Early Cardiovascular Disease Research Articles

Association between the weight-adjusted-waist index and Familial hypercholesterolemia: a cross-sectional study

ObjectiveThe weight-adjusted-waist index (WWI) is a novel obesity measurement indicator, and this study aims to determine the relationship between WWI and Familial hypercholesterolemia (FH).MethodsUsing data from the National Health and Nutrition Examination Survey (NHANES) from 2017 to March 2020, cross-sectional data from 3698 participants were analyzed. The study examined the correlation between WWI and FH using multivariate logistic regression and smooth curve fitting, and conducted subgroup analysis and interaction tests.ResultsThe study sample consisted of 3698 subjects for whom the overall probable prevalence of FH was 5.43% and increased with WWI tertile (quantile 1: 4.00%; quantile 2: 4.94%; quantile 3: 7.34%); individuals with the highest WWI tertile were significantly more likely to have FH than those with the lowest tertile (OR = 4.60,95% CI: 2.00-10.60). Subgroup analysis and interaction tests showed significant significance between WWI and personal history of early Atherosclerotic cardiovascular disease (ASCVD), family history of early ASCVD and probable prevalence of FH (p < 0.05).ConclusionThis study demonstrates a nonlinear positive correlation between WWI and FH. This may provide new ideas for the prevention and treatment of FH in the future.

Developing a question prompt tool to prevent and manage early cardiovascular disease after hypertensive pregnancy: qualitative interviews with women and clinicians

BackgroundPersons (henceforth, women) who have hypertensive disorders of pregnancy (HDP) are at risk of premature cardiovascular disease (CVD). While largely preventable through lifestyle management, many women and clinicians are unaware of the risk. Based on prior research, we developed a question prompt tool (QPT) on preventing and managing CVD after HDP. The purpose of this study was to refine QPT design.MethodsWe recruited Canadian women who had HDP and clinicians who might care for them using multiple strategies, conducted telephone interviews with consenting participants, and used qualitative description and inductive content analysis to derive themes.ResultsWe interviewed 21 women who varied in HDP type, CVD status, years since HDP pregnancy, age, geography and ethno-cultural group; and 21 clinicians who varied in specialty (midwife, nurse practitioner, family physician, internist, obstetrician, cardiologist), geography and years in practice. Participating women and clinicians agreed on needed improvements: more instructions, lay and gender-neutral language, links to additional information, more space for answers, graphic appeal, and both print and electronic format. Both groups identified similar barriers: clinicians lack time/willingness, and low language/health literacy and access to technology among women; enablers: translated, credible source/endorser, culturally relevant, organized by health trajectory stages; and likely benefits: raise awareness, empower women, encourage them to adopt healthy lifestyle. Women desired exposure to the QPT before or during pregnancy, while clinicians recommended waiting until postpartum to avoid overwhelming women. Similarly, most women said the QPT should be available through multiple avenues to empower them for health self-advocacy, while clinicians thought they should introduce the QPT to women, and decide when and which questions to address. To mitigate reluctance, clinicians recommended self-directed educational materials accompany the QPT.ConclusionsWe will use this information to refine QPT design and plan for future evaluation. If found to be effective and widely disseminated, the QPT could improve awareness and communication about this issue, and may reduce CVD risk in many women who have hypertensive pregnancies. Ongoing research is needed to more fully understand how QPTs support patient-clinician communication, and how to alert and prime both patients and clinicians to use QPTs.

Advances in ECG and PCG-based cardiovascular disease classification: a review of deep learning and machine learning methods

Cardiovascular diseases (CVD) have been found to be prevalent in society, frequently ending in death. According to the findings of a recent survey, the mortality rate is increasing due to the prevalence of adult cigarette consumption, elevated blood pressure, high cholesterol levels, and obesity. The previously mentioned causes are exacerbating the severity of the condition. A pressing necessity exists for a study on the variability of these factors and their impact on cardiovascular disease (CVD). This involves the use of advanced tools to detect the disease early on and aid in the reduction of fatality rates. With their extensive methodologies that would help in the early CVD prediction and recognition of behavioral patterns in large amounts of data, artificial intelligence, and data mining disciplines offer a broad study potential. The results of these predictions will help physicians make decisions and early diagnoses, decreasing the risk of patient death. This work compares and reports the classification, machine learning, and deep learning algorithms that predict cardiovascular illnesses. For this study, articles from 2012 to 2023 were considered; after filtering, 82 articles were chosen for primary research. Future researchers will benefit from this review on cardiovascular disorders by better understanding the Deep Learning and Machine Learning models now in the healthcare sector. The review encompasses commonly employed methodologies such as support vector machine, decision tree, random forest, and convolutional neural networks (CNNs). Additionally, this survey aggregates and presents information on the performance metrics used to report accuracy. It also goes over the most popular datasets used by various diagnostic models (ECG and PCG signals datasets). In addition, it emphasizes prominent publishers, journals, and conferences that serve as platforms for the evaluation of scholarly works. Additionally, it will facilitate their understanding of the unresolved challenges or hurdles experienced by past researchers. A lack of more extensive and consistent datasets was the most common issue, followed by the need to improve existing models.

