Identification Of Individuals Research Articles

The Impact of Character Creation and Customization on Self-Expression and Identity in RPGs

While a large of research investigates the importance of character creation and customization in role-playing games (RPGs) on players’ motivation and enjoyment to play, little is known about the impact of character creation and customization on players’ self-expression and identity exploration. Video games have been a venue for both developers and gamers to explore complex stories and ideas. Role-playing games (RPGs) are a type of video game in which players can take the role of a character in a fictional setting and interact with the game’s narrative. This paper is part of a larger research on the construction of individual identities using RPG character creation and customization. Building upon existing research on identity adaptation theory, queer theory, the proteus effect theory, and the performance of identity, this paper finds that players’ choices of character creation and customization contribute to opportunities for self-expression and individual agency. This paper also examines the character Creation and Customization preferences among different demographic groups and the association between in-game identity and real-life self-perception and social behaviors of players.

Characterization of individuals with atherosclerotic cardiovascular disease, with and without chronic kidney disease: results from a UK electronic health record database

Abstract Introduction Elevated C-reactive protein (CRP) levels are often present in individuals with atherosclerotic cardiovascular disease (ASCVD), including in those who also have chronic kidney disease (CKD), and are associated with an increased risk of major adverse cardiovascular events (MACE). To understand unmet needs in the identification and management of at-risk individuals, we characterized patients with ASCVD in a UK database, according to CKD stage and CRP levels. Methods This study comprised separate retrospective cross-sectional and longitudinal cohort analyses of patients in the Clinical Practice Research Datalink (CPRD) Aurum database, a representative UK database of anonymized primary care records. In the cross-sectional analysis, CPRD Aurum was linked with Hospital Episode Statistics (HES) and Index of Multiple Deprivation data to assess patient characteristics on 1 Jan 2021 (defined as index date). In the cohort analysis, CPRD Aurum was linked with HES and Office for National Statistics mortality data to determine the incidence of 3-point MACE (3P-MACE) over approximately 5 years of follow-up (index date: 1 Jan 2016). Both analyses included individuals ≥ 18 years old with an ASCVD diagnosis in both primary and secondary care in the 5 years pre-index. Data were analysed using descriptive statistics. Patient characteristics were assessed by CKD stage; for individuals with CKD stage 3–4, characteristics were also assessed by CRP levels. Results In total, 225,767 and 230,055 individuals were included in the cross-sectional and cohort analyses, respectively. The prevalence of CKD stage 3–4 among individuals with ASCVD was 24.3% (Table 1). The proportion of individuals with elevated CRP (CRP ≥ 2 mg/L) and anaemia increased for successively higher CKD severity groups; in addition, glycated haemoglobin (HbA1c) ≥ 7% and elevated blood pressure were generally more frequently observed in individuals with CKD than in those without (Table 1). Individuals with ASCVD, CKD stage 3–4 and elevated CRP had higher HbA1c and total cholesterol levels and a greater proportion had obesity, anaemia and elevated blood pressure, compared with individuals with CRP < 2 mg/L (Table 2). Over 5 years’ follow-up, the incidence of 3P-MACE increased with CKD severity (Table 1) and with increasing CRP levels in individuals with ASCVD and CKD stage 3–4 (Table 2). However, only 28% of individuals with ASCVD and CKD stage 3–4 had CRP measurements. Conclusion Adults with ASCVD, CKD and elevated CRP identified from primary and secondary UK care records had a higher disease burden than individuals with CRP levels < 2 mg/L. In the UK, testing CRP levels is not routinely recommended, resulting in a high proportion of missing CRP measurements in this study. Routine testing of CRP may help in identifying patients with residual cardiovascular risk and has the potential to support risk assessment and management of patients with ASCVD and CKD.

Using Social Network Analysis during COVID-19 contact tracing in Libya, 2020

Abstract Background Social network analysis (SNA) may provide valuable insights into individual-level transmission dynamics, allowing for targeted contact tracing interventions. We used SNA to understand the variability in SARS-CoV-2 transmission at the case level during the first wave in Libya. Methods We analyzed the spread of COVID-19 in Libya from March to August 2020 using contact tracing data from 287 confirmed cases. We created a network to visualize the transmission patterns, representing each case as a node and connections as links. We focused on direct contacts identified through contact tracing and calculated network measures such as out-degree centrality, in-degree centrality, and betweenness centrality. Gephi software was used for data visualization and understanding the network structure. Results Of the 287 COVID-19 cases, 264 (92%) had nodes with zero outdegrees, indicating that they did not contribute to infection transmission. The remaining 23 (8%) had non-zero out-degree measures and transmitter infection to other cases, 10 (4%) of whom were super-spreaders (outdegree≥5) causing 70% of the transmission. Six (2%) had no epidemiological contact, indicated by zero in-degree centrality measures; 63% had zero betweenness centrality measures, suggesting they did not act as intermediaries connecting separate transmission chains within the network. The network contained nine clusters of connected nodes, with the largest having 91 (24%) cases; the mean path length between cases was 1.846, and the network diameter was 4. Conclusions The study revealed distinct clusters of connected cases, along with the presence of super-spreaders. This network structure underscores the critical role of early identification and isolation of individuals with high transmission potential to effectively contain outbreaks. This revised conclusion directly references the findings of nine clusters and super-spreaders, demonstrating how these results support the importance of early intervention. Key messages • Social network analysis identifies clusters and super-spreaders in COVID-19 transmission, emphasizing the need for targeted interventions. • Connected clusters and super-spreaders highlight the importance of contact tracing and early isolation for effective control of COVID-19 outbreaks.

