Disease Risk Assessment Research Articles

Kidney Disease Prediction with Encrypted Data Sharing in Healthcare

A vital component of contemporary healthcare is disease risk assessment, which makes it possible to estimate a person's propensity to develop particular medical problems. Analyzing a variety of risk factors, including age, gender, lifestyle decisions, past medical history, and genetic susceptibility, is part of this procedure. The ability to access vast healthcare datasets and the development of sophisticated machine learning algorithms have greatly increased the accuracy of illness risk prediction. Using machine learning techniques, this proposed strategy offers an outline of the methodology involved in kidney disease prediction. Kidney disease Prediction seeks to enable early detection and intervention by utilizing machine learning (ML) techniques, such as Support Vector Machines (SVM), to predict the risk of kidney disease based on user query data. The project intends to increase treatment efficiency, improve quality of life for those at risk of renal disease, and expedite healthcare delivery through the integration of an appointment booking system and secure exchange of prescriptions and ideas. Utilizing the findings of the disease risk assessment, customized preventative plans are created based on the risk profile of the individual. These tactics could involve genetic counselling, specialized screening techniques, lifestyle adjustments, or preventative medicine. In order to keep models current with the most recent developments in medicine and data patterns, they must be continuously monitored and adjusted

Assessing, communicating and managing cardiovascular disease risk: a practical summary of the 2023 guideline.

The outdated cardiovascular disease risk calculator has been reported to overestimate cardiovascular disease risk for a contemporary Australian population, and does not include relevant variables, such as socioeconomic disadvantage, which has been shown to increase the incidence of both heart attack and stroke. The 2023 Australian Guideline for Assessing and Managing Cardiovascular Disease Risk marks a major milestone as the first update to Australia's cardiovascular disease prevention guideline in over a decade. The new guideline may help to refine and recategorise risk estimates, hence improving the discriminatory and predictive value of the new calculator. The new Australian Cardiovascular Disease Risk Calculator expresses risk scores as a percentage estimate of a person's probability of dying or being hospitalised due to cardiovascular disease within the next 5 years. The new calculator expresses risk scores as low (less than 5%), intermediate (5% to less than 10%), or high (10% or higher) risk over 5 years. Reclassification factors built into the new calculator are designed to help clinicians individualise risk estimates. These factors include ethnicity (e.g. First Nations status), family history of premature cardiovascular disease, severe mental illness, kidney disease and coronary artery calcium score. The new calculator also uses optional diabetes-specific variables (supporting a more granular cardiovascular disease risk assessment of people with type 2 diabetes). People who meet the clinically determined high-risk criteria (chronic kidney disease, familial hypercholesterolaemia) should not progress through the Australian Cardiovascular Disease risk calculator, but move straight to management. For a person with a cardiovascular disease risk score recorded from the outdated calculator, clinicians may want to reassess their risk using the new calculator the next time the person attends.