Risk of Major Adverse Cardiovascular Outcomes in Families With MASLD: A Population-Based Multigenerational Cohort Study.

Metabolic dysfunction-associated steatotic liver disease (MASLD) is a risk factor for cardiovascular disease. However, whether family members of individuals with MASLD also share an increased cardiovascular risk is unknown. We created a nationwide multigenerational cohort study identifying all family members of Swedish adults diagnosed with biopsy-proven MASLD (1969-2017) and of matched general population comparators (by age, sex, calendar year, and county of residence). We calculated incidence rates and used Cox models to calculate adjusted hazard ratios (aHRs) and 95% CIs for incident major adverse cardiovascular events (MACE), including acute myocardial infarction, stroke, hospitalization for heart failure, or cardiovascular death. Cox models were adjusted for education, country of birth, diabetes, hypertension, obesity, dyslipidemia, chronic kidney disease, chronic obstructive pulmonary disease, and the Charlson comorbidity index. We identified 22 267 MASLD first-degree relatives (FDRs; parents, siblings, and offspring) and 5687 MASLD spouses, as well as 118 056 comparator FDRs and 29 389 comparator spouses without earlier cardiovascular disease. Overall, the mean age was 41.8 years (SD, 18.0), and 51.5% were females. Over a median of 24.6 years, the incidence rate for MACE was higher in MASLD FDRs than in comparator FDRs (65.0 versus 62.5/10 000 person-years; aHR, 1.06 [95% CI, 1.01-1.11]). MASLD FDRs had higher rates of acute myocardial infarction (23.0 versus 20.9/10 000 person-years; aHR, 1.09 [95% CI, 1.01-1.18]) and cardiovascular death (aHR, 1.09 [95% CI, 1.01-1.18]). Across generations of FDRs, the risk of MACE was uniformly increased with no differences by relationship (ie, parents, siblings, and offspring; Pinteraction>0.05). MASLD spouses were also at an increased risk of MACE (117.6 versus 103.5/10 000 person-years; aHR, 1.09 [95% CI, 1.01-1.18]). First-degree relatives of individuals with biopsy-proven MASLD are at slightly higher risk of incident MACE, but absolute risks do not support early screening for cardiovascular disease. Shared lifestyle factors may be the main contributors, as spouses of MASLD patients also had higher risks of MACE.

Protein quantitative trait loci identifies genetic biomarkers of proteins associated with measures of myocardial fibrosis in new-onset, treatment naive rheumatoid arthritis

Abstract Introduction The excess risk of cardiovascular disease (CVD) in patients with rheumatoid arthritis (RA) compared to the general population is attributed to chronic inflammation. Identifying the underlying mechanisms and shared inflammatory pathways more precisely would enable tailored treatment. Purpose To identify genetic variants associated with the cardio-metabolic-inflammatory proteomic signature in patients with early, treatment-naïve RA and low CVD risk. Methods The Coronary Artery Disease Evaluation in RA (CADERA) study was a bolt-on to a RCT of patients with early, treatment-naïve RA and low CVD risk profile (maximum of 1 traditional CVD risk factor and no diabetes mellitus). Normalised expression of 334 proteins from 75 CADERA patients who underwent cardiovascular magnetic resonance imaging (CMR) was measured using the Olink Proximity Extension Immunoassay across Inflammation, Cardiovascular II, III and Cardiometabolic panels. Genome-wide single nucleotide polymorphism (SNP) data were generated using the Illumina Global Screening Array and was imputed and checked for data quality using standard approaches. Proteins associated with CMR parameters were identified using Bayesian mixed effects linear regression. Protein quantitative trait locus (pQTL) analysis was then performed using linear regression models adjusted for age, gender, and systolic blood pressure. Cis-pQTLs were defined as SNPs within 1Mb of the protein-encoding gene, and significance threshold was set at 1e-5. Due to sample size, trans-pQTLs were not considered in this analysis. Linkage disequilibrium (LD) clumping was performed using PLINK software to identify index SNPs defined as the most significant variant in LD (r2 &amp;lt;0.5) within a region of +/- 250Kb. Results Following quality control 318 proteins and 5,971,781 SNPs were available for analysis in 75 CADERA patients. The median age was 51 years [inter-quartile range (IQR) 40 - 61] with 52/75 (69%) females, 40/75 (53%) current/ex-smokers, and median body mass index (BMI) 26.4kg/m2 (IQR 23.3 - 28). Participants had a median RA symptom duration of 21.9 weeks (IQR 14.4 - 32.7). Only 8/75 (10%) reported a history of hypertension, hypercholesteroaemia or family history of CVD. 54 out of 318 proteins were associated with CMR measures of myocardial fibrosis and/or vascular stiffness. 460 cis-pQTLs were identified for 20 out of the 318 proteins. LD clumping revealed 40 cis-pQTLs independently associated with these 20 proteins. Of these, NOTCH3 (rs34763630) was positively associated with extracellular volume, and SELPLG (rs7980427) was negatively associated with late gadolinium enhancement as detected on CMR (table 1). No other proteins with genetic support were associated with CMR measures. Conclusion This study for the first time, implicates genetic markers associated with two proteins as putative candidate biomarkers of CMR-identified myocardial fibrosis in new-onset RA that now warrant validation in an independent cohort.

