Clinical Risk Factors Research Articles

Infliximab (IFX) is commonly used in the management of acute severe ulcerative colitis (ASUC), yet up to 30% of individuals still require colectomy within 1 year. Clinical data characterizing these patients, however, are limited. We aimed to determine risk factors for colectomy among patients with ASUC who received in-hospital IFX treatment. We performed a retrospective analysis of patients with ASUC who were treated with at least one dose of IFX while admitted between 2014 and 2022. Cox proportional hazards (PH) models were used to assess demographic, clinical, and laboratory risk factors for colectomy within 30 days and 1 year of IFX initiation. Overall, 36/170 (21.2%) patients underwent colectomy within 1 year of IFX initiation, with 22 (12.9%) individuals requiring colectomy within 30 days. On univariable analysis, concomitant Clostridioides difficile infection during admission, a ≤50% decrease in C-reactive protein (CRP) and experiencing 3 or more bowel movements per day within 48 hours after an initial IFX dose were significantly associated with 1-year colectomy. On multivariable Cox PH analysis, C. difficile infection during admission (aHR=2.92, 95% CI: 1.12-7.58) and a higher CRP/albumin ratio on admission (aHR=1.13, 95% CI: 1.01-1.27) were associated with increased colectomy risk within 1 year of IFX initiation. C. difficile infection and a higher CRP/albumin ratio on admission are associated with decreased time to colectomy within 1 year of IFX among patients presenting with ASUC. These factors may aid in early risk stratification to minimize delays in JAK-inhibitor initiation or surgical referral.

ABSTRACT Purpose To investigate the clinical characteristics, risk factors, and prognostic factors of cytomegalovirus retinitis (CMVR) after hematopoietic stem cell transplantation (HSCT). Methods A retrospective cohort study was conducted involving 473 HSCT patients (193 allogeneic, 280 autologous) from 2019 to 2023. Results In this cohort, 12 patients (21 eyes) developed CMVR. The cumulative incidence was 2.54%. CMVR was bilateral in 75% of cases, with a mean onset of 136.4 ± 59.1 days post-transplantation. CMV DNA was detected in 88.9% (16/18) of aqueous humor samples. Retinal detachment was the most common complication, affecting 52.4% of eyes. All patients had three or more risk factors. Median visual acuity declined from 20/80 (range, hand motion-20/20) initially to 20/125 (range, no light perception-20/25) at final follow-up. CMVR was the first symptom of CMV infection in 16.7% of cases. The recurrence rate was 25.00% (3/12), with recurrent cases linked to EBV viremia episodes. Correlation analysis revealed that CMVR type and initial BCVA were significantly associated with RD (p < 0.05). Conclusions These findings highlight the critical importance of the first year post-HSCT for CMVR detection. Increased ophthalmic screening is advised for patients with multiple risk factors and hemorrhagic types to improve outcomes.

An increase in mental disorders has been suggested, but the interpretation of such trends remains unclear. This study examines changes in the 12-month prevalence of anxiety and mood disorders over 12 years and evaluates whether clinical characteristics or sociodemographic, vulnerability and health-lifestyle risk factors contributed to these trends. To assess trends in the 12-month prevalence of anxiety disorders (i.e. panic disorder, agoraphobia, social anxiety disorder or generalised anxiety disorder) and mood disorders (major depressive disorder, dysthymia or bipolar disorder) and explore whether changes in clinical profiles or risk factors influenced these trends. Data from 11 615 respondents (mean age 43.5 years, 53.5% female) in the Netherlands Mental Health Survey and Incidence Studies (NEMESIS) were analysed, covering 2007-2009 (NEMESIS-2, n = 6646) and 2019-2022 (NEMESIS-3, n = 4969). Diagnoses were determined using the Composite International Diagnostic Interview 3.0. The 12-month prevalence of all anxiety and mood disorders was significantly higher in 2019-2022 compared to 2007-2009, with relative increases across disorders ranging from approximately a half to more than double their previous rates. Any anxiety or mood disorder increased from 10.2 to 16.7%. Clinical profiles were equally severe in 2019-2022; rather, there was increased mental health care use, a higher number of comorbid disorders and earlier onset. Examination of 14 risk factors showed no consistent evidence of greater prevalence or increased relative impact over time. There was a consistent rise in the 12-month prevalence of anxiety and mood disorders over 12 years. This increase was not explained by changes in risk factors or less severe disorder reporting. Instead, these findings suggest a concerning decline in public mental health, highlighting the need for effective prevention strategies, timely interventions and better mental health resource allocation to address growing clinical demands.

