Individual Risk Research Articles

Multi-target stool DNA test adherence among average-risk 45- to 49-year-old patients from 2017-2023.

102 Background: Colorectal cancer (CRC) is the third most common cancer in the United States and the second most common cause of cancer-related mortality. In 2021, the US Preventive Services Task Force (USPSTF) lowered the age of initial CRC screening from 50 to 45 years of age for average risk individuals. At that time, only 20% of adults aged 45-49 years were up-to-date with CRC screening. With its at-home use, accessibility, and patient navigation, the multi-target stool DNA test (mt-sDNA) could help support screening uptake in this population. Here, we analyzed national claims data to investigate mt-sDNA adherence rates among average-risk adults aged 45-50 years. Methods: Using a national claims database of over 165 million individuals linked with Exact Sciences Laboratories data, mt-sDNA orders for AA patients were retrospectively identified from 2017 to 2023. Average CRC risk individuals aged ≥45 years, and new to mt-sDNA, were included in this analysis. Primary outcome of interest was adherence, defined as mt-sDNA kit return rate within 365-days from shipment. Test return time was examined as the secondary outcome. Demographics were age, sex, ordering provider, residential geography, payor, and outreach preference. Covariates on adherence were examined using logistic regression. Results: A total of 607,388 mt-sDNA orders were prescribed during the study period, with an overall 365-day return rate of 65.4%. Average time to test return was 30.4 days. Women outnumbered men in the cohort (58.6%), and most mt-sDNA orders were prescribed by primary care providers (59.4%). The majority of individuals preferred ‘Digital SMS’ communication (62%), and nearly 90% of the cohort was Commercially insured (87.6%). Among those in the cohort participants’ self-reported race/ethnicity was: White (37.3%), Hispanic or Latino (10.1%), Black or African American (8.8%), unknown (8.8%), Asian or pacific islander (4.6%), and other (3.7%). Adherences for the four identified races/ethnicities were all near, or above 60%. Men demonstrated higher adherence than women (66.7% vs 64.5%; p<0.0001). Time to test return was shortest in men (28.9 days) and African American persons (28.5 days). Tests ordered by gastroenterologists yielded the quickest adherence times amongst providers (26 days). After controlling for race, gender, payor class, setting (urban/rural classification), and preferred language, digital outreach and provider type were significantly associated with increased mt-sDNA adherence. Conclusions: This large, national analysis reports a substantially higher rate of adherence among individuals ages 45-49 with the mt-sDNA test than the current nationally reported rates. At-home testing, along with standard and preference-based navigation may have contributed to high mt-sDNA completion rates. More research is warranted to explore screening behaviors and the benefits of screening within this younger population.

Multi-target stool DNA test adherence in average-risk African Americans from 2017-2024.

99 Background: Colorectal cancer (CRC) stands as the second most common cause of cancer-related mortality in the United States, while being the third most common cancer in the country. African Americans (AA) are disproportionately affected with CRC (incidence and mortality), yet the disease can be prevented through screening and early detection. Data reveals that only 59% of AA between ages 45-75 were up to date with CRC screening as of 2021. Broad availability of the navigation-supported, at home multi-target stool DNA (mt-sDNA) test may help mitigate barriers to screening adherence in this population. We examined multi-target stool-DNA (mt-sDNA) test adherence rates in a nationally representative AA population. Methods: Using a national claims database of over 165 million individuals linked with Exact Sciences Laboratories data, mt-sDNA orders for AA patients were retrospectively identified from 2016 to 2024. Average CRC risk individuals aged ≥45 years, and new to mt-sDNA, were included in this analysis. Primary outcome of interest was adherence, defined as mt-sDNA kit return rate within 365-days from shipment. Time to test return was examined as the secondary outcome. Demographics were age, sex, ordering provider, residential geography, payor, and outreach preference. Covariates on adherence were examined using logistic regression. Results: Among 434,951 mt-sDNA orders from 2017-2024, the overall adherence rate was 62%, and the average time to test return was 27.6 days. The cohort included higher percentages of women (61.7%), ages 50-64 years (57.8%), located in Southern US (51.4%), and metro areas (90.9%). Most patients were also commercially insured (56.1%) and received their mt-sDNA order from their primary care provider (65.4%). Digital SMS was the most preferred outreach option (47%). Patients ages ≥76 years demonstrated the highest adherence (70.3%), with all age categories near or above 60% (45-49 yrs [59.4%]; 50-64 yrs [60.8%], 65-75 yrs [64.3%]). Across payors, patients with Medicare had the highest adherence rates (66.5%). Across outreach, highest adherence was among those who preferred ‘Digital SMS + Email’ outreach (64.3%). Men were quicker to return their test than women (26.8 days vs 28 days, p<0.001). Those ages ≥76 years and with tests ordered by GIs had the shortest time to test return (21.3 and 22.2 days, respectively). Adjusted analyses revealed multiple factors were associated with increased odds of adherence, including older age (≥76 years; OR=1.54; p<0.001), ‘Digital SMS + Email’ outreach (OR=1.25; p<0.001), rural residence (OR=1.23; p<0.001), and males (OR=1.05; p<0.001). Conclusions: Mt-sDNA adherence was higher for AA patients in this large, national cohort as compared to previously reported overall CRC screening adherence data for this population group. Findings suggest mt-sDNA utilization, with its included patient navigation, may help reduce screening disparities among AA patients.

