Aging is a highly complex and heterogeneous process that progresses at different rates across individuals, making biological age (BA) a more accurate indicator of physiological decline than chronological age. While previous studies have built aging clocks using single-omics data, they often fail to capture the full molecular complexity of human aging. In this work, we leveraged the Human Phenotype Project, a large-scale cohort of 10,000 adults aged 40-70 years, with extensive longitudinal profiling that includes clinical, behavioral, environmental, and multi-omics datasets spanning transcriptomics, lipidomics, metabolomics, and the microbiome. By employing advanced machine learning frameworks capable of modeling nonlinear biological dynamics, we developed and rigorously validated a multi-omics aging clock that robustly predicts diverse health outcomes and future disease risk. Unsupervised clustering of the integrated molecular profiles from multi-omics uncovered distinct biological subtypes of aging, revealing striking heterogeneity in aging trajectories and pinpointing pathway-specific alterations associated with different aging patterns. These findings demonstrate the power of multi-omics integration to decode the molecular landscape of aging and lay the groundwork for personalized healthspan monitoring and precision strategies to prevent age-related diseases.

As global life expectancy increases, so does the burden of chronic diseases, yet individuals exhibit considerable variability in the rate at which they age. Identifying biomarkers that distinguish fast from slow ageing is crucial for understanding the biology of ageing, enabling early disease detection, and improving prevention strategies. Using contrastive deep learning, we show that skin biopsy images alone are sufficient to determine an individual's age. We then use visual features in histopathology slides of the skin biopsies to construct a novel biomarker of ageing. By linking with comprehensive health registers in Denmark, we demonstrate that visual features in histopathology slides of skin biopsies predict mortality and the prevalence of chronic age-related diseases. Our work highlights how routinely collected health data can provide additional value when used together with deep learning, by creating a new biomarker for ageing which can be actively used to determine mortality over time.

Aging is a process that is characterized by physiological and molecular changes that increase an individual's risk of developing diseases and eventually dying. Being able to measure and estimate the biological signatures of aging can help researchers identify preventive measures to reduce disease risk and impact. Researchers have developed "aging clocks" based on markers such as blood proteins or DNA methylation to measure individuals' biological age, which is distinct from one's chronological age. These aging clocks help predict the risk of age-related diseases. But because protein and methylation markers require a blood draw, non-invasive ways to find similar measures could make aging information more accessible.

To solve the most pressing scientific problems, scientists today often face enormous hurdles when it comes to gathering the data they need to embark on research. Enter Ramkumar Hariharan, a data scientist and computational biologist at Northeastern University in Seattle. A scientist and an engineer, Hariharan's current research is centered around an emerging scientific field called geroscience, or the "study of aging as it relates to age-related diseases." Hariharan has been trying to understand the reasons why some cancer patients respond better to certain kinds of immunotherapies. To do so requires lots of information about the patients themselves, the specific forms of cancer and the drugs used to treat patients.

The founder of an up and coming artificial intelligence (AI) company said targeting disease is a challenging if not herculean process, and it will take the science and skills of AI to conquer it. But it also takes a ton of money. That's why Insilico Medicine CEO Alex Zhavoronkov said his latest list of venture capitalists (VCs) are so crucial. They give an enormous amount of credibility to the new technology his company is developing . Finding the biological origin of a disease--the potential target for intervention--is the first step in discovering medicines to combat that disease.

The new collaboration is an indication of research into aging biomarkers together with the interventions discovered over the past five years is advancing. The collaboration is through the startup Napa Therapeutics, Ltd. Napa is to develop drugs against a novel aging-related target, as Business Insider reports. In terms of what each company will offer, the Buck Institute is one of the primary research centers focused on research into aging and the elimination of age-related disease. Insilico Medicine is an artificial intelligence pioneer, which is looking at on a range of factors associated with aging.

The longevity and biotechnology industries are focusing on aging in a big way, and it's beginning to show. The fields of Artificial Intelligence (AI) and regenerative medicine are putting their money on combating aging and age-related diseases, and the benefits are likely to be immense. While biotechnology and AI are relatively new concepts, the announcements of funding and collaboration yesterday by and between three companies are bringing those concepts that much closer to the forefront of medicine. Insilico Medicine, a Baltimore-based next-generation AI company specializing in the application of deep learning for target identification, drug discovery and aging research, yesterday announced a collaboration agreement with WuXi AppTec, a leading global contract research outsourcing provider based in Shanghai, China, serving the pharmaceutical, biotech, and medical device industries. "It's a big step not only for Insilico Medicine but for AI and the pharmaceutical industries," said Alex Zhavoronkov, PhD, CEO of Insilico Medicine, Inc.

The longevity and biotechnology industries are focusing on aging in a big way, and it's beginning to show. The fields of Artificial Intelligence (AI) and regenerative medicine are putting their money on combating aging and age-related diseases, and the benefits are likely to be immense. While biotechnology and AI are relatively new concepts, the announcements of funding and collaboration yesterday by and between three companies are bringing those concepts that much closer to the forefront of medicine. Insilico Medicine, a Baltimore-based next-generation AI company specializing in the application of deep learning for target identification, drug discovery and aging research, yesterday announced a collaboration agreement with WuXi AppTec, a leading global contract research outsourcing provider based in Shanghai, China, serving the pharmaceutical, biotech, and medical device industries. "It's a big step not only for Insilico Medicine but for AI and the pharmaceutical industries," said Alex Zhavoronkov, PhD, CEO of Insilico Medicine, Inc.

The longevity and biotechnology industries are focusing on aging in a big way, and it's beginning to show. The fields of Artificial Intelligence (AI) and regenerative medicine are putting their money on combating aging and age-related diseases, and the benefits are likely to be immense. While biotechnology and AI are relatively new concepts, the announcements of funding and collaboration yesterday by and between three companies are bringing those concepts that much closer to the forefront of medicine. Insilico Medicine, a Baltimore-based next-generation AI company specializing in the application of deep learning for target identification, drug discovery and aging research, yesterday announced a collaboration agreement with WuXi AppTec, a leading global contract research outsourcing provider based in Shanghai, China, serving the pharmaceutical, biotech, and medical device industries. "It's a big step not only for Insilico Medicine but for AI and the pharmaceutical industries," said Alex Zhavoronkov, PhD, CEO of Insilico Medicine, Inc.

Recent advances in modern artificial intelligence demonstrated promising results in both biomarker development and drug discovery and the leading academic and research institutions are expanding their AI groups. The Buck Institute is the leading research institution with massive amounts of biological data coming from high-throughput experiments and with the unique multidisciplinary expertise in aging research. Alex Zhavoronkov works on the intersection of the next-generation of artificial intelligence and aging research and will help support the AI efforts at the Buck. "We are incredibly excited about the potential of AI to accelerate aging research," said Dr. Eric Verdin, President and CEO of the Buck Institute. "The Buck has been at the forefront of asking the most important questions in the field. Now, with the latest in bioinformatics and artificial intelligence, and with the involvement of world-class experts like Dr. Zhavoronkov, we will finally have the tools to answer them. Fully utilizing these powerful technologies, we will dramatically increase our understanding of how aging works, and what we can do about it."