This study investigates risk factors for the development of preeclampsia (PE) in women with twin pregnancies and constructs and validates a column-line diagram prediction model for clinical decision-making. Records of 70 women with PE and 70 women without PE were selected from twin pregnancies who underwent labor and delivery at Huzhou Maternity and Child Health Care Hospital between September 2021 and June 2023. The cohort was then divided into a training set (98 cases) and a validation set (42 cases) in the ratio of 7:3 using a simple random sampling method. Clinical risk factors, blood biochemical indexes, and uterine artery pulsatility index of all pregnant women were collected to assess the risk of PE. The results were presented as odds ratios (OR) with 90% confidence intervals (CI). Least absolute shrinkage and selection operator regression analysis was used to screen the predictors and establish an optimized, multifactorial logistic regression-based columnar graph model. Distinction, calibration, and clinical utility of the columnar plot model were evaluated by using the receiver operator characteristic curve, calibration plot, and decision curve analysis. Age (OR = 13.39, 95% CI = 2.152-157.0, P = .014), prepregnancy body mass index (OR = 5.979, 95% CI = 1.365-34.27, P = .027), mode of conception (OR = 3.498, 95% CI = 1.071-12.79, P = .045), serum homocysteine cysteine level (OR = 2.079, 95% CI = 1.193-4.005, P = .016), serum β-human chorionic gonadotropin level (Log10; OR = 9.984, 95% CI = 1.467-82.77, P = .024), uterine artery pulsatility index (per0.1; OR = 1.347, 95% CI = 1.11-1.7, P = .005) were independent risk factors for PE (P < .05), and the column-line graph prediction model based on the above 6 risk factors had a good discriminatory degree (area under curve value: 0.880, 95% CI = 0.817-0.944 for training set validation, and 0.831, 95% CI = 0.704-0.958 for validation set validation). The calibration curve showed good agreement between the predicted and actual probabilities of the model (P > .05), and the decision curve analysis showed that the model had a high net clinical benefit (threshold probability values: >2.5% for the training set, 18% to 75% for the validation set). The column-line diagram model developed in this study can more accurately predict the risk of developing PE in women with twin pregnancies.

Prolongation of the QT interval in the ECG is a critical finding that signifies an extended duration from the onset of ventricular depolarization to the end of ventricular repolarization. It can predispose patients to life-threatening arrhythmias, such as Torsades de Pointes (TdP). Long QT syndromes (LQTS) are defined by mutations in ion channel genes, particularly those encoding cardiac potassium and sodium channels and are characterized by a significant risk for sudden cardiac death if untreated. However, besides these clearly defined entities various medications have been implicated in causing QT interval prolongation. There is increasing evidence for a genetically determined risk for drug-induced QT prolongation. In addition, due to numerous clinical factors influencing the QT interval, QT prolongation increases the risk of TdP particularly in multi-morbid patients necessitating vigilant monitoring in at-risk populations. This review gives an overview of mechanisms and conditions which induce QT prolongation, the clinical assessment of QT interval duration, thereby highlighting quantitative variations in measurement techniques and heart-rate correction, as well as in demographic interpretation of normal values. The risk of cardiac arrhythmia is discussed, in both patients with congenital LQTS and acquired QT prolongation, along with influencing pharmacokinetic/pharmacodynamic, non-pharmacologic and genetic risk factors for TdP. Finally, clinical implications for individual patient management, including risk-adapted drug-prescription and use of ECG monitoring to mitigate the risks associated with QT prolongation, are summarized. Understanding the interplay between pharmacokinetics, pharmacodynamics, genetic predisposition and co-morbidities is essential for optimizing treatment in the context of prolonged QT intervals, preventing adverse cardiovascular events, and improving cardiac safety. Comprehensive drug labelling regarding exposure-QT relationships and available pharmacovigilance data are important sources of information enhancing patient safety.

This study aimed to evaluate the value of contrast-enhanced computed tomography (CT) imaging radiomics in distinguishing malignant lesions from benign ones in the orbit. A retrospective analysis was conducted on CT imaging data from 139 patients with orbital tumor lesions, all of whom underwent contrast-enhanced CT scans within 2 weeks before diagnosis. Of these, 45 cases were benign lesions and 94 were malignant lesions. Radiomic features were extracted from the contrast-enhanced CT images, and 12 features were selected through the minimum redundancy maximum relevance and least absolute shrinkage and selection operator regression methods. The selected features were used to build models using logistic regression, Naive Bayes Classifier (NaiveBayes), support vector machine (SVM), Extra Trees Classifier (ExtraTrees), and multilayer perceptron, with the best-performing model identified. Multivariate logistic regression was employed to identify clinical risk factors for malignant orbital lesions, and a nomogram model was developed by combining radiomic features and clinical variables. The predictive performance of each model was evaluated using the area under the receiver operating characteristic curve. Among the 3 machine learning models, the SVM model demonstrated the best predictive performance and robustness across datasets. Therefore, the SVM model was used to construct the nomogram. The nomogram achieved area under the receiver operating characteristic curve values of 0.957 and 0.833 in the training and testing cohorts, respectively, both of which were higher than 0.80. The performance of the nomogram was significantly superior to that of the clinical model (De-long test, P < .05), but no statistically significant difference was observed when compared to the radiomics model (De-long test, P > .05). Contrast-enhanced CT radiomics can effectively differentiate between malignant and benign orbital lesions. Both the nomogram and radiomics models exhibited high predictive performance, offering valuable insights for clinical decision-making.

