Integrated Machine Learning and Deep Learning Models for Cardiovascular Disease Risk Prediction: A Comprehensive Comparative Study

Cardiovascular Diseases (CVDs) pose a significant global health challenge, necessitating accurate risk prediction for effective preventive measures. This comprehensive comparative study explores the performance of traditional Machine Learning (ML) and Deep Learning (DL) models in predicting CVD risk, utilizing a meticulously curated dataset derived from health records. Rigorous preprocessing, including normalization and outlier removal, enhances model robustness. Diverse ML models (Logistic Regression, Random Forest, Support Vector Machine, K-Nearest Neighbor, Decision Tree, and Gradient Boosting) are compared with a Long Short-Term Memory (LSTM) neural network for DL. Evaluation metrics include accuracy, ROC AUC, computation time, and memory usage. Results identify the Gradient Boosting Classifier and LSTM as top performers, demonstrating high accuracy and ROC AUC scores. Comparative analyses highlight model strengths and limitations, contributing valuable insights for optimizing predictive strategies. This study advances predictive analytics for cardiovascular health, with implications for personalized medicine. The findings underscore the versatility of intelligent systems in addressing health challenges, emphasizing the broader applications of ML and DL in disease identification beyond cardiovascular health.

Dynamics of Advantageous Mutant Spread in Spatial Death-Birth and Birth-Death Moran Models

The spread of an advantageous mutation through a population is of fundamental interest in population genetics. While the classical Moran model is formulated for a well-mixed population, it has long been recognized that in real-world applications, the population usually has an explicit spatial structure which can significantly influence the dynamics. In the context of cancer initiation in epithelial tissue, several recent works have analyzed the dynamics of advantageous mutant spread on integer lattices, using the biased voter model from particle systems theory. In this spatial version of the Moran model, individuals first reproduce according to their fitness and then replace a neighboring individual. From a biological standpoint, the opposite dynamics, where individuals first die and are then replaced by a neighboring individual according to its fitness, are equally relevant. Here, we investigate this death-birth analogue of the biased voter model. We construct the process mathematically, derive the associated dual process, establish bounds on the survival probability of a single mutant, and prove that the process has an asymptotic shape. We also briefly discuss alternative birth-death and death-birth dynamics, depending on how the mutant fitness advantage affects the dynamics. We show that birth-death and death-birth formulations of the biased voter model are equivalent when fitness affects the former event of each update of the model, whereas the birth-death model is fundamentally different from the death-birth model when fitness affects the latter event.

Fatty liver is associated with an increased risk of diabetes and cardiovascular disease- Evidence from three different disease models: NAFLD, HCV and HIV

Fatty liver, which frequently coexists with necroinflammatory and fibrotic changes, may occur in the setting of nonalcoholic fatty liver disease(NAFLD) and chronic infections due to either hepatitis C virus(HCV) or human immunodeficiency virus(HIV). These three pathologic conditions are associated with an increased prevalence and incidence of cardiovascular disease(CVD) and type 2 diabetes(T2D). In this multidisciplinary clinical review, we aim to discuss the ever-expanding wealth of clinical and epidemiological evidence supporting a key role of fatty liver in the development of T2 D and CVD in patients with NAFLD and in those with HCV or HIV infections. For each of these three common diseases, the epidemiological features, pathophysiologic mechanisms and clinical implications of the presence of fatty liver in predicting the risk of incident T2 D and CVD are examined in depth. Collectively, the data discussed in this updated review, which follows an innovative comparative approach, further reinforce the conclusion that the presence of fatty/inflamed/fibrotic liver might be a shared important determinant for the development of T2 D and CVD in patients with NAFLD, HCV or HIV. This review may also open new avenues in the clinical and research arenas and paves the way for the planning of future, well-designed prospective and intervention studies.