Dynamic Importance of Genomic and Clinical Risk for Coronary Artery Disease Over the Life Course.

Earlier identification of high coronary artery disease (CAD) risk individuals may enable more effective prevention strategies. However, existing 10-year risk frameworks are ineffective at earlier identification. We sought to understand how the variable importance of genomic and clinical factors across life stages may significantly improve lifelong CAD event prediction. A longitudinal study was performed using data from 2 cohort studies: the FOS (Framingham Offspring Study) with 3588 participants aged 19 to 57 years and the UKB (UK Biobank) with 327 837 participants aged 40 years to 70 years. A total of 134 765 and 3 831 734 person-time years were observed in FOS and UKB, respectively. Hazard ratios for CAD were calculated for polygenic risk score (PRS) and clinical risk factors at each age of enrollment. The relative importance of PRS and pooled cohort equations in predicting CAD events was also evaluated by age groups. The importance of CAD PRS diminished over the life course, with a hazard ratio of 3.58 (95% CI, 1.39-9.19) at the age of 19 years in FOS and a hazard ratio of 1.51 (95% CI, 1.48-1.54) by the age of 70 years in UKB. Clinical risk factors exhibited similar age-dependent trends. PRS significantly outperformed pooled cohort equations in identifying subsequent CAD events in the 40- to 45-year age group, with 3.2-fold more appropriately identified events. Overall, adding PRS improved the area under the receiving operating curve of the pooled cohort equations by an average of +5.1% (95% CI, 4.9%-5.2%) across all age groups; among individuals <55 years, PRS augmented the area under the curve-ROC of the pooled cohort equations by 6.5% (95% CI, 5.5%-7.5%; P<0.001). Genomic and clinical risk factors for CAD display time-varying importance across the lifespan. The study underscores the added value of CAD PRS, particularly among individuals younger than 55 years, for enhancing early risk prediction and prevention strategies. All results are available at https://surbut.github.io/dynamicHRpaper/index.html.

Lee's TRIAD-osteoporosis, fragility fracture, and bone health optimization.

The primary goal of osteoporosis management is to prevent fragility fractures, which occur from falls from standing height or less in individuals over fifty. However, the management of bone health optimization is often neglected in patients undergoing elective surgeries, such as arthroplasty and spinal surgeries. The objective of this article is to link all these three conditions into a TRIAD so that surgeons and physicians can collaborate more effectively, utilizing similar principles and strategies for better management. Clinical approaches based on country-specific guidelines are commonly used to manage osteoporosis. However, skeletal assessments are rarely conducted before or after elective procedures, leading to overlooked conditions such as osteoporosis, osteopenia, and fragility fracture risk factors. These three conditions are illustrated from the patient case study shown, to highlight the importance of not neglecting bone health optimization in high risk individuals undergoing elective surgery, with underlying osteopenia and multiple risk factors who sustained fragility fracture intraoperatively. Patients undergoing elective surgeries often have their bone health neglected, leading to a higher incidence of complications such as aseptic loosening and peri-prosthetic fractures due to poor bone quality and density. Bone health assessment and optimization therefore is essential in patients with osteoporosis, osteopenia with clinical risk factors, and patients with history of fragility fracture, to ensure implants sit on bone with good density and quality to minimize the complications. By combining osteoporosis, fragility fractures, and bone health optimization into a TRIAD, "Lee's TRIAD," surgeons and physicians can collaborate more effectively, utilizing similar principles and strategies for better management.