Objectives This study aimed to identify critical risk factors for acute kidney injury (AKI) following cardiac surgery. By integrating patient data from the MIMIC-IV database with large language models (LLMs) and machine learning algorithms, we ensured the clinical relevance of the selected risk factors, providing robust insights for the early identification and intervention of postoperative AKI. Methods Intensive care unit (ICU) data of patients from the MIMIC-IV database undergoing cardiac surgery were analyzed. Lasso regression and random forest algorithms were used to select significant predictive features from high-dimensional data. Model evaluation involved 10-fold cross-validation and metrics including accuracy, sensitivity, specificity, and the area under the curve. To enhance clinical relevance, LLMs-simulated expert judgment in cardiology and nephrology, which was further validated through discussions with clinical experts. Results In the cohort consisting of 4,565 patients, a total of 113 important and shared risk factors for AKI were identified, including variables such as anion gap, arterial partial pressure of oxygen (PaO 2 ), and fraction of inspired oxygen (FiO 2 ). Among these, 18 key variables were identified as postoperative AKI predictors via machine learning and LLMs-simulated expert validation. These included anchor age, Creatinine (serum), BUN (Blood Urea Nitrogen), Potassium (serum), Sodium (serum), Lactic Acid, Troponin-T, Furosemide (Lasix), Vancomycin (Random), Gentamicin (Trough), Albumin 5%, ART BP Mean, Cardiac Output (thermodilution), Brain Natriuretic Peptide (BNP), Absolute Count - Lymphs, Absolute Count - Monos, and Absolute Count - Neuts. The integration of LLMs with machine learning algorithms proved effective in accurately identifying clinically relevant risk factors. Conclusion The proposed risk prediction approach for postoperative AKI following cardiac surgery, based on the collaborative analysis of machine learning and large language models (LLMs), effectively identified and validated key clinical risk factors. By simulating expert clinical reasoning, the LLMs significantly enhanced the medical relevance of feature selection and improved the clinical interpretability of the model. This approach provides a solid theoretical and practical foundation for the precise early identification and clinical intervention of postoperative AKI in cardiac surgery patients.

Background Post-COVID-19 pulmonary fibrosis (PCPF) is a significant long-term complication in survivors of COVID-19. In this study, we aimed to identify clinical risk factors for PCPF and evaluate the impact of COVID-19–related therapies. Methods We retrospectively studied hospitalized adults with confirmed COVID-19 across three hospitals in South Korea from 2020 to 2022. Inclusion required chest computed tomography (CT) imaging both before and after COVID-19 infection. PCPF was defined as fibrotic changes seen on follow-up CT performed at least one month after recovery. Results Among 5,720 hospitalized adults with COVID-19, 688 met the inclusion criteria, and 87 (12.6%) developed PCPF based on follow-up CT. In the multivariate logistic regression, pre-existing renal disease (adjusted odds ratio [aOR] 3.287; 95% confidential interval [CI]: 1.260–8.580; p = 0.014), higher hemoglobin levels (aOR: 1.194; 95% CI: 1.032–1.387; p = 0.018) and elevated CRP (aOR: 1.005; 95% CI: 1.001–1.009; p = 0.022) were independently associated with increased risk of PCPF. Remdesivir use was significantly associated with a reduced risk of PCPF (aOR: 0.359; 95% CI: 0.176–0.734; p = 0.005), whereas baricitinib use was associated with an increased risk (aOR: 5.633; 95% CI: 1.642–19.548; p = 0.006). Conclusion PCPF remains a relevant sequela in COVID-19 survivors. Remdesivir and baricitinib use were associated with a reduced and increased risk of PCPF, respectively. Although adjusted for multiple confounders, residual indication bias of each treatment cannot be completely excluded. Therefore, prospective studies are needed to validate these associations.