Morphological Features of Complex Congenital Cardiovascular Anomalies in Fetuses:as Evaluated by Cast Models

Accurate prenatal diagnosis of complex congenital cardiovascular anomalies, vascular ones in particular, is still challenging. A fetal cardiovascular cast model can provide a copy of the cardiac chambers and great vessels with normal or pathological structures. This study was aimed to demonstrate three-dimensional anatomy of complex congenital cardiovascular anomalies in fetuses by means of corrosion casting. Twenty fetuses with prenatal-ultrasound-diagnosed complex cardiovascular anomalies were enrolled in this study（19 to 35 gestational weeks）. Fetal cardiovascular cast models were made by a corrosion casting technique. The specimens were injected with casting material via the umbilical vein, and then immersed in strong acid after casting fluid was solidified, to disclose the geometries of cardiovascular cavities. Nineteen cast models were successfully made from 20 specimens. The casts distinctly showed the morphological malformations and spatial relationship between cardiac chambers and great vessels. One hundred and eleven abnormalities were revealed by casting in the 19 specimens, including 34 abnormalities located in the cardiac chambers（3, 4 and 27 anomalies in the atria, atrioventricular valves and ventricles, respectively）, and 77 in the great vessels（28, 20, 24 and 5 anomalies in the aorta and its branches, the pulmonary artery, the ductus arteriosus and the major veins, respectively）. Corrosion casting can display three-dimensional anatomy of fetal complex cardiovascular anomalies. This improves our understanding of related pathomorphology and prenatal diagnosis.

Discrimination and stratification tests of cardiovascular disease risk assessment models against ultrasound detection of carotid plaques in type 2 diabetics

Atherosclerosis is the early stage of arterial disease, and underlies development of cardiovascular disease (CVD) and stroke. Although sophisticated models for assessing CVD and stroke risk have been derived based on large-scale prospective studies, their abilities in detecting the presence or absence of atherosclerotic plaque have not been investigated. This study aimed to evaluate and compare discriminatory and risk stratifying abilities of 13 CVD risk assessment models against the ultrasound detection of carotid plaques in type 2 diabetes mellitus (T2DM) patients. Forty-nine T2DM subjects were recruited with informed consent, and major anthropomorphic and biomarker data for these models were collected. The model risk scores were evaluated against the carotid plaques detected by Doppler ultrasound. Only the FHS-Lpts-CHD-10Y model, which is a variant of the Framingham model, revealed an area under the receiver operating curve (AUROC) that was significantly different from a random scoring approach (AUROC: 0.681, p was able to stratify the risk levels of carotid plaque presence (Chi-Square statistic: 5.99,

The Importance of Setting Treatment Goals for Cardiovascular Diseases

Background: Guidelines are issued by most major organizations that focus on a specific disease entity. Guidelines should be a significant help to the practicing physician who may not be up-to-date with the recent medical literature. Unfortunately, when conflicting guidelines for a specific disease are published, confusion results. Purpose: This article provides a suggested guideline outcome measure that would benefit the physician and patient. Methods: A review of 19 different guidelines for cardiovascular disease treatment is one example of the lack of specific outcomes that currently exist. The basic problem with most guidelines is that they do not state the expected end result (i.e., the benefit to the patient) if that guideline is followed. When guidelines use cardiovascular disease risk factors to dictate therapy, the end benefit is never stated so that the patient can make an appropriate choice of which (if any) guideline to follow. Results: A good example is guidelines published by the American Heart Association for reducing cardiovascular disease. These guidelines are risk factor based and only indicate that cardiovascular disease would be reduced if followed. No specific percentage in the reduction of the incidence of disease is given. In contrast, when elimination of the disease is the stated goal of the guideline, the end result is clear. To date, this goal has been stated by only one organization devoted to eliminating cardiovascular disease. Conclusion: Guidelines need to be written to provide the physician and the patient with a specific end point that is expected when the guideline is followed. Patient acceptance and compliance will be much improved if the patient knows the risk/benefit of following the guideline’s recommendations.

The relationship between compartment models and their stochastic counterparts:A comparative study with examples of the COVID-19 epidemic modeling

