Voiko tekoäly ennustaa henkilön metabolomisen iän ja räätälöidä terveyssuunnitelmat sen mukaisesti?

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.

Chronological Clock vs Biological Clock

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.

Chronological Clocks

Kronologinen ikäsi on ikäsi, joka perustuu elämiesi päivien määrään. Kronologinen ikäsi voi auttaa terveydenhuollon ammattilaisia ymmärtämään, mitä kohtuudella voidaan odottaa kehostasi terveyden tilan, ikääntymisen merkkien, elämäntapojen ja muun suhteen. Se voi myös auttaa ammattilaisia kehittämään tiettyihin ikäryhmiin räätälöityjä lääkkeitä, mikä mahdollistaa tehokkaamman hoidon. Huomionarvoista on, että kronologista ikääsi ei voida muuttaa.

Biological Clocks

Biologinen ikäsi kuvaa molekyylisen ja solullisen vaurion vaihetta. Toisin kuin kronologinen ikääntyminen, biologista ikääntymistä voidaan muuttaa muuttamalla elämäntapoja, ympäristöjä, ruokavalioita ja muuta. Biologinen ikäsi ilmenee suorituskyvyn ja kykyjen tasossa, joka poikkeaa kronologisen iän odotuksista. Biologisten kellojen päätavoitteena on seurata ja osoittaa, miten aineenvaihduntasi muuttuu ajan myötä.

Study AI-Powered Metabolomic Age Clocks

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.

Metabolites

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.

Metabolomic Age – 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.

Lisäksi tutkijat havaitsivat, että korkeamman MileAgen omaavilla oli paljon suurempi alttius riski- ja terveystekijöille. Heidän kehonsa olivat myös heikompia eivätkä parantuneet yhtä nopeasti. Huomionarvoista on, että heidän solunsa osoittivat lyhyemmät telomeerit, mikä on toinen merkki heikentyvästä terveydestä ja ikääntymisestä. Metaboliittien ennustaman iän ja kronologisen iän välistä eroa kutsutaan MileAge Delta:ksi.

Lähde – Science.org

Metabolomic Age Study Test

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.

Huomionarvoista on, että osallistujien ikä vaihteli 40–69 vuotta. Algoritmit ohjelmoitiin käyttäen 168 plasmametaboliittia, jotka peräisin UK Biobank -datasta. Se sisälsi keski-ikäisiä ja vanhempia aikuisia.

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.

Metabolomic Age Study Results

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.

Benefits of the Metabolomic Age Study

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.

Using Metabolic Age as an Early Warning Indicator

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.

Use Cases for Metabolic Age

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.

Health Assessments

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.

Risk Stratification

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.

Proactive Health Tracking

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.

Metabolic Age Researchers

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.

Companies that Can Benefit from Metabolomic Age Study

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 ) on edistynyt tieteellinen tutkimus yritys, joka on erikoistunut geenisekvensointiteknologiaan. Yritys tuli markkinoille vuonna 2012 nimellä Avante Biosystems, Inc. ennen kuin se uudelleenbrändäsi 10x Genomicsiksi. Projekti perustettiin Serge Saxonovin, Ben Hindsonin ja Kevin Nessin toimesta parantamaan biologian ymmärtämistä terveydenhuollon asiantuntijoille. Nykyään yritys tarjoaa erilaisia palveluita, jotka kattavat immunologian ja neurotieteen.

Vuonna 2018 10x Genomics teki useita korkean tason yritysostoja, jotka paransivat sen teknologiaa ja markkina-asemaa. Esimerkiksi yritys hankki Epinomicsin ja Spatial Transcriptomicsin. Molemmat toimet paransivat sen tarjontaa ja antoivat yritykselle pääsyn kehittyneisiin teknologioihin.

Huomionarvoista on, että TXG on suosittu osake, joka jatkaa sijoittajien huomion herättämistä yrityksen saavutusten ohella. Tähän mennessä yritys on saanut useita palkintoja, mukaan lukien The Scientist Top 10 Innovations -palkinto vuosina 2017, 2018, 2019, 2020 ja 2021. Lisäksi sitä pidetään yhtenä maailman 100 parhaasta tutkimuslaitoksesta ja se sijoittuu jatkuvasti maailman 20 parhaan lääketieteellisen tutkimuksen yrityksen joukkoon.

Metabolic Age Study Future

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.

Metabolic Age – Tracking Your Health on New Levels.

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.

Tutustu muihin hienoihin terveys-teknologioihin nyt.

Study Reference:

^{1. Mutz, J., Iniesta, R., & Lewis, C. M. (2024). Metabolominen ikä (MileAge) ennustaa terveyttä ja elinikää: Useiden koneoppimisalgoritmien vertailu. Science Advances, 10(51), eadp3743. https://doi.org/10.1126/sciadv.adp3743}