This study aimed to assess the incremental prognostic value of ischemic score quantified by stress dynamic CT myocardial perfusion imaging (CT-MPI) in patients with and without diabetes mellitus (DM). 331 patients (92 patients with DM) with suspected coronary artery disease (CAD) who underwent coronary computed tomography angiography (CCTA) and CT-MPI were included. Major adverse cardiovascular events (MACE) included cardiac death, acute coronary syndrome (ACS), late revascularization, and hospitalizations attributable to heart failure. Ischemic score was assessed based on reduced myocardial blood flow across 16 segments. An ischemic perfusion defect (IPD) was identified as ischemic score ≥ 4. During a median follow-up of 15 (9, 40) months, 49 patients (14.8%) experienced MACE. IPD was an independent predictor for the development of MACE in both DM (HR: 11.64, p = 0.019) and non-DM patient groups (HR: 8.66, p = 0.004). Adding IPD to clinical risk factors and CCTA characteristics increased the C-index for predicting MACE from 0.72 to 0.80 in DM patients (p = 0.005) and from 0.76 to 0.83 in non-DM patients (p = 0.006). IPD quantified by stress dynamic CT-MPI had incremental prognostic value over traditional clinical risk factors and anatomic parameters in both DM and non-DM patients.

The ultimate goal of a genome-wide association study (GWAS) is to translate its discoveries into clinical practice. To explore the clinical use of GWAS findings in the bone field, we conducted a GWAS of dual-energy X-ray absorptiometry (DXA)-derived bone mineral density (BMD) traits at 11 skeletal sites, within over 30,000 European individuals from the UK Biobank. A total of 91 unique and independent loci were identified for 11 DXA-derived BMD traits and fractures, including five novel loci (harboring the genes ABCA1, CHSY1, CYP24A1, SWAP70, and PAX1) for six BMD traits. These loci exhibited evidence of association in both males and females, which could serve as independent replication. We demonstrated that each polygenic risk score (PRS) was independently associated with fracture risk. Although incorporating multiple PRSs (ie, metaPRS) with clinical risk factors from the Fracture Risk Assessment Tool exhibited the highest predictive performance, the improvement was modest in fracture prediction. Additionally, we uncovered genetic correlation and shared polygenicity between head BMD and intracranial aneurysm. Finally, by integrating gene expression and GWAS datasets, we prioritized genes (e.g., ESR1 and SREBF1) encoding druggable human proteins along with their respective inhibitors/antagonists. In conclusion, this comprehensive investigation revealed a new genetic basis for BMD and its clinical relevance to fracture prediction. More importantly, it is suggested that head BMD was genetically correlated with intracranial aneurysm. The prioritization of genetically supported targets implies the potential repurposing of drugs (e.g., the n-3 PUFA supplement) for the prevention of osteoporosis.

Background/Objectives: Secondary hyperparathyroidism (SHPT) affects 30–50% of end-stage renal disease patients. Parathyroidectomy (PTX), while effective for medication-refractory SHPT, carries 20–70% risk of hungry bone syndrome (HBS)—severe sustained hypocalcemia requiring intensive care and prolonged hospitalization. Accurate preoperative risk stratification using biochemical markers and validated prediction tools is critical for optimal preventive management. Methods: We conducted a comprehensive narrative review synthesizing evidence on HBS predictors after PTX in SHPT, evaluating traditional and novel bone turnover markers, clinical risk factors, and multivariate prediction models, through a structured literature search and analysis. Results: Preoperative bone turnover status represents the strongest contributor to HBS risk. Traditional biomarkers—particularly parathyroid hormone (PTH &gt; 1000–2400 pg/mL) and alkaline phosphatase (ALP &gt; 150–300 U/L)—demonstrate moderate-to-strong individual predictive power. Novel bone turnover markers (bone-specific ALP, P1NP, TRAP-5b) offer incremental value, especially in CKD populations where renal clearance affects traditional markers. Combined risk prediction models substantially outperform single biomarkers, achieving area under curve values of 0.87–0.95. The simple NYU 2-point score (ALP &gt; 150 U/L + PTH &gt; 1000 pg/mL) showed 96.8% accuracy, with 100% negative predictive value. More complex tools like nomograms (C-index 0.92–0.94) and machine-learning algorithms (AUC 0.88) provide enhanced discrimination by integrating multiple continuous parameters. Additional clinical factors—younger age (&lt;48 years), prolonged dialysis (≥5 years), low preoperative calcium, high gland weight, and absence of autotransplantation—further refine risk assessment. Postoperative calcium typically reaches nadir at 48–72 h, defining the critical monitoring window. Conclusions: High-turnover bone biomarkers and combined risk models effectively identify high-risk SHPT patients. Risk-stratified protocols (i.e., prophylactic supplementation, intensive monitoring, and selective ICU admission) can substantially reduce HBS-related morbidity. Ongoing efforts should focus on validating these predictive tools across diverse populations and integrating them into clinical practice, thereby facilitating real-time HBS risk assessment and protocol-driven care.