2023 Australian guideline for assessing and managing cardiovascular disease risk.

The 2023 Australian guideline for assessing and managing cardiovascular disease risk provides updated evidence-based recommendations for the clinical assessment and management of cardiovascular disease (CVD) risk for primary prevention. It includes the new Australian CVD risk calculator (Aus CVD Risk Calculator), based on an equation developed from a large New Zealand cohort study, customised and recalibrated for the Australian population. The new guideline replaces the 2012 guideline that recommended CVD risk assessment using the Framingham risk equation. The new guideline recommends CVD risk assessment in people without known CVD: all people aged 45-79 years, people with diabetes from 35 years, and First Nations people from 30 years. The new Aus CVD Risk Calculator should be used to estimate and categorise CVD risk into low (< 5% risk over five years), intermediate (5% to < 10% risk over five years) or high risk (≥ 10% over five years). The following reclassification factors may be applied to recategorise calculated risk to improve accuracy of risk prediction, particularly in individuals close to a risk threshold: Indigenous status/ethnicity, estimated glomerular filtration rate, urine albumin to creatinine ratio measurements, severe mental illness, coronary artery calcium score and family history of premature CVD. A variety of communication formats is available to communicateCVD risk to help enable shared decision making. Healthy lifestylemodification, including smoking cessation, nutrition, physical activity and limiting alcohol, is encouraged for all individuals. Blood pressure-lowering and lipid-modifying pharmacotherapies should be prescribed for high risk and considered for intermediate risk individuals, unless contraindicated or clinically inappropriate. Reassessment of CVD risk should be considered within five years for individuals at low risk and within two years for those with intermediate risk. Reassessment of CVD risk is not recommended for individuals at high risk. CHANGES IN ASSESSMENT AND MANAGEMENT AS A RESULT OF THE GUIDELINE: The updated guideline recommends assessment over a broader age range and uses the Aus CVD Risk Calculator, which replaces the previous Framingham-based equation. It incorporates new variables: social disadvantage, diabetes-specific risk markers, diagnosis of atrial fibrillation and use of blood pressure-lowering and lipid-modifying therapies. Reclassification factors are also a new addition. Updated risk categories and thresholds are based on the new AusCVD Risk Calculator. The proportion of the population in the high risk category (≥ 10% over five years) is likely to be broadly comparable to more than 15% risk from the Framingham-based equation. The full guideline and Aus CVD Risk Calculator can be accessed at www.cvdcheck.org.au.

Urinary vanin-1 as a novel biomarker for survival in peripheral artery disease.

Chronic kidney disease is associated with increased rates of incidence, morbidity, and mortality in lower-extremity peripheral artery disease (PAD). No specific marker for a functional risk assessment of kidney disease in PAD is known, especially at the early stages. Thus, we speculated that urinary vanin-1 (uVNN1), a marker of oxidative stress even in early kidney injury, could further stratify outcome assessment in patients with PAD. Patients with stable PAD (n = 304) of the Vienna medical cohort were followed up for up to 10 years and the outcome was assessed by central death database queries. uVNN1 was measured by enzyme-linked immunosorbent assay (ELISA) at study inclusion and normalized to urinary creatinine (uVNN1/Cr). During the observation time (9.3, 7.0-9.8 years), 104 patients died, 54.8% of which were due to cardiovascular causes. uVNN1/Cr was associated with a urine albumin-creatinine ratio (UACR) (R = 0.166, p = 0.004) but not with an estimated glomerular filtration rate (R = 0.102, p = 0.077). Levels of uVNN1/Cr did not differ between asymptomatic and symptomatic PAD (p = 0.406). Kaplan-Meier curves showed a clear-cut association with higher all-cause (log-rank p = 0.034) and cardiovascular mortality (log-rank p = 0.032) with higher uVNN1/Cr levels. Similarly, significant associations for all-cause (hazard ratio [HR] 1.34, 95% CI [1.08-1.67], p = 0.009) and cardiovascular mortality (HR 1.45, 95% CI [1.06-1.99], p = 0.020) could be seen in multivariable Cox regression models. uVNN1/Cr showed an independent association with both all-cause and cardiovascular mortality in patients with PAD and was associated with early kidney disease. Thus, uVNN1 could be a useful marker for risk stratification of kidney disease in PAD.

Cardiovascular disease risk assessment through sensing the circulating microbiome with perovskite quantum dots leveraging deep learning models for bacterial species selection.

Perovskite quantum dots (PQDs) are novel nanomaterials wherein perovskites are used to formulate quantum dots (QDs). The present study utilizes the excellent fluorescence quantum yields of these nanomaterials to detect 16S rRNA of circulating microbiome for risk assessment of cardiovascular diseases (CVDs). A long short-term memory (LSTM) deep learning model was used to find the association of the circulating bacterial species with CVD risk, which showed the abundance of three different bacterial species (Bauldia litoralis (BL), Hymenobacter properus (HYM), and Virgisporangium myanmarense (VIG)). The observations suggested that the developed nano-sensor provides high sensitivity, selectivity, and applicability. The observed sensitivities for Bauldia litoralis, Hymenobacter properus, and Virgisporangium myanmarense were 0.606, 0.300, and 0.281fg, respectively. The developed sensor eliminates the need for labelling, amplification, quantification, and biochemical assessments, which are more labour-intensive, time-consuming, and less reliable. Due to the rapid detection time, user-friendly nature, and stability, the proposed method has a significant advantage in facilitating point-of-care testing of CVDs in the future. This may also facilitate easy integration of the approach into various healthcare settings, making it accessible and valuable for resource-constrained environments.

