هل يمكن للذكاء الاصطناعي التنبؤ بالعمر الميتابولومي للفرد وتخصيص خطط صحية وفقًا لذلك؟

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.

الساعة الزمنية مقابل الساعة البيولوجية

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.

الساعات الزمنية

عمرك الزمني هو عمرك المستند إلى أيام حياتك. يمكن لعمرك الزمني مساعدة المتخصصين في الرعاية الصحية على فهم ما يمكن توقعه بشكل معقول من جسمك من حيث الحالة الصحية، علامات الشيخوخة، أنماط الحياة، وأكثر. كما يمكن أن يساعد المتخصصين على تطوير أدوية مخصصة لمجموعات عمرية معينة، مما يتيح رعاية أكثر فعالية. ومن الجدير بالذكر أن عمرك الزمني لا يمكن تغييره.

الساعات البيولوجية

عمرك البيولوجي يمثل مرحلة الضرر الجزيئي والخلوي في جسمك. على عكس الشيخوخة الزمنية، يمكن تعديل الشيخوخة البيولوجية عن طريق تغيير أنماط الحياة، والبيئات، والأنظمة الغذائية، وأكثر. ستظهر الشيخوخة البيولوجية في مستوى الأداء والقدرات بناءً على توقعات عمرك الزمني. الهدف الرئيسي من الساعات البيولوجية هو تتبع وإظهار كيف يتغير أيضك مع مرور الوقت.

دراسة الساعات العمرية الميتابولومية المدعومة بالذكاء الاصطناعي

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.

الميتابوليتات

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.

العمر الميتابولومي – 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.

المصدر – Science.org

اختبار دراسة العمر الميتابولومي

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.

نتائج دراسة العمر الميتابولومي

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.

فوائد دراسة العمر الميتابولومي

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.

استخدام العمر الأيضي كمؤشر إنذار مبكر

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.

حالات الاستخدام للعمر الأيضي

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.

تقييمات صحية

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.

تصنيف المخاطر

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.

تتبع الصحة الاستباقي

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.

باحثو العمر الأيضي

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.

الشركات التي يمكنها الاستفادة من دراسة العمر الميتابولومي

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 ) هي شركة أبحاث علمية متقدمة مؤسسة تتخصص في تقنية تسلسل الجينات. دخلت الشركة السوق في عام 2012 باسم  Avante Biosystems, Inc. قبل أن تعيد تسمية نفسها إلى 10x Genomics.  تم تأسيس المشروع بواسطة  Serge Saxonov, Ben Hindson, و Kevin Ness لتحسين فهم علم الأحياء للخبراء في مجال الرعاية الصحية. اليوم، تقدم الشركة خدمات متنوعة تمتد عبر علم المناعة وعلم الأعصاب.

في عام 2018، قامت 10x Genomics بالعديد من الاستحواذات على مستوى عالٍ التي حسّنت تقنيتها وموقعها في السوق. على سبيل المثال، استحوذت الشركة على Epinomics و Spatial Transcriptomics. كلا الإجراءين حسّنا عروضها ومنحا الشركة إمكانية الوصول إلى تقنيات متقدمة.

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.

مستقبل دراسة العمر الأيضي

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.

العمر الأيضي – تتبع صحتك على مستويات جديدة.

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.

تعرف على تقنيات صحية أخرى رائعة الآن.

مرجع الدراسة:

^{1. Mutz, J., Iniesta, R., & Lewis, C. M. (2024). العمر الميتابولومي (MileAge) يتنبأ بالصحة وطول العمر: مقارنة بين عدة خوارزميات تعلم آلي. Science Advances, 10(51), eadp3743. https://doi.org/10.1126/sciadv.adp3743}