Extended-spectrum β-lactamase (ESBL)-producing Escherichia coli is among the most important multidrug-resistant pathogens causing bloodstream infections (BSIs). Cefotaximase (CTX-M) enzymes are the most common and highly diverse ESBL family in E. coli. CTX-M-15 in group CTX-M-1 and CTX-M-14 in group CTX-M-9 are the most extensively disseminated enzymes. Multidrug-resistant E. coli strains complicate empirical therapy and increase healthcare burden globally and in Korea. We investigated the molecular epidemiology, sequence types (STs), and ESBL genotypes of E. coli bloodstream isolates in Korea and identified clinical risk factors for cefotaxime resistance. We collected all non-duplicated isolates of E. coli and related clinical information from patients with BSIs at eight sentinel hospitals in the Korean Global Antimicrobial Resistance Surveillance System (Kor-GLASS) collection network during 2017-2021. Duplicate isolates were removed to ensure representativeness of the data. Antimicrobial susceptibility was tested using disk diffusion tests, and multilocus sequence typing and beta-lactamase genotyping were performed. Among 9,232 E. coli blood isolates, resistance rates to cefotaxime and ceftazidime were 36.4% and 11.4%, respectively. Among the clinical factors, age >65 yrs (adjusted odds ratio [aOR], 1.36), hospital-origin infection (aOR, 2.55), and admission type (intensive care unit [ICU] vs. general ward; aOR, 1.34) were significant cefotaxime resistance risk factors. ST131 was the most prevalent among cefotaxime-resistant E. coli (64.8%, 2,180/3,363), followed by ST1193 (5.3%, N=177), and ST69 (5.1%, N=170). ST131, ST648, ST405, and ST410 cefotaxime-resistant E. coli isolates frequently harbored blaCTX-M-15, whereas ST1193 and ST68 showed a high proportion of blaCTX-M-27 carriers, and most ST457 and ST5150 isolates carried blaCTX-M-55. Continuous monitoring of ESBL-producing E. coli is required to prevent further dissemination, guide empirical therapy, inform infection control policies, and ensure early detection of multidrug-resistant clones with the potential for widespread transmission.

Relevance. In the context of economic instability and declining birth rates, reproductive health care is becoming particularly important. The problem of infertility, especially in cases of unexplained origin, requires a comprehensive study from both medical and social perspectives. Psychological factors are considered potential clinical risk factors that can reduce both female and male fertility and, once pregnancy occurs, may affect its course and perinatal outcomes. Aim of the study. To assess the psychological status of pregnant women with a history of psychosomatic infertility. Materials and methods. A total of 87 pregnant women were examined. They were divided into three groups according to the duration of infertility of unexplained origin in their medical history: Group 1 – 1.5–2.5 years (32 women, 36.8%), Group 2 – 2.6–4.5 years (34 women, 39.1%), and Group 3 – 4.6–5.5 years (21 women, 24.1%). The psychological state was assessed by trimester using the Hospital Anxiety and Depression Scale (HADS). Statistical processing of the results was performed with the Microsoft Excel statistical analysis package. Results. Analysis of the course of pregnancy in women with a history of unexplained infertility indicated a complicated course. The frequency of gestational complications increased with pregnancy duration and was higher in women who had experienced longer periods of infertility before conception. Assessment of the psychological state using the HADS revealed an increase in anxiety and depression among women who developed gestational complications. Throughout pregnancy, fluctuations in the severity of both anxiety and depressive symptoms were noted, ranging from normal (0–7) to subclinical (8–10) and clinically significant (≥11) levels. The results demonstrated a predominance of anxiety disorders over depressive ones. Changes in the psychological state of women with a history of infertility corresponded to changes in the type and frequency of gestational complications. Conclusion. The findings confirm the role of the maternal psychogenic state as a risk factor for gestational complications, emphasizing the need for timely assessment and correction both during preconception preparation and throughout pregnancy.

Background: New-onset atrial fibrillation (AFib) increases the risk of recurrent stroke. Early identification of stroke survivors at short-term risk may guide monitoring and prevention. Single-lead ECG data from consumer wearables, such as the Apple Watch, interpreted via artificial intelligence (AI), could offer a novel, scalable AFib risk-monitoring approach. Goal: To evaluate the performance of the Wake Forest 1-Year AFib Risk Prediction Model (WF-AFib) in stroke survivors with no prior AFib history and assess feasibility of remote AFib risk monitoring via Apple Watch ECGs. Methods: WF-AFib is a deep learning model (modified ResNet) trained on over 3 million lead I ECGs from more than 600,000 patients at Wake Forest Baptist Health, achieving an AUC of 0.77 in the general population. We externally validated WF-AFib using data from stroke survivors at the University of Tennessee Health Science Center (UTHSC), Memphis, TN. Performance was compared with CHARGE-AF and with a logistic regression model (LR-AI) combining WF-AFib predictions with clinical risk factors. We also assessed agreement between WF-AFib results from clinical ECGs and Apple Watch ECGs in a convenience sample of 243 adult participants from the St. Jude Lifetime Cohort (SJLIFE), all childhood cancer survivors. Results: The UTHSC cohort included 3,086 ECGs from stroke survivors (mean age 63±14 years; 48.7% male; 29.2% White, 68.3% Black) without prior AFib. Stroke was defined by ICD-10 codes I60–I63, I69. Within one year, 350 ECGs (11.3%) were linked to new onset incident AFib. CHARGE-AF and WF-AFib both yielded AUCs of 0.71 (p=0.861). LR-AI significantly outperformed both (AUC=0.79; p&lt;0.001), with 87% specificity, 93% NPV, and 33% PPV at 50% sensitivity. In the SJLIFE sample (mean age 35±10 years; 49.6% male; 83.1% White, 13.7% Black), WF-AFib categorized 88% of participants similarly using clinical and Apple Watch ECGs (Spearman ρ=0.61, p&lt;0.001). Conclusions: AI-based ECG analysis shows promise for predicting AFib risk in stroke survivors. We found high concordance between clinical and wearable ECGs in a non-stroke population, but larger, representative wearable ECG datasets from stroke patients are needed to confirm feasibility of scalable remote monitoring.

