A IA pode prever a idade metabolômica de uma pessoa e adaptar planos de saúde de acordo?

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 study¹ in which AI algorithms were used to predict the metabolic age of patients. Here’s what you need to know.

Relógio Cronológico vs Relógio Biológico

Relógios Cronológicos

Sua idade cronológica é a idade baseada nos dias de sua vida. Sua idade cronológica pode ajudar os profissionais de saúde a entender o que razoavelmente se pode esperar do seu corpo em termos de estado de saúde, sinais de envelhecimento, estilos de vida e mais. Também pode ajudar os profissionais a desenvolver medicamentos direcionados a determinados grupos etários, permitindo um cuidado mais eficaz. Notavelmente, sua idade cronológica não pode ser alterada.

Relógios Biológicos

Sua idade biológica representa o estágio de dano molecular e celular. Ao contrário do envelhecimento cronológico, o envelhecimento biológico pode ser alterado mudando estilos de vida, ambientes, dietas e mais. Seu envelhecimento biológico será detectável no nível de desempenho e capacidades com base nas expectativas da sua idade cronológica. O objetivo principal dos relógios biológicos é rastrear e demonstrar como seu metabolismo muda ao longo do tempo.

Estudo de Relógios de Idade Metabolômica Alimentados 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.

Metabólitos

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.

Idade Metabolômica – 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.

Fonte – Science.org

Teste do Estudo de Idade Metabolômica

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 do Estudo de Idade Metabolômica

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.

Benefícios do Estudo de Idade Metabolômica

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.

Usando a Idade Metabólica como Indicador de Aviso Prévio

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 a Idade 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.

Avaliações de Saúde

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.

Estratificação de Risco

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.

Monitoramento Proativo da Saúde

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.

Pesquisadores da Idade 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 Podem se Beneficiar do Estudo de Idade Metabolômica

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 ) é uma corporação de pesquisa científica avançada corporation que se especializa em tecnologia de sequenciamento genético. A empresa entrou no mercado em 2012 como  Avante Biosystems, Inc. before rebranding to 10x Genomics.  The project was founded by  Serge Saxonov, Ben Hindson, and Kevin Ness to improve the understanding of biology for healthcare experts. Today, the company provides various services that span across immunology and neuroscience.

In 2018, 10x Genomics made several high-level acquisitions that improved its technology and market positioning. For example, the firm acquired Epinomics and Spatial Transcriptomics. Both maneuvers improved its offerings and provided the company with access to advanced technologies.

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 do Estudo da Idade 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.

Idade Metabólica – Monitorando Sua Saúde em Novos Níveis.

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.

Learn about other cool health techs now.

Referência do Estudo:

^{1. Mutz, J., Iniesta, R., & Lewis, C. M. (2024). Idade metabolômica (MileAge) prevê saúde e longevidade: Uma comparação de múltiplos algoritmos de aprendizado de máquina. Science Advances, 10(51), eadp3743. https://doi.org/10.1126/sciadv.adp3743}