AI could predict who will have a heart attack

For all the modern marvels of cardiology, we struggle to predict who will have a heart attack. Many people never get screened at all. Now, startups like Bunkerhill Health, Nanox.AI, and HeartLung Technologies are applying AI algorithms to screen millions of CT scans for early signs of heart disease. This technology could be a breakthrough for public health, applying an old tool to uncover patients whose high risk for a heart attack is hiding in plain sight. But it remains unproven at scale while raising thorny questions about implementation and even how we define disease. 

Last year, an estimated 20 million Americans had chest CT scans done, after an event like a car accident or to screen for lung cancer. Frequently, they show evidence of coronary artery calcium (CAC), a marker for heart attack risk, that is buried or not mentioned in a radiology report focusing on ruling out bony injuries, life-threatening internal trauma, or cancer.

Dedicated testing for CAC remains an underutilized method of predicting heart attack risk. Over decades, plaque in heart arteries moves through its own life cycle, hardening from lipid-rich residue into calcium. Heart attacks themselves typically occur when younger, lipid-rich plaque unpredictably ruptures, kicking off a clotting cascade of inflammation that ultimately blocks the heart’s blood supply. Calcified plaque is generally stable, but finding CAC suggests that younger, more rupture-prone plaque is likely present too. 

Coronary artery calcium can often be spotted on chest CTs, and its concentration can be subjectively described. Normally, quantifying a person’s CAC score involves obtaining a heart-specific CT scan. Algorithms that calculate CAC scores from routine chest CTs, however, could massively expand access to this metric. In practice, these algorithms could then be deployed to alert patients and their doctors about abnormally high scores, encouraging them to seek further care. Today, the footprint of the startups offering AI-derived CAC scores is not large, but it is growing quickly. As their use grows, these algorithms may identify high-risk patients who are traditionally missed or who are on the margins of care. 

Historically, CAC scans were believed to have marginal benefit and were marketed to the worried well. Even today, most insurers won’t cover them. Attitudes, though, may be shifting. More expert groups are endorsing CAC scores as a way to refine cardiovascular risk estimates and persuade skeptical patients to start taking statins. 

The promise of AI-derived CAC scores is part of a broader trend toward mining troves of medical data to spot otherwise undetected disease. But while it seems promising, the practice raises plenty of questions. For example, CAC scores ­haven’t proved useful as a blunt instrument for universal screening. A 2022 Danish study evaluating a population-based program, for example, showed no benefit in mortality rates for patients who had undergone CAC screening tests. If AI delivered this information automatically, would the calculus really shift? 

And with widespread adoption, abnormal CAC scores will become common. Who follows up on these findings? “Many health systems aren’t yet set up to act on incidental calcium findings at scale,” says Nishith Khandwala, the cofounder of Bunkerhill Health. Without a standard procedure for doing so, he says, “you risk creating more work than value.” 

There’s also the question of whether these AI-generated scores would actually improve patient care. For a symptomatic patient, a CAC score of zero may offer false reassurance. For the asymptomatic patient with a high CAC score, the next steps remain uncertain. Beyond statins, it isn’t clear if these patients would benefit from starting costly cholesterol-lowering drugs such as Repatha or other PCSK9-inhibitors. It may encourage some to pursue unnecessary but costly downstream procedures that could even end up doing harm. Currently, AI-derived CAC scoring is not reimbursed as a separate service by Medicare or most insurers. The business case for this technology today, effectively, lies in these potentially perverse incentives. 

At a fundamental level, this approach could actually change how we define disease. Adam Rodman, a hospitalist and AI expert at Beth Israel Deaconess Medical Center in Boston, has observed that AI-derived CAC scores share similarities with the “incidentaloma,” a term coined in the 1980s to describe unexpected findings on CT scans. In both cases, the normal pattern of diagnosis—in which doctors and patients deliberately embark on testing to figure out what’s causing a specific problem—were fundamentally disrupted. But, as Rodman notes, incidentalomas were still found by humans reviewing the scans. 

Now, he says, we are entering an era of “machine-based nosology,” where algorithms define diseases on their own terms. As machines make more diagnoses, they may catch things we miss. But Rodman and I began to wonder if a two-tiered diagnostic future may emerge, where “haves” pay for brand-name algorithms while “have-nots” settle for lesser alternatives. 

