logo
Heart failure impacts millions of Americans. Now, researchers say it's linked to cognitive decline

Heart failure impacts millions of Americans. Now, researchers say it's linked to cognitive decline

Independent16-05-2025

More than six million Americans have heart failure. The progressive condition contributes to hundreds of thousands of deaths each year.
People develop it after the heart's muscle is injured and gradually loses its ability to pump enough blood to supply the body's needs. Now, researchers say those affected by it may also have to worry about a link to cognitive decline.
'Heart failure is a disease that never goes away, and treating it relies heavily on a patient's ability to follow specific instructions, monitor their symptoms and keep up with many different medications,' Dr. Supriya Shore, clinical assistant professor of internal medicine-cardiology at the University of Michigan Medical School, said in a statement.
'Seeing this cognitive decline among patients, and how it worsens over time after a diagnosis of heart failure, should be a warning for providers to assess a patient's cognitive ability early and factor it into the care plan,' she said.
Shore was the lead author of the research, which was recently published in the journal Circulation: Heart Failure.
To reach these conclusions, the authors examined the cognitive abilities of nearly 30,000 adults. They compared the health of those who did and did not develop heart failure, finding that heart failure was associated with a significant decrease in cognition at the time of diagnosis.
In addition, they found that cognition and executive functioning also declined more rapidly over the years after diagnosis. Executive functioning refers to the ability to engage in higher-level cognitive skills, according to Health.
People with heart failure mentally aged the equivalent of 10 years within just seven years of a diagnosis. Adults with heart failure would reach the threshold for meaningful decline in global cognition nearly six years earlier than people without it, they discovered.
Typical risk factors for cognitive impairment did not explain the accelerated decline they observed.
Furthermore, they found that the largest decrease in global cognition occurred among older adults, women, and white participants. Global cognition includes a range of mental processes, such as thinking and learning.
While these findings are alarming, they may help to better treat and monitor people with heart failure now and in the future. The number of Americans who have heart failure is expected to increase to 8.7 million by 2030. It currently directly accounts for about 8.5 percent of all heart disease deaths in the U.S.
'Regular cognitive monitoring of older adults with heart failure would help identify individuals with the earliest signs of cognitive decline who require supportive care,' said senior author Dr. Deborah Levine, a professor of internal medicine and neurology at the University of Michigan Medical School.
'We need a better understanding of the mechanisms driving accelerated cognitive decline after heart failure to develop interventions that halt or slow the decline,' she added.

Orange background

Try Our AI Features

Explore what Daily8 AI can do for you:

Comments

No comments yet...

Related Articles

How to Work Smarter, Not Harder, with Artificial Intelligence
How to Work Smarter, Not Harder, with Artificial Intelligence