Development and validation of risk prediction model for recurrent cardiovascular events among Chinese: the Personalized CARdiovascular DIsease risk Assessment for Chinese model.

Cardiovascular disease (CVD) is a leading cause of mortality, especially in developing countries. This study aimed to develop and validate a CVD risk prediction model, Personalized CARdiovascular DIsease risk Assessment for Chinese (P-CARDIAC), for recurrent cardiovascular events using machine learning technique. Three cohorts of Chinese patients with established CVD were included if they had used any of the public healthcare services provided by the Hong Kong Hospital Authority (HA) since 2004 and categorized by their geographical locations. The 10-year CVD outcome was a composite of diagnostic or procedure codes with specific International Classification of Diseases, Ninth Revision, Clinical Modification. Multivariate imputation with chained equations and XGBoost were applied for the model development. The comparison with Thrombolysis in Myocardial Infarction Risk Score for Secondary Prevention (TRS-2°P) and Secondary Manifestations of ARTerial disease (SMART2) used the validation cohorts with 1000 bootstrap replicates. A total of 48 799, 119 672 and 140 533 patients were included in the derivation and validation cohorts, respectively. A list of 125 risk variables were used to make predictions on CVD risk, of which 8 classes of CVD-related drugs were considered interactive covariates. Model performance in the derivation cohort showed satisfying discrimination and calibration with a C statistic of 0.69. Internal validation showed good discrimination and calibration performance with C statistic over 0.6. The P-CARDIAC also showed better performance than TRS-2°P and SMART2. Compared with other risk scores, the P-CARDIAC enables to identify unique patterns of Chinese patients with established CVD. We anticipate that the P-CARDIAC can be applied in various settings to prevent recurrent CVD events, thus reducing the related healthcare burden.

Low income and education are associated with greater ASCVD risk scores among adults in the US

IntroductionSocial determinants of health (SDOH) are fundamental causes of poor cardiovascular health, yet cardiovascular disease (CVD) risk assessment tools exclude SDOH. Our objective was to determine whether SDOH are independently associated with CVD risk in US adults. MethodsUtilizing the National Health and Nutrition Examination Survey (NHANES), we combined years 1999–2018 and included participants aged 40–79 without history of CVD and with information to calculate CVD risk (n = 21,694). Ten-year risk of atherosclerotic CVD (ASCVD) was calculated using the American Heart Association/American College of Cardiology (AHA/ACC) pooled cohort equations. We used linear regression models to estimate the association between SDOH and ASCVD risk, after adjusting for demographic factors. All analyses accounted for the complex survey design. ResultsMean age was 54.7 years, with 52.7 % female, 73.8 % non-Hispanic White, 9.4 % non-Hispanic Black, and 10.7 % Hispanic. From adjusted models, compared with an income of ≥ $75 K, ASCVD risk was greater by 3.06 (95 % CI: 2.65, 3.47) among those with income < $25 K, by 1.55 (95 % CI: 1.21, 1.89) among those with income $25 K–<$55 K, and by 1.20 (95 % CI: 0.84, 1.56) among those with income $55 K–<$75 K. Compared to college graduates, ASCVD risk was greater by 3.09 (95 % CI: 2.56, 3.62) among those with less than a high school education, by 1.65 (95 % CI: 1.31, 200) among those who were high school graduates, and by 1.41 (95 % CI: 1.11, 1.72) among those with some college education. ConclusionWe found strong graded associations between lower income and lower educational attainment with greater CVD risk.