For patients who have no risk factors or are detached from regular medical care, an AI-derived CAC score could potentially catch problems earlier and rewrite the script. But how these scores reach people, what is done about them, and whether they can ultimately improve patient outcomes at scale remain open questions. For now—holding the pen as they toggle between patients and algorithmic outputs—clinicians still matter. 

Vishal Khetpal is a fellow in cardiovascular disease. The views expressed in this article do not represent those of his employers. 

This retina implant lets people with vision loss do a crossword puzzle

Science Corporation—a competitor to Neuralink founded by the former president of Elon Musk’s brain-interface venture—has leapfrogged its rival after acquiring, at a fire-sale price, a vision implant that’s in advanced testing,.

The implant produces a form of “artificial vision” that lets some patients read text and do crosswords, according to a report published in the New England Journal of Medicine today.

The implant is a microelectronic chip placed under the retina. Using signals from a camera mounted on a pair of glasses, the chip emits bursts of electricity in order to bypass photoreceptor cells damaged by macular degeneration, the leading cause of vision loss in elderly people.

“The magnitude of the effect is what’s notable,” says José-Alain Sahel, a University of Pittsburgh vision scientist who led testing of the system, which is called PRIMA. “There’s a patient in the UK and she is reading the pages of a regular book, which is unprecedented.”  

Until last year, the device was being developed by Pixium Vision, a French startup cofounded by Sahel, which faced bankruptcy after it couldn’t raise more cash.  

That’s when Science Corporation swept in to purchase the company’s assets for about €4 million ($4.7 million), according to court filings.

“Science was able to buy it for very cheap just when the study was coming out, so it was good timing for them,” says Sahel. “They could quickly access very advanced technology that’s closer to the market, which is good for a company to have.”

Science was founded in 2021 by Max Hodak, the first president of Neuralink, after his sudden departure from that company. Since its founding, Science has raised around $290 million, according to the venture capital database Pitchbook, and used the money to launch broad-ranging exploratory research on brain interfaces and new types of vision treatments.

“The ambition here is to build a big, standalone medical technology company that would fit in with an Apple, Samsung, or an Alphabet,” Hodak said in an interview at Science’s labs in Alameda, California in September. “The goal is to change the world in important ways … but we need to make money in order to invest in these programs.”

By acquiring the PRIMA implant program, Science effectively vaulted past years of development and testing. The company has requested approval to sell the eye chip in Europe and is in discussions with regulators in the US.

Unlike Neuralink’s implant, which records brain signals so paralyzed recipients can use their thoughts to move a computer mouse, the retina chip sends information into the brain to produce vision. Because the retina is an outgrowth of the brain, the chip qualifies as a type of brain-computer interface.

Artificial vision systems have been studied for years and one, called the Argus II, even reached the market and was installed in the eyes of about 400 people. But that product was later withdrawn after it proved to be a money-loser, according to Cortigent, the company that now owns that technology.

Thirty-eight patients in Europe received a PRIMA implant in one eye. On average, the study found, they were able to read five additional lines on a vision chart—the kind with rows of letters, each smaller than the last. Some of that improvement was due to what Sahel calls “various tricks” like using a zoom function, which allows patients to zero in on text they want to read.

The type of vision loss being treated with the new implant is called geographic atrophy, in which patients have peripheral vision but can’t make out objects directly in front of them, like words or faces. According to Prevent Blindness, an advocacy organization, this type of central vision loss affects around one in 10 people over 80.  

The implant was originally designed starting 20 years ago by Daniel Palanker, a laser expert and now a professor at Stanford University, who says his breakthrough was realizing that light beams could supply both energy and information to a chip placed under the retina. Other implants, like Argus II, use a wire, which adds complexity.

“The chip has no brains at all. It just turns light into electrical current that flows into the tissue,” says Palanker. “Patients describe the color they see as yellowish blue or sun color.”

The system works using a wearable camera that records a scene and then blasts bright infrared light into the eye, using a wavelength humans can’t see. That light hits the chip, which is covered by “what are basically tiny solar panels,” says Palanker. “We just try to replace the photoreceptors with a photo-array.”

