Publications by authors named "Brant L"
Background And Aims: Current heart failure (HF) risk stratification strategies require comprehensive clinical evaluation. In this study, artificial intelligence (AI) applied to electrocardiogram (ECG) images was examined as a strategy to predict HF risk.
Methods: Across multinational cohorts in the Yale New Haven Health System (YNHHS), UK Biobank (UKB), and Brazilian Longitudinal Study of Adult Health (ELSA-Brasil), individuals without baseline HF were followed for the first HF hospitalization.
Objectives: Analyze the burden of diseases attributable to risk factors (RF) in Brazil according to age, sex, and Brazilian states between 1990 and 2021.
Methods: This study used data from the Global Burden of Disease study 1990 to 2021. The metrics used in this analysis included: mortality rates, disability-adjusted life years (DALYs) and Summary Exposure Value (SEV).
Introduction: Herpes simplex virus 1 (HSV-1) infection alters critical markers of Alzheimer's Disease (AD) in neurons. One key marker of AD is the hyperphosphorylation of Tau, accompanied by altered levels of Tau isoforms. However, an imbalance in these Tau splice variants, specifically resulting from altered 3R to 4R splicing of exon 10, has yet to be directly associated with HSV-1 infection.
View Article and Find Full Text PDFBackground And Aims: AI-enhanced 12-lead ECG can detect a range of structural heart diseases (SHDs) but has a limited role in community-based screening. We developed and externally validated a noise-resilient single-lead AI-ECG algorithm that can detect SHD and predict the risk of their development using wearable/portable devices.
Methods: Using 266,740 ECGs from 99,205 patients with paired echocardiographic data at Yale New Haven Hospital, we developed ADAPT-HEART, a noise-resilient, deep-learning algorithm, to detect SHD using lead I ECG.
Background: Identifying structural heart diseases (SHDs) early can change the course of the disease, but their diagnosis requires cardiac imaging, which is limited in accessibility.
Objective: To leverage images of 12-lead ECGs for automated detection and prediction of multiple SHDs using an ensemble deep learning approach.
Methods: We developed a series of convolutional neural network models for detecting a range of individual SHDs from images of ECGs with SHDs defined by transthoracic echocardiograms (TTEs) performed within 30 days of the ECG at the Yale New Haven Hospital (YNHH).
Objectives: The aim of this study was to analyse the burden of disease due to noncommunicable diseases (NCDs) between 1990 and 2021 in Brazil. In addition, this study compared mortality from NCDs with mortality from all causes and COVID-19, analysed NCD mortality trends and projections for 2030, and analysed NCD mortality rates and risk factors attributed to these deaths among the 27 states of Brazil.
Study Design: Ecological studies.
We developed a study to determine the ideal level of inclusion of soybean oil (SBOil) in the diet without affecting the quantitative and qualitative parameters of the carcass and meat of lambs in a feedlot system; therefore, determining the ideal level of inclusion. Forty male lambs (Santa Inês breed) were used. The initial body weight and age averaged 34.
View Article and Find Full Text PDFMore With Less: Diffusing Innovations in Cardiovascular Service Delivery.
Circ Cardiovasc Qual Outcomes
August 2024
In Latin America and the Caribbean (LAC), sociodemographic context, socioeconomic disparities and the high level of urbanization provide a unique entry point to reflect on the burden of cardiometabolic disease in the region. Cardiovascular diseases are the main cause of death in LAC, precipitated by population growth and ageing together with a rapid increase in the prevalence of cardiometabolic risk factors, predominantly obesity and diabetes mellitus, over the past four decades. Strategies to address this growing cardiometabolic burden include both population-wide and individual-based initiatives tailored to the specific challenges faced by different LAC countries, which are heterogeneous.
View Article and Find Full Text PDFArticle Synopsis
- The study assesses the effectiveness of US-based cardiovascular disease (CVD) risk scores in a Brazilian cohort, revealing that these scores often overestimate risk for individuals in low- and middle-income countries (LMICs).
- A total of 12,155 adults aged 40-75 were analyzed, and all risk scores had good overall prediction ability, but particularly struggled with accuracy in white women, showing an AUC score below 0.6.
- The findings emphasize the need for customized risk assessment tools that reflect the specific demographics and health challenges of populations in LMICs, as current scores can overestimate risk by 32%-170%.
Tau protein aggregation is a hallmark of several neurodegenerative diseases, including Alzheimer's disease, frontotemporal dementia (FTD) and progressive supranuclear palsy (PSP), spurring development of tau-lowering therapeutic strategies. Here, we report fully human bifunctional anti-tau-PEST intrabodies that bind the mid-domain of tau to block aggregation and degrade tau via the proteasome using the ornithine decarboxylase (ODC) PEST degron. They effectively reduced tau protein in human iPSC-derived cortical neurons in 2D cultures and 3D organoids, including those with the disease-associated tau mutations R5L, N279K, R406W, and V337M.