Geeky Gadgets

time19 minutes ago

  • Geeky Gadgets

How to Work Smarter, Not Harder, with Artificial Intelligence

What if the future of work isn't about competing with machines, but mastering the skills to work alongside them? As artificial intelligence (AI) continues to transform industries—from healthcare and finance to entertainment and education—the demand for professionals who understand its inner workings is skyrocketing. Yet, navigating the world of AI can feel overwhelming, with its complex algorithms, vast datasets, and ever-evolving tools. The good news? You don't need to be a genius mathematician or a coding prodigy to thrive in this space. By focusing on a few essential AI skills, you can position yourself at the forefront of this technological wave, ready to innovate, adapt, and lead. Tina Huang breaks down the core competencies that every aspiring AI professional needs to succeed, from mastering foundational programming languages like Python to understanding the ethical implications of AI-driven systems. You'll discover how skills like data handling and machine learning form the backbone of AI innovation, while communication and collaboration ensure your ideas make an impact beyond the technical realm. Whether you're a curious beginner or a seasoned tech enthusiast, this guide will help you uncover the tools and knowledge to not just keep up with AI but to shape its future. After all, the key to thriving in an AI-driven world isn't just knowing what to learn—it's understanding why it matters. Essential AI Skills Guide Programming: The Foundation of AI Programming forms the cornerstone of AI development. Proficiency in programming languages is essential for building AI models, implementing algorithms, and creating scalable solutions. Key languages include: Python: Known for its simplicity and versatility, Python is the most widely used language in AI. Its extensive libraries, such as TensorFlow, PyTorch, and Scikit-learn, streamline the development of machine learning and deep learning models. Known for its simplicity and versatility, Python is the most widely used language in AI. Its extensive libraries, such as TensorFlow, PyTorch, and Scikit-learn, streamline the development of machine learning and deep learning models. R: A powerful tool for statistical analysis and data visualization, R is particularly useful for exploratory data analysis and research-focused AI applications. A powerful tool for statistical analysis and data visualization, R is particularly useful for exploratory data analysis and research-focused AI applications. Java: Frequently used in enterprise-level AI applications, Java is valued for its scalability and performance in large-scale systems. Mastering these languages enables you to write efficient, maintainable, and scalable code, a fundamental skill for any AI professional. Mathematics and Statistics: The Core of AI Models AI is deeply rooted in mathematical principles, which provide the theoretical foundation for algorithms and models. Key areas of focus include: Linear Algebra: Essential for understanding data representation in AI models, such as vectors and matrices used in neural networks. Essential for understanding data representation in AI models, such as vectors and matrices used in neural networks. Calculus: Critical for optimizing algorithms, particularly through techniques like gradient descent, which is used to minimize errors in machine learning models. Critical for optimizing algorithms, particularly through techniques like gradient descent, which is used to minimize errors in machine learning models. Probability and Statistics: Fundamental for designing systems that handle uncertainty, make predictions, and evaluate performance using statistical methods. A strong grasp of these concepts ensures you can develop robust and reliable AI systems capable of solving complex problems. Every Essential AI Skill You Need to Know Watch this video on YouTube. Here are additional guides from our expansive article library that you may find useful on AI skills. Machine Learning: The Heart of AI Machine learning (ML) is the driving force behind AI, allowing systems to learn from data and improve over time. To excel in ML, you must understand its key methodologies: Supervised Learning: Involves training models on labeled datasets for tasks like classification (e.g., spam detection) and regression (e.g., predicting housing prices). Involves training models on labeled datasets for tasks like classification (e.g., spam detection) and regression (e.g., predicting housing prices). Unsupervised Learning: Focuses on identifying patterns in unlabeled data, such as clustering customers based on purchasing behavior or reducing data dimensions for visualization. Focuses on identifying patterns in unlabeled data, such as clustering customers based on purchasing behavior or reducing data dimensions for visualization. Reinforcement Learning: Teaches agents to make decisions by interacting with their environment, commonly applied in robotics, gaming, and autonomous systems. These techniques allow you to select the most effective approach for addressing specific challenges, making ML expertise indispensable in AI development. Data Handling: Fueling AI Systems Data is the lifeblood of AI, and effective data handling is crucial for building accurate and reliable models. Key aspects include: Preprocessing: Cleaning and organizing raw data to remove inconsistencies, noise, and errors, making sure the dataset is ready for analysis. Cleaning and organizing raw data to remove inconsistencies, noise, and errors, making sure the dataset is ready for analysis. Exploratory Data Analysis (EDA): Identifying patterns, trends, and anomalies in data to guide model development and improve decision-making. Identifying patterns, trends, and anomalies in data to guide model development and improve decision-making. Data Quality Assurance: Making sure datasets are accurate, unbiased, and representative to avoid flawed predictions and unintended consequences. By mastering data handling techniques, you can ensure that your AI systems are built on a solid foundation of high-quality data. Deep Learning: Unlocking Complex AI Tasks Deep learning, a subset of machine learning, focuses on neural networks that mimic the human brain's structure and function. It is particularly effective for solving complex problems. Key concepts include: Convolutional Neural Networks (CNNs): Specialized for image recognition and processing, CNNs are widely used in applications like facial recognition and medical imaging. Specialized for image recognition and processing, CNNs are widely used in applications like facial recognition and medical imaging. Recurrent Neural Networks (RNNs): Designed for sequential data, such as time series or text, RNNs are commonly used in natural language processing and speech recognition. Frameworks like TensorFlow and PyTorch are essential tools for designing, training, and deploying deep learning models, allowing you to tackle advanced tasks such as computer vision and natural language understanding. AI Ethics: Building Responsible Systems As AI becomes more integrated into daily life, ethical considerations are paramount. Developing responsible AI systems involves addressing: Bias Mitigation: Making sure training datasets are diverse and representative to prevent biased outcomes. Making sure training datasets are diverse and representative to prevent biased outcomes. Data Privacy: Protecting sensitive information and adhering to regulations like GDPR to maintain user trust. Protecting sensitive information and adhering to regulations like GDPR to maintain user trust. Societal Impact: Evaluating how AI technologies affect employment, equity, and decision-making processes. By prioritizing ethical practices, you contribute to the creation of fair, transparent, and trustworthy AI solutions. Cloud Computing: Scaling AI Solutions Cloud computing platforms like AWS, Google Cloud, and Microsoft Azure are indispensable for deploying and scaling AI models. These platforms offer: Computational Power: Providing the resources needed for training complex models, such as deep learning networks. Providing the resources needed for training complex models, such as deep learning networks. Storage Solutions: Managing vast datasets efficiently and securely. Managing vast datasets efficiently and securely. Scalability: Allowing AI applications to handle increasing user demands without compromising performance. Familiarity with cloud computing ensures your AI solutions are both powerful and accessible, meeting the needs of diverse applications. Communication Skills: Bridging the Technical Gap Effective communication is a vital skill for AI professionals, allowing you to convey complex concepts to non-technical audiences. Key aspects include: Presenting Insights: Translating technical findings into actionable recommendations that drive business decisions. Translating technical findings into actionable recommendations that drive business decisions. Collaborating Across Teams: Making sure alignment between technical development and organizational goals by working closely with stakeholders. Strong communication skills foster collaboration, enhance understanding, and ensure the successful implementation of AI projects. Media Credit: Tina Huang Filed Under: AI, Guides Latest Geeky Gadgets Deals Disclosure: Some of our articles include affiliate links. If you buy something through one of these links, Geeky Gadgets may earn an affiliate commission. Learn about our Disclosure Policy.