A diagram of how a visual scene could be represented by a retinal implant.
COURTESY SCIENCE CORPORATION

The current system produces about 400 spots of vision, which lets users make out the outlines of words and objects. Palanaker says a next-generation device will have five times as many “pixels” and should let people see more: “What we discovered in the trial is that even though you stimulate individual pixels, patients perceive it as continuous. The patient says ‘I see a line,’ “I see a letter.’”

Palanker says it will be important to keep improving the system because “the market size depends on the quality of the vision produced.”

When Pixium teetered on insolvency, Palanker says, he helped search for a buyer, meeting with Hodak. “It was a fire sale, not a celebration,” he says. “But for me it’s a very lucky outcome, because it means the product is going forward. And the purchase price doesn’t really matter, because there’s a big investment needed to bring it to market. It’s going to cost money.”  

Photo of the PRIMA Glasses and Pocket Processor.
The PRIMA artificial vision system has a battery pack/controller and an eye-mounted camera.
COURTESY SCIENCE CORPORATION

During a visit to Science’s headquarters, Hodak described the company’s effort to redesign the system into something sleeker and more user-friendly. In the original design, in addition to the wearable camera, the patient has to carry around a bulky controller containing a battery and laser, as well as buttons to zoom in and out. 

But Science has already prototyped a version in which those electronics are squeezed into what look like an extra-large pair of sunglasses.

“The implant is great, but we’ll have new glasses on patients fairly shortly,” Hodak says. “This will substantially improve their ability to have it with them all day.” 

Other companies also want to treat blindness with brain-computer interfaces, but some think it might be better to send signals directly into the brain. This year, Neuralink has been touting plans for “Blindsight,” a project to send electrical signals directly into the brain’s visual cortex, bypassing the retina entirely. It has yet to test the approach in a person.

This startup thinks slime mold can help us design better cities

It is a yellow blob with no brain, yet some researchers believe a curious organism known as slime mold could help us build more resilient cities.

Humans have been building cities for 6,000 years, but slime mold has been around for 600 million. The team behind a new startup called Mireta wants to translate the organism’s biological superpowers into algorithms that might help improve transit times, alleviate congestion, and minimize climate-related disruptions in cities worldwide.

Mireta’s algorithm mimics how slime mold efficiently distributes resources through branching networks. The startup’s founders think this approach could help connect subway stations, design bike lanes, or optimize factory assembly lines. They claim its software can factor in flood zones, traffic patterns, budget constraints, and more.

“It’s very rational to think that some [natural] systems or organisms have actually come up with clever solutions to problems we share,” says Raphael Kay, Mireta’s cofounder and head of design, who has a background in architecture and mechanical engineering and is currently a PhD candidate in materials science and mechanical engineering at Harvard University.

As urbanization continues—about 60% of the global population will live in metropolises by 2030—cities must provide critical services while facing population growth, aging infrastructure, and extreme weather caused by climate change. Kay, who has also studied how microscopic sea creatures could help researchers design zero-energy buildings, believes nature’s time-tested solutions may offer a path toward more adaptive urban systems.

Officially known as Physarum polycephalum, slime mold is neither plant, animal, nor fungus but a single-­celled organism older than dinosaurs. When searching for food, it extends tentacle-like projections in multiple directions simultaneously. It then doubles down on the most efficient paths that lead to food while abandoning less productive routes. This process creates optimized networks that balance efficiency with resilience—a sought-after quality in transportation and infrastructure systems.

The organism’s ability to find the shortest path between multiple points while maintaining backup connections has made it a favorite among researchers studying network design. Most famously, in 2010 researchers at Hokkaido University reported results from an experiment in which they dumped a blob of slime mold onto a detailed map of Tokyo’s railway system, marking major stations with oat flakes. At first the brainless organism engulfed the entire map. Days later, it had pruned itself back, leaving behind only the most efficient pathways. The result closely mirrored Tokyo’s actual rail network.

Since then, researchers worldwide have used slime mold to solve mazes and even map the dark matter holding the universe together. Experts across Mexico, Great Britain, and the Iberian peninsula have tasked the organism with redesigning their roadways—though few of these experiments have translated into real-world upgrades.

