AI가 개인의 대사 연령을 예측하고 그에 맞는 건강 계획을 맞춤화할 수 있을까?

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.

연대 시계와 생물학적 시계

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.

연대 시계

연대 연령은 당신이 살아온 일수에 기반한 나이입니다. 연대 연령은 의료 전문가가 당신의 신체 상태, 노화 징후, 생활 방식 등을 합리적으로 예상하는 데 도움을 줍니다. 또한 특정 연령대에 맞춘 약물을 개발하여 보다 효과적인 치료를 가능하게 합니다. 주목할 점은 연대 연령은 변경할 수 없다는 것입니다.

생물학적 시계

생물학적 연령은 분자 및 세포 손상의 정도를 나타냅니다. 연대 연령과 달리, 생물학적 연령은 생활 방식, 환경, 식단 등을 변화시켜 조절할 수 있습니다. 생물학적 연령은 연대 연령에 대한 기대치와 비교해 성능 및 능력 수준에서 감지됩니다. 생물학적 시계의 주요 목표는 시간이 지남에 따라 당신의 대사가 어떻게 변하는지를 추적하고 보여주는 것입니다.

AI 기반 대사 연령 시계 연구

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.

대사물질

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.

대사 연령 – 마일에이지

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.

출처 – Science.org

대사 연령 연구 테스트

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.

대사 연령 연구 결과

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.

대사 연령 연구의 이점

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.

대사 연령을 조기 경고 지표로 활용하기

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.

대사 연령의 활용 사례

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.

건강 평가

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.

위험 계층화

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.

선제적 건강 추적

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.

대사 연령 연구자들

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.

대사 연령 연구로 혜택을 받을 수 있는 기업

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 )는 유전자 시퀀싱 기술을 전문으로 하는 고급 과학 연구 기업입니다. 이 회사는 2012년에 Avante Biosystems, Inc.로 시장에 진입했으며 이후 10x Genomics로 브랜드를 변경했습니다. 이 프로젝트는 Serge Saxonov, Ben Hindson, Kevin Ness에 의해 설립되어 의료 전문가들의 생물학 이해를 향상시키고자 했습니다. 오늘날, 이 회사는 면역학 및 신경과학 전반에 걸친 다양한 서비스를 제공하고 있습니다.

2018년에 10x Genomics는 기술 및 시장 입지를 강화하기 위해 여러 대규모 인수를 진행했습니다. 예를 들어, Epinomics와 Spatial Transcriptomics를 인수했으며, 이 두 움직임은 제품 라인을 강화하고 고급 기술에 대한 접근성을 제공했습니다.

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.

대사 연령 연구의 미래

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.

대사 연령 – 새로운 수준에서 당신의 건강을 추적하기.

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.

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연구 참고문헌:

^{1. Mutz, J., Iniesta, R., & Lewis, C. M. (2024). 대사 연령 (MileAge)이 건강 및 수명을 예측함: 다중 머신러닝 알고리즘 비교. Science Advances, 10(51), eadp3743. https://doi.org/10.1126/sciadv.adp3743}