¿Puede la IA predecir la edad metabolómica de una persona y adaptar los planes de salud en consecuencia?

Artificial intelligence continues to push the boundaries of science and health exploration. From mapping proteins to neurological networks, these systems continue to make game-changing impacts on the sector. This month, a group of advantageous researchers from the Institute of Psychiatry, Psychology & Neuroscience took AI integration further. They released a estudio¹ in which AI algorithms were used to predict the metabolic age of patients. Here’s what you need to know.

Reloj Cronológico vs Reloj Biológico

To grasp the importance of this development, it’s vital to understand the differences between your chronological and biological age. These two methods of determining the age of your body are used in different ways by healthcare professionals. As such, it’s crucial to be able to determine both accurately when providing health care strategies.

Relojes Cronológicos

Tu edad cronológica es la edad basada en los días de tu vida. La edad cronológica puede ayudar a los profesionales de la salud a comprender qué esperar razonablemente de tu cuerpo en términos de estado de salud, signos de envejecimiento, estilos de vida y más. También puede ayudar a los profesionales a desarrollar fármacos adaptados a ciertos grupos de edad, lo que permite una atención más eficaz. Cabe destacar que tu edad cronológica no puede cambiarse.

Relojes Biológicos

Tu edad biológica representa el nivel de daño molecular y celular. A diferencia del envejecimiento cronológico, el envejecimiento biológico puede modificarse al cambiar estilos de vida, entornos, dietas y más. Tu envejecimiento biológico será detectable en el nivel de rendimiento y capacidades en comparación con las expectativas basadas en tu edad cronológica. El objetivo principal de los relojes biológicos es rastrear y demostrar cómo tu metabolismo cambia con el tiempo.

Estudio de Relojes de Edad Metabolómica impulsados por IA

AI-powered biological aging clocks could revolutionize the sector. These algorithms can connect huge swatches of data to create in-depth maps that integrate advanced metabolite data. Notably, this study was the first to attempt to use AI to map and rate metabolites.

Metabolitos

Metabolites are markers found in the blood that appear after metabolism. They serve multiple roles and can indicate certain aspects of health. These molecules are a relatively unexplored method of determining a patient’s health status and expectations.

In this approach, engineers introduce statistical or machine-learning algorithms. These systems are specifically designed to locate relationships between chronological age and molecular data. The results are a more in-depth and individualized understanding of a person’s health age.

Edad Metabolómica – MileAge

As part of the study, the engineers created a Metabolic Age ranking system. Each patient was given a MileAge based on the difference between metabolite-predicted and chronological age. The MileAge approach leverages a number of metabolic markers. Specifically, the engineers discovered that a select panel of metabolites explained more than half of the variance in chronological age.

Additionally, researchers found that those with higher MileAge were far more susceptible to risk and health factors. Their bodies were also weaker and couldn’t heal as fast. Notably, their cells showed shorter telomeres, which is another sign of decaying health and aging. The difference between metabolite-predicted age and chronological age is called the MileAge Delta.

Fuente – Science.org

Prueba del Estudio de Edad Metabolómica

To discover the optimal MileAge algorithms, engineers had to test many different machine learning algorithms. Notably, the team leveraged nuclear magnetic resonance spectroscopy to monitor cellular changes. They began by developing 17 machine learning algorithms based on data gathered from blood from over 225,000 UK Biobank participants.

Notably, the age of participants ranged from 40 – 69 years. The algorithms were programmed utilizing 168 plasma metabolites derived from UK Biobank data. It included middle-aged and older adults.

The next step was to compare the AI algorithms to see which ones were the most accurate. The team used nuclear magnetic resonance (NMR) spectroscopy to acquire data from the blood banks to benchmark each model. The comparison yielded some interesting results.

Resultados del Estudio de Edad Metabolómica

The study demonstrated that AI algorithms are not all equal in terms of their ability to accurately predict MileAge. The engineers noticed that Cubist rule-based regression and linear algorithms worked the most effectively at determining aging signals.

The study revealed that 116 metabolites serve roles in this function, with GlycA, omega-3, and DHA having the highest correlation with age. These revelations demonstrated that more complex nonlinear AI systems were the best options.

Beneficios del Estudio de Edad Metabolómica

This metabolic age study brings several key benefits to the table. For one, it will allow people to track their health by empowering proactive behavior. It will also be a key tool used by professionals to determine the best care practices and strategies to implement based on individual patient needs.