Deterministic compartment models(CMs)and stochastic models,including stochastic CMs and agent-based models,are widely utilized in epidemic modeling.However,the relationship between CMs and their corresponding stochastic models is not well understood.The present study aimed to address this gap by conducting a comparative study using the susceptible,exposed,infectious,and recovered(SEIR)model and its extended CMs from the coronavirus disease 2019 modeling literature.We demonstrated the equivalence of the numerical solution of CMs using the Euler scheme and their stochastic counterparts through theoretical analysis and simulations.Based on this equivalence,we proposed an efficient model calibration method that could replicate the exact solution of CMs in the corresponding stochastic models through parameter adjustment.The advancement in calibration techniques enhanced the accuracy of stochastic modeling in capturing the dynamics of epidemics.However,it should be noted that discrete-time stochastic models cannot perfectly reproduce the exact solution of continuous-time CMs.Additionally,we proposed a new stochastic compartment and agent mixed model as an alternative to agent-based models for large-scale population simulations with a limited number of agents.This model offered a balance between computational efficiency and accuracy.The results of this research contributed to the comparison and unification of deterministic CMs and stochastic models in epidemic modeling.Furthermore,the results had implications for the development of hybrid models that integrated the strengths of both frameworks.Overall,the present study has provided valuable epidemic modeling techniques and their practical applications for understanding and controlling the spread of infectious diseases.

Cardiovascular diseases in European ethnic minorities: Beyond the traditional cardiovascular risk factors

This editorial is intended to be a reflection on cardiovascular disease(CVD)burden in European ethnic minorities.In some European countries,ethnic minority realities,due to their recent appearance,are still to be studied in depth.The experience of several European countries,where the migration processes started earlier,even more than a century ago,can help by being an example.Many studies have shown that major differences in CVD burden exist not only between countries,but also within the same country when considering different social strata and ethnic groups.The CV risk factors underlying heart disease have been well established.Important epidemiological studies have helped us understand that the underlying causes of heart disease as well as the behaviors that can help prevent them are the same.We are now well aware that CVD should be treated by considering a holistic approach.This is why the social determinants(SDs)of health that may worsen the disease burden or that,vice versa,may improve the treatment,and even more significantly,the prognosis of a patient’s illness should be taken into consideration.For ethnic minority patients,this holistic,hermeneutic approach is of importance.Several SDs of health that influence CVDs have been identified but their relevance for the health of ethnic minorities has not yet been clearly defined.In some European countries,most ethnic minorities are largely also religious minorities.Only a few studies have evaluated the role of religion,which is an important SD that affects the probability of having CV risk factors and diseases.Adolescents,particularly those belonging to the second generation,seem to be the weak link.If we believe that these young people are really citizens of their country of birth,then a way of recognizing their belonging to the community starts from a will to better understand their condition,in order to assist them while they grow physically and mentally.Thinking about safeguarding the health of this population should be more than a health task,rather a goal of social justice.

Sex and racial disparities in non-alcoholic fatty liver disease-related cardiovascular events: National inpatient sample analysis (2019)

BACKGROUND Non-alcoholic fatty liver disease(NAFLD)increases cardiovascular disease(CVD)risk irrespective of other risk factors.However,large-scale cardiovascular sex and race differences are poorly understood.AIM To investigate the relationship between NAFLD and major cardiovascular and cerebrovascular events(MACCE)in subgroups using a nationally representative United States inpatient sample.METHODS We examined National Inpatient Sample(2019)to identify adult hospitalizations with NAFLD by age,sex,and race using ICD-10-CM codes.Clinical and demographic characteristics,comorbidities,and MACCE-related mortality,acute myocardial infarction(AMI),cardiac arrest,and stroke were compared in NAFLD cohorts by sex and race.Multivariable regression analyses were adjusted for sociodemographic characteristics,hospitalization features,and comorbidities.RESULTS We examined 409130 hospitalizations[median 55(IQR 43-66)years]with NFALD.NAFLD was more common in females(1.2%),Hispanics(2%),and Native Americans(1.9%)than whites.Females often reported non-elective admissions,Medicare enrolment,the median age of 55(IQR 42-67),and poor income.Females had higher obesity and uncomplicated diabetes but lower hypertension,hyperlipidemia,and complicated diabetes than males.Hispanics had a median age of 48(IQR 37-60),were Medicaid enrollees,and had non-elective admissions.Hispanics had greater diabetes and obesity rates than whites but lower hypertension and hyperlipidemia.MACCE,all-cause mortality,AMI,cardiac arrest,and stroke were all greater in elderly individuals(P<0.001).MACCE,AMI,and cardiac arrest were more common in men(P<0.001).Native Americans(aOR 1.64)and Asian Pacific Islanders(aOR 1.18)had higher all-cause death risks than whites.CONCLUSION Increasing age and male sex link NAFLD with adverse MACCE outcomes;Native Americans and Asian Pacific Islanders face higher mortality,highlighting a need for tailored interventions and care.