Background: Abdominal aortic aneurysm (AAA) is a common and potentially fatal condition when rupture occurs. Current US guidelines recommend screening by ultrasound in men aged 65–75 with a smoking history. However, AAA remains underdiagnosed using these limited clinical criteria, underscoring the need to explore additional risk factors. We evaluated whether the addition of a polygenic risk score (PRS) for AAA helps identify individuals at higher risk among patients with established cardiometabolic disease. Methods: A genetic analysis was performed by pooling individual patient-level data from 3 TIMI trials (FOURIER, ENGAGE-AF and PEGASUS). AAA events were reported by investigators as adverse events, and blinded study physicians independently reviewed and confirmed all cases. We utilized a recently validated AAA PRS (Nat Genet, 2023) and assessed its association with AAA using Cox proportional hazard models adjusted for age, sex, smoking, history of ASCVD, HTN, hyperlipidemia, DM, trial and ancestry (PC1–5). PRS was analyzed continuously (per 1-SD) and categorically (high [top 20%], intermediate [middle 60%], low [bottom 20%]). Results: A total of 39,960 patients were included (median f/u 2.6 years). Individuals in higher PRS categories had a higher rate of AAA events over time ( Figure 1A ). Even after adjusting for clinical risk factors, a 1-SD higher PRS was independently associated with a 54% higher risk of AAA (HR-adj 1.54 [95% CI 1.35–1.76], p&lt;0.001). Compared with low PRS, an intermediate PRS was associated with a 2-fold risk (HR-adj 2.03 [1.23–3.34], p&lt;0.001), and a high PRS with a 3-fold risk (HR-adj 3.12 [1.82–5.36], p&lt;0.001). The magnitude of risk conferred by a high PRS was comparable to traditional clinical risk factors ( Figure 1B ). Coclusion: In patients with cardiometabolic disease, an AAA PRS was independently associated with risk of AAA events, with a magnitude of risk on par with traditional clinical risk factors.

Background: Due to the availability of effective lipid-lowering therapies, familial hypercholesterolemia (FH) case finding is adopted by many healthcare systems. In parallel, polygenic risk scores (PRS) have emerged as a powerful tool for capturing the effect of common genetic variants associated with CAD. Research Question: What proportion of individuals at high PRS for CAD exhibit equivalent risk compared to FH variant carriers, and what are the potential clinical benefits of treating these individuals with statins. Methods: We analyzed data from the UK Biobank, identifying FH carriers via whole-exome sequencing and defining polygenic risk using a CAD PRS. Incident CAD events in statin-naive individuals were compared between FH carriers and individuals with high PRS. Number-needed-to-treat (NNT) with statins and number needed to screen (NNS) were calculated to assess therapeutic impact in high PRS individuals. Results: The top 8% of the PRS distribution exhibited comparable or higher CAD risk than FH carriers. This high-PRS group accounted for between 18 and 29 times more CAD events than FH carriers, depending on age. In the untreated high PRS group the rate of CAD events was 10.28 per 1000 person years (9.01 - 11.67) compared to 6.78 per 1000 person years (5.39 - 8.42) in the treated high PRS group, representing a relative risk reduction (RRR) of 0.34 (0.15 - 0.49). In the median PRS group, the rate of CAD events was 5.28 per 1000 persons (4.67 - 5.94) compared to 3.93 per 1000 person years (3.21 - 4.75) in the treated group, representing a RRR of 0.26 (0.07 - 0.41). The NNT in the high PRS group was 286 compared to 741 in the median PRS group, indicating that statins were roughly 2.6 fold more effective, after matching on clinical risk factors. The NNT (286) for statins in the high PRS group is similar to literature based estimates for statin treatment in FH (222) but the number needed to screen is approximately 21 fold higher for FH than for high PRS. Conclusion: In the population, screening for high CAD PRS individuals will prevent more CAD cases than screening for FH. This is because high PRS individuals are much more prevalent than FH individuals and have enhanced therapeutic benefit from statin treatment (compared to those without high PRS). The polygenic component of CAD is a major, treatable risk factor that is not routinely evaluated in prevention programs, despite a growing body of evidence that this can be implemented easily into clinical practice.