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- The study highlights the challenge of effectively stratifying heart failure (HF) risk despite existing therapies, proposing that portable devices that record single-lead electrocardiograms (ECGs) could improve community-based assessments.
- An artificial intelligence (AI) algorithm was evaluated for its ability to predict HF risk from these single-lead ECGs, using data from multiple cohorts including Yale New Haven Health System, UK Biobank, and ELSA-Brasil.
- Results indicated that individuals screened positively by the AI-ECG model had a significantly higher risk for developing HF, with increases in risk correlating with higher model probabilities, suggesting it could be a valuable tool for early identification of at-risk patients
Article Synopsis
- Researchers are exploring the use of artificial intelligence (AI) on electrocardiograms (ECGs) to predict the risk of heart failure (HF), which traditionally relies on specific blood tests or thorough clinical assessments.
- The study involved data from multiple cohorts, including the Yale New Haven Health System and UK Biobank, focusing on individuals without HF at the start and monitoring their hospitalization for HF over several years.
- Results showed that a positive AI-ECG screening significantly increased the risk of developing HF, with the hazard ratios indicating a much higher risk in the UK and Brazil compared to the Yale cohort, highlighting the model's effectiveness in predicting incident HF risk.
Alpha-synuclein (αSyn) aggregation and the formation of Lewy pathology (LP) is a foundational pathophysiological phenomenon in synucleinopathies. Delivering therapeutic single-chain and single-domain antibodies that bind pathogenic targets can disrupt intracellular aggregation. The fusion of antibody fragments to a negatively-charged proteasomal targeting motif (PEST) creates bifunctional constructs that enhance both solubility and turnover.
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- The American Heart Association created guidelines for tracking cardiovascular health (CVH) to shift focus from managing risk factors to preventing cardiovascular disease, but knowledge about CVH differences between high- and low-income countries remains scarce.
- A study analyzed survey data from Ethiopia, Bangladesh, Brazil, England, and the US to score CVH using specific health metrics, revealing that higher-income countries had lower high CVH scores, particularly as age increased.
- The research concluded that while CVH declines with age is a global issue, tailored interventions are necessary to maintain health across different populations, especially in high-CVH countries aiming to combat risk-factor increases.
Background: Central Illustration : Higher Arterial Stiffness Predicts Chronic Kidney Disease in Adults: The ELSA-Brasil Cohort Study.
Background: Arterial stiffening can directly affect the kidneys, which are passively perfused by a high flow. However, whether the relation between arterial stiffness and renal function depends on diabetes and hypertension conditions, is a matter of debate.
Article Synopsis
- The study investigates how improving hypertension care in low- and middle-income countries might affect different socioeconomic groups, particularly focusing on wealth quintiles.
- Researchers simulated better diagnosis and treatment levels for hypertension and assessed the resulting changes in cardiovascular disease (CVD) risk across various wealth groups.
- Results indicated that lower-income groups, especially in lower-middle-income countries, would experience the greatest health benefits, emphasizing that targeted improvements in hypertension management could help reduce health inequities.
Cardiovascular diseases (CVDs) remain the leading cause of death and disability worldwide. Digital health technologies are important public health interventions for addressing the burden of cardiovascular disease. In this article, we discuss the importance of translating digital innovations in research-funded projects to low-resource settings globally to advance global cardiovascular health equity.
View Article and Find Full Text PDFInequities in global health research are well documented. For example, training opportunities for US investigators to conduct research in low-income and middle-income countries (LMIC) have exceeded opportunities for LMIC investigators to train and conduct research in high-income countries. Reciprocal innovation addresses these inequities through collaborative research across diverse global settings.
View Article and Find Full Text PDFObjective: Evaluate the longitudinal association between BP control and the use of antihypertensive classes with arterial stiffness (AS) in Brazilian adults.
Methods: This study included 1830 participants with arterial hypertension (1092 participants with controlled BP and 738 participants with uncontrolled BP) from the Longitudinal Study of Adult Health (ELSA-Brasil). AS was assessed by pulse wave velocity (PWV) and pulse pressure (PP) at baseline and repeated after approximately 9 years.
This study aimed to estimate the prevalence of possible cases of FH and analyze associated factors in the adult Brazilian population. Cross-sectional study with laboratory data from the Brazilian National Health Survey, with 8521 participants. Possible cases of FH were defined according to the Dutch Lipid Clinic Network criteria.
View Article and Find Full Text PDFObjective: Expressing the cardiovascular disease (CVD) risk in relation to peers may complement the estimation of absolute CVD risk. We aimed to determine 10-year CVD risk percentiles by sex and age in the Brazilian population and evaluate their association with estimated long-term atherosclerotic CVD (ASCVD) risk.
Methods: A cross-sectional analysis of baseline data from the ELSA-Brasil study was conducted in individuals aged 40-74 years without prior ASCVD.