A candidate panel of eight urinary proteins shows potential of early diagnosis and risk assessment for diabetic kidney disease in type 1 diabetes

Diabetic kidney disease (DKD) poses a significant health challenge for individuals with diabetes. At its initial stages, DKD often presents asymptomatically, and the standard for non-invasive diagnosis, the albumin-creatinine ratio (ACR), employs discrete categorizations (normal, microalbuminuria, macroalbuminuria) with limitations in sensitivity and specificity across diverse population cohorts. Single biomarker reliance further restricts the predictive value in clinical settings. Given the escalating prevalence of diabetes, our study uses proteomic technologies to identify novel urinary proteins as supplementary DKD biomarkers. A total of 158 T1D subjects provided urine samples, with 28 (15 DKD; 13 non-DKD) used in the discovery stage and 131 (45 DKD; 40 pDKD; 46 non-DKD) used in the confirmation. We identified eight proteins (A1BG, AMBP, AZGP1, BTD, RBP4, ORM2, GM2A, and PGCP), all of which demonstrated excellent area-under-the-curve (AUC) values (0.959 to 0.995) in distinguishing DKD from non-DKD. Furthermore, this multi-marker panel successfully segregated the most ambiguous group (microalbuminuria) into three distinct clusters, with 80% of subjects aligning either as DKD or non-DKD. The remaining 20% exhibited continued uncertainty. Overall, the use of these candidate urinary proteins allowed for the better classification of DKD and offered potential for significant improvements in the early identification of DKD in T1D populations.

Developing and Testing PREDHIS (Pregnant Dental Health Information System): A Self-Detection Tool for Oral Disease Risk Factors in Pregnant Women

During pregnancy, gum sensitivity and the possibility of gum bleeding increases. Thedental and oral examination program during pregnancy can be carried out in conjunction with thepregnancy check-up program schedule and suggests the use of the Pregnant Women's Healthy Dental Card (KASIH) for dental and oral disease risk assessment since early pregnancy which is then referred to the Dental Clinic. However, the implementation has not been maximized due to the lack of cooperation of the party in charge of health services, as well as the insignificant number of visits of pregnant women referred to the dental clinic from the MCH clinic due to the mother's perception of the need for a healthy oral cavity. This study aims to produce a detection tool application that is feasible according to experts and acceptable to pregnant women as a tool that is able to detect risk factors for oral and dental disease in pregnant women independently. This study was Research and Development with a sample of 15 pregnant women as a test sample for acceptance of the development model. the result is PREDHIS as self-detection tool which android- based mobile apps, said to be very feasible according to the assessment of information technology experts, dental health experts and maternal and child health experts, and has a good level of user acceptance in providing usefulness and convenience based on the assessment of pregnant women. Hence, this study shown that a system can be built, namely the PREDHIS which is able to detect risk factors for oral and dental disease in pregnant women independently which is feasible and acceptable according to needs assessment.

Cáncer de mama: valor pronóstico del estado «TN» versus perfil de expresión génica tumoral

ObjectivesTo analyze the prognostic value of the “TNM” system, and specifically the “TN” status, compared to the tumor gene expression profile, when evaluating the risk of metastatic disease. Materials and methodsRetrospective review of 224 cases of infiltrating breast cancer. Genomic risk was established using the 70-gene gene expression platform (MammaPrint®). The result obtained with the genomic risk assessment has been considered “gold standard”. Results126 cases (56.25%) showed a “low” risk genomic profile and 98 cases (43.75%) showed a “high” risk. Among those with low genomic risk, 26.19% of tumors classified “T2”, “T3” and “T4” were observed. On the contrary, among those with “high” risk, 67.35% of tumors classified as “T1” were observed. Regarding the “N” status, among the “low” risk tumors, 26.98% of cases classified as “N1”, “N2” and “N3” were observed. On the other hand, among those with “high” genomic risk, 77.14% of cases classified as “N0” were observed. Among a total of 131 “T1N0” tumors, 57 (43.51%) showed “high” genomic risk. No significant differences have been observed in terms of risk assessment of metastatic disease (high vs. low) between the various “TN” categories studied. ConclusionsThe evaluation of the risk of metastatic disease using the “TN” system is not accurate. The detection of early tumors does not necessarily mean a good prognosis. On the contrary, the detection of “T2” or “N1” tumors does not imply a poor prognosis in all cases.

