The extension of Lifespan and Health span is possible with AI.
Saheed Oyedele B.Tech., M.Sc., M.Sc., Doctoral Cand.
??Electrical & Electronics Engr. | Network Engineering Specialist | Data Analytics Pro | Cybersecurity Professional | Risk Analyst | Ethics & Compliance Advocate | Doctoral Candidate in Cybersecurity & Info Assurance??
This is the approach to understanding aging, learning about the underlying mechanisms of healthy longevity from humans who are already aging well. Aging is an almost universal unifying feature possessed by all living organisms, tissues, and cells. It includes using AI and machine learning to analyze the distinctive molecular features of people who live the healthiest, longest lives, and then use that knowledge to develop therapies that could help everyone age more successfully. It requires understanding aging, learning about the underlying mechanisms of healthy longevity from humans who are already aging well.The discovery process begins with the aging cohorts — precious samples collected from thousands of people over decades — coupled with detailed records of their health and mortality. The attractive feature of AI is its ability to identify relevant patterns within complex, nonlinear data, without the need for any a priori mechanistic understanding of the biological processes. AI unveils the mechanistic relationships taking place within the body.
People are living longer than ever before — and this era of longevity is being shaped by rapid advancements in technologies driven by artificial intelligence (AI).
As we all know, aging drives disease. Aging is the primary cause of many chronic diseases, including devastating illnesses like cancers and Alzheimer’s.
A central aspect of the approach is discovering pathways which, when they are active in certain ways, result in a healthier person. Advances in artificial intelligence, combined with the availability of large datasets, have led to a boom in the field, increasing the variety of biomarkers that could be considered candidates as potential age predictors. One promising development that considers multiple combinations of these different predictors could shed light on the aging process and provide a further understanding of what contributes to healthy aging. There are two kinds of age: chronological age, which is the number of years one has lived, and biological age, which is influenced by our genes, lifestyle, behavior, the environment, and other factors. Biological age is the superior measure of true age and is the most biologically relevant feature, as it closely correlates with mortality and health status. Very often when someone looks older than their chronological age, they are sick. Scientists are working round the clock, using AI as their magic wand in their relentless search for natural compounds that can slow down the aging process.
Scientists used a machine learning model trained on mountains of data about known chemicals and their effects, along with so much more, to predict whether a compound could extend the life of a translucent worm that shares a similar metabolism to humans. Thanks to this information, this whiz kid machine learning model could eventually help predict which compounds might keep us looking like we have just taken a dip in the fountain of youth.
AI rose to the challenge, unearthing three compounds with potential anti-aging properties. The scientists developed a model trained to recognize chemical features that have senolytic properties. Senolytics are a class of small molecules under intense study for their ability to suppress age-related processes such as fibrosis, inflammation, and cancer by eliminating aged, dysfunctional cells without harming healthy cells. These three chemicals ginkgetin, periplocin and oleandrin – demonstrated the ability to remove deteriorating cells effectively. Among the three, oleandrin was found to be the most potent. These compounds are both from natural products found in traditional herbal medicines. AI has been known to spot what a human cannot be due to its ability to analyze data more closely and at greater volumes. Zebra Medical Computer Vision AI medical imaging tool can be used to analyze data, including medical imaging, to diagnose diseases, such as bone, liver, lung, and cardiovascular illnesses. A good example of a targeted immunotherapy of advanced forms of cancer with a high curability is CAR-T. It is a novel type of cell immunotherapy that uses a patient’s own T lymphocytes cells (a type of white blood cells), to fight cancer. These cells are genetically engineered to express chimeric antigen receptors (CARs), which enable them to recognize and attack only cancer cells in the body, sparing normal cells. Artificial intelligence models are currently being used to conduct genomic analysis and identify specific genes associated with healthy human lifespan. One such project is Calico Labs’ collaboration with the well-known platform AncestryDNA, which analyzes a vast range of data to establish hereditary factors in longevity.
?Drug discovery and development timelines can be further optimized by using DL and AI technologies to characterize drug candidates according to likely efficacy and safety for longevity. Regenerative medicine aims to provide patients with improved treatment and faster recovery for health life span.?These techniques could also be used to grow tissues and organs and transplant them into the body, eliminating potential organ transplant rejection. If applied at a larger scale, this could help to address the shortage of organs available for transplants. Gene therapy is an experimental technique that uses genes to treat or prevent diseases, including inherited disorders, some types of cancer, and certain viral infections. In practice, this technique is designed to introduce genetic material into cells to compensate for abnormal genes or to make a beneficial protein. AI ability to learn, predict, and advice based on vast amounts of data, AI technology can identify patterns that can be used to predict the prognosis of patients and advise medical practitioners with different options available ranging from available personalized medicine to clinical trials with experimental therapies. Demographic data, or life tables, such as the ones from the Human Mortality Database provide information to analyze demographic trends including mortality and fertility rates. Using life tables, one can extract survival curves showing the proportion of individuals surviving to each age for a given species. The analysis of these curves demonstrate that they elicit specific topological features which provide information about the specific aging patterns of each species. AI algorithms are adapted to analyze the complexity of demographic data. Analysis can include the extraction of the most important features used to reproduce the topology of species.