Long-term outcomes after transcatheter aortic valve implantation: derivation and validation of the long-TAVI prediction model

Abstract Background Identification of high-risk individuals after transcatheter aortic valve implantation (TAVI) is essential to personalized care. Established risk models require a plethora of variables, fail to account for biomarker dynamics and imaging parameters, mostly focus on the short term, and offer limited predictive performance. Purpose We aimed to develop an easily applicable prediction model for long-term mortality or heart failure (HF) admission following TAVI. Methods Patients undergoing TAVI at a high-volume university centre in the United Kingdom (n = 990) were recruited into a prospective cohort study. Enrolled patients were divided into a development cohort (07/2007 – 12/2016) and a validation cohort (01/2017 – 11/2018) and followed for up to 15.1 (median 5.3, interquartile range [IQR] 2.8 – 7.4) years. Patient characteristics along with pre- and post-procedural imaging results and brain natriuretic peptide (BNP) levels before the procedure and at 3 months were recorded. A clinical risk score for long-term mortality and HF admission (i.e., Long-TAVI risk score) was derived using Cox regression and evaluated using time-dependent area under the receiver operating characteristics curve (AUC). Results Age at presentation was 79.8 (standard deviation [SD] 8.9) years, 961 (99.0%) patients had signs of heart failure, left ventricular ejection fraction was 52 (SD 11.2), and pre-TAVI BNP levels were 298.0 (interquartile range [IQR] 159.0 – 571.5) pg/mL. High BNP levels at presentation (higher vs. lower than 400 pg/mL: HR 1.50, 95% CI 1.17 – 1.92, P < 0.001) and dynamic changes in BNP with the first 3 months (low/high vs. low/low: 2.61, 95% CI 1.17 – 5.86, P = 0.020; high/high vs. low/low: 2.41, 95% CI 1.26 – 4.64, P = 0.008) were independent predictors mortality or HF admission. The simple 8-item Long-TAVI score accounts for periprocedural BNP dynamics and shows good discrimination (AUC 0.76, 95% CI 0.72 – 0.79) and calibration in the development cohort performing similarly well in the external validation cohort (AUC 0.70, 95% CI 0.63 – 0.77). Long-TAVI significantly outperformed the logistic EURO score (AUC 0.56, 95% CI 0.52 – 0.59, P < 0.001). Conclusion Both pre-TAVI BNP levels and periprocedural BNP trajectories are independently associated with long-term outcomes after TAVI. Accounting for clinical characteristics, imaging parameters, and circulating BNP levels, the newly developed Long-TAVI risk score achieves high performance and provides the first prediction model for long-term mortality or HF admission. Long-TAVI represents a novel tool for personalized risk assessment in patients undergoing TAVI.Correlation at baseline A) and FUP B)Score performance A) and risk groups B)

Discrimination and calibration are insensitive metrics for comparing the performance of cardiovascular risk prediction models

Abstract Background/Introduction Accurate cardiovascular disease (CVD) risk prediction models can facilitate the identification and cost-effective treatment of high-risk individuals. Discrimination and calibration are the most commonly recommended metrics for evaluating model performance. However, both are population-level measures, and it remains unclear whether these metrics are sufficiently sensitive to assess the performance of models designed for use with individual patients. Purpose Using a real-world dataset, we aimed to compare CVD risk estimates for example individuals (individual-level measures) and discrimination and calibration metrics (population-level measures) from two CVD risk prediction models; one with "age" as the only predictor and one with multiple predictors. Methods We constructed a cohort of almost all New Zealanders without CVD or heart failure, alive and aged 30–79 years in 2014, with follow-up linkage to hospitalisations and mortality until 2018 (N = 2,098,359). We derived two sets of sex-specific Cox regression models to predict 5-year CVD risk. The age-only models included "age" as the only predictor, and the full models used nine pre-defined routinely available CVD risk predictors with interaction terms. We calculated and compared individuals’ 5-year CVD risk from the age-only and full models and assessed model performance using Harrell’s C-statistics and calibration plots. Results Risk estimates for individuals using the two models showed substantial differences. A 65-year-old woman had predicted 5-year risks of 4.9% with the age-only model but 16.1% with the full model; a 74-year-old man had predicted risks of 15.2% and 9.2%, respectively, with age-only and full models (Figure 1). However, the models had similar discrimination and calibration (Figure 2). Age-only models had good discrimination with C-statistics of 0.773 (95% CI: 0.771–0.775) in women and 0.740 (0.738–0.742) in men, and calibrated well in both sexes. The full models had slightly better discrimination (C-statistics: 0.814 [0.812–0.816] in women and 0.769 [0.767–0.771] in men), and very similar calibration to the age-only models in both sexes. Conclusions Age-only models showed good discrimination and were well calibrated, but predicted very different risks in individual case studies compared to models with multiple predictors. This study calls into question the use of discrimination and calibration metrics as the main measures for comparing the performance of different CVD risk prediction models. We recommend that examples of predicted risk for individuals and an assessment of the magnitude and prevalence of cardiovascular predictors should also be considered when comparing the performance of prognostic models.