Elevated cardiovascular risk and acute events in hospitalized colon cancer survivors:A decade-apart study of two nationwide cohorts

BACKGROUND Over the years,strides in colon cancer detection and treatment have boosted survival rates;yet,post-colon cancer survival entails cardiovascular disease(CVD)risks.Research on CVD risks and acute cardiovascular events in colorectal cancer survivors has been limited.AIM To compare the CVD risk and adverse cardiovascular outcomes in current colon cancer survivors compared to a decade ago.METHODS We analyzed 2007 and 2017 hospitalization data from the National Inpatient Sample,studying two colon cancer survivor groups for CVD risk factors,mortality rates,and major adverse events like pulmonary embolism,arrhythmia,cardiac arrest,and stroke,adjusting for confounders via multivariable regression analysis.RESULTS Of total colon cancer survivors hospitalized in 2007(n=177542)and 2017(n=178325),the 2017 cohort often consisted of younger(76 vs 77 years),male,African-American,and Hispanic patients admitted non-electively vs the 2007 cohort.Furthermore,the 2017 cohort had higher rates of smoking,alcohol abuse,drug abuse,coagulopathy,liver disease,weight loss,and renal failure.Patients in the 2017 cohort also had higher rates of cardiovascular comorbidities,including hypertension,hyperlipidemia,diabetes,obesity,peripheral vascular disease,congestive heart failure,and at least one traditional CVD(P<0.001)vs the 2007 cohort.On adjusted multivariable analysis,the 2017 cohort had a significantly higher risk of pulmonary embolism(PE)(OR:1.47,95%CI:1.37-1.48),arrhythmia(OR:1.41,95%CI:1.38-1.43),atrial fibrillation/flutter(OR:1.61,95%CI:1.58-1.64),cardiac arrest including ventricular tachyarrhythmia(OR:1.63,95%CI:1.46-1.82),and stroke(OR:1.28,95%CI:1.22-1.34)with comparable all-cause mortality and fewer routine discharges(48.4%vs 55.0%)(P<0.001)vs the 2007 cohort.CONCLUSION Colon cancer survivors hospitalized 10 years apart in the United States showed an increased CVD risk with an increased risk of acute cardiovascular events(stroke 28%,PE 47%,arrhythmia 41%,and cardiac arrest 63%).It is vital to regularly screen colon cancer survivors with concomitant CVD risk factors to curtail long-term cardiovascular complications.

Smaller & Smarter: Score-Driven Network Chaining of Smaller Language Models

With the continuous evolution and expanding applications of Large Language Models (LLMs), there has been a noticeable surge in the size of the emerging models. It is not solely the growth in model size, primarily measured by the number of parameters, but also the subsequent escalation in computational demands, hardware and software prerequisites for training, all culminating in a substantial financial investment as well. In this paper, we present novel techniques like supervision, parallelization, and scoring functions to get better results out of chains of smaller language models, rather than relying solely on scaling up model size. Firstly, we propose an approach to quantify the performance of a Smaller Language Models (SLM) by introducing a corresponding supervisor model that incrementally corrects the encountered errors. Secondly, we propose an approach to utilize two smaller language models (in a network) performing the same task and retrieving the best relevant output from the two, ensuring peak performance for a specific task. Experimental evaluations establish the quantitative accuracy improvements on financial reasoning and arithmetic calculation tasks from utilizing techniques like supervisor models (in a network of model scenario), threshold scoring and parallel processing over a baseline study.

Large animal models for Huntington's disease research

Huntington'sdisease(HD)isahereditary neurodegenerative disorder for which there is currently no effectivetreatmentavailable.Consequently,the development of appropriate disease models is critical to thoroughly investigate disease progression.The genetic basis of HD involves the abnormal expansion of CAG repeats in the huntingtin(HTT)gene,leading to the expansion of a polyglutamine repeat in the HTT protein.Mutant HTT carrying the expanded polyglutamine repeat undergoes misfolding and forms aggregates in the brain,which precipitate selective neuronal loss in specific brain regions.Animal models play an important role in elucidating the pathogenesis of neurodegenerative disorders such as HD and in identifying potential therapeutic targets.Due to the marked species differences between rodents and larger animals,substantial efforts have been directed toward establishing large animal models for HD research.These models are pivotal for advancing the discovery of novel therapeutic targets,enhancing effective drug delivery methods,and improving treatment outcomes.We have explored the advantages of utilizing large animal models,particularly pigs,in previous reviews.Since then,however,significant progress has been made in developing more sophisticated animal models that faithfully replicate the typical pathology of HD.In the current review,we provide a comprehensive overview of large animal models of HD,incorporating recent findings regarding the establishment of HD knock-in(KI)pigs and their genetic therapy.We also explore the utilization of large animal models in HD research,with a focus on sheep,non-human primates(NHPs),and pigs.Our objective is to provide valuable insights into the application of these large animal models for the investigation and treatment of neurodegenerative disorders.