Background: Many patients with peripheral artery disease (PAD) have concomitant coronary artery disease (CAD). Patients with concomitant PAD and CAD, known as polyvascular disease, are a high-risk population. Platelets have potent mechanistic effects in the pathogenesis of cardiovascular (CV) disease. We investigated CV risk, platelet aggregation, and the platelet transcriptome in patients with polyvascular disease versus PAD alone. Methods: Patients with symptomatic PAD scheduled for lower extremity revascularization (LER) were included in the study. Patients were followed for a median of 18 months after LER for adverse clinical events. The primary endpoint was major adverse cardiovascular or limb events (MACLE), a composite of death, myocardial infarction, stroke, and major amputation. Light transmission aggregometry (LTA) was used to measure platelet aggregation in response to submaximal agonist stimulation. Platelet RNA was isolated and sequenced, and differential expression analysis was performed stratified by polyvascular disease, adjusted for age, sex, race, and ethnicity. Results: Among 289 subjects undergoing LER, 160 (55%) had polyvascular disease (mean age 70, 68% male, 68% white), and 129 (45%) had PAD alone (mean age 69, 66% male, 54% white). After multivariable adjustment of demographic and clinical risk factors, polyvascular disease patients had a higher incidence of MACLE (45.0% vs 26.4% in PAD alone, aHR 2.3, 95% CI 1.4-3.8, p=0.001; Panel A). Among 97 subjects matched for receiving both aspirin and clopidogrel, there was no difference in platelet aggregation in response to arachidonic acid (AA) and adenosine diphosphate (ADP). In contrast, platelet aggregation in response to submaximal epinephrine was higher in patients with vs without polyvascular disease (Panel B). Platelet RNA sequencing comparing groups (72 polyvascular vs 62 PAD alone) revealed 182 differentially expressed genes (p &lt; 0.01), 77 upregulated and 105 downregulated. Gene set enrichment analysis revealed enrichment of pathways involved in platelet activation, platelet aggregation, and cell junction organization in patients with polyvascular disease compared to PAD alone (Panel C). Conclusion: Patients with polyvascular disease have a higher risk of CV events, increased platelet aggregation, and a hyperreactive platelet transcriptome. Altogether, these data suggest that platelets may mediate, in part, the heightened risk of CV events in patients with polyvascular disease.

Introduction: Postoperative atrial fibrillation (poAF) is a serious complication following cardiac surgery, contributing to increased ICU and hospital length of stay, heightened stroke risk, and increased morbidity and mortality. While clinical risk factors such as obesity, hypertension, and a history of atrial fibrillation are known contributors, the genetic mechanisms driving poAF in cardiac surgery patients are not well understood. Thus, understanding the specific gene expression changes in atrial tissues associated with poAF may provide insights into pathophysiology and highlight novel therapeutic targets. Hypothesis: This study aims to compare left atrial transcriptome profile in patients with poAF to those without to identify the genes that are differentially expressed. Methods/Approach: We performed bulk-RNA sequencing on left atrial tissue collected from 86 patients undergoing mitral valve surgery. Differential gene expression (DGE) analysis was then performed to determine genes significantly associated with poAF status using DESeq2, accounting for key covariates including patient age, sex, sequencing batch year, left atrial size, left ventricular ejection fraction, and prior history of atrial fibrillation. P-values following DGE were then adjusted using Benjamini-Hochberg correction to minimize False Discovery Rate. Results: We identified 35 genes demonstrating statistically significant expression (adjusted p-value &lt; 0.05, absolute log2 fold change &gt; 1) between patients who developed poAF and those who did not. Among the identified differential expressed genes, immune-related genes such as JCHAIN were upregulated and regulatory non-coding RNAs including ARHGAP26-AS1 and LIN28AP2 were highly downregulated. Notably, RPL21P70, a pseudogene of the RPL21 gene expressed in heart muscle, was heavily downregulated. These genes may contribute to atrial remodeling and inflammatory processes involved in atrial fibrillation. Conclusion: Our study revealed a set of differentially expressed genes associated with the development of post-operative atrial fibrillation in cardiac surgery patients. While several of these genes play roles in immune signaling and RNA regulation, a notable portion consisted of pseudogenes, whose biological function remains poorly understood. Future exploration into upregulation and downregulation of identified genes provide a basis for further study and highlight potential targets for therapeutic intervention.