The effect of the LPA gene on aortic valve calcification in a Portuguese population

Abstract Introduction Aortic valve calcium (AVC) deposition is one of the mechanisms behind aortic sclerosis and stenosis. There is a known association between LPA gene rs3798220 T>C polymorphism and the development of severe aortic stenosis. However, it is still unknown if it has been a driving factor since the early stages of the disease. Objective Investigate, in our population, the association between the LPA gene variant rs3798220 T>C and aortic valve calcification. Methods AVC was measured in 451 consecutive individuals from the prospective arm of the GENEMACOR study with a mean age of 63.4±9.7 years, 58.8% male. AVC score was performed by cardiac computed tomography and reported as Agatston units. Results were separated into two groups (Group A: AVC=0; Group B: AVC>0). Lp(a) levels were analyzed in the different groups. Each individual was genotyped for LPA rs3798220 T>C. This variant has a minor allele frequency (MAF)<2%; hence, the risk homozygous CC is a rare genotype, and we used the heterozygous CT in our analysis. The bivariate and multivariate logistic regression analysis evaluated the presence of AVC adjusting to traditional risk factors (TRFs) and the LPA heterozygous genotype (CT). Results LP(a) levels were higher in the CT allele versus the homozygous TT, but this difference did not reach a statistical significance. After multivariate analysis, the heterozygous LPA rs3798220 T>C polymorphism remained an independent risk factor for the presence of aortic valve calcification (OR=12.14, p=0.002) together with age (OR=1.13, p<0.0001), arterial hypertension (AHT) (OR=1.96, p=0.006) and alcohol consumption (OR=2.46, p=0.016). Conclusion The LPA gene variant rs3798220 T>C is associated with the presence of aortic valve calcification in a Portuguese population. In such an age-dependent disease as aortic stenosis, the inversion in the age pyramid will see a dramatic increase in the prevalence of aortic stenosis. The early identification of high-risk individuals might enable prompt traditional risk factors control and disease prevention. Larger population studies may help to determine if this difference is driven by Lp(a) levels.

Exome sequencing in over 63,000 patients in TIMI trials uncovers pathogenic cardiomyopathy variant carriers with high risk for heart failure and cardiovascular death

Abstract Background Rare genetic variants predisposing to cardiomyopathy (CMP) confer increased risk for adverse events. The prevalence of CMP variants in contemporary cardiovascular (CV) trials and their association with prevalent heart failure (HF) and incident HF hospitalizations (HHF) is not established. Purpose We aimed to identify carriers of rare pathogenic/likely pathogenic (PLP) variants for dilated cardiomyopathy (DCM), hypertrophic cardiomyopathy (HCM), and arrhythmogenic right ventricular cardiomyopathy (ARVC) in well-phenotyped clinical trial cohorts and to evaluate their prognostic significance. Methods Rare variants were identified from high-quality exome sequencing data from 5 large clinical trials (ENGAGE AF-TIMI 48, SAVOR-TIMI 53, PEGASUS-TIMI 54, DECLARE-TIMI 58, FOURIER (TIMI 59)). Putatively pathogenic variants for cardiomyopathy were identified using phenotype- and gene-specific curation of protein-truncating variants and high-confidence variant classifications from ClinVar. We conducted logistic regression for prevalent disease and Cox proportional hazard models for incident HHF, both adjusted by age, sex, trial, and first 10 principal components and using those without CMP variants as the reference. Sensitivity analysis by history of HF at baseline was performed for prospective outcomes. Results In 63,700 participants, we identified 866 CMP variant carriers of which 518 identified as DCM carriers (age 65±9 yrs, male 66%), 194 HCM carriers (65.5±9 yrs, male 69%), and 125 ARVC carriers (66±9 yrs, male 77%). Out of the 518 DCM variant carriers, 402 had a PLP variant in TTN. DCM variant carriers had higher odds of history of HF (Odds ratio [OR] 1.94, 1.57-2.39, p<0.0001) whereas this was not observed for HCM variant carriers (OR 1.18, 0.85-1.65, p=0.33) or ARVC variant carriers (OR 1.23, 0.80-1.90, p=0.35). Over a mean follow-up of 2.5 years, the rate of HHF was significantly higher for DCM variant carriers (Hazard ratio [HR] 1.58, 1.14-2.20, p=0.006), HCM variant carriers (HR 2.78, 1.87-4.13, p<0.001), and ARVC variant carriers (HR 1.89, 1.02-3.53, p=0.04). These associations were, if anything, stronger in patients without HF at baseline (HR 2.06, 1.30-3.25) than in patients with HF at baseline (HR 1.48, 1.12-1.95). DCM variant carriers also had a higher rate of CV death (HR 1.50, 1.05-2.12, p=0.02), which was not observed for HCM (HR 1.10, 0.57-2.11, p=0.78) or ARVC (HR 0.35, 0.09-1.41, p=0.14) variant carriers. Conclusion In 5 contemporary CV trials, CMP variant carriers have an increased rate of HHF, while DCM variant carriers also have a higher rate of CV death. These findings motivate better identification of these high-risk individuals and the incorporation of genetic testing in CV trials.