Assessment of body composition in young adulthood and its associations to early changes in cardiovascular phenotypes: a cross-sectional study

Abstract Introduction Obesity in young adulthood is associated with various cardiovascular structural and functional changes, commonly used as markers of early subclinical cardiovascular disease. Whether these changes occur predominantly in relation to increased fat mass (FM) or are instead more closely related to simultaneous increases in underlying fat-free mass (FFM) that are known to occur in obesity, is incompletely understood. Purpose To investigate associations between both FM and FFM and a wide range of cardiac (left ventricular mass, LVM; end-diastolic volume, EDV; cardiac output, CO; stroke volume, SV), vascular (systolic blood pressure, SBP; diastolic blood pressure, DBP; mean arterial pressure, MAP; systemic vascular resistance, SVR) and autonomic (heart rate variability triangular index, HRVTI; baroreceptor reflex sensitivity, BRS) phenotypes commonly used to monitor the early evolution of subclinical CVD. Methods The study used data from extensive adipose and cardiovascular phenotyping of 408 healthy young adults (mean age 21 years) participating in a sub-study of the long-running ALSPAC birth cohort. We performed cross-sectional analysis using multiple imputation, multivariable linear regression and interaction testing to assess the association between both FM and FFM and cardiovascular phenotypes. Results Our study revealed that FFM resulted in a much greater increase in CO (difference in outcome per 1-SD difference in exposure β=0.58L/min, p&amp;lt;0.01, 95% CI=0.38, 0.77) than FM (β=0.20L/min, p=0.01, 95% CI=0.05, 0.36), following adjustment for both tissue types. FFM was also associated with an increase in LVM (β=19.1g, p&amp;lt;0.01, 95% CI=16.61, 21.58), EDV (β=22ml, p&amp;lt;0.01, 95% CI=18.18, 25.13) and SV (β=13ml, p&amp;lt;0.01, 95% CI=10.96, 15.75). FFM was also associated with improved autonomic and peripheral vascular function, including lower HR (β=-4bpm, p&amp;lt;0.01, 95% CI=-6.12, -1.95), higher HRVTI (β=2.1, p&amp;lt;0.01, 95% CI=0.72, 3.48) and lower SVR (β=-1.1, p&amp;lt;0.01, 95% CI=-2.12, -1.07), resulting in unchanged MAP despite elevations in CO. On the contrary, FM was not associated with LVM (β=-0.91g, p=0.35, 95% CI=-2.85, 1.02), and was associated with lower EDV (β=-4.62ml, p&amp;lt;0.01, 95% CI=-7.34, -1.88) and reduced autonomic function, as seen with HRVTI (β=-1.83, p&amp;lt;0.01, 95% CI=-3.02, -0.64) and BRS (β=-2.47, p=0.01, 95% CI=-4.27, -0.67). FM was also associated with increased HR (β=3.26bpm, p&amp;lt;0.01, 95% CI=1.57, 4.94) and increased SBP (β=2.14mmHg, p&amp;lt;0.01, 95% CI=1.01, 3.27) DBP (β=2.68mmHg, p&amp;lt;0.01, 95% CI=1.60, 3.77), and MAP (β=2.34mmHg, p&amp;lt;0.01, 95% CI=1.34, 3.34). Conclusion In young adults, changes in surrogate clinical markers of cardiac, vascular, and autonomic function commonly attributed solely to increases in fat mass should be interpreted with caution as they may be associated with underlying changes in FFM. Future work should focus on longitudinal studies of this cohort to confirm remodelling patterns based on these clinical markers.