P1288 The Association of Long-term Night Shift Work and Related Circulating Proteins with Inflammatory Bowel Disease: A Prospective Cohort and Multi-omics Study

Abstract Background The association between night shift work and the risk of inflammatory bowel disease (IBD) remains unclear. We aim to investigate the association between night shift work and the risk of IBD, ulcerative colitis (UC), and Crohn’s disease (CD), while further investigating the roles of genetic susceptibility and circulating proteins. Methods This cohort study included 149,014 UK Biobank participants without IBD at baseline, with 28,772 undergoing proteomic detection. Cox regression assessed associations between night shift work, circulating proteins, and IBD risk, while multinomial logistic regression identified proteins linked to night shift work. Bulk RNA-seq (1,030 blood, 453 intestinal tissue, and 176 Ustekinumab-treated intestinal samples) and single-cell transcriptomics (51 samples, 163,336 cells) were integrated to explore the roles of night shift-related proteins in IBD development and progression. Results During a mean follow-up of 13.66 years, 818 incident IBD (556 UC and 262 CD) cases were documented. Compared to day workers, there was a significant increasing trend in the frequency of current shift work and the risk of incident IBD (Ptrend=0.018). Among individuals with usual or permanent night shifts work, the risk of IBD incidence increased by 56% compared to those with no shift work, with a hazard ratio (HR) of 1.56 (95% CI: 1.15-2.13), especially for in those with intermediate genetic susceptibility to IBD. Additionally, there was a significant increasing trend in the frequency of current shift work and the risk of incident UC (Ptrend=0.037). High genetic risk individuals with usual or permanent night shifts work were respectively associated with a 3.60-fold increase in the risk of IBD, a 3.13-fold increase in the risk of CD, and a 2.77-fold increase in the risk of UC. Compared with shift workers never working nights, participants with an average frequency of more than 8 nights per month of night shift work exposure possessed higher IBD risk (HR 1.54, 95%Cl: 1.07-2.23). Six circulating proteins (IL18R1, GDNF, SOD1, TNFRSF6B, MMP12, and CCL7) related to night shift work were associated with the risk of incident IBD. Multi-omics evidence indicated that MMP12 increased UC risk through macrophage function and reduces the efficacy of Ustekinumab, while CCL7+ monocytes, possibly in conjunction with ACKR1, may facilitate the initiation of inflammation in CD. Conclusion Night shift work has been associated with an increased risk of IBD, CD, and UC, with MMP12 and CCL7 identified as potential drivers of this association. Further research is needed to assess whether reducing night shift frequency and duration, combined with targeted protein interventions, could mitigate IBD risk and improve prognosis.

P1276 Persistent Insomnia Ignites the Flames of Crohn's Disease by Elevating Systemic Inflammation

Abstract Background The association between insomnia and the risk of inflammatory bowel disease (IBD) remains inconclusive. We aim to investigate the association between insomnia and the risk of IBD, Crohn's disease (CD) and ulcerative colitis (UC), while further considering genetic vulnerability and inflammatory mediation. Methods This cohort study included 480,252 participants without IBD at baseline in the UK Biobank. The mean age of the population was 56.53 years old and 54.3% were female. Cox regression models examined the associations between insomnia and the incident of IBD, CD and UC. Polygenic risk score (PRS) for IBD, UC and CD was calculated. Subgroup and mediation analyses were conducted. Results A total of 480,252 participants were included. The mean age of the population was 56.53 years old and 54.3% were female. During a mean follow-up of 13.20 years, 3,219 incident IBD (1,005 CD and 2,207 UC) cases were documented. Compared to non-insomniacs, there was a significant increasing trend in the frequency of insomnia and the risk of incident IBD (P for trend=0.040) and CD (P for trend=0.003). Among individuals who usually have insomnia, the risk of CD incidence increased by 31% compared to those free of insomnia (hazard ratio [HR] 1.31; 95% confidence interval [CI]: 1.09-1.57; P=0.004), but no significant association was found between usually having insomnia and the risk of IBD (HR 1.10; 95% CI: 1.00-1.22; P=0.051) and UC (HR 1.03; 95% CI 0.91-1.16; P=0.678). Consistent results were observed in subgroup and sensitivity analyses. The connection between insomnia and CD was especially significant for participants with intermediate genetic susceptibility. High genetic risk individuals with usual insomnia were associated with a 2.51-fold increased risk of CD. The association between usually having insomnia and the risk of CD was partially mediated low-grade inflammation (INFLA) score by 6.36% (natural indirect effect [NIE] 1.018; 95% CI 1.013-1.023). Conclusion Insomnia exposure was associated with increased risk of CD but not UC. Further study is warranted to determine if modifying insomnia could mitigate CD risk.

