashes. An anatomical injury severity scoring system, termed as Injury Severity Score (ISS), is analyzed providing an overall score by accounting for the possibility of multiple injuries to different body parts of a rider. An ISS ranges from 1 to 75, averaging at 10.32 for this sample (above 9 is considered serious injury), with a spike at 1 (very minor injury). Preliminary cross-tabulation analysis mapped ISS to the Abbreviated Injury Scale (AIS) injury classification and examined the strength of associations between the two. While the study finds a strong correlation between AIS and ISS classification (Kendall’s tau of 0.911), significant contrasts are observed in that, when compared to ISS, AIS tends to underestimate the severity of an injury sustained by a rider. For modeling, fixed and random parameter Tobit modeling frameworks were used in a corner-solution setting to account for the left-tail spike in the distribution of ISS and to account for unobserved heterogeneity. The developed random parameters Tobit framework additionally accounts for the interactive effects of key risk factors, allowing for possible correlations among random parameters. A correlated random parameter Tobit model significantly out-performed the uncorrelated random parameter Tobit and fixed parameter Tobit models. While controlling for various other factors, we found that motorcycle-specific shoes and retroreflective upper body clothing correlate with lower ISS on-average by 5.94 and 1.88 units respectively. Riders with only partial helmet coverage on-average sustained more severe injuries, whereas, riders with acceptable helmet fit had lower ISS Methodologically, not only do the individual effects of several key risk factors vary significantly across observations in the form of random parameters, but the interactions between unobserved factors characterizing random parameters significantly influence the injury severity score as well. The implications of the findings are discussed.

KF-finder: identification of key factors from host-microbial networks in cervical cancer

BackgroundThe human body is colonized by a vast number of microbes. Microbiota can benefit many normal life processes, but can also cause many diseases by interfering the regular metabolism and immune system. Recent studies have demonstrated that the microbial community is closely associated with various types of cell carcinoma. The search for key factors, which also refer to cancer causing agents, can provide an important clue in understanding the regulatory mechanism of microbiota in uterine cervix cancer.ResultsIn this paper, we investigated microbiota composition and gene expression data for 58 squamous and adenosquamous cell carcinoma. A host-microbial covariance network was constructed based on the 16s rRNA and gene expression data of the samples, which consists of 259 abundant microbes and 738 differentially expressed genes (DEGs). To search for risk factors from host-microbial networks, the method of bi-partite betweenness centrality (BpBC) was used to measure the risk of a given node to a certain biological process in hosts. A web-based tool KF-finder was developed, which can efficiently query and visualize the knowledge of microbiota and differentially expressed genes (DEGs) in the network.ConclusionsOur results suggest that prevotellaceade, tissierellaceae and fusobacteriaceae are the most abundant microbes in cervical carcinoma, and the microbial community in cervical cancer is less diverse than that of any other boy sites in health. A set of key risk factors anaerococcus, hydrogenophilaceae, eubacterium, PSMB10, KCNIP1 and KRT13 have been identified, which are thought to be involved in the regulation of viral response, cell cycle and epithelial cell differentiation in cervical cancer. It can be concluded that permanent changes of microbiota composition could be a major force for chromosomal instability, which subsequently enables the effect of key risk factors in cancer. All our results described in this paper can be freely accessed from our website at http://www.nwpu-bioinformatics.com/KF-finder/.

Population attributable fractions and joint effects of key risk factors for multiple sclerosis

Aim: We examined the combined effect of having multiple key risk factors and the interactions between the key risk factors of multiple sclerosis (MS). Methods: We performed an incident case-control study including cases with a first clinical diagnosis of central nervous system demyelination (FCD) and population-based controls. Results: Compared to those without any risk factors, those with one, two, three, and four or five risk factors had increased odds of being an FCD case of 2.12 (95% confidence interval (CI), 1.11–4.03), 4.31 (95% CI, 2.24–8.31), 7.96 (95% CI, 3.84–16.49), and 21.24 (95% CI, 5.48–82.40), respectively. Only HLA-DR15 and history of infectious mononucleosis interacted significantly on the additive scale (Synergy index, 3.78; p = 0.03). The five key risk factors jointly accounted for 63.8% (95% CI, 43.9–91.4) of FCD onset. High anti-EBNA IgG was another important contributor. Conclusions: A high proportion of FCD onset can be explained by the currently known risk factors, with HLA-DR15, ever smoking and low cumulative sun exposure explaining most. We identified a significant interaction between HLA-DR15 and history of IM in predicting an FCD of CNS demyelination, which together with previous observations suggests that this is a true interaction.

Bayesian mapping of HIV infection among women of reproductive age in Rwanda.