Screening for subclinical atherosclerosis in patients clinically diagnosed with familial hypercholesterolemia and determination of imaging-guided patient management strategy

Abstract Introduction Familial hypercholesterolemia (FH) is a monogenic dyslipidemia characterized by elevated plasma LDL-C levels, leading to early cardiovascular disease. The Dutch Lipid Clinic Network (DLCN) score is the most widely used clinical scoring system in current practice. Subclinical atherosclerosis is defined as the detection of atheromatous plaques in arterial territories, such as coronary, carotid or iliofemoral, before a symptom or event occurs. It is an early indicator of atherosclerotic burden. In this study, we investigated the presence of subclinical atherosclerosis in FH patients with a definite or probable clinical diagnosis according to DLCN score. We also tried to identify independent predictors of subclinical atherosclerosis. Methods This study is a single-center, prospective, and cross-sectional investigation involving a cohort of 215 FH patients. These patients are primary prevention patients and among them, 120 patients received a definite clinical diagnosis, while 95 patients received a probable clinical diagnosis based on the DLCN score. Carotid USG and Femoral USG were performed to screen for subclinical atherosclerosis and CAC score was calculated by non-contrast CT. Apo A-I, Apo B and Lp (a) measurements were analyzed by immunonephelometric method. Results The study population consisted of 136 (63%) women and 79 (37%) men. The mean age was 54 years (43-62) and the stigmata rate was 18%. Initially, 17% of patients were on aspirin, and 32% were receiving statin treatment; however, following subclinical atherosclerosis screening, these proportions significantly increased (aspirin usage to %32 and statin usage to %96) within the population. 148 patients (69%) had subclinical atherosclerosis in at least one site. Upon regional analysis, subclinical atherosclerosis rates were 48% in the coronary arteries, 47.5% in the carotid bifurcation region, and 40.5% in the femoral bifurcation region. Additionally, 25% of patients had atherosclerosis at one site, 27% at two sites, and 17% at all three sites. Age, male gender, pretreatment LDL-C level, Apo A-I/Apo B ratio and DM were identified as independent predictors of the presence of subclinical atherosclerosis in the FH patients. Furthermore age, male gender, pretreatment nonHDL-C level, Lp (a) ≥ 30 mg/dl and presence of femoral plaque were identified as independent predictors of coronary atherosclerosis. Conclusion Subclinical atherosclerosis is prevalent among FH patients, and this study has identified independent predictors associated with presence of subclinical atherosclerosis. However, despite this heightened risk, medication utilization among these patients remains below optimal levels and only a small proportion of the population has achieved their treatment goals. Our study highlights the influence of subclinical atherosclerosis on treatment approaches, as demonstrated by the notable rise in statin and aspirin utilization observed after screening.Clinical and biochemical characteristicsSubclinical Atherosclerosis Rates

Metabolomic profiling of the entire UK Biobank enables interpretation of familial hypercholesterolemia mutations and prediction of severe cardiovascular outcomes

Abstract Background Familial hypercholesterolemia (FH) is an inherited disorder resulting in high levels of LDL-C from birth. Premeature coronary heart disease occurs in half of affected men by age 50 years(1). While many genetic variants that cause FH are well understood, there are also many variants in FH genes that are of uncertain clinical significance. Furthermore, the variability in residual cardiovascular risk of FH carriers after statin treatment is not fully understood. We here investigate whether metabolomic and genomic data from the 500,000 person UK Biobank cohort can aid in interpretation of candidate pathogenic variants for FH, and characterise future risk of severe cardiac events in the carriers of such variants. Methods We have quantified 249 metabolomic parameters in 490,830 blood samples from UK Biobank cohort. Among 186,483 participants with exome sequence data we identified 896 who carried known pathogenic FH variants and 405 who carried variants of unknown significance in an established FH gene. We fitted a logistic predictive model to identify pathogenic mutations using LDL levels, statin use and 17 principal components of the metabolomic parameters. We further tested whether metabolomic and genomic scores for predicting future risk of myocardial infarction (MI), that were trained in the general population (i.e. individuals without FH), could predict risk in FH carriers. Results A model including all metabolites had a higher accuracy at identifying known pathogenic FH variants (36/56 at FDR &amp;lt; 0.05), compared to LDL and statin use alone (30/56). Our model also identified 8 FH variants marked as uncertain significance in ClinVar with significant (FDR &amp;lt; 0.05) evidence of predicted pathogenicity. We found that our metabolomic risk score predicted 10-year risk of MI among FH carriers (HR=1.96 per SD, p = 0.0011). This was indeed stronger than the effect in the general population (HR = 1.64 per SD, p = 2.5e-168). A combined metabolomic and polygenic risk score had an even larger effect at predicting MI in FH carriers (HR = 3.06 per SD, p = 2.0e-6), and this score was significantly better at predicting MI in FH carriers than in non-carriers (HR = 3.06 vs 1.88 per SD, interaction p = 0.042). Conclusion Familial hypercholesterolemia is an important source of risk for early cardiovascular disease. Population-scale metabolomics can help distinguish pathogenic from benign variants that have not yet been characterised. Metabolomic and genomic scores can also identify individuals at most elevated risk, and provide even more information to FH individuals than in the general population.