Systematic review of the application of the Kidney Failure Risk Equation and Oxford classification in estimating prognosis in IgA Nephropathy

BackgroundIgA nephropathy (IgAN) is the most common primary glomerulonephritis in the world and is an important cause of chronic kidney disease (CKD) and kidney failure. Outcomes are heterogeneous, and accurate risk stratification is important to identify the highest risk individuals for treatment and to help prevent disease progression. The Oxford classification (OC) is an internationally adopted standard for renal biopsy reporting in IgAN, which measures the degree of histological abnormalities and predicts prognosis. The kidney failure risk equation (KFRE) was developed to predict kidney failure in all causes of CKD and has been shown to be highly accurate across diverse etiologies. This review aimed to compare the KFRE with formulae incorporating the OC in accurately determining the risk of kidney failure in IgAN.MethodsA systematic review was conducted in accordance with the Cochrane library guidelines and PRISMA statement for reporting of systematic reviews. Studies comparing the accuracy of the KFRE with the OC in predicting disease progression and kidney failure in IgAN were evaluated. The search strategy and analysis were performed independently by two reviewers. Studies that were eligible for inclusion compared the KFRE with any tool incorporating the OC in a cohort of individuals with IgAN. Eligible outcomes were reduction of estimated glomerular filtration rate (eGFR) or end-stage renal disease (ESRD), and prognostic tools were required to assess the accuracy of these formulae by discrimination and/or calibration.ResultsAfter searching several databases, only one study was eligible for inclusion in the review. This study of 2300 Chinese individuals with IgAN had a median follow-up of 2.5 years. Two-hundred eighty-eight individuals had a composite outcome of 50% decline in eGFR or ESRD, and 214 individuals developed ESRD. Both the KFRE and the IgAN Risk Prediction (IRP) tool (incorporating the OC) were highly accurate at predicting ESRD with a C-statistic of 0.90 and 0.91, respectively. Subgroup analysis demonstrated improved performance of IRP over KFRE in discrimination for individuals with preserved eGFR (> 60 ml/min/1.73 m2) at baseline. The risk of bias was high due to insufficient follow-up and handling of missing data, so overall confidence in findings is very low.ConclusionThere is currently insufficient evidence to compare the accuracy of the KFRE and OC in determining outcomes in IgAN. Further research is required in this field.Systematic review registrationPROSPERO CRD42022364569.

Predicting conversion to psychosis using machine learning: response to Cannon

BackgroundWe previously reported that machine learning could be used to predict conversion to psychosis in individuals at clinical high risk (CHR) for psychosis with up to 90% accuracy using the North American Prodrome Longitudinal Study-3 (NAPLS-3) dataset. A definitive test of our predictive model that was trained on the NAPLS-3 data, however, requires further support through implementation in an independent dataset. In this report we tested for model generalization using the previous iteration of NAPLS-3, the NAPLS-2, using the identical machine learning algorithms employed in our previous study.MethodStandard machine learning algorithms were trained to predict conversion to psychosis in clinical high risk individuals on the NAPLS-3 dataset and tested on the NAPLS-2 dataset.ResultsNAPLS-2 and -3 individuals significantly differed on most features used in machine learning models. All models performed above chance, with Naive Bayes and random forest methods showing the best overall performance. Importantly, however, overall performance did not match those previously observed when using only NAPLS-3 data.ConclusionThe results of this study suggest that a machine learning model trained to predict conversion to psychosis on one dataset can be used to train an independent dataset. Performance on the test set was not in the range necessary for clinical application, however. Possible reasons that limited performance are discussed.

Lung cancer screening in India: Preparing for the future using smart tools &amp; biomarkers to identify highest risk individuals

There is a growing burden of lung cancer cases in India, incidence projected to increase from 63,708 cases (2015) to 81,219 cases (2025). The increasing numbers are attributed to smoking (India currently has nearly 100 million adult smokers) and environmental pollution. Most patients present with advanced disease (80-85% are incurable), causing nearly 60,000 annual deaths from lung cancer. Early detection through lung cancer screening (LCS) can result in curative therapies for earlier stages of lung cancer and improved survival. Annual low-dose computerized tomography (LDCT) is the standard method for LCS. Usually, high-risk populations (age&gt;50 yr and &gt;20 pack-years of smoking) are considered for LCS, but even such focused screening may be challenging in resource-limited countries like India. However, developing a smart LCS programme with high yield may be possible by leveraging demographic and genomic data, use of smart tools, and judicious use of blood-based biomarkers. Developing this model over the next several years will facilitate a structured cancer screening programme for populations at the highest risk of lung cancer. In this paper, we discuss the demographics of lung cancer in India and its relation to smoking patterns. Further, we elaborate on the potential applications and challenges of bringing a smart approach to LCS in high-risk populations in India.

Machine learning analysis of population-wide plasma proteins identifies hormonal biomarkers of Parkinson's Disease.