Barriers in translating existing nutrigenetics insights to precision nutrition for cardiometabolic health in ethnically diverse populations.

Cardiometabolic diseases pose a significant threat to global public health, with a substantial majority of cardiovascular disease mortality (more than three-quarters) occurring in low- and middle-income countries. There have been remarkable advances in recent years in identifying genetic variants that alter disease susceptibility by interacting with dietary factors. Despite the remarkable progress, several factors need to be considered before the translation of nutrigenetics insights to personalised and precision nutrition in ethnically diverse populations. Some of these factors include variations in genetic predispositions, cultural and lifestyle factors as well as socioeconomic factors. This review aims to explore the factors that need to be considered in bridging the gap between existing nutrigenetics insights and the implementation of personalised and precision nutrition across diverse ethnicities. Several factors might influence variations among individuals with regards to dietary exposures and metabolic responses and these include genetic diversity, cultural and lifestyle factors as well as socioeconomic factors. A multi-omics approach involving disciplines such as metabolomics, epigenetics and the gut microbiome might contribute to improved understanding of the underlying mechanisms of gene-diet interactions and the implementation of precision nutrition although more research is needed to confirm the practicality and effectiveness of this approach. Conducting gene-diet interaction studies in diverse populations is essential and studies utilising large sample sizes are required as this improves the power to detect interactions with minimal effect sizes. Future studies should focus on replicating initial findings to enhance reliability and promote comparison across studies. Once findings have been replicated in independent samples, dietary intervention studies will be required to further strengthen the evidence and facilitate their application in clinical practice. Nutrigenetics has a potential role to play in the prevention and management of cardiometabolic diseases. Conducting gene-diet interaction studies in diverse populations is essential giving the genetic diversity and variations in dietary patterns. Integrating data from disciplines such as metabolomics, epigenetics and the gut microbiome could help in early identification of individuals at risk of cardiometabolic diseases as well as the implementation of precise dietary interventions for preventing and managing cardiometabolic diseases.

Electrocardiogram-based artificial intelligence to identify prevalent coronary artery disease

Abstract Background Coronary artery disease (CAD) often goes undetected for years before it manifests and results in substantial morbidity and mortality. Some patients have neither typical risk factors nor symptomatic angina despite progressive disease. Purpose Develop a deep-learning model to detect prevalent CAD and identify people at risk for adverse events using electrocardiograms (ECG) in a primary care setting. Methods We developed a convolutional neural network using 12-lead ECG waveforms to discriminate the presence of CAD defined using diagnostic codes ("ECG2CAD"). ECG2CAD was trained on 764,670 ECGs from 137,199 individuals at the Massachusetts General Hospital (MGH). Model performance for discrimination of prevalent CAD was measured using AUROC and AUPRC, and compared against a model comprising age and sex, as well as the Pooled Cohort Equations (PCE), in three test sets independent of model training: MGH, Brigham and Women’s Hospital (BWH) and UK Biobank. ECG2CAD was assessed across subgroups of age, sex and self-reported ethnicity and for incident CAD-related events in the BWH primary care subset. Results ECG2CAD was evaluated in MGH (N=18,706 [N=6,051 cases], age 57±16 years), BWH (N=88,270 [N=27,898 cases], age 57±16 years), and UK Biobank (N=42,147 [N=1,509 cases], age 65±8 years). ECG2CAD consistently discriminated prevalent CAD (MGH: AUROC 0.782, AUPRC 0.639; BWH: AUROC 0.747, AUPRC 0.588; UK Biobank: AUROC 0.760, AUPRC 0.155) and incrementally improved upon both a model based on age and sex (p<0.01) and the PCE (p<0.01) in MGH and BWH. In the BWH primary care subset (N=51,808), model performance was consistent in subgroups defined by age, sex and self-reported ethnicity/race. The incorporation of ECG2CAD in addition to age and sex consistently improved discrimination (AUROC delta of 0.011-0.086) in all subgroups. In the BWH primary care subset with over 10 years of follow-up, we observed that either the presence of ECG2CAD-predicted CAD (HR 2.18, 95% CI 2.00-2.38, P<0.001) and ICD-based known CAD (HR 2.13, 95% CI 1.86-2.45, P<0.001) were associated with increased risk for incident myocardial infarction. The presence of both definitions was associated with an almost five-fold increase in risk (HR 4.97, 95% CI 4.51-5.48, P<0.001). The risk of incident adverse events was highest among people in the top quintile of ECG-based predicted risk (high risk) compared with people from quintile 4-2 (intermediate risk) or the lowest quintile (low risk), even when excluding those with known prevalent CAD at baseline. Conclusions Artificial intelligence-enabled analysis of the 12-lead ECG may facilitate efficient identification of individuals with possible undiagnosed CAD and inform downstream testing and preventive measures.