Historically, researchers working with the organism would print a physical map and add slime mold onto it. But Kay believes that Mireta’s approach, which replicates slime mold’s pathway-building without requiring actual organisms, could help solve more complex problems. Slime mold is visible to the naked eye, so Kay’s team studied how the blobs behave in the lab, focusing on the key behaviors that make these organisms so good at creating efficient networks. Then they translated these behaviors into a set of rules that became an algorithm.

Some experts aren’t convinced. According to Geoff Boeing, an associate professor at the University of Southern California’s Department of Urban Planning and Spatial Analysis, such algorithms don’t address “the messy realities of entering a room with a group of stakeholders and co-visioning a future for their community.” Modern urban planning problems, he says, aren’t solely technical issues: “It’s not that we don’t know how to make infrastructure networks efficient, resilient, connected—it’s that it’s politically challenging to do so.”

Michael Batty, a professor emeritus at University College London’s Centre for Advanced Spatial Analysis, finds the concept more promising. “There is certainly potential for exploration,” he says, noting that humans have long drawn parallels between biological systems and cities. For decades now, designers have looked to nature for ideas—think ventilation systems inspired by termite mounds or bullet trains modeled after the kingfisher’s beak

Like Boeing, Batty worries that such algorithms could reinforce top-down planning when most cities grow from the bottom up. But for Kay, the algorithm’s beauty lies in how it mimics bottom-up biological growth—like the way slime mold starts from multiple points and connects organically rather than following predetermined paths. 

Since launching earlier this year, Mireta, which is based in Cambridge, Massachusetts, has worked on about five projects. And slime mold is just the beginning. The team is also looking at algorithms inspired by ants, which leave chemical trails that strengthen with use and have their own decentralized solutions for network optimization. “Biology has solved just about every network problem you can imagine,” says Kay.

Elissaveta M. Brandon is an independent journalist interested in how design, culture, and technology shape the way we live.

The race to make the perfect baby is creating an ethical mess

Consider, if you will, the translucent blob in the eye of a microscope: a human blastocyst, the biological specimen that emerges just five days or so after a fateful encounter between egg and sperm. This bundle of cells, about the size of a grain of sand pulled from a powdery white Caribbean beach, contains the coiled potential of a future life: 46 chromosomes, thousands of genes, and roughly six billion base pairs of DNA—an instruction manual to assemble a one-of-a-kind human.

Now imagine a laser pulse snipping a hole in the blastocyst’s outermost shell so a handful of cells can be suctioned up by a microscopic pipette. This is the moment, thanks to advances in genetic sequencing technology, when it becomes possible to read virtually that entire instruction manual.

An emerging field of science seeks to use the analysis pulled from that procedure to predict what kind of a person that embryo might become. Some parents turn to these tests to avoid passing on devastating genetic disorders that run in their families. A much smaller group, driven by dreams of Ivy League diplomas or attractive, well-behaved offspring, are willing to pay tens of thousands of dollars to optimize for intelligence, appearance, and personality. Some of the most eager early boosters of this technology are members of the Silicon Valley elite, including tech billionaires like Elon Musk, Peter Thiel, and Coinbase CEO Brian Armstrong. 

Embryo selection is less like a build-a-baby workshop and more akin to a store where parents can shop for their future children from several available models—complete with stat cards.

But customers of the companies emerging to provide it to the public may not be getting what they’re paying for. Genetics experts have been highlighting the potential deficiencies of this testing for years. A 2021 paper by members of the European Society of Human Genetics said, “No clinical research has been performed to assess its diagnostic effectiveness in embryos. Patients need to be properly informed on the limitations of this use.” And a paper published this May in the Journal of Clinical Medicine echoed this concern and expressed particular reservations about screening for psychiatric disorders and non-­disease-related traits: “Unfortunately, no clinical research has to date been published comprehensively evaluating the effectiveness of this strategy [of predictive testing]. Patient awareness regarding the limitations of this procedure is paramount.”    

Moreover, the assumptions underlying some of this work—that how a person turns out is the product not of privilege or circumstance but of innate biology—have made these companies a political lightning rod. 

SELMAN DESIGN

As this niche technology begins to make its way toward the mainstream, scientists and ethicists are racing to confront the implications—for our social contract, for future generations, and for our very understanding of what it means to be human.