Turmeric as a therapeutic agent against arsenic-induced metabolic dysregulation in rat models

Ethnopharmacological relevanceHealth hazards stemming from metalloid consumption, such as arsenic, pose a global concern. This study aimed to assess the protective effects of turmeric against arsenic-induced alterations in lipid profiles and energy metabolism in rats. MethodsThirty male rats were divided into three groups over a 90–180-day exposure period: Group 1 (Control) received standard rat chow; Group 2 received an arsenic diet; and Group 3 received arsenic plus turmeric supplemented diet. Blood and liver tissue samples were collected for lipid profiles, glucose levels, energy metabolism parameters, and PPAR-α mRNA expression using qRT-PCR. Statistical analysis was performed (p<0.05). ResultsResults indicated significant (p<0.05) increases in lipid profiles among arsenic-exposed rats after 180 days, elevating the risk of coronary artery diseases. Both arsenic and turmeric-exposed groups showed heightened total cholesterol (TC), triglycerides (TG), and low-density lipoprotein (LDL) levels at 90 and 180 days, with nuanced differences suggesting dyslipidemia and atherosclerosis. Blood glucose observations revealed hypoglycemia initially in arsenic-exposed rats, progressing to hyperglycemia by 180 days. Arsenic significantly (p<0.05) inhibited pyruvate kinase, hexokinase, and isocitrate dehydrogenase activities in the liver after 180 days, while turmeric exposure showed no significant (p<0.05) changes. Additionally, both arsenic and turmeric groups exhibited downregulation of PPAR-α expression at 180 days. In-silico analysis identified curcumin as a promising component for anti-atherosclerosis and arsenic-related conditions due to its strong binding affinities and multiple hydrogen bond formations. ConclusionIn conclusion, long-term turmeric consumption may mitigate arsenic-induced oxidative damage associated with early hyperglycemia, diabetes mellitus, cardiovascular diseases, and atherosclerosis. These findings underscore turmeric’s potential therapeutic role against arsenic toxicity, suggesting avenues for future research and public health interventions.

Phonocardiogram (PCG) Murmur Detection Based on the Mean Teacher Method.

Cardiovascular diseases (CVDs) are among the primary causes of mortality globally, highlighting the critical need for early detection to mitigate their impact. Phonocardiograms (PCGs), which record heart sounds, are essential for the non-invasive assessment of cardiac function, enabling the early identification of abnormalities such as murmurs. Particularly in underprivileged regions with high birth rates, the absence of early diagnosis poses a significant public health challenge. In pediatric populations, the analysis of PCG signals is invaluable for detecting abnormal sound waves indicative of congenital and acquired heart diseases, such as septal defects and defective cardiac valves. In the PhysioNet 2022 challenge, the murmur score is a weighted accuracy metric that reflects detection accuracy based on clinical significance. In our research, we proposed a mean teacher method tailored for murmur detection, making full use of the Phyionet2022 and Phyionet2016 PCG datasets, achieving the SOTA (State of Art) performance with a murmur score of 0.82 and an AUC score of 0.90, providing an accessible and high accuracy non-invasive early stage CVD assessment tool, especially for low and middle-income countries (LMICs).

Death of a Parent, Racial Inequities, and Cardiovascular Disease Risk in Early to Mid-adulthood.

Black Americans experience the death of a parent much earlier in the life course than White Americans on average. However, studies have not considered whether the cardiovascular health consequences of early parental death vary by race. Using data from the National Longitudinal Study of Adolescent to Adult Health, we explore associations between early parental death and cardiovascular disease (CVD) risk in early to mid-adulthood (N = 4,193). We find that the death of a parent during childhood or adolescence (ages 0-17) or the transition to adulthood (ages 18-27) is associated with increased CVD risk for Black Americans, whereas parental death following the transition to adulthood (ages 28+) undermines cardiovascular health for both Black Americans and White Americans. These findings illustrate how a stress and life course perspective can help inform strategies aimed at addressing both the unequal burden of bereavement and high cardiovascular risk faced by Black Americans.