Moderna falls on fears new CDC vaccine advisory panel could cloud RSV shot prospects
Moderna falls on fears new CDC vaccine advisory panel could cloud RSV shot prospects

Reuters

time24 minutes ago

  • Reuters

Moderna falls on fears new CDC vaccine advisory panel could cloud RSV shot prospects

June 13 (Reuters) - Moderna's shares (MRNA.O), opens new tab fell 2.5% before the bell on Friday, on concerns whether a new CDC advisory panel would back the use of the company's respiratory syncytial virus vaccine in a broader age group. The RSV shot, mRESVIA, secured U.S. approval on Thursday for expanded use in at-risk adults aged 18 to 59 years, but needs the U.S. Centers for Disease Control and Prevention's recommendation before it is made available for the age group. It is already approved for use in adults aged 60 or older. The biotech's stock extended its decline into the second session, following the appointment of the new panel late Wednesday. Some of the new appointees have openly expressed anti-vaccine views, including against the mRNA vaccine technology on which Moderna's shots are based. Earlier this week, Health Secretary Robert F. Kennedy Jr. fired all 17 members of the panel, known as the Advisory Committee on Immunization Practices (ACIP), saying it would "re-establish public confidence in vaccine science" - a move health experts warn could undermine public confidence in available vaccines. The ACIP advises the CDC on who should get the shots after they are approved by the U.S. Food and Drug Administration. The panel meeting for Moderna's RSV shot, scheduled for later this month, will be the "first time for investors to see the refreshed ACIP in action and will be important for sentiment in the vaccine sector, which continues to decline in our view," said William Blair analyst Myles Minter. U.S.-listed shares of GSK (GSK.L), opens new tab and Pfizer (PFE.N), opens new tab fell marginally and those of smaller Novavax (NVAX.O), opens new tab were down 1.7% at $7.03 in premarket trade. The CDC currently recommends Moderna's vaccine for adults aged 75 and older, as well as for at-risk adults aged 60 to 74. In April, the ACIP recommended the use of approved RSV vaccines in at-risk adults aged 50 to 59. However, the CDC is yet to sign off on the recommendation.

Do people consider T2DM an autoimmune condition?
Do people consider T2DM an autoimmune condition?

Medical News Today

time35 minutes ago

  • Medical News Today

Do people consider T2DM an autoimmune condition?

No, type 2 diabetes mellitus (T2DM) is not an autoimmune disease. The term autoimmune disease describes when the immune system attacks and damages healthy cells in the body by mistake. Although some research suggests that the immune system may play a role in the development of T2DM, most health experts classify it as a chronic metabolic disorder . This describes conditions that affect any aspect of metabolism. Conversely, unlike T2DM, type 1 diabetes mellitus (T1DM) is an autoimmune disease. Health experts consider T1DM an autoimmune condition because it occurs when the body's immune system mistakenly attacks and destroys the beta cells in the pancreas, which are responsible for producing insulin. T2DM happens when the body cannot use insulin effectively and the cells in the body eventually develop insulin resistance, or impaired insulin sensitivity. This causes insulin to be less effective. As a result, more insulin is necessary to enable cells to use glucose in the blood. Over time, this can cause the beta cells in the pancreas to wear out. Eventually, the pancreas is no longer able to produce enough insulin to overcome the cells' resistance. This leads to higher blood glucose levels, and ultimately T2DM. While researchers do not fully understand what causes problems with insulin production and resistance, risk factors for T2DM can include : having obesity being 35 or older a family history of T2DM or personal history of gestational diabetes not being physically active smoking having prediabetes being African American, American Indian, Asian American, Hispanic, Latino, or Pacific Islander

DOWNLOAD THE APP

Get Started Now: Download the App

Ready to dive into the world of global news and events? Download our app today from your preferred app store and start exploring.
app-storeplay-store