As the number of Parkinson's patients is expected to increase with the growth of the aging population there is a growing need to identify new diagnostic markers that can be used cheaply and routinely to monitor the population, stratify patients towards treatment paths and provide new therapeutic leads. Genetic predisposition and familial forms account for only around 10% of PD cases [1] leaving a large fraction of the population with minimal effective markers for identifying high risk individuals. The establishment of population-wide omics and longitudinal health monitoring studies provides an opportunity to apply machine learning approaches on these unbiased cohorts to identify novel PD markers. Here we present the application of three machine learning models to identify protein plasma biomarkers of PD using plasma proteomics measurements from 43,408 UK Biobank subjects as the training and test set and an additional 103 samples from Parkinson's Progression Markers Initiative (PPMI) as external validation. We identified a group of highly predictive plasma protein markers including known markers such as DDC and CALB2 as well as new markers involved in the JAK-STAT, PI3K-AKT pathways and hormonal signaling. We further demonstrate that these features are well correlated with UPDRS severity scores and stratify these to protective and adversarial features that potentially contribute to the pathogenesis of PD.

Self-Transcendence as a Risk and Resilience Factor in Individuals at Clinical High Risk for Psychosis.

Self-transcendence is a personality feature and psychological resource that involves feelings of connectedness with the universe, all of humanity, and the individual self. Self-transcendence has been positively associated with both positive psychotic symptoms and clinical high risk for developing psychosis status, but studies reporting these findings focus solely on the connectedness-with-universe aspect of self-transcendence. The broader self-transcendence literature, which also includes connection with humanity and oneself, robustly supports self-transcendence as an indicator of well-being. Given this discrepancy, we sought to understand whether self-transcendence should be considered a risk or resilience factor for youth at clinical high risk. We operationalised self-transcendence using two more holistic measures novel to the clinical high risk population. Clinical high risk participants (n = 42) and healthy controls (n = 44) completed the Adult Self-Transcendence Inventory and participated in narrative life story interviews which were coded for self-transcendence themes. Clinical high risk individuals scored lower than healthy controls on measures of self-transcendence, functioning, and life satisfaction. However, there were no group differences in the relationships between self-transcendence and measures of well-being. Our findings suggest self-transcendence is a part of healthy personality development that may be impacted in clinical high risk individuals yet may still function as a psychological resource for this population, pointing toward new avenues for intervention in clinical high risk and other mental health populations.

Centering autonomy and choice to support oral PrEP utilization among people who inject drugs: qualitative lessons from HPTN 094 INTEGRA

BackgroundOral Pre-exposure prophylaxis (PrEP) is effective in preventing HIV transmission. However, despite high rates of HIV risk behaviors among people who inject drugs (PWID), this population remains underserved by current HIV prevention efforts in the United States. To address this challenge, we conducted an in-depth exploration of perspectives on using oral PrEP among PWID engaged in the HIV Prevention Trials Network (HPTN) 094 INTEGRA Study.MethodsGuided by the Practical, Robust, Implementation, and Sustainability Model (PRISM), our qualitative study drew on semi-structured interviews conducted as part of the embedded implementation science evaluation of HPTN 094 INTEGRA. Seventy-seven PWID participants from five sites across New York City, Houston, Los Angeles, Philadelphia, and Washington DC were interviewed to assess intervention delivery, care access, and engagement sustainability. Audio files were transcribed verbatim and analyzed via an inductive and deductive thematic approach.ResultsMost participants (n = 46, 59.7%) discussed oral PrEP during their interview, though not directly prompted. Participants discussing PrEP had a mean age of 41.6 years and were predominantly white (54.3%) and cisgender men (60.9%). Among these, 15 participants described using PrEP. All participants had facilitated access to oral PrEP. Yet, the choice to use PrEP was influenced by personal risk perceptions, (mis)information about PrEP, and external factors (i.e. housing, financial security), which, for some, limited the autonomy to use PrEP. Two key themes emerged among participants using PrEP: ease of access and perceptions of high HIV risk. Those not using PrEP described two themes: low risk perception and prioritizing more urgent needs. Among participants not using PrEP a subgroup commonly described ambivalent interest, PrEP knowledge gaps, and PrEP readiness (i.e., contemplation).ConclusionsQualitative findings highlight that facilitated PrEP access was insufficient to motivate use for many participants. Rather, PrEP decision-making process (i.e., choice) was linked to risk perception and individuals’ capability to leverage PrEP as a resource based on their circumstances (i.e., autonomy). Participants’ descriptions of the centrality of choice and autonomy for PrEP use underscore that ease of access is a necessary pre-condition, but person-centered interventions should also address housing, financial stability, and urgent medical conditions to promote PrEP utilization among PWID.Clinical trial registrationNCT04804027.