Harnessing AI for Early Detection of Cardiovascular Diseases: Insights from Predictive Models Using Patient Data

Cardiovascular diseases (CVDs) continue to be the leading cause of death in the world, taking millions of lives every year. Early detection and treatment of these diseases are critical in preventing deaths and illnesses. Currently, traditional diagnostic methods like routine clinical examinations and other standard risk assessment tools fail to detect CVDs in early on set stages, posing numerous limitations for preventable interventions. This work explores incorporating artificial intelligence (AI) to catalyze early CVD diagnosis using AI models for patient data including electrocardiogram (ECG), wearable-generated metrics, and medical history. The research showcased the ability of AI to predict with high accuracy when CVD would start using machine learning methods such as Random Forest, Support Vector Machines (SVM) and Neural Networks. The powerful AI models were carefully constructed by training and testing them on an extensive dataset that contained measurements of diverse patient characteristics including many other readouts related to cardiovascular risk. Compared to traditional methods, the models outperformed, and Neural Networks had an accuracy of 92% in identifying high-risk patients. A hallmark of this work is that AI identifies subtle patterns and relationships within patient data ones that conventional approaches might not find. These AI-based predictive models might soon become part of the routine clinical evaluation for cardiovascular disease, providing a personalized and early health intervention to empower your cardiovascular care. Timely identification of these high-risk patients could allow for targeted interventions, leading to improved health and reductions in healthcare spending. Despite the promising potential that AI holds in healthcare, it also has brought up issues of data privacy, ethical use and the need for extensive clinical validation. This paper suggests a predictive strategy could be implemented using AI algorithms to lead early detection and management of CVD, which opens an opportunity for individualized care accompanied by big data era. Future studies should focus on improving such models with more diverse and larger datasets as well as overcoming logistical difficulties encountered in integrating AI into clinical workflows.

A Plasmonic Nanoledge Array Sensor for Selective Detection of Cardiovascular Disease Biomarkers in Human Whole Blood.

Optical sensors face challenges when detecting ultralow amounts of analytes in whole blood, including signal quenching due to optical absorption and false positives due to nonspecific binding. This study introduces gold nanoscale array features termed nanoledges (NLs), which interact with incident white light to produce a transmitted surface plasmon resonance (tSPR) signal. This extraordinary optical transmission (EOT) spectrum occurs in the near-infrared (NIR) region, thereby minimizing signal quenching caused by visible-light absorption from blood proteins and pigments. To develop a sensitive, selective, and label-free optical biosensor for detecting various levels of cardiac troponin I (cTnI) in very small volumes of whole blood samples, DNA aptamers are tethered to the NL surface, specifically binding to the cTnI biomarker. This biological binding activity alters the refractive index at the NL surface, causing a peak shift in the EOT spectrum and enabling quantification of cTnI levels. The NL array chip demonstrated high sensitivity for cTnI detection in buffer, human serum (HS), and human whole blood (HB), with detection limits of 0.079, 0.084, and 0.097 ng/mL, respectively. Control measurements using blank target mediums and those containing up to 125 ng/mL of other proteins, such as myoglobin, creatine kinase, and heparin, showed minimal interference and high specificity. The NL plasmonic array's performance in biosensing underscores its promise for clinical analysis and its potential development as a point-of-care platform for early cardiovascular disease (CVD) diagnostics.

The Impact of Clinical Pharmacists on Cardiovascular Disease Management in Spain: A Community Pharmacy-Based Intervention Study

Background: Cardiovascular Disease (CVD) is a significant global health concern, causing 17.3 million deaths annually. In Spain, CVD accounts for 14.6% of mortality, necessitating effective management strategies. Clinical Pharmacists (CPs) have emerged as crucial players in early CVD identification and management, particularly through community pharmacies. Methods: This study enrolled 900 individuals across 55 pharmacies, with 820 meeting inclusion criteria based on existing CVD risk factors. Participants were randomly assigned to either a control group receiving standard treatment (n = 353) or an intervention group receiving enhanced guidance from CPs (n = 375). Outcomes measured included changes in CVD risk factors, blood pressure, lipid profiles, and smoking cessation over a three-month period. Results: The intervention group exhibited a statistically significant reduction in projected CVD risk, with a mean decrease of 6.42% (p &lt; 0.001). Improvements were noted in LDL cholesterol, blood pressure, and body mass index in both groups, underscoring the effectiveness of CP-led interventions. No adverse events were reported. Conclusion: CPs play a vital role in managing CVD risk factors through education, counseling, and medication management. This study highlights the effectiveness of CP interventions in community pharmacy settings, emphasizing the need for further research to evaluate long-term clinical and humanistic outcomes. Expanding the role of CPs in the healthcare system could significantly alleviate the burden of CVD in Spain.

Association Between Body Composition Patterns, Cardiovascular Disease, and Risk of Neurodegenerative Disease in the UK Biobank.