Genetically modified pigs:Emerging animal models for hereditary hearing loss

Hereditary hearing loss(HHL),a genetic disorder that impairs auditory function,significantly affects quality of life and incurs substantial economic losses for society.To investigate the underlying causes of HHL and evaluate therapeutic outcomes,appropriate animal models are necessary.Pigs have been extensively used as valuable large animal models in biomedical research.In this review,we highlight the advantages of pig models in terms of ear anatomy,inner ear morphology,and electrophysiological characteristics,as well as recent advancements in the development of distinct genetically modified porcine models of hearing loss.Additionally,we discuss the prospects,challenges,and recommendations regarding the use pig models in HHL research.Overall,this review provides insights and perspectives for future studies on HHL using porcine models.

Risk of cardiovascular death in patients with hepatocellular carcinoma based on the Fine-Gray model

BACKGROUND Hepatocellular carcinoma(HCC)is one of the most common types of cancers worldwide,ranking fifth among men and seventh among women,resulting in more than 7 million deaths annually.With the development of medical tech-nology,the 5-year survival rate of HCC patients can be increased to 70%.How-ever,HCC patients are often at increased risk of cardiovascular disease(CVD)death due to exposure to potentially cardiotoxic treatments compared with non-HCC patients.Moreover,CVD and cancer have become major disease burdens worldwide.Thus,further research is needed to lessen the risk of CVD death in HCC patient survivors.METHODS This study was conducted on the basis of the Surveillance,Epidemiology,and End Results database and included HCC patients with a diagnosis period from 2010 to 2015.The independent risk factors were identified using the Fine-Gray model.A nomograph was constructed to predict the CVM in HCC patients.The nomograph performance was measured using Harrell’s concordance index(C-index),calibration curve,receiver operating characteristic(ROC)curve,and area under the ROC curve(AUC)value.Moreover,the net benefit was estimated via decision curve analysis(DCA).RESULTS The study included 21545 HCC patients,of whom 619 died of CVD.Age(<60)[1.981(1.573-2.496),P<0.001],marital status(married)[unmarried:1.370(1.076-1.745),P=0.011],alpha fetoprotein(normal)[0.778(0.640-0.946),P=0.012],tumor size(≤2 cm)[(2,5]cm:1.420(1.060-1.903),P=0.019;>5 cm:2.090(1.543-2.830),P<0.001],surgery(no)[0.376(0.297-0.476),P<0.001],and chemotherapy(none/unknown)[0.578(0.472-0.709),P<0.001]were independent risk factors for CVD death in HCC patients.The discrimination and calibration of the nomograph were better.The C-index values for the training and validation sets were 0.736 and 0.665,respectively.The AUC values of the ROC curves at 2,4,and 6 years were 0.702,0.725,0.740 in the training set and 0.697,0.710,0.744 in the validation set,respectively.The calibration curves showed that the predicted probab-ilities of the CVM prediction model in the training set vs the validation set were largely consistent with the actual probabilities.DCA demonstrated that the prediction model has a high net benefit.CONCLUSION Risk factors for CVD death in HCC patients were investigated for the first time.The nomograph served as an important reference tool for relevant clinical management decisions.

Predicting the Risk of Arterial Stiffness in Coal Miners Based on Different Machine Learning Models

Coal is one of the world’s main energy resources,accounting for approximately 68%of China’s current total power generation.However,several studies have demonstrated that dust,exhaust fumes,and other harmful factors in coal mines increase the risk of cardiovascular disease(CVD)among miners^([1]).Arterial stiffness(AS)is an independent risk factor of CVD,and epidemiological studies have shown that AS plays a vital role in assessing the risk of CVD^([2]).