Enhancing Sustainable Decision Making: The Impact of Emotional Valence, Arousal, and Personality on Risk Recognition During Contract Review

The capacity to identify risks during the pre-contract phase is crucial for effective contract management. Among the various factors that can influence this ability, emotions play a significant role in determining the risk recognition capabilities of individuals. This study aims to investigate the complex interplay between emotional states, personality traits, learning styles, and risk recognition during contract review with physiological and psychological responses. Firstly, a theoretical framework that delineates the relationship among the demographic and emotional factors and risk recognition performance during contract review is proposed. Secondly, an experiment is conducted to record the physiological and psychological responses. Finally, a hierarchical regression model is employed. The results indicated that emotional valence and arousal significantly influence risk recognition performance (p &lt; 0.001), while individual factors such as personality traits and risk propensity indirectly affect risk recognition performance through the mediating role of emotions (p &lt; 0.01). The research contributes to the existing literature by elucidating the indirect pathways through which individual characteristics influence risk perception. These insights can inform sustainable risk management strategies, helping organizations and individuals make more informed and effective decisions in contract management.

Abstract 4134977: Utility of Coronary Artery Calcium Scoring in Low-Risk Patients: the Multi-Ethnic Study of Atherosclerosis (MESA)

Introduction: Coronary artery calcium (CAC) scoring is a powerful tool for atherosclerotic cardiovascular disease (ASCVD) risk assessment. Guidelines recommend consideration of CAC scoring in intermediate risk patients, but it’s utility in low and borderline risk patients is less clear. Methods: We used data from 2,894 participants from MESA with low (&lt;5%) or borderline (5-&lt;7.5%) 10-year ASCVD risk defined by the pooled cohort equations (PCE). We evaluated the association between CAC score and coronary heart disease (CHD) and ASCVD risk using multivariable adjusted Cox proportional hazard models. We also evaluated if the addition of CAC to the pooled cohort equations (PCE) improved risk prediction using Harrell’s C-index and net reclassification improvement (NRI). Results/Data: Mean age was 54.1 ± 5.8 years with 66.9% women. Among low-risk individuals, the ASCVD event rate was 3.3% with CAC=0, and 7.8% with CAC&gt;0, and 4.7% vs. 11.4% among borderline-risk individuals. Natural-log transformed CAC score was associated with increased ASCVD risk in low-risk (HR 1.35, 95% CI 1.22-1.50) and borderline-risk (HR 1.29, 95% CI 1.15-1.45) individuals. Similar results were seen for CHD with higher HRs ( Table 1 ). The addition of CAC to the PCE improved the C-index (SE) among low/borderline risk individuals from 0.620 (0.023) to 0.671 (0.024). Continuous NRI was also significant with the addition of CAC to the PCE in low/borderline risk (0.422, 95% CI 0.248-0.611). Greater improvement was seen for CHD ( Table 2 ). Conclusions: The presence of CAC in low and borderline risk individuals is associated with increased ASCVD and CHD risk. The addition of CAC scoring to the PCE improved risk prediction in low and borderline risk individuals, suggesting potential clinical utility in this population.

Abstract 4139199: Quantifying the Burden of High Polygenic Risk for Cardiometabolic Disease in a US-based Healthcare System Biobank

Background: Healthcare systems are increasingly collecting genotype data on patients. Polygenic risk scores (PRS), which quantify inherited risk of a given disease, have the potential to leverage this data to identify high risk individuals. While PRS for single traits have often been evaluated, the same data can be used to calculate PRSs across traits and evaluate the overall burden of high cardiometabolic polygenic risk using a single test. Methods: We examined the association of cardiometabolic PRS from the Polygenic Score Catalog among 236,393 All of Us (AoU) participants for 6 target traits of interest, using all available PRS for coronary artery disease (CAD; 57 PRS), atrial fibrillation (AF; 30 PRS), heart failure (HF; 9 PRS), type 2 diabetes mellitus (T2DM; 133 PRS), venous thromboembolism (VTE; 9 PRS), and thoracic aortic aneurysm (TAA; 2 PRS). PRSMix is a tool that incorporates information across several PRS for a given trait to improve prediction accuracy for a target population. We generated a PRSMix for each trait and externally benchmarked its performance against individual PRS in the Mass General Brigham Biobank (MGBB). Phenotypes were ascertained using hospital diagnosis and procedural codes. Logistic regression was performed, adjusting for age, sex, and the first 10 principal components of inferred genetic similarity. Results: Of the 53,306 genotyped MGBB participants, 55.6% are female, mean enrollment age is 53 ± 17 years. Each trait-specific PRSMix demonstrated generally stronger associations with the target trait compared to any individual contributing score. This included trait-specific PRSMix associations (OR/SD, [95%CI]) of 2.07 [1.99-2.15] for CAD, 1.88 [1.82-1.95] for AF, 1.47 [1.43-1.51] for HF, 2.07 [2.00-2.14] for T2DM, 1.43 [1.39-1.47] for VTE, and 1.40 [1.34-1.46] for TAA. Findings were consistent across inferred genetically similar ancestry groups, with stronger PRSMix associations for CAD detected in the EUR group (2.14 [2.05-2.22]) compared to the AFR group (1.43 [1.21-1.70]). Approximately 37% of people in MGBB have 3-fold greater risk of at least one cardiometabolic disease, with 22% at risk for CAD, 14% for T2DM, 13% for Afib, 3% for VTE, 2% for HF and &lt;1% for TAA. Conclusion: We developed PRSMix scores for cardiometabolic traits in AoU and validated them in MGBB. We identified 37% of biobank participants with at least 3-fold greater disease risk associated with high polygenic scores for at least one cardiometabolic outcome.