Uso de la Edad Metabólica como Indicador de Alerta Temprana

Checking your MileAge could provide you with added confidence in your health. This system makes it easier to see early signs of declining health. As such, healthcare professionals can recommend more effective measures to combat disease. It will also play a major role in improving preventive measures by making it easy to see who is at high risk.

Casos de Uso para la Edad Metabólica

There are several immediate and distant use case scenarios for MileAge-based technologies. These options range from enabling people to monitor their health better, all the way to creating advanced care and treatment methods that integrate key metabolic markers.

Evaluaciones de Salud

At the core of this technology will include the ability to create more useful and specific health assessments. Insurance companies and healthcare professionals will need to leverage this tech to improve and monitor their healthcare strategies currently. In the future, you may need to get a MileAge checkup prior to gaining coverage.

Estratificación de Riesgo

Another key science that can benefit from MileAge integration is risk stratification. Research will leverage this data to find out what areas and people are at the highest risk of certain disorders and diseases. Determining the risk level of populations will help researchers locate and eliminate unnecessary risks presented to patients based on their location or habits.

Seguimiento Proactivo de la Salud

The rise of wearables has introduced a new level of health trackability. Now, the metabolic age scale could help to improve personal health tracking even further. Imagine taking a test or even just clicking a button on your watch to find out the key areas you need to improve your health and bring you up to par with others in your area and age group.

Investigadores de la Edad Metabólica

The MileAge research was conducted by engineers from the Institute of Psychiatry, Psychology & Neuroscience. The study received funding from various backers, including the National Institute for Health and Care Research (NIHR) and Maudsley Biomedical Research Centre (BRC). Additionally, the UK Biobank played a vital role in providing access to data and samples.

Empresas que pueden beneficiarse del Estudio de Edad Metabolómica

There are several companies that could integrate this technology to improve their offerings and services. These firms work in the healthcare industry and already provide some form of service that utilizes a person’s health status as an indicator or metric when determining the products or offerings.

10x Genomics Inc

10x Genomics Inc (TXG ) es una corporación de investigación científica avanzada corporación que se especializa en tecnología de secuenciación génica. La empresa ingresó al mercado en 2012 como  Avante Biosystems, Inc. antes de renombrarse a 10x Genomics.  El proyecto fue fundado por  Serge Saxonov, Ben Hindson y Kevin Ness para mejorar la comprensión de la biología para expertos en salud. Hoy, la compañía ofrece diversos servicios que abarcan inmunología y neurociencia.

En 2018, 10x Genomics realizó varias adquisiciones de alto nivel que mejoraron su tecnología y posicionamiento en el mercado. Por ejemplo, la empresa adquirió Epinomics y Spatial Transcriptomics. Ambas maniobras mejoraron sus ofertas y le brindaron acceso a tecnologías avanzadas.

Notably, TXG is a popular stock that continues to draw investor attention alongside the company’s accomplishments. To date, the firm received multiple awards including The Scientist Top 10 Innovations award in 2017, 2018, 2019, 2020, and 2021. Additionally, it’s considered one of the top 100 global research institutions and consistently ranks among the top 20 global pharmaceutical research firms.

Futuro del Estudio de la Edad Metabólica

Now that engineers have determined that the MileAge marker provides more accurate results compared to previous methods, there’s sure to be additional peer review prior to integration. This method of tracking health will quickly catch on as more agencies realize the added accuracy and savings it provides.

Edad Metabólica – Seguimiento de tu Salud en Nuevos Niveles.

The average person gains a lot from this study in regard to their future healthcare. For one, insurance companies, healthcare providers, and drug manufacturers will all be able to leverage their information to more accurately provide services to the population. As such, there is a strong demand for this data to receive priority in terms of integration. Regardless of the time frame it takes to institute, MileAge markers will soon become crucial in determining health care plans and more.

Descubre otras tecnologías de salud interesantes ahora.

Referencia del Estudio:

^{1. Mutz, J., Iniesta, R., & Lewis, C. M. (2024). La edad metabolómica (MileAge) predice la salud y la esperanza de vida: una comparación de múltiples algoritmos de aprendizaje automático. Science Advances, 10(51), eadp3743. https://doi.org/10.1126/sciadv.adp3743}