SCORE2-ASIA risk prediction algorithms: revised models to estimate 10-year risk of cardiovascular disease in the Asia Pacific Region

Abstract Background Most current cardiovascular disease (CVD) prediction tools have been developed and validated solely in Western countries, while there are significant differences between Asian and Western populations in risk factor distributions and CVD incidence patterns. This study outlines the recalibration and validation of the SCORE2-ASIA model to estimate 10-year fatal and non-fatal CVD risk among adults aged 40-69 years with no previous CVD or diabetes in the Asia Pacific Region. Methods The sex-specific and competing risk-adjusted SCORE2 models were systematically recalibrated to reflect CVD incidence in the Asian clinical practice. We defined four risk regions in Asia according to country-specific CVD mortality rates (low risk (<100 CVD deaths per 100,000), moderate risk (100 to <150 CVD deaths per 100,000), high risk (150 to <300 CVD deaths per 100,000), and very high risk (≥300 CVD deaths per 100,000)). Models were recalibrated using region representative data on expected incidences and risk factor distributions, utilising the methodological approaches developed in recalibration of the SCORE2 models for Europe. The SCORE-ASIA model was externally validated in all risk regions using population-based cohort data from independents cohort data. To ensure the European coefficients well applied to Asian populations, the SCORE2 model derivation process was repeated in external validation cohorts. Results Region-specific incidence was estimated using country-specific data on CVD mortality and data on the ratio of fatal to total events on 9,496,212 individuals from 5 Asian countries (496,212 CVD events). For external validation, we analysed data from 12 additional cohorts in 13 Asian countries (4,775,976 individuals, 176,011 CVD events). After applying the derived risk prediction models to external validation cohorts, the pooled external validation C-index was 0.709 (95%CI 0.676-0.743). In the nationally representative CDMD cohort from Singapore (population completely separate from recalibration data) there was adequate agreement between predicted and observed risks. Risk factor coefficients derived in Asian populations were largely similar to the original SCORE2 coefficients, indicating that the original SCORE2 coefficients apply well to the Asian population. Predicted CVD risk varied several-fold across Asian risk regions. For example, the estimated 10-year CVD risk for a 50-year-old non-smoker, with a systolic blood pressure of 140mmHg, total cholesterol of 5.5mmol/L, and HDL-cholesterol of 1.3mmol/L, ranged from 12% for men in low-risk countries to 26% for men in very-high-risk countries, and from 7% for women in low-risk countries to 26% for women in very-high-risk countries. Conclusions The SCORE2-ASIA model accurately predicts 10-year risk of CVD for apparently healthy people in Asian countries after systematic recalibration, thereby enhancing the identification of individuals at higher risk of developing CVD across the Asia Pacific Region.

Metabolomic alterations in the plasma of patients with various clinical manifestations of COVID-19

BackgroundThe metabolomic profiles of individuals with different clinical manifestations of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection have not been clearly characterized.MethodsWe performed metabolomics analysis of 166 individuals, including 62 healthy controls, 16 individuals with asymptomatic SARS-CoV-2 infection, and 88 patients with moderate (n = 42) and severe (n = 46) symptomatic 2019 coronavirus disease (COVID-19; 17 with short-term and 34 with long-term nucleic-acid test positivity). By examining differential expression, we identified candidate metabolites associated with different SARS-CoV-2 infection presentations. Functional and machine learning analyses were performed to explore the metabolites’ functions and verify their candidacy as biomarkers.ResultsA total of 417 metabolites were detected. We discovered 70 differentially expressed metabolites that may help differentiate asymptomatic infections from healthy controls and COVID-19 patients with different disease severity. Cyclamic acid and N-Acetylneuraminic Acid were identified to distinguish symptomatic infected patients and asymptomatic infected patients. Shikimic Acid, Glycyrrhetinic acid and 3-Hydroxybutyrate can supply significant insights for distinguishing short-term and long-term nucleic-acid test positivity.ConclusionMetabolomic profiling may highlight novel biomarkers for the identification of individuals with asymptomatic SARS-CoV-2 infection and further our understanding of the molecular pathogenesis of COVID-19.

The role of coronary artery calcium score and european score2 in primary prevention of cardiovascular disease