Preimplantation genetic testing (PGT), while still relatively rare, is not new. Since the 1990s, parents undergoing in vitro fertilization have been able to access a number of genetic tests before choosing which embryo to use. A type known as PGT-M can detect single-gene disorders like cystic fibrosis, sickle cell anemia, and Huntington’s disease. PGT-A can ascertain the sex of an embryo and identify chromosomal abnormalities that can lead to conditions like Down syndrome or reduce the chances that an embryo will implant successfully in the uterus. PGT-SR helps parents avoid embryos with issues such as duplicated or missing segments of the chromosome.

Those tests all identify clear-cut genetic problems that are relatively easy to detect, but most of the genetic instruction manual included in an embryo is written in far more nuanced code. In recent years, a fledgling market has sprung up around a new, more advanced version of the testing process called PGT-P: preimplantation genetic testing for polygenic disorders (and, some claim, traits)—that is, outcomes determined by the elaborate interaction of hundreds or thousands of genetic variants.

In 2020, the first baby selected using PGT-P was born. While the exact figure is unknown, estimates put the number of children who have now been born with the aid of this technology in the hundreds. As the technology is commercialized, that number is likely to grow.

Embryo selection is less like a build-a-baby workshop and more akin to a store where parents can shop for their future children from several available models—complete with stat cards indicating their predispositions.

A handful of startups, armed with tens of millions of dollars of Silicon Valley cash, have developed proprietary algorithms to compute these stats—analyzing vast numbers of genetic variants and producing a “polygenic risk score” that shows the probability of an embryo developing a variety of complex traits.  

For the last five years or so, two companies—Genomic Prediction and Orchid—have dominated this small landscape, focusing their efforts on disease prevention. But more recently, two splashy new competitors have emerged: Nucleus Genomics and Herasight, which have rejected the more cautious approach of their predecessors and waded into the controversial territory of genetic testing for intelligence. (Nucleus also offers tests for a wide variety of other behavioral and appearance-related traits.) 

The practical limitations of polygenic risk scores are substantial. For starters, there is still a lot we don’t understand about the complex gene interactions driving polygenic traits and disorders. And the biobank data sets they are based on tend to overwhelmingly represent individuals with Western European ancestry, making it more difficult to generate reliable scores for patients from other backgrounds. These scores also lack the full context of environment, lifestyle, and the myriad other factors that can influence a person’s characteristics. And while polygenic risk scores can be effective at detecting large, population-level trends, their predictive abilities drop significantly when the sample size is as tiny as a single batch of embryos that share much of the same DNA.

The medical community—including organizations like the American Society of Human Genetics, the American College of Medical Genetics and Genomics, and the American Society for Reproductive Medicine—is generally wary of using polygenic risk scores for embryo selection. “The practice has moved too fast with too little evidence,” the American College of Medical Genetics and Genomics wrote in an official statement in 2024.

But beyond questions of whether evidence supports the technology’s effectiveness, critics of the companies selling it accuse them of reviving a disturbing ideology: eugenics, or the belief that selective breeding can be used to improve humanity. Indeed, some of the voices who have been most confident that these methods can successfully predict nondisease traits have made startling claims about natural genetic hierarchies and innate racial differences.

What everyone can agree on, though, is that this new wave of technology is helping to inflame a centuries-old debate over nature versus nurture.


The term “eugenics” was coined in 1883 by a British anthropologist and statistician named Sir Francis Galton, inspired in part by the work of his cousin Charles Darwin. He derived it from a Greek word meaning “good in stock, hereditarily endowed with noble qualities.”

Some of modern history’s darkest chapters have been built on Galton’s legacy, from the Holocaust to the forced sterilization laws that affected certain groups in the United States well into the 20th century. Modern science has demonstrated the many logical and empirical problems with Galton’s methodology. (For starters, he counted vague concepts like “eminence”—as well as infections like syphilis and tuberculosis—as heritable phenotypes, meaning characteristics that result from the interaction of genes and environment.)