The Impact of an Educational Intervention on Enhancing Clinical Knowledge of Physicians and Pharmacists Regarding Statins and Monitoring Parameters: The Experience of a Tertiary Teaching Hospital.

Understanding the latest guideline recommendations is crucial for healthcare professionals to apply statin therapy effectively. Thus, the purpose of this study was to evaluate the efficacy of an educational intervention in enhancing the awareness and understanding of physicians and pharmacists concerning risk assessment of Atherosclerotic cardiovascular disease (ASCVD) and the role of statin therapy. This pre- and post-intervention study was conducted in Sana'a, Yemen's capital city, at the University of Science and Technology Hospital. The study was done between 11/2021-12/2021, and two separate educational sessions were ‎held. The McNemar's test and Wilcoxon signed-rank test were employed as necessary. Participants' awareness of the Framingham CVD risk calculator improved significantly from 40.4% pre-intervention to 78.7% post-intervention. Similarly, understanding of the parameters used in the 10-year ASCVD Risk calculator rose from 46.8% pre-intervention to 76.6% post-intervention. The ability to identify high, moderate, and low-intensity statin therapy, for instance, increased from 34% to 63.8% post-intervention. Regarding statins' contraindications, safety, and efficacy monitoring parameters, pre-intervention knowledge was unsatisfactory, and the educational intervention improved it significantly (p <0.05). For physicians, the median ASCVD risk assessment knowledge score was significantly improved from ‎‎4 (IQR = 3-5) pre-intervention to 7 (6.25-8) immediately post-intervention, while the statin therapy clinical knowledge median score ‎significantly improved from 3 (1.25-6.5) to 9 (7.25-14.75) post-education intervention, p-values were 0.002 and 0.003; respectively. For pharmacists, a similar significant improvement (p <0.05) in the overall knowledge scores for both ASCVD risk ‎assessment and statin therapy was noted. The educational intervention improved participants' knowledge of statin therapy and ASCVD risk assessment. Therefore, further education lectures and training programs through continuing ‎medical ‎education on the up-to-date guidelines' recommendations should be regularly implemented to raise ‎awareness and improve ‎the clinical knowledge and appropriateness of statins use in clinical settings.‎.

Retrospective Assessment of Metabolic Syndrome and Cardiovascular Disease Risk Following Monthly and Three-Month Long-Acting Paliperidone Palmitate Treatment in Schizophrenia

IntroductionPatients with schizophrenia exhibit a higher prevalence of metabolic syndrome and cardiovascular diseases compared to the general population, resulting in increased mortality rates. The extent of this risk may vary based on the specific treatment employed.ObjectivesThis study aims to compare the risk assessments of metabolic syndrome and cardiovascular diseases in schizophrenia patients who transitioned from monthly long-acting paliperidone palmitate (PP1M) treatment to three-month long-acting paliperidone palmitate (PP3M) treatment during both treatment periods.MethodsThe research was conducted at the Psychiatry Clinic and Psychotic Disorders Outpatient Clinic of Selcuk University Faculty of Medicine. Eligible participants included patients under PP3M treatment for a minimum of 6 months and undergoing continuous monitoring in the psychotic disorders outpatient clinic. Sociodemographic and clinical data, scales, laboratory values, and measurements taken both before and during the use of PP3M and PP1M were retrieved from file records, encompassing assessments, analyses, and examinations conducted in accordance with the “Psychotic Disorders - Treatment Monitoring Protocol.” Ethical approval was obtained from the Selcuk University Ethics Committee.Results Among the 31 patients transitioning from PP1M to PP3M treatment, 15 (48.4%) were female. The mean age of the patients was 44.4±14.4 years. No statistically significant differences were observed in the mean values of clinical evaluation and side effect assessment scales, body mass index (BMI), waist-to-hip ratio, systolic blood pressure, glucose levels, cholesterol levels, prolactin levels, and thyroid-stimulating hormone (TSH) measurements between the pre- and post-treatment phases (p&gt;0.05). However, a significant difference was identified in the mean Qrisk3 values, a cardiovascular risk index, in two distinct measurements (10-year risk score: PP1M 3.7±4.2 and PP3M 4.6±4.8, p=0.003).Conclusions Our study, designed to investigate the impact of the monthly and three-month long-acting formulations of the same antipsychotic drug on patients’ clinical status, side effects, and general health parameters, found that PP3M treatment did not significantly differ from PP1M treatment in terms of Qrisk3 values. Despite the lack of statistical significance between the parameters used in Qrisk3 calculation, the observed significant difference in Qrisk3 values is attributed to variations in age. In order to promote the widespread adoption of long-acting treatments in schizophrenia management, clinicians should engage in comprehensive comparative studies assessing both efficacy and side effects.Disclosure of InterestNone Declared