Abstract Background There is limited evidence about the role of using coronary artery calcium (CAC) score in conjunction with the European SCORE2 (systematic coronary risk evaluation) for the identification of individuals at higher risk of cardiovascular disease. Purpose To evaluate the relationship between CAC score and the European SCORE2 for cardiovascular risk stratification in an asymptomatic population in primary prevention. Methods This is a preliminary analysis from a single-centre, prospective, randomised 1:1, controlled trial assessing the use of CAC score vs standard preventive strategies in asymptomatic individuals undergoing primary cardiovascular prevention. The 10-year risk of cardiovascular events was assessed with either SCORE2 (ages 45-69) or SCORE2-OP (ages 70-89). Subjects were classified as 1) low to moderate risk, 2) high risk, and 3) very high risk according to SCORE2/SCORE2-OP. CAC score was assessed by ECG gated low-dose computed tomography with no contrast administration. A CAC score of zero was considered to reclassify risk one level downward in high or very-high risk patients. CAC score ≥ 100 was used to reclassify risk one level upward in patients with low to moderate risk and a CAC score > 1000 to reclassify risk upward in subjects with high risk. Results A total of 1,252 participants: 627 in the CAC group and 625 in the control group, median age 55 yrs, 25th-75th 50-61 yrs, with availability of SCORE2 and SCORE2-OP were included in this analysis. SCORE2/SCORE 2-OP was similar in the CAC and control groups. Among the subjects in the CAC group, there was a highly significant difference in SCORE2/SCORE 2-OP between participants with CAC score = zero and those with CAC > zero (median 5.6, 25th-75th 3.6-8.55 vs 2.9, 1.8-4.6, p< 0.001) as well as between those with CAC ≥ 100 and those with CAC score < 100 [median 6.8, 25th-75th (10.3-4.5) vs 3.3 (2.0-5.3), p<0.001]. The prevalence of CAC score = zero was 75% in the low to moderate risk group, 47% in the high risk, and 20% in the very high risk, p<0.001. By contrast the prevalence of CAC score ≥ 100 was 7% in the low to moderate risk group, 24% in the high risk, and 45% in the very high risk, p< 0.001. The proportion of patients classified as low to moderate risk group was 72% with CAC score vs 59% without CAC score, high risk 23% with CAC score vs 35% without CAC score, very high risk 5% vs 6% respectively, p<0.001. Using CAC score risk reclassification occurred in 48% downward and 2% upward in originally classified high risk subjects, 7% upward in originally classified low to moderate risk, and 20% downward in originally classified very high risk subjects. Conclusions CAC score may lead to risk reclassification in a substantial proportion of individuals, in particular among those originally classified as high risk by the SCORE2/SCORE2-OP. The cardiovascular risk prediction accuracy of adding CAC score to SCORE2/SCORE2-OP awaits validation at long-term follow-up.

Diabetes and sudden cardiac death in a nationwide, unselected population

Abstract Background and purpose Sudden cardiac death (SCD) remains a prominent cause of mortality, with individuals afflicted by diabetes mellitus (DM) bearing a heightened risk of ventricular arrhythmias and SCD. However, comprehensive investigations in a nationwide, unselected context are lacking. Therefore, this study aimed to compare the incidence rates (IR) of SCD among individuals with Type 1 Diabetes Mellitus (T1DM) and Type 2 Diabetes Mellitus (T2DM) to the general population. Methods The study included the entire Danish population (approximately 5.5 million individuals) in 2010. SCD cases were identified through the detailed Danish death certificates and discharge summaries, yielding 6851 cases of SCD. The Danish Register of Medicinal Product Statistics, containing comprehensive prescription data from Danish pharmacies, facilitated the identification of individuals with T1DM and T2DM. Loss of life years was estimated for both T1DM and T2DM patients. Results Among the identified 6851 SCD cases, 272 (4%) were diagnosed with T1DM, and 833 (12%) with T2DM. Incidence rates of SCD were consistently elevated across all age groups for individuals with diabetes, compared to the general population (Figure 1). Patients with T1DM exhibited higher incidence rates than patients with T2DM (Figure 1). Notably, individuals aged 30-40 years with DM showed the greatest differences in incidence rates of SCD compared to the general population, with incidence rate ratios of 20.0 (95% confidence interval: 11.8-80.0) and 5.6 (95% confidence interval: 2.7-14.0) for T1DM and T2DM, respectively. On average, each 30-year-old patient with either T1DM or T2DM experiences a reduction of 3.7 and 3 life-years, respectively, due to the increased risk of SCD in these groups (Figure 2). Conclusion This nationwide study examines the incidence rates of SCD among individuals with and without a history of DM across all age brackets. Notably, patients with DM display consistently elevated incidence rates across age groups, with a particularly pronounced risk observed in those with T1DM. For both T1DM and T2DM, SCD is an important contributing factor to their shorter life expectancy compared to the general population. The heightened risk among individuals with DM underscores the importance of improved risk stratification for these patients.Incidence rates of SCDStacked cumulative incidence curves

Blood pressure polygenic risk scores associate with resistant hypertension in individuals with type 1 diabetes