Effects of loneliness and isolation on cardiovascular diseases:a two sample Mendelian Randomization Study

BACKGROUND Loneliness and isolation are associated with multiple cardiovascular diseases(CVDs),but there is a lack of research on whether they were causally linked.We conducted a Mendelian Randomization(MR)study to explore causal relationships between loneliness and isolation and multiple CVDs.METHODS Single nucleotide polymorphisms associated with loneliness and isolation were identified from a genome-wide association study(GWAS)of 455,364 individuals of European ancestry in the IEU GWAS database.Summary data for 15 CVDs were also obtained from the IEU GWAS database.We used three MR methods including inverse variance weighting,MR-Egger,and weighted median estimation to assess the causal effect of exposure on outcomes.Cochran's Q test and MR-Egger intercept test were used to evaluate the heterogeneity and pleiotropy.RESULTS MR analysis showed that loneliness and isolation were significantly associated with essential hypertension(OR=1.07,95%CI:1.03-1.12),atherosclerotic heart disease(OR=1.04;95%CI:1.02-1.06),myocardial infarction(OR=1.02;95%CI:1-1.04)and angina(OR=1.04;95%CI=1.02-1.06).No heterogeneity and pleiotropy effects were found in this study.CONCLUSIONS Causal relationship of loneliness and isolation with CVDs were found in this study.

Use of machine learning models for the prognostication of liver transplantation: A systematic review

BACKGROUND Liver transplantation(LT)is a life-saving intervention for patients with end-stage liver disease.However,the equitable allocation of scarce donor organs remains a formidable challenge.Prognostic tools are pivotal in identifying the most suitable transplant candidates.Traditionally,scoring systems like the model for end-stage liver disease have been instrumental in this process.Nevertheless,the landscape of prognostication is undergoing a transformation with the integration of machine learning(ML)and artificial intelligence models.AIM To assess the utility of ML models in prognostication for LT,comparing their performance and reliability to established traditional scoring systems.METHODS Following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines,we conducted a thorough and standardized literature search using the PubMed/MEDLINE database.Our search imposed no restrictions on publication year,age,or gender.Exclusion criteria encompassed non-English studies,review articles,case reports,conference papers,studies with missing data,or those exhibiting evident methodological flaws.RESULTS Our search yielded a total of 64 articles,with 23 meeting the inclusion criteria.Among the selected studies,60.8%originated from the United States and China combined.Only one pediatric study met the criteria.Notably,91%of the studies were published within the past five years.ML models consistently demonstrated satisfactory to excellent area under the receiver operating characteristic curve values(ranging from 0.6 to 1)across all studies,surpassing the performance of traditional scoring systems.Random forest exhibited superior predictive capabilities for 90-d mortality following LT,sepsis,and acute kidney injury(AKI).In contrast,gradient boosting excelled in predicting the risk of graft-versus-host disease,pneumonia,and AKI.CONCLUSION This study underscores the potential of ML models in guiding decisions related to allograft allocation and LT,marking a significant evolution in the field of prognostication.

Occupational physical activity, all-cause, cardiovascular disease, and cancer mortality in 349,248 adults: Prospective and longitudinal analyses of the MJ Cohort

Background:Evidence on the health benefits of occupational physical activity(OPA)is inconclusive.We examined the associations of baseline OPA and OPA changes with all-cause,cardiovascular disease(CVD),and cancer mortality and survival times.Methods:This study included prospective and longitudinal data from the MJ Cohort,comprising adults over 18 years recruited in 1998-2016,349,248 adults(177,314 women)with baseline OPA,of whom 105,715(52,503 women)had 2 OPA measures at 6.3±4.2 years(mean±SD)apart.Exposures were baseline OPA,OPA changes,and baseline leisure-time physical activity.Results:Over a mean mortality follow-up of 16.2±5.5 years for men and 16.4±5.4 years for women,11,696 deaths(2033 of CVD and 4631 of cancer causes)in men and 8980 deaths(1475 of CVD and 3689 of cancer causes)in women occurred.Combined moderately heavy/heavy baseline OPA was beneficially associated with all-cause mortality in men(multivariable-adjusted hazard ratio(HR)=0.93,95%confidence interval(95%CI):0.89-0.98 compared to light OPA)and women(HR=0.86,95%CI:0.79-0.93).Over a mean mortality follow-up of 12.5±4.6 years for men and 12.6±4.6 years for women,OPA decreases in men were detrimentally associated(HR=1.16,95%CI:1.01-1.33)with all-cause mortality,while OPA increases in women were beneficially(HR=0.83,95%CI:0.70-0.97)associated with the same outcome.Baseline or changes in OPA showed no associations with CVD or cancer mortality.Conclusion:Higher baseline OPA was beneficially associated with all-cause mortality risk in both men and women.Our longitudinal OPA analyses partly confirmed the prospective findings,with some discordance between sex groups.