Genetic and multi-omic risk assessment of Alzheimer's disease implicates core associated biological domains.

Alzheimer's disease (AD) is the predominant dementia globally, with heterogeneous presentation and penetrance of clinical symptoms, variable presence of mixed pathologies, potential disease subtypes, and numerous associated endophenotypes. Beyond the difficulty of designing treatments that address the core pathological characteristics of the disease, therapeutic development is challenged by the uncertainty of which endophenotypic areas and specific targets implicated by those endophenotypes to prioritize for further translational research. However, publicly funded consortia driving large-scale open science efforts have produced multiple omic analyses that address both disease risk relevance and biological process involvement of genes across the genome. Here we report the development of an informatic pipeline that draws from genetic association studies, predicted variant impact, and linkage with dementia associated phenotypes to create a genetic risk score. This is paired with a multi-omic risk score utilizing extensive sets of both transcriptomic and proteomic studies to identify system-level changes in expression associated with AD. These two elements combined constitute our target risk score that ranks AD risk genome-wide. The ranked genes are organized into endophenotypic space through the development of 19 biological domains associated with AD in the described genetics and genomics studies and accompanying literature. The biological domains are constructed from exhaustive Gene Ontology (GO) term compilations, allowing automated assignment of genes into objectively defined disease-associated biology. This rank-and-organize approach, performed genome-wide, allows the characterization of aggregations of AD risk across biological domains. The top AD-risk-associated biological domains are Synapse, Immune Response, Lipid Metabolism, Mitochondrial Metabolism, Structural Stabilization, and Proteostasis, with slightly lower levels of risk enrichment present within the other 13 biological domains. This provides an objective methodology to localize risk within specific biological endophenotypes and drill down into the most significantly associated sets of GO terms and annotated genes for potential therapeutic targets.

A Retrospective Study of Biological Risk Factors Associated with Primary Knee Osteoarthritis and the Development of a Nomogram Model.

A high percentage of the elderly suffer from knee osteoarthritis (KOA), which imposes a certain economic burden on them and on society as a whole. The purpose of this study is to examine the risk of KOA and to develop a KOA nomogram model that can timely intervene in this disease to decrease patient psychological burdens. Data was collected from patients with KOA and without KOA at our hospital from February 2021 to February 2023. Initially, a comparison was conducted between the variables, identifying statistical differences between the two groups. Subsequently, the risk of KOA was evaluated using the Least Absolute Shrinkage and Selection Operator method and multivariate logistic regression to determine the most effective predictive index and develop a prediction model. The examination of the disease risk prediction model in KOA includes the corresponding nomogram, which encompasses various potential predictors. The assessment of disease risk entails the application of various metrics, including the consistency index (C index), the area under the curve (AUC) of the receiver operating characteristic curve, the calibration chart, the GiViTi calibration band, and the model for predicting KOA. Furthermore, the potential clinical significance of the model is explored through decision curve analysis (DCA) and clinical influence curve analysis. The study included a total of 582 patients, consisting of 392 patients with KOA and 190 patients without KOA. The nomogram utilized age, haematocrit, platelet count, apolipoprotein a1, potassium, magnesium, hydroxybutyrate dehydrogenase, creatine kinase, and estimated glomerular filtration rate as predictors. The C index, AUC, calibration plot, Giviti calibration band, DCA and clinical influence KOA indicated the ability of nomogram model to differentiate KOA. Using nomogram based on disease risk, high-risk KOA can be identified directly without imaging.