Abstract Background Hypertension is a major risk factor of global disease burden, but treatment goals are often not met. A subset of individuals with hypertension is affected by treatment-resistant hypertension (RHTN), and they suffer from increased cardiovascular morbidity and mortality. Early identification of individuals at risk of RHTN could lead to more efficient antihypertensive drug treatment. Purpose The aim of this study was to investigate whether polygenic risk scores (PRS) for systolic (SBP) and diastolic blood pressure (DBP) predict RHTN in individuals with or without type 1 diabetes (T1D). Methods We performed association analyses between PRSs and RHTN in two Finnish cohorts: The Finnish Diabetic Nephropathy Study (FinnDiane) and the Finnish individuals of the LIFE Study (LIFE-Fin). FinnDiane is an ongoing, observational nationwide study aiming to identify genetic and clinical risk factors for complications of T1D. We included data from 778 adults (476 with RHTN and 302 with controlled hypertension). LIFE is a randomized, double-blind study evaluating long-term effects of losartan compared with atenolol in patients with signs of left ventricular hypertrophy. We used SBP and DBP data from 378 Finnish individuals (173 with RHTN and 205 with controlled hypertension) at the four-year visit of the study. In FinnDiane, RHTN was defined as SBP ≥130 or DBP ≥85 mmHg and the use of three or more antihypertensive drugs, or SBP <130 and DBP <85 mmHg and the use of four or more antihypertensive drugs. In LIFE-Fin, RHTN was defined as SBP ≥140 or DBP ≥90 mmHg and the use of three or more antihypertensive drugs. Controlled hypertension was defined as the use of three or fewer antihypertensive drugs and SBP <130 and DBP <85 mmHg in FinnDiane, and as SBP <140 and DBP <90 mmHg in LIFE-Fin. We calculated a PRS for each participant using genome-wide association study data for 1,098,015 genetic variants; the variant-specific data were derived from UK Biobank based PRS-CS-method computed PRSs (1). Results We observed significant associations of SBP and DBP PRSs with RHTN in FinnDiane (OR 1.56 [1.30; 1.87], P = 1.8e-6, and OR 1.25 [1.04; 1.50], P = 0.02, per one-SD increase of the PRS value) when adjusted for age, sex, estimated glomerular filtration rate and body mass index. The association between SBP PRS and RHTN remained significant when T1D individuals with or without albuminuria were analysed separately. Neither SBP nor DBP PRSs were significantly associated with RHTN in LIFE-Fin. Conclusion Higher SBP PRSs associate with higher risk of RHTN in individuals with T1D, with or without albuminuria, but not in nondiabetic individuals with hypertension. It remains to be investigated whether general blood pressure genetics contribute to RHTN in the general population.

Prediction of overweight/obesity risk in adolescence using static and dynamic multi-exposure models

Abstract Background Studying the risk factors for obesity development and timely identification of individuals at increased risk are key for obesity prevention. We aimed to assess the associations of multiple prenatal, infancy and childhood exposures with overweight/obesity at 13 years (y) and develop static and dynamic multiple-exposure prediction models. Methods In 4232 mother-child pairs from the Generation XXI birth cohort, we assessed, during pregnancy/infancy and childhood, 55 sociodemographic factors, anthropometric measures, lifestyle behaviors, and clinical exposures related to the mother and the child and categorized body mass index z-scores as normal weight and overweight/obesity at 13y. Static models for pregnancy/infancy, 4, 7 and 10y, and a dynamic model were fitted using Elastic Net and logistic regression and were compared in terms of predictive performance. Results Mothers with lower educational level and household income, no partner, overweight/obesity before and after pregnancy, excessive gestational weight gain, pregnancy complications and who were smokers during pregnancy had children with a higher risk of overweight/obesity at 13y. Children with higher BMI during infancy, overweight/obesity during childhood, an early introduction of solid foods (before 4 months), a higher consumption of soft drinks at 4y and females had a higher risk of overweight/obesity at 13y. We observed the lowest predictive performance for the pregnancy/infancy model (prediction error of 36%) and the highest predictive performance for the 10y and dynamic models (prediction errors of 17%). Conclusions We identified the most important predictors of overweight/obesity in adolescence, most of them modifiable, and able to be targeted in prevention strategies. Predicting overweight/obesity risk in adolescence might be suboptimal if only considering pregnancy/infancy data and improves as closer to the age of the outcome, with no additional benefit in using a complex dynamic model. Key messages • Prevention of overweight/obesity should start during prenatal life. • Children at higher risk of overweight/obesity may be identified years earlier using prediction models in clinical practice.

Predicting and Monitoring Anxiety and Depression: Advanced Machine Learning Techniques for Mental Health Analysis

Anxiety and depression are considered among the most prevailing mental illnesses; they affect millions in the USA and worldwide. Besides being highly prevalent, these conditions have major implications for individuals and American society as a whole. The prime objective of this research project was to design and evaluate advanced machine learning methodologies for the monitoring and prediction of anxiety and depression. The rise in recent advances in Machine Learning and AI technologies has unleashed tremendous potential in the diagnosis and monitoring of mental health conditions such as anxiety and depression. Predictive models, powered by Machine Learning algorithms, process vast amounts of data and detect patterns that might have evaded human clinicians. This dataset for the current research project was retrieved from the website kaggle.com and shared publicly with anyone by the Harvard Data Verse repository. The dataset contained behavioral, psychophysiological, and demographic data that were collected from 593 participants aged 18-35 years for the prediction of anxiety and depression disorder risk. For this study, three machine learning algorithms were deployed: Logistic Regression, XG-Boost, and Random Forest. To assess and evaluate the performance of the algorithms, two key performance evaluation metrics were utilized MSE & R-squared. By reviewing the performance of the aforementioned three machine learning models, Linear Regression, Random Forest Regressor, and XG-Boost Regressor, using evaluation metrics MSE and R-squared are compared in a tabular form. Retrospectively, all three models performed remarkably well, with very low MSE values and R-squared values close to 1. Linear Regression marginally outperformed the others, but all models were successful in predicting the anxiety or depression indicator accurately. The proposed models are valid and reliable models for predicting mental health, therefore enabling the identification of at-risk individuals well in advance, allowing early intervention to prevent symptom onsets or advancements in their course and thus improve overall outcomes.