Cardiovascular risk burden,dementia risk and brain structural imaging markers:a study from UK Biobank

Background Cardiovascular risk burden is associated with dementia risk and neurodegeneration-related brain structure,while the role of genetics and incident cardiovascular disease(CVD)remains unclear.Aims To examine the association of overall cardiovascular risk burden with the risk of major dementia subtypes and volumes of related brain regions in a large sample,and to explore the role of genetics and CVD onset.Methods A prospective study among 354 654 participants free of CVD and dementia(2006-2010,mean age 56.4 years)was conducted within the UK Biobank,with brain magnetic resonance imaging(MRl)measurement availablefor 15104participants since 2014.CVD risk burden was evaluated by the Framingham General Cardiovascular Risk Score(FGCRS).Dementia diagnosis was ascertained from inpatient and death register data.Results Overamedian 12.0-yearfollow-up,3998 all-cause dementia cases were identified.Higher FGCRS was associated with increasedall-cause dementia risk after adjusting for demographic,major lifestyle,clinical factors and the polygenic risk score(PRS)of Alzheimer's disease.Comparing the high versus low tertile of FGCRS,the odds ratios(ORs)and 95%confidence intervals(Cls)were 1.26(1.12 to 1.41)for all-cause dementia,1.67(1.33 to 2.09)for Alzheimer's disease and 1.53(1.07 to 2.16)for vascular dementia(all p_(treng)<0.05).Incident stroke and coronary heart disease accounted for 14%(95%Cl:9% to 21%)of the association between FGCRS and all-cause dementia.Interactions were not detected for FGCRS and PRS on the risk of any dementia subtype.We observed an 83%(95%Cl:47%to 128%)higher all-cause dementia risk comparing the high-high versus low-low FGCRS-PRS category.For brain volumes,higher FGCRS was associated with greater log-transformed white matter hyperintensities,smaller cortical volume and smaller grey matter volume.Conclusions Our findings suggest that the positive association of cardiovascular risk burden with dementia risk also applies to major dementia subtypes.The association of cardiovascular risk burden with all-cause dementia is largely independent of CVD onset and genetic predisposition to dementia.

Diabetes Screening and Cardiovascular Risk in Tuberculosis Patients in Conakry and Surrounding Towns

Objective: To determine the prevalence of diabetes and the level of cardiovascular risk in tuberculosis patients treated in Conakry and surrounding towns. Method: This was a cross-sectional study carried out at tuberculosis treatment centers in Conakry and surrounding towns (Coyah and Dubréka) on World TB Day, November 14, 2022, among 350 tuberculosis patients. We determined the proportion of TB patients with diabetes and high hyperglycemia. We determined the Chi-square to identify the factors associated with the occurrence of diabetes in tuberculosis patients, and a significance threshold with p Results: The average age of participants was 33.5 ± 14.5 years, and they were predominantly male (59.7%). The majority of participants lived in Conakry (96.3%). 5.7% were known diabetics and 4.6% were known hypertensives. We found a prevalence of diabetes of 9.4%. Among diabetics, 39% were diabetes discoveries. 30% were hypertensive or had high blood pressure. Of these hypertensive patients, 26.4% were unrecognized hypertensives. Age and hypertension were statistically significantly associated with the onset of diabetes in tuberculosis patients. Medium, high and very high levels of cardiovascular risk were present in 23.4%, 8.9% and 3.4% respectively. Conclusion: Diabetes is common in tuberculosis patients. Cardiovascular risk factors, notably hypertension and cardiovascular risk level, are frequently associated with tuberculosis. Tuberculosis patients with cardiovascular risk factors should be screened for diabetes.