Yet even today, Galton’s influence lives on in the field of behavioral genetics, which investigates the genetic roots of psychological traits. Starting in the 1960s, researchers in the US began to revisit one of Galton’s favorite methods: twin studies. Many of these studies, which analyzed pairs of identical and fraternal twins to try to determine which traits were heritable and which resulted from socialization, were funded by the US government. The most well-known of these, the Minnesota Twin Study, also accepted grants from the Pioneer Fund, a now defunct nonprofit that had promoted eugenics and “race betterment” since its founding in 1937. 

The nature-versus-nurture debate hit a major inflection point in 2003, when the Human Genome Project was declared complete. After 13 years and at a cost of nearly $3 billion, an international consortium of thousands of researchers had sequenced 92% of the human genome for the first time.

Today, the cost of sequencing a genome can be as low as $600, and one company says it will soon drop even further. This dramatic reduction has made it possible to build massive DNA databases like the UK Biobank and the National Institutes of Health’s All of Us, each containing genetic data from more than half a million volunteers. Resources like these have enabled researchers to conduct genome-wide association studies, or GWASs, which identify correlations between genetic variants and human traits by analyzing single-nucleotide polymorphisms (SNPs)—the most common form of genetic variation between individuals. The findings from these studies serve as a reference point for developing polygenic risk scores.

Most GWASs have focused on disease prevention and personalized medicine. But in 2011, a group of medical researchers, social scientists, and economists launched the Social Science Genetic Association Consortium (SSGAC) to investigate the genetic basis of complex social and behavioral outcomes. One of the phenotypes they focused on was the level of education people reached.

“It was a bit of a phenotype of convenience,” explains Patrick Turley, an economist and member of the steering committee at SSGAC, given that educational attainment is routinely recorded in surveys when genetic data is collected. Still, it was “clear that genes play some role,” he says. “And trying to understand what that role is, I think, is really interesting.” He adds that social scientists can also use genetic data to try to better “understand the role that is due to nongenetic pathways.”

Many on the left are generally willing to allow that any number of traits, from addiction to obesity, are genetically influenced. Yet heritable cognitive ability seems to be “beyond the pale for us to integrate as a source of difference.”

The work immediately stirred feelings of discomfort—not least among the consortium’s own members, who feared that they might unintentionally help reinforce racism, inequality, and genetic determinism. 

It’s also created quite a bit of discomfort in some political circles, says Kathryn Paige Harden, a psychologist and behavioral geneticist at the University of Texas in Austin, who says she has spent much of her career making the unpopular argument to fellow liberals that genes are relevant predictors of social outcomes. 

Harden thinks a strength of those on the left is their ability to recognize “that bodies are different from each other in a way that matters.” Many are generally willing to allow that any number of traits, from addiction to obesity, are genetically influenced. Yet, she says, heritable cognitive ability seems to be “beyond the pale for us to integrate as a source of difference that impacts our life.” 

Harden believes that genes matter for our understanding of traits like intelligence, and that this should help shape progressive policymaking. She gives the example of an education department seeking policy interventions to improve math scores in a given school district. If a polygenic risk score is “as strongly correlated with their school grades” as family income is, she says of the students in such a district, then “does deliberately not collecting that [genetic] information, or not knowing about it, make your research harder [and] your inferences worse?”

To Harden, persisting with this strategy of avoidance for fear of encouraging eugenicists is a mistake. If “insisting that IQ is a myth and genes have nothing to do with it was going to be successful at neutralizing eugenics,” she says, “it would’ve won by now.”

Part of the reason these ideas are so taboo in many circles is that today’s debate around genetic determinism is still deeply infused with Galton’s ideas—and has become a particular fixation among the online right. 

SELMAN DESIGN

After Elon Musk took over Twitter (now X) in 2022 and loosened its restrictions on hate speech, a flood of accounts started sharing racist posts, some speculating about the genetic origins of inequality while arguing against immigration and racial integration. Musk himself frequently reposts and engages with accounts like Crémieux Recueil, the pen name of independent researcher Jordan Lasker, who has written about the “Black-White IQ gap,” and i/o, an anonymous account that once praised Musk for “acknowledging data on race and crime,” saying it “has done more to raise awareness of the disproportionalities observed in these data than anything I can remember.” (In response to allegations that his research encourages eugenics, Lasker wrote to MIT Technology Review, “The popular understanding of eugenics is about coercion and cutting people cast as ‘undesirable’ out of the breeding pool. This is nothing like that, so it doesn’t qualify as eugenics by that popular understanding of the term.” After going to print, i/o wrote in an email, “Just because differences in intelligence at the individual level are largely heritable, it does not mean that group differences in measured intelligence … are due to genetic differences between groups,” but that the latter is not “scientifically settled” and “an extremely important (and necessary) research area that should be funded rather than made taboo.” He added, “I’ve never made any argument against racial integration or intermarriage or whatever.” X and Musk did not respond to requests for comment.)