Introduction: Improving the risk stratification of cardiovascular events based on the analysis of structural changes in the coronary arteries in patients (pts) with ACS is relevant and significant. Purpose: Development of a "Risk Score Scale" ("RSS") of MACE based on computed tomographic angiography (CTA) data for pts with ACS. Methods: The study included 249 pts with ACS (77.5% men, age 58.2 ±10.7 years). Unstable angina occurred in 26.5% of pts, MI – in 73.5%. PCI for culprit lesions was performed in 90% of pts. On the 3rd-7th day of hospitalization CTA was performed by 64 - row CT scanner in 16% of pts and 320 - row CT scanner in others. The characteristics of 785 plaques were determined in pts, including 609 uncalcified ones. We assessed coronary obstruction, morphology and signs of uncalcified plaques instability, such as the presence of low-attenuation area &lt;30 HU, napkin-ring sign, positive remodeling, spotty calcifications, rough contour. The total number of plaques and the number of plaques with certain characteristics were also evaluated. Results: During 39.1 [18.0;57.4] months of follow-up, 71 (28.5%) pts had MACE (at least one of the following events: nonfatal MI, unstable angina, cardiac death, unplanned PCI, ischemic stroke). In the univariate Cox analysis a significant association with the MACE was found in 14 out of 30 CTA characteristics. Multifactorial Cox analysis showed that 11 of the 14 predictors are independent of known clinical risk factors. ROC analysis of significant univariate CT predictors was used to develop the “RSS”. The uncensored period was 305 days. 26 MACE emerged during this period. For each of the significant predictors, the cut-off values were calculated using the Yuden method, as well as corresponding AUC, Sn, Sp, PPV and NPV. The "RSS" includes 8 most optimal predictors. Their values of the points awarded are proportional to the values of their AUCs (Table). For the resulting "RSS" the threshold value calculated by the Yuden method was 3 points. A score of &gt;3 points indicates a high risk of MACE: odds ratio = 7.2, 95% CI: 2.6-19.7, p&lt;0.0001. The AUC of the scale was 0.77 (Figure), Sn = 0.81, Sp = 0.63, PPV = 0.21, NPV = 0.97. Conclusion: The use of “RSS” based on CTA characteristics is a new and practically significant technique that makes it possible to improve the risk stratification of adverse outcomes in the first year after ACS.

Background: Peripheral artery disease (PAD) is a vascular condition marked by arterial narrowing that affects 8.5 million adults in the U.S. Contributions of circulating biomarkers reflecting different immune pathways have not been well established in large population-based studies of older adults. Research Questions: We hypothesized that nine inflammatory biomarkers would be associated with increased risk of incident clinical PAD and incident low ankle-brachial index (ABI), independent of risk factors. Methods: We used available data from 5888 adults ≥65 years enrolled in the Cardiovascular Health Study. Nine inflammatory biomarkers measured at baseline were evaluated: soluble CD14 (sCD14), interleukin-6 (IL-6), high sensitivity C-reactive protein (hsCRP), white blood cell count (WBC), soluble CD163 (sCD163), interleukin-18 (IL-18), interleukin-1 receptor antagonist (IL-1RA), soluble tumor necrosis factor receptor 1 (sTNFR1), and soluble interleukin-2 receptor alpha (sIL2Rα). Clinical PAD was defined as exertional lower extremity discomfort relieved by rest, imaging evidence, revascularization procedure, or claudication. ABI was assessed in 1989-90 in the first CHS cohort of predominantly white participants, or in 1992-93 for the second CHS cohort of predominantly black participants (baseline), and again in 1998-99 (follow-up). Incident low ABI was defined as a &gt;0.15 drop in ABI and follow-up ABI&lt;0.9. Cox regression was used for incident PAD, and Poisson regression for incident low ABI. Model adjusted for demographic, behavioral, and clinical risk factors. Results: After excluding participants with prevalent PAD or ABI&lt;0.9 at baseline, 5061 (41% men, 85% white) were included for the incident clinical PAD analysis. Over a median follow-up of 13.9 years, 260 participants developed clinical PAD. For incident low ABI, further exclusions for ABI&gt;1.4 or missing follow-up ABI yielded 2435 participants for analysis, of which 359 had incident low ABI over a median interval of 8 years. Five biomarkers, sCD14, hsCRP, WBC, sTNFR1, and sIL2Rα, were associated with incident PAD. While three biomarkers, IL-6, WBC, and IL-1RA, were associated with incident low ABI. Conclusions: Several markers of inflammation, monocyte stimulation, and adaptive immune activation were associated with incident PAD, while those signaling general inflammation and NLRP3 activation were associated with incident low ABI. Additional work will identify specific inflammatory pathways as targets for PAD in elders.