Office-based risk equation of Globorisk for prediction of ten-years cardiovascular risk among Iranian population: findings from Fasa PERSIAN cohort study

BackgroundGloborisk is one of the prediction tools for 10-year risk assessment of cardiovascular disease, featuring an office-based (non-laboratory-based) version. This version does not require laboratory tests for determining the CVD risk. The present study aims to determine the 10-year CVD risk using the office-based Globorisk model and factors associated with the 10-year CVD risk.MethodsIn this study, baseline data from 6810 individuals participating in the Fasa cohort study, with no history of CVD or stroke, were utilized. The risk equation of the office-based Globorisk model incorporates age, sex, systolic blood pressure (SBP), body mass index (BMI), and smoking status. The Globorisk model categorizes the risk into three groups: low risk (< 10%), moderate risk (10% to < 20%), and high risk (≥ 20%). To identify factors associated with the 10-year CVD risk, the predicted risk was categorized into two groups: <10% and ≥ 10%. Multivariable logistic regression analysis was employed to determine factors associated with an increased CVD risk.ResultsAccording to the 10-year CVD risk categorization, 78.3%, 16.4%, and 5.3% of men were in the low, moderate, and high risk groups, respectively, while 85.8%, 10.0%, and 4.2%, of women were in the respective risk groups. Multivariable logistic regression results indicated that in men, the 10-year CVD risk decreases with being an opium user, and increases with being illiterate, having abdominal obesity, and low or moderate physical activity compared to high physical activity. In women, being married, and higher fiber consumption decrease the 10-year CVD risk, while being illiterate, low or moderate physical activity compared to high physical activity, having abdominal obesity, opium use, and being in wealth quintiles 1 to 4 compared to quintile 5 increase the risk.ConclusionsConsidering the factors associated with increased CVD risk, there is a need to enhance awareness and modify lifestyle to mitigate and reduce the risk of CVD. Additionally, early identification of individuals at moderate to high risk is essential for preventing disease progression. The use of the office-based Globorisk model can be beneficial in settings where resources are limited for determining the 10-year CVD risk.

Making Sense of ‘Us’: Mechanisms Linking Attachment Avoidance and Couple Identity Clarity

ABSTRACTJust as people strive to understand their own individual identities—to form a clear and coherent sense of who they are—they also seek to gain a clear understanding of who and what they are as a couple. However, some people may struggle in this goal; specifically, people high in attachment avoidance, who face barriers unique to the specific nature of their insecurity. We investigated whether attachment avoidance is negatively correlated with couple identity clarity and tested potential mechanisms for this association. We proposed that less interpersonal closeness and self‐verification from their partner would mediate the association between attachment avoidance and couple identity clarity. We found support for these hypotheses across three studies (total N = 912). Thus, attachment avoidance may create identity‐specific challenges in relationships.

Social entrepreneurship and value creation in the cultural sector. An empirical analysis using the multidimensional controlling model

Purpose Social entrepreneurship plays a crucial role in the contemporary economic and social ecosystem, defining a value proposition that incorporates a plurality of dimensions to be considered. This research work analyzes the externalities of social enterprises driven by arts and culture for the territories they operate in. Design/methodology/approach A mixed-method approach of a single case study is performed to consider multiple dimensions in the development of controlling models in the managerial field. The framework adopted is then implemented through a longitudinal analysis over the last three years. Findings The empirical evidence shows the evolution of the economic and financial performance, the social effectiveness and the institutional legitimacy of the case, explaining the role played in the reference community, even during crisis time. The predictive power of performance changes emerges as a direct link to socially responsible indexes that amplify the attractiveness of value proposition processes. Research limitations/implications The results obtained are aligned with Betzlet et al. (2021) key predictor of the success of social entrepreneurship. A wide sample with multiple social enterprises operating in the cultural and creative field needs to be considered in the future, to further advance the literature through a comparative analysis. Originality/value This paper reveals that, in the cultural and creative sector, social entrepreneurship value creation is intrinsically linked to the individual and collective identity dimension able to promote cohesion and community well-being.

‘Resilient survivors’: narratives of violence against women of refugee background

ABSTRACT Scholarship on migrant deservingness explores how host society institutions require specific types of performances, particularly of vulnerability/ powerlessness and resilience/ self-sufficiency, sometimes in temporal succession. Such performances are also required of victim-survivors of violence against women, a category that overlaps considerably with refugees. This study brings together feminist literature related to violence against women with migrant deservingness literature, to observe how cultural factors interact with host society institutions. The article draws on an empirical study about narratives of violence in the lives of women of refugee background in Ireland, centring five in-depth interviews with self-identified victim-survivors, alongside six focus group discussions with women of refugee background. Using narrative analysis, I explore individual and collective identities in response to violence. Narrative identities of resilience are strongly present, while vulnerability is not. I conclude that, in line with scholarship on migrant deservingness, neoliberal institutional discourses of ‘resilient survivors’ support individual women to express agentic, albeit individualising and responsibilitising narratives, and I add that this simultaneously upholds patriarchal cultures of silence and inaction about violence. I offer conceptual thinking on ‘space for action’ as an alternative approach for scholars and practitioners to construct and consider the victim-survivor of violence against refugee women.