Accumulating evidence connects diverse components of body composition (e.g., fat, muscle, and bone) to neurodegenerative disease risk, yet their interplay remains underexplored. This study examines the associations between patterns of body composition and the risk of neurodegenerative diseases, exploring the mediating role of cardiovascular diseases (CVDs). This retrospective analysis used data from the UK Biobank, a prospective community-based cohort study. We included participants free of neurodegenerative diseases and with requisite body composition measurements at recruitment, who were followed from 5 years after recruitment until April 1, 2023, to identify incident neurodegenerative diseases. We assessed the associations between different components and major patterns of body composition (identified by principal component analysis) with the risk of neurodegenerative diseases, using multivariable Cox models. Analyses were stratified by disease susceptibility, indexed by polygenetic risk scores for Alzheimer and Parkinson diseases, APOE genotype, and family history of neurodegenerative diseases. Furthermore, we performed mediation analysis to estimate the contribution of CVDs to these associations. In addition, in a subcohort of 40,790 participants, we examined the relationship between body composition patterns and brain aging biomarkers (i.e., brain atrophy and cerebral small vessel disease). Among 412,691 participants (mean age 56.0 years, 55.1% female), 8,224 new cases of neurodegenerative diseases were identified over an average follow-up of 9.1 years. Patterns identified as "fat-to-lean mass," "muscle strength," "bone density," and "leg-dominant fat distribution" were associated with a lower rate of neurodegenerative diseases (hazard ratio [HR] = 0.74-0.94) while "central obesity" and "arm-dominant fat distribution" patterns were associated with a higher rate (HR = 1.13-1.18). Stratification analysis yielded comparable risk estimates across different susceptibility groups. Notably, 10.7%-35.3% of the observed associations were mediated by CVDs, particularly cerebrovascular diseases. The subcohort analysis of brain aging biomarkers corroborated the findings for "central obesity," "muscle strength," and "arm-dominant fat distribution" patterns. Our analyses demonstrated robust associations of body composition patterns featured by "central obesity," "muscle strength," and "arm-dominant fat distribution" with both neurodegenerative diseases and brain aging, which were partially mediated by CVDs. These findings underscore the potential of improving body composition and early CVD management in mitigating risk of neurodegenerative diseases.

Cardiovascular Risk in HIV Patients: Ageing Analysis of the Involved Genes

Acquired immunodeficiency syndrome (AIDS) has transitioned from a progressive, fatal disease to a chronic, manageable disease thanks to better defining of antiretroviral therapy, contributing to increased life expectancy. In parallel, a growing number of subjects without clinical signs of disease but living with chronic HIV infection (also indicated as PLWHs, i.e., People Living With HIV) are experiencing early cardiovascular disease, and the risk increases with age. However, a progressive increase in the prevalence of multiple comorbidity diseases has been reported as these patients age, including cardiovascular disease (CVD). Cardiovascular mortality can be related to viral infection, a progressive reduction in response to antiretroviral therapy, chronic inflammation, and lifestyle. Cardiovascular ageing represents a relevant issue in the management of HIV-infected patients. Although the exact pathophysiological mechanism that leads PLWHs to develop cardiovascular disease is not entirely understood, there is substantial evidence that they accumulate age-related conditions earlier than the general population. Furthermore, since the proportion of PLWHs growing older than 50 years has progressively increased, this results in a complex interaction between disease-related pathophysiology and the exposition of a growing burden of cardiovascular risk factors. We performed a study to relate the effect ageing gas on genes associated with HIV and cardiovascular diseases. We performed a systematic review of the genes most frequently associated with ageing in HIV-infected subjects, followed by a bioinformatic analysis to explore the biological impact of the ageing-related genes.

The association of cardiovascular disease risk with coronary artery calcification and thoracic aortic dilation: a study in idiopathic inflammatory myopathies and systemic lupus erythematosus.

We aim to explore the prevalence of coronary artery calcification (CAC) and ascending/descending thoracic aorta (AA/DA) dilation in idiopathic inflammatory myopathies (IIM) and systemic lupus erythematosus (SLE) patients, and to assess associations between cardiovascular disease (CVD) risk factors and these imaging signatures. This study recruited 151 IIM patients, 140 SLE patients, and 195 controls. The CAC and AA/DA diameters were quantified using non-gated chest CT images. The independent samples t-test or Mann-Whitney test was chosen for comparisons of continuous variables between patients and healthy controls. For categorical data, comparisons were made using the chi-square test or Fisher's exact test. Multivariate regression or Spearman's correlation analysis was employed to probe the associations between CVD risk factors and Framingham risk score (FRS) with imaging signatures. The IIM and SLE patients showed significantly higher prevalence of CAC and AA/DA dilatation (P < 0.01). Age was a risk factor for both CAC and AA/DA dilatation in all cohorts (P < 0.01). In IIM patients, the AA/DA dilatation was associated with BMI (P = 0.05). In SLE patients, CAC was associated with the elevated CRP level (P = 0.05). Without CAC, both IIM and SLE patients showed significant correlations between AA/DA diameters and FRS (P < 0.01, P < 0.01). Only in SLE patients, the interleukin-6 (IL-6) level correlated with AA/DA diameters. The IIM and SLE patients more commonly exhibit CAC and AA/DA dilation. These subclinical atherosclerosis signs are associated with traditional CVD risk factors. For AID patients without CAC, AA/DA diameters could serve as a potential biomarker for early CVD risk. Key Points • The study characterized the manifestation of subclinical atherosclerosis imaging biomarkers (CAC, AA/DA dilation) in IIM and SLE patients. • AA/DA diameters could serve as an early imaging biomarker in clinical management for IIM and SLE patients with early-onset and no CAC present.