Association of the glucose patterns after a single non-standardized meal with the habitual diet composition and features of the daily glucose profile in individuals without diabetes

BackgroundThe postprandial glucose response (PPGR), contributing to the glycemic variability (GV), is positively associated with cardiovascular risk in people without diabetes, and can thus represent a target for cardiometabolic prevention strategies. ObjectiveThe study aimed to distinguish patterns of PPGR after a single non-standardized meal and to evaluate their relationship with the habitual diet and the daily glucose profile (DGP) in individuals at high-cardiometabolic risk. MethodsBaseline four-day continuous glucose monitoring (CGM) was performed in 159 adults recruited in the MEDGI-Carb trial. After a non-standardized breakfast, parameters of the PPGR were estimated by a mechanistic model: baseline glucose; amplitude – the magnitude of post-meal glucose concentrations; frequency – the velocity of post-meal glucose oscillations; damping – the rate of post-meal glucose decay. PPGR patterns were identified by cluster analysis. Differences between clusters and the relationship between PPGR parameters and individual features were explored by one-way ANOVA and correlation analysis, respectively. ResultsTwo patterns of PPGR emerged. Pattern A had higher baseline, amplitude, frequency, and damping than B. Individuals in cluster A compared to B had a higher energy (2,002±526 vs. 1,766±455 Kcal, p=0.025), protein (82±22 vs. 72±21 g, p=0.028) and fat (87±30 vs. 75±22 g, p=0.041), but not carbohydrate habitual intake. Pattern A associated with a higher average daily glucose (6.12±0.50 vs. 5.88±0.62 mmol/L, p=0.019) and lower GV (11.67±3.52 vs. 13.43±3.78%, p=0.010). Mean daily glucose correlated directly with baseline (rs=0.419, p<0.001) and amplitude (rs=0.189, p=0.022) of the PPGR, while DGP variability correlated directly with amplitude (rs=0.218, p=0.008), frequency (rs=-0.179, p=0.031) and damping (rs=-0.309, p<0.001). ConclusionsTwo PPGR patterns after a single non-standardized breakfast were identified in high-cardiometabolic risk individuals. The habitual diet was associated with the patterns and their dynamic parameters, which, in turn, could predict the individuals’ DGP. Our findings could support the implementation of dietary strategies targeting the PPGR to ameliorate the cardiometabolic risk profile.Clinical Trial ID: NCT03410719

AI and cybersecurity: a risk society perspective

IntroductionThe rapid evolution of Artificial Intelligence (AI) has introduced transformative potential across various sectors, while simultaneously posing significant cybersecurity risks.MethodsThe aim of this paper is to examine the debates on AI-related cybersecurity risks through the lens of Beck’s theory of the risk society. Utilizing thematic content analysis, we explored public discourse on AI and cybersecurity as presented in articles published by WIRED.ResultsOur analysis identified several key themes: the global nature of AI risks, their pervasive influence across multiple sectors, the alteration of public trust, the individualization of risk, and the uneven distribution of AI risks and benefits.DiscussionThe editorial choices in WIRED predominantly favor a functionalist and solutionist perspective on AI cybersecurity risks, often marginalizing the opinions of ordinary individuals and non-Western voices. This editorial bias tends to limit diversity and underrepresent key opposing viewpoints, potentially hindering a more comprehensive and nuanced debate on AI and cybersecurity issues.