Harden, though, warns against discounting the work of an entire field because of a few noisy neoreactionaries. “I think there can be this idea that technology is giving rise to the terrible racism,” she says. The truth, she believes, is that “the racism has preexisted any of this technology.”


In 2019, a company called Genomic Prediction began to offer the first preimplantation polygenic testing that had ever been made commercially available. With its LifeView Embryo Health Score, prospective parents are able to assess their embryos’ predisposition to genetically complex health problems like cancer, diabetes, and heart disease. Pricing for the service starts at $3,500. Genomic Prediction uses a technique called an SNP array, which targets specific sites in the genome where common variants occur. The results are then cross-checked against GWASs that show correlations between genetic variants and certain diseases.

Four years later, a company named Orchid began offering a competing test. Orchid’s Whole Genome Embryo Report distinguished itself by claiming to sequence more than 99% of an embryo’s genome, allowing it to detect novel mutations and, the company says, diagnose rare diseases more accurately. For $2,500 per embryo, parents can access polygenic risk scores for 12 disorders, including schizophrenia, breast cancer, and hypothyroidism. 

Orchid was founded by a woman named Noor Siddiqui. Before getting undergraduate and graduate degrees from Stanford, she was awarded the Thiel fellowship—a $200,000 grant given to young entrepreneurs willing to work on their ideas instead of going to college—back when she was a teenager, in 2012. This set her up to attract attention from members of the tech elite as both customers and financial backers. Her company has raised $16.5 million to date from investors like Ethereum founder Vitalik Buterin, former Coinbase CTO Balaji Srinivasan, and Armstrong, the Coinbase CEO.

In August Siddiqui made the controversial suggestion that parents who choose not to use genetic testing might be considered irresponsible. “Just be honest: you’re okay with your kid potentially suffering for life so you can feel morally superior …” she wrote on X.

Americans have varied opinions on the emerging technology. In 2024, a group of bioethicists surveyed 1,627 US adults to determine attitudes toward a variety of polygenic testing criteria. A large majority approved of testing for physical health conditions like cancer, heart disease, and diabetes. Screening for mental health disorders, like depression, OCD, and ADHD, drew a more mixed—but still positive—response. Appearance-related traits, like skin color, baldness, and height, received less approval as something to test for.

Intelligence was among the most contentious traits—unsurprising given the way it has been weaponized throughout history and the lack of cultural consensus on how it should even be defined. (In many countries, intelligence testing for embryos is heavily regulated; in the UK, the practice is banned outright.) In the 2024 survey, 36.9% of respondents approved of preimplantation genetic testing for intelligence, 40.5% disapproved, and 22.6% said they were uncertain.

Despite the disagreement, intelligence has been among the traits most talked about as targets for testing. From early on, Genomic Prediction says, it began receiving inquiries “from all over the world” about testing for intelligence, according to Diego Marin, the company’s head of global business development and scientific affairs.

At one time, the company offered a predictor for what it called “intellectual disability.” After some backlash questioning both the predictive capacity and the ethics of these scores, the company discontinued the feature. “Our mission and vision of this company is not to improve [a baby], but to reduce risk for disease,” Marin told me. “When it comes to traits about IQ or skin color or height or something that’s cosmetic and doesn’t really have a connotation of a disease, then we just don’t invest in it.”

Orchid, on the other hand, does test for genetic markers associated with intellectual disability and developmental delay. But that may not be all. According to one employee of the company, who spoke on the condition of anonymity, intelligence testing is also offered to “high-roller” clients. According to this employee, another source close to the company, and reporting in the Washington Post, Musk used Orchid’s services in the conception of at least one of the children he shares with the tech executive Shivon Zilis. (Orchid, Musk, and Zilis did not respond to requests for comment.)