HIV prevalence is rising and has been consistently higher among women in Rwanda whereas a decreasing national HIV prevalence rate in the adult population has stabilised since 2005. Factors explaining the increased vulnerability of women to HIV infection are not currently well understood. A statistical mapping at smaller geographic units and the identification of key HIV risk factors are crucial for pragmatic and more efficient interventions. The data used in this study were extracted from the 2010 Rwanda Demographic and Health Survey data for 6952 women. A full Bayesian geo-additive logistic regression model was fitted to data in order to assess the effect of key risk factors and map district-level spatial effects on the risk of HIV infection. The results showed that women who had STIs, concurrent sexual partners in the 12 months prior to the survey, a sex debut at earlier age than 19 years, were living in a woman-headed or high-economic status household were significantly associated with a higher risk of HIV infection. There was a protective effect of high HIV knowledge and perception. Women occupied in agriculture, and those residing in rural areas were also associated with lower risk of being infected. This study provides district-level maps of the variation of HIV infection among women of child-bearing age in Rwanda. The maps highlight areas where women are at a higher risk of infection; the aspect that proximate and distal factors alone could not uncover. There are distinctive geographic patterns, although statistically insignificant, of the risk of HIV infection suggesting potential effectiveness of district specific interventions. The results also suggest that changes in sexual behaviour can yield significant results in controlling HIV infection in Rwanda.

Few studies addressing toothbrushing frequency and periodontitis.

PubMed and seven other databases. English-language articles evaluating the association between the frequency of tooth brushing and periodontitis were considered. Two reviewers independently conducted study selection with study quality being assessed using the Health Evidence Bulletin (HEB) Wales checklist. Relevant study characteristics were extracted and where necessary, effect measures and corresponding confidence intervals (CIs) were recalculated from the reported papers. Random and fixed effects meta-analyses were conducted. Fourteen studies were included (12 cross-sectional, two case-controlled). Sample sizes ranged from 44 - 9203. A fixed effects meta-analysis (12 cross sectional studies) found a significant overall odds ratio (OR) 1.41 (95%CI: 1.25-1.58, P <0.0001). A random effects meta-analysis (all 14 studies) found OR =1.44 (95%CI: 1.21-1.71, P < <0.0001) a slight indication for heterogeneity (I(2) = 48%, P = 0.02). There are relatively few studies evaluating the association between tooth brushing frequency and periodontitis. A clear effect was observed, indicating that infrequent tooth brushing was associated with severe forms of periodontal disease. Further epidemiological studies are needed to precisely estimate the effect of key risk factors for periodontitis and their interaction effects.

Is frequency of tooth brushing a risk factor for periodontitis? A systematic review and meta-analysis.

The epidemiology of periodontitis regarding oral-hygiene practices particularly the frequency of tooth brushing has been the subject of relatively few dedicated studies. This paper provides a systematic review of available relevant epidemiological studies and a meta-analysis of the effect of tooth brushing frequency on periodontitis. To review and to quantify the risk for periodontitis associated with frequency of tooth brushing. Systematic literature search was conducted in nine online resources (PUBMED, ISI and 7 additional databases). Related and cross-referencing publications were reviewed. Papers published until end of March 2013 reporting associations between tooth brushing frequency and periodontitis were considered. A meta-analysis was performed to quantify this association. Fourteen studies were identified. The test of heterogeneity for cross-sectional studies was not significant (P = 0.31). A fixed-effects model yielded a significant overall odds ratio estimate of 1.41 (95%CI: 1.25-1.58, P < 0.0001) for infrequent compared to frequent tooth brushing. For all fourteen studies, there was a slight indication for heterogeneity (I² = 48%, P = 0.02) and the corresponding result with a random-effects model was 1.44 (95%CI: 1.21-1.71, P < 0.0001). There are relatively few studies evaluating the association between tooth brushing frequency and periodontitis. A clear effect was observed, indicating that infrequent tooth brushing was associated with severe forms of periodontal disease. Further epidemiological studies are needed to precisely estimate the effect of key risk factors for periodontitis and their interaction effects.

Effects of leading mortality risk factors among trauma patients vary by age

Patient age is well recognized as a factor that contributes to increased mortality risk among trauma patients. Less well recognized is the potential that the strength of the effects of other risk factors that increase mortality risk may depend on a patient's age. This study examines whether the statistical relationship between trauma patient survival and key mortality risk factors varies significantly by patient age in years, across mechanisms of injury. The statistical interaction between age and values of key risk factors included in the Trauma Quality Improvement Program mortality risk adjustment model is assessed using patient data included in the 2008 National Trauma Data Bank National Sample Program. Multivariable logistic regression analysis is used to assess the statistical significance of the interaction effect on patient morality risk for key mortality risk factors and patient age in years, across mechanisms of injury. Statistically significant interactions (p < 0.01) occurred between age and each of the selected risk factors, for each common mechanism of injury. Differences also occurred in the direction of the interactions between age and selected risk factors, across mechanism of injury. The effects of key risk factors included in trauma patient mortality risk adjustment models vary depending on patient age in years, for each commonly occurring mechanism of injury. Statistical models assessing patient mortality risk could be meaningfully improved by accounting for these interaction effects. Prognostic and epidemiologic study, level III.