Predictive Modeling for Cardiovascular Disease Risk Assessment

Abstract: Heart disease remains one of the leading causes of mortality worldwide. Early detection and risk assessment are crucial for effective prevention and management. This research paper presents a novel approach utilizing machine learning techniques, particularly decision trees, for predicting heart disease risk. The study utilizes a dataset sourced from the UCI Machine Learning Repository, encompassing diverse features such as age, gender, height, weight, cholesterol levels, and other relevant attributes. The proposed model aims to accurately classify individuals into risk categories based on their demographic and health-related information. Additionally, a user-friendly web application is developed using Python Flask, enabling users to input their data and receive instant risk assessments. Through rigorous experimentation and evaluation, the efficacy and reliability of the predictive model are demonstrated, offering valuable insights for early intervention and personalized healthcare strategies in the fight against heart disease.

Contemporary attitudes and beliefs on coronary artery calcium from social media using artificial intelligence

Coronary artery calcium (CAC) is a powerful tool to refine atherosclerotic cardiovascular disease (ASCVD) risk assessment. Despite its growing interest, contemporary public attitudes around CAC are not well-described in literature and have important implications for shared decision-making around cardiovascular prevention. We used an artificial intelligence (AI) pipeline consisting of a semi-supervised natural language processing model and unsupervised machine learning techniques to analyze 5,606 CAC-related discussions on Reddit. A total of 91 discussion topics were identified and were classified into 14 overarching thematic groups. These included the strong impact of CAC on therapeutic decision-making, ongoing non-evidence-based use of CAC testing, and the patient perceived downsides of CAC testing (e.g., radiation risk). Sentiment analysis also revealed that most discussions had a neutral (49.5%) or negative (48.4%) sentiment. The results of this study demonstrate the potential of an AI-based approach to analyze large, publicly available social media data to generate insights into public perceptions about CAC, which may help guide strategies to improve shared decision-making around ASCVD management and public health interventions.

Associations between hypertension with reproductive and menopausal factors: An integrated women's health programme (IWHP) study.

Women are less likely to have classic cardiovascular risk factors than men, and events during their reproductive and menopausal years may increase hypertension risk. The aim of this study is to examine woman-specific factors, including menstrual, reproductive and pregnancy complications, in relation to the prevalence of hypertension in mid-life Asian women. This is a cross-sectional study of 1146 healthy women aged 45-69 years, from a multi-ethnic Asian cohort. The women completed an extensive questionnaire that included their sociodemographic details, medical history, lifestyle and physical activity, and reproductive and menopausal history. They also underwent objectively measured physical performance tests and a dual X-ray absorptiometry scan. Hypertension was defined as a systolic BP ≥140 and/or diastolic BP ≥90mm Hg, past diagnosis by a physician, or use of antihypertensive medications. Multivariable logistic regression was used to assess the independent risk factors for hypertension. The average age of the 1146 women analysed was 56.3 (SD 6.2) years, and 55.2 percent of them were hypertensive. The prevalence of gestational diabetes and gestational hypertension was 12.6% and 9.4%, respectively. Besides age, abnormal menstrual cycle length at 25 years of age (OR:2.35, CI:1.34-4.13), preeclampsia (OR:2.46, CI:1.06-5.74), increased visceral adiposity (OR:4.21, CI:2.28-7.79) and reduced physical performance (OR:2.83, CI:1.46-5.47) were independently associated with hypertension in Asian women. Our findings highlight the necessity of including features of menstrual and reproductive history as possible indicators of hypertension risk in cardiovascular disease risk assessment and prevention among Asian women. Reducing visceral adiposity and exercise to improve physical performance may help women avoid developing hypertension.

