Why biological clocks get our'true age' wrong - and how AI could help Your chronological age can't always tell you the state of your health, which is why biological clocks have been developed to show our risk of developing diseases or dying - but they're not all they are cracked up to be, says columnist Graham Lawton You may be chronologically older than your "true age" When I first started writing about ageing years ago, there was a buzz around something called biological clocks, also known as ageing clocks or "true age" measurements. In principle, these are quite simple: we all have a chronological age, the number of years since birth, but this doesn't necessarily reflect how far we are down the slippery slope from birth to decrepitude. On average, this follows a fairly predictable trajectory, with gradual declines in almost every physical and mental attribute throughout adulthood. When we judge how old somebody is, we are intuitively totting up many of these tell-tale signs we see - the wrinkles and grey hair, or changes in posture, gait, voice, mental acuity and so on. The goal of measuring biological age is to capture this decline in a single metric, evaluated scientifically and expressed in years. The results tell us something we intuitively know: some people age better than others.

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.

When used correctly, they can help us unpick some of the mysteries of our biology, and our mortality. Be honest: Have you ever looked up someone from your childhood on social media with the sole intention of seeing how they've aged? One of my colleagues, who shall remain nameless, certainly has. He recently shared a photo of a former classmate. "Can you believe we're the same age?" he asked, with a hint of glee in his voice. A relative also delights in this pastime. "Wow, she looks like an old woman," she'll say when looking at a picture of someone she has known since childhood. The years certainly are kinder to some of us than others. But wrinkles and gray hairs aside, it can be difficult to know how well--or poorly--someone's body is truly aging, under the hood. A person who develops age-related diseases earlier in life, or has other biological changes associated with aging (such as elevated cholesterol or markers of inflammation), might be considered "biologically older" than a similar-age person who doesn't have those changes. Some 80-year-olds will be weak and frail, while others are fit and active. Longevity clinics offer a mix of services that largely cater to the wealthy.

Tech millionaire turned longevity pioneer Bryan Johnson devotes more than 6 hours a day to trialling different methods to turn back the clock. Can the rest of us learn anything from his radical approach? Bryan Johnson is finishing his 6.5-hour morning routine when I sign on to Zoom for my allotted 15-minute call with him (a constraint of what a member of his team describes as his "crazy" schedule). The tech millionaire turned longevity pioneer is standing in front of a cement wall in his California home, the coldness of which is relieved by green bursts of tropical houseplants. Wearing a helmet-like headset, a few wires trailing out and down past the screen, together with a black T-shirt bearing the words "Don't Die", the effect is somewhere between a luxury Balinese villa and a VR store designed by Apple.

A study reveals that extreme heat accelerates biological aging even more than smoking or drinking. It is well known that heat causes exhaustion in the body due to dehydration. A recent study concluded that extreme heat accelerates the aging of the human body, a worrying fact given the increasing frequency of heat waves due to climate change. The researchers are not talking about the effects of solar radiation on the skin, but biological aging. Unlike chronological age--that answer that you give when asked how old you are--your biological age reflects how well your cells, tissues, and organs are functioning.

Predicting an individual's aging trajectory is a central challenge in preventative medicine and bioinformatics. While machine learning models can predict chronological age from biomarkers, they often fail to capture the dynamic, longitudinal nature of the aging process. In this work, we developed and validated a machine learning pipeline to predict age using a longitudinal cohort with data from two distinct time periods (2019-2020 and 2021-2022). We demonstrate that a model using only static, cross-sectional biomarkers has limited predictive power when generalizing to future time points. However, by engineering novel features that explicitly capture the rate of change (slope) of key biomarkers over time, we significantly improved model performance. Our final LightGBM model, trained on the initial wave of data, successfully predicted age in the subsequent wave with high accuracy ($R^2 = 0.515$ for males, $R^2 = 0.498$ for females), significantly outperforming both traditional linear models and other tree-based ensembles. SHAP analysis of our successful model revealed that the engineered slope features were among the most important predictors, highlighting that an individual's health trajectory, not just their static health snapshot, is a key determinant of biological age. Our framework paves the way for clinical tools that dynamically track patient health trajectories, enabling early intervention and personalized prevention strategies for age-related diseases.

What if a simple selfie was enough to show scientifically how well or badly we’re ageing? That moment’s getting closer …

Fox News anchor Bret Baier has the latest on the Murdoch Children's Research Institute's partnership with the Gladstone Institutes for the'Decoding Broken Hearts' initiative on'Special Report.' A simple selfie could hold hidden clues to one's biological age -- and even how long they'll live. That's according to researchers from Mass General Brigham, who developed a deep-learning algorithm called FaceAge. Using a photo of someone's face, the artificial intelligence tool generates predictions of the subject's biological age, which is the rate at which they are aging as opposed to their chronological age. FaceAge also predicts survival outcomes for people with cancer, according to a press release from MGB.

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.

For an increasing number of women over 40, age really is just a number. It may not be possible to stop the passage of time -- but certain healthy habits can help slow down biological age, experts say. "As we age, our abilities to perform certain physical and cognitive tasks decline, while our risks for disease and ultimately death increase," Chris Mirabile, CEO and founder of NOVOS, a longevity supplements company in New York, told Fox News Digital. "Although these changes are correlated with chronological age, biological age is a more accurate predictor, because it looks at individuals and how well – or poorly – they are aging." If a 40-year-old woman has a biological age of 35, it implies that she is biologically in the same place as an average 35-year-old, Mirabile said – which means a significant reduction in risk for disease and death, plus an increased capacity for activities associated with a high quality of life.