Depressive symptom clusters and biomarkers of monocyte activation, inflammation, and coagulation in people with HIV and depression.

We assess associations of somatic and cognitive/affective depressive symptom clusters with monocyte activation (soluble (s)CD14, sCD163), systemic inflammation (interleukin-6 (IL-6), high-sensitivity C-reactive protein (hsCRP)), and coagulation (D-dimer, fibrinogen) in people with HIV (PWH) on suppressive antiretroviral therapy with depression. Utilizing baseline data from a randomized controlled trial, we found no significant associations in linear regression models examining individual depressive symptom clusters; however, models examining both clusters simultaneously showed that the somatic cluster was positively associated with inflammation biomarkers, while the cognitive/affective cluster was negatively associated with inflammation and coagulation biomarkers (suggesting a cooperative suppression effect). Our findings indicate a differential association with depressive symptom clusters and biological mechanisms underlying cardiovascular disease (CVD) in HIV, which may be driven by unique components of each depressive symptom cluster. This line of research could identify subgroups of PWH with depression at elevated CVD risk needing early CVD prevention approaches. Supported by R01 HL126557.

Abstract Tu111: Effect Of Grandmaternal Diabetes and High Fat Diet on Cardiometabolic Dysfunction in the Second Generation (F2) Newborns

Infants exposed to diabetic pregnancy are at higher risk of cardiomyopathy at birth and early onset cardiovascular disease (CVD) as adults. We have previously used a rat model to demonstrate that a combination of high fat diet (HFD) and late gestational diabetes causes impaired cardiac function in first generation (F1) newborn and adult offspring via mitochondrial dysfunction and metabolic stress. The objective of this study was to determine whether grandmaternal HFD and diabetes can cause inheritable CVD in second generation offspring (F2), even if the first generation is cross fostered during lactation and fed a standard control diet throughout life. Myocardial function in the F2 newborns was assessed using echocardiography and mitochondrial function in the isolated neonatal cardiomyocytes (NRCM) was assessed using extracellular flux analyses. Exposed F2 newborns had a higher birthweight (p=0.03) and blood glucose (p&lt;0.0001) compared to control newborns and F1 generation parents (p=0.0002 and p&lt;0.0001). Of significant interest, exposed F2 newborns had higher heart rates (p&lt;0.0001), heart:body weight ratios (p&lt;0.0001) and systolic dysfunction which was evident from lower ejection fraction (p&lt;0.0001) and fractional shortening (p&lt;0.0001) compared to control F2 newborns. While control and combination exposed NRCM had similar basal respiration, exposed F2 NRCM had a higher proton leak (p=0.0004) which indicates damage to the inner mitochondrial membrane and/or electron transport chain complex. There was also shift to higher glycolysis at baseline (p=0.005), but blunted glucose-induced glycolytic rate (p=0.04) in F2 newborns compared to controls. Overall results suggest that maternal HFD and diabetes causes multigenerational programming of cardiometabolic dysfunction in F2 offspring despite the F1 parent having normal diet, weight, and euglycemia during the pregnancy.

Clinical Management of Familial Hypercholesterolemia in Children.

Familial Hypercholesterolemia (FH) is one of the best-characterized inborn errors of metabolism, with an estimated prevalence of 1:250 in the general population. Only approximately 10% of the affected subjects are diagnosed and, therefore, under medical care, including drug therapy or, in severe cases, apheresis. Screening at the age of 6-10 years would be useful and cost-effective. There is enough evidence that children and adolescents with FH should be treated in order to reduce elevated Total cholesterol and LDL-c levels and, therefore, avoid the risk of early cardiovascular diseases. As FH was described more than 130 years ago, it is surprising that the knowledge of that very important metabolic disorder is insufficient. The present report describes clinical and pathophysiological characteristics and nutritional and medical therapies in children with familial hypercholesterolemia.