Multi-Factor Coral Disease Risk: A new product for early warning and management.

Ecological forecasts are becoming increasingly valuable tools for conservation and management. However, there are few examples of near-real-time forecasting systems that account for the wide range of ecological complexities. We developed a new coral disease ecological forecasting system that explores a suite of ecological relationships and their uncertainty and investigates how forecast skill changes with shorter lead times. The Multi-Factor Coral Disease Risk product introduced here uses a combination of ecological and marine environmental conditions to predict the risk of white syndromes and growth anomalies across reefs in the central and western Pacific and along the east coast of Australia and is available through the US National Oceanic and Atmospheric Administration Coral Reef Watch program. This product produces weekly forecasts for a moving window of 6 months at a resolution of ~5 km based on quantile regression forests. The forecasts show superior skill at predicting disease risk on withheld survey data from 2012 to 2020 compared with predecessor forecast systems, with the biggest improvements shown for predicting disease risk at mid- to high-disease levels. Most of the prediction uncertainty arises from model uncertainty, so prediction accuracy and precision do not improve substantially with shorter lead times. This result arises because many predictor variables cannot be accurately forecasted, which is a common challenge across ecosystems. Weekly forecasts and scenarios can be explored through an online decision support tool and data explorer, co-developed with end-user groups to improve use and understanding of ecological forecasts. The models provide near-real-time disease risk assessments and allow users to refine predictions and assess intervention scenarios. This work advances the field of ecological forecasting with real-world complexities and, in doing so, better supports near-term decision making for coral reef ecosystem managers and stakeholders. Secondarily, we identify clear needs and provide recommendations to further enhance our ability to forecast coral disease risk.

Deciphering thyroid function and CIMT: a Mendelian randomization study of the U-shaped influence mediated by apolipoproteins.

Carotid Intima-Media Thickness (CIMT) is a key marker for atherosclerosis, with its modulation being crucial for cardiovascular disease (CVD) risk assessment. While thyroid function's impact on cardiovascular health is recognized, the causal relationship and underlying mechanisms influencing CIMT remain to be elucidated. In this study, Mendelian Randomization (MR) was employed to assess the causal relationship between thyroid function and CIMT. Thyroid hormone data were sourced from the Thyroidomics Consortium, while lipid traits and CIMT measurements were obtained from the UK Biobank. The primary analysis method was a two-sample MR using multiplicative random effects inverse variance weighting (IVW-MRE). Additionally, the study explored the influence of thyroid hormones on lipid profiles and assessed their potential mediating role in the thyroid function-CIMT relationship through multivariate MR analysis. The study revealed that lower levels of Free Thyroxine (FT4) within the normal range are significantly associated with increased CIMT. This association was not observed with free triiodothyronine (FT3), thyroid-stimulating hormone (TSH), or TPOAb. Additionally, mediation analysis suggested that apolipoprotein A-I and B are involved in the relationship between thyroid function and CIMT. The findings indicate a potential U-shaped curve relationship between FT4 levels and CIMT, with thyroid hormone supplementation in hypothyroid patients showing benefits in reducing CIMT. This research establishes a causal link between thyroid function and CIMT using MR methods, underscoring the importance of monitoring thyroid function for early cardiovascular risk assessment. The results advocate for the consideration of thyroid hormone supplementation in hypothyroid patients as a strategy to mitigate the risk of carotid atherosclerosis. These insights pave the way for more targeted approaches in managing patients with thyroid dysfunction to prevent cardiovascular complications.

PREVENT Equations: A New Era in Cardiovascular Disease Risk Assessment.

PREVENT Equations: A New Era in Cardiovascular Disease Risk Assessment.