Scientists do not have to look into the technical aspect of what is going on behind the scenes but to have insights from knowing their data in much more detail through exploratory data analysis. AI should not only provide correct predictions, but also give information about the features used to obtain the predictions. The dream of modern medical science is to be able to bend the trajectory of disease which can be made possible by artificial intelligence. AI creates a different approach taking a fresh approach to understanding, and hopefully treating, these devastating diseases, which include frailty, loss of the immune system as we age, chronic heart disease, etc. Artificial intelligence and computer vision, a very precise method of measuring the age of cells that was taken out of a body which is used score the actual biologic age of cells we treat with drugs. Artificial intelligence platform could be used to envision a future where, not only do we have a set of drugs for something like Parkinson’s disease or Alzheimer’s but be able to use cells from body as a diagnostic to predict that a particular therapy may work better for you than another person. 1% of Americans live to age 100. It is believed that these centenarians, and similarly long-lived individuals, may have protective molecular factors that help reduce risk or delay the onset of age-related disabilities and diseases.The analysis of data sets that map the complex, multilayered interplay of genetics, metabolism, proteins, and other variables was done. As usual, the twin technologies of artificial intelligence (AI) and machine learning (ML) can help human investigators sort, analyze, and navigate this data much more swiftly and efficiently.?Another way AI is contributing to anti-aging is through personalized medicine. By analyzing an individual’s genetic information and medical history, AI can predict the risk of age-related diseases and identify personalized treatment plans that are tailored to the patient’s specific needs. This approach can help prevent diseases before they occur and improve treatment outcomes. AI is helping in the fight against aging is through wearable technology. AI-powered wearables can monitor vital signs and other health metrics in real-time, providing insights into an individual’s health and wellness. Most recently, scientists explored how ChatGPT, an AI-based language model, was able to predict Alzheimer’s in 80% of cases when analyzing speech. However, it is not the only implementation.
Health literacy, which refers to the patient’s ability to understand and use health-related information, is one of the most important factors for level of health and longevity and it is strongly correlated with the level of education. It has been linked to healthier behaviors such as exercising regularly, eating a balanced diet, avoiding smoking, and limiting alcohol consumption. These behaviors can contribute to better health outcomes over time. Research has consistently shown that individuals with higher levels of education tend to have better health outcomes compared to those with lower levels of education. Education can improve a person’s health literacy, which refers to their ability to understand and use health-related information. AI-powered virtual assistants personalized health recommendations and reminders to older adults, helping them manage chronic conditions and improve their overall quality of life. By analyzing an individual’s medical history and health data, virtual assistants can provide tailored recommendations for exercise, nutrition, and lifestyle changes that can promote longevity. AI algorithms can analyze large amounts of personal health data and identify patterns that may predict the likelihood of developing certain disease and assist physicians and other healthcare professionals to reach accurate diagnoses in difficult cases. This will allow physicians to prescribe and take preventative measures (so-called primary prevention), to reduce the risk of developing such conditions, several years before they will occur, such as, for example, for dementia, heart failure, diabetes, hypertension and cancer. Gene editing and genetic engineering will have a tremendous potential to increase human longevity by allowing us to directly manipulate the genetic code that governs many biological processes in the human body. It will be used, for example, for anti-aging therapies, by manipulating genes that we know now or in the future that control aging processes, such as telomere length or cellular senescence. By slowing down or reversing the aging process, we could potentially extend enormously the human lifespan, at the same time preserving healthy individuals. This possibility has already being proven in many animal studies, and eventually will be applied to human beings.
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Conclusion:
It is important to note that many of these technologies are still relatively new and untested in human beings. Besides unexpected adverse effects that may occur, there are still many ethical and safety considerations that must be addressed before widespread use in humans. However, the potential benefits of these technologies for increasing human longevity are significant and could have a profound impact on our health and lifespan in the future. AI has the potential to revolutionize healthcare and increase human longevity by improving disease prevention, diagnosis, and treatment. As technology advances, we can expect AI to play an even greater role in improving our health and longevity. Despite the achievements, AI is not a flawless solution, and people who apply the technology in their work are advised to do so with caution to ensure that any solutions created operate efficiently and can be used responsibly. For example, biased data introduced into AI datasets can discriminate against or favor certain groups. In addition, such models may contain incomplete or inaccurate data, making them ineffective. The volume of available medical and genetic information is mind-blowing and awaits its proper analysis. Future AI systems combining the recent breakthroughs in computer and physical sciences will unravel the intricate relationship between aging and chronic diseases and lead to the development of transformative medicines.
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