Recent Insights in the Treatment for Clinical High Risk for Psychosis and Recent Onset Psychosis

Objectives: This study aims to assess the effectiveness of early interventions in preventing psychosis transition, promoting remission, and reducing hospitalization rates in individuals at high risk for psychosis and those with recent onset psychosis (ROP).Methods: A systematic review and meta-analysis were conducted, comparing early intervention strategies such as cognitive-behavioral therapy and psychosocial support to no intervention. The study focused on outcomes related to psychosis transition, remission rates, and prevention of psychiatric hospitalization.Results: Although only a subset of clinical high risk (CHR) individuals transition to full psychosis, non-pharmacological treatments like cognitive-behavioral therapy are generally recommended as a first-line approach. In ROP patients, early pharmacological treatment reduces relapse rates, while psychosocial interventions aim to improve various functional outcomes. The meta-analysis results of this study did not show a significant reduction in psychosis transition rates with specialized interventions for CHR patients. For ROP patients, early interventions initially reduced hospitalization rates, but this effect was not sustained in mid-term follow-up results.Conclusion: While early interventions offer short-term benefits in reducing psychosis transition and hospitalization, additional research is needed to determine their long-term effectiveness in functional recovery and overall patient outcomes.

Knowledge, attitude, and practice of inpatients with cardiovascular disease regarding obstructive sleep apnea

There is a significant interrelationship between cardiovascular disease and obstructive sleep apnea (OSA), as they share common risk factors and comorbidities. This study aimed to investigate the knowledge, attitude, and practice (KAP) of inpatients with cardiovascular disease towards OSA. This cross-sectional study was conducted between January, 2022 and January, 2023 at Zhongda Hospital Affiliated to Southeast University among inpatients with cardiovascular disease using a self-administered questionnaire. A self-designed questionnaire was used to assess KAP, and the STOP-Bang questionnaire was applied to evaluate participants’ OSA risk. Spearman correlation and path analyses were conducted to explore relationships among KAP scores and high OSA risk. Subgroup analyses were conducted within the high-risk population identified by the STOP-Bang questionnaire. In a study analyzing 591 questionnaires, 66.33% were males. Mean scores were 6.81 ± 4.903 for knowledge, 26.84 ± 4.273 for attitude, and 14.46 ± 2.445 for practice. Path analysis revealed high risk of OSA positively impacting knowledge (β = 2.351, P < 0.001) and practice (β = 0.598, P < 0.001) towards OSA. Knowledge directly affected attitude (β = 0.544) and practice (β = 0.139), while attitude influenced practice (β = 0.266). Among high OSA risk individuals, knowledge directly impacted attitude (β = 0.645) and practice (β = 0.133). Knowledge indirectly influenced practice via attitude (β = 0.197). Additionally, attitude directly affected practice (β = 0.305). These findings provide insights into the interplay between OSA risk, knowledge, attitude, and practice. Inpatients with cardiovascular disease demonstrated inadequate knowledge, moderate attitude, and practice towards OSA. The findings highlighting the need for targeted educational interventions to improve awareness and management of OSA.

Rosuvastatin effect on atherosclerotic plaque microcalcification

Abstract Background and Aims Atherosclerotic plaque fluorine-18 sodium fluoride (18F-NaF) uptake on positron emission tomography with computed tomography (PET-CT) identifies active microcalcification and has been shown to correlate with clinical instability in patients with cardiovascular (CV) disease. Statin therapy promotes coronary macrocalcification over time. Our aim was to investigate rosuvastatin effect on atheroma 18F-NaF uptake. Methods Subjects with high CV risk but without CV events underwent 18F-NaF-PET-CT in a single-centre. Those with subclinical atherosclerosis and significant 18F-NaF plaque uptake were included in a single-arm clinical trial, treated with rosuvastatin 20mg/daily for six months, and re-evaluated by 18F-NaF-PET-CT. Primary endpoint was reduction in maximum atheroma 18F-NaF uptake in the coronary, aortic or carotid arteries, assessed by the tissue-to-background ratio (TBR). The secondary endpoint was corrected uptake per lesion (CUL) variation. Results Forty individuals were enrolled and 38 included in the pharmacological trial; mean age was 64 years-old, two-thirds were male and most were diabetic. The 10-year expected CV risk was 9.5% (6.0-15.3) for SCORE2 and 31.7±18.7% for ASCVD systems. After six months of rosuvastatin treatment (n=34), low-density lipoprotein cholesterol lowered from 133.6±33.8 to 58.8±20.7 mg.dL-1 (60% relative reduction, p&amp;lt;0.01). There was a significant 19% reduction in maximum plaque 18F-NaF uptake after treatment, from 1.96 (1.78-2.22) to 1.53 (1.40-2.10), p&amp;lt;0.001 (primary endpoint analysis). The secondary endpoint CUL was reduced by 23% (p=0.003). Conclusion In a single-centre non-randomized clinical trial of high CV risk individuals with subclinical atherosclerosis, the maximum atherosclerotic plaque 18F-NaF uptake was significantly reduced after six months of high-intensity statin.Graphical abstractTrial design