I met Kian Sadeghi, the 25-year-old founder of New York–based Nucleus Genomics, on a sweltering July afternoon in his SoHo office. Slight and kinetic, Sadeghi spoke at a machine-gun pace, pausing only occasionally to ask if I was keeping up. 

Sadeghi had modified his first organism—a sample of brewer’s yeast—at the age of 16. As a high schooler in 2016, he was taking a course on CRISPR-Cas9 at a Brooklyn laboratory when he fell in love with the “beautiful depth” of genetics. Just a few years later, he dropped out of college to build “a better 23andMe.” 

His company targets what you might call the application layer of PGT-P, accepting data from IVF clinics—and even from the competitors mentioned in this story—and running its own computational analysis.

“Unlike a lot of the other testing companies, we’re software first, and we’re consumer first,” Sadeghi told me. “It’s not enough to give someone a polygenic score. What does that mean? How do you compare them? There’s so many really hard design problems.”

Like its competitors, Nucleus calculates its polygenic risk scores by comparing an individual’s genetic data with trait-associated variants identified in large GWASs, providing statistically informed predictions. 

Nucleus provides two displays of a patient’s results: a Z-score, plotted from –4 to 4, which explains the risk of a certain trait relative to a population with similar genetic ancestry (for example, if Embryo #3 has a 2.1 Z-score for breast cancer, its risk is higher than average), and an absolute risk score, which includes relevant clinical factors (Embryo #3 has a minuscule actual risk of breast cancer, given that it is male).

The real difference between Nucleus and its competitors lies in the breadth of what it claims to offer clients. On its sleek website, prospective parents can sort through more than 2,000 possible diseases, as well as traits from eye color to IQ. Access to the Nucleus Embryo platform costs $8,999, while the company’s new IVF+ offering—which includes one IVF cycle with a partner clinic, embryo screening for up to 20 embryos, and concierge services throughout the process—starts at $24,999.

“Maybe you want your baby to have blue eyes versus green eyes,” Nucleus founder Kian Sadeghi said at a June event. “That is up to the liberty of the parents.”

Its promises are remarkably bold. The company claims to be able to forecast a propensity for anxiety, ADHD, insomnia, and other mental issues. It says you can see which of your embryos are more likely to have alcohol dependence, which are more likely to be left-handed, and which might end up with severe acne or seasonal allergies. (Nevertheless, at the time of writing, the embryo-screening platform provided this disclaimer: “DNA is not destiny. Genetics can be a helpful tool for choosing an embryo, but it’s not a guarantee. Genetic research is still in it’s [sic] infancy, and there’s still a lot we don’t know about how DNA shapes who we are.”)

To people accustomed to sleep trackers, biohacking supplements, and glucose monitoring, taking advantage of Nucleus’s options might seem like a no-brainer. To anyone who welcomes a bit of serendipity in their life, this level of perceived control may be disconcerting to say the least.

Sadeghi likes to frame his arguments in terms of personal choice. “Maybe you want your baby to have blue eyes versus green eyes,” he told a small audience at Nucleus Embryo’s June launch event. “That is up to the liberty of the parents.”

On the official launch day, Sadeghi spent hours gleefully sparring with X users who accused him of practicing eugenics. He rejects the term, favoring instead “genetic optimization”—though it seems he wasn’t too upset about the free viral marketing. “This week we got five million impressions on Twitter,” he told a crowd at the launch event, to a smattering of applause. (In an email to MIT Technology Review, Sadeghi wrote, “The history of eugenics is one of coercion and discrimination by states and institutions; what Nucleus does is the opposite—genetic forecasting that empowers individuals to make informed decisions.”)

Nucleus has raised more than $36 million from investors like Srinivasan, Alexis Ohanian’s venture capital firm Seven Seven Six, and Thiel’s Founders Fund. (Like Siddiqui, Sadeghi was a recipient of a Thiel fellowship when he dropped out of college; a representative for Thiel did not respond to a request for comment for this story.) Sadeghi has even poached Genomic Prediction’s cofounder Nathan Treff, who is now Nucleus’s chief clinical officer.

Sadeghi’s real goal is to build a one-stop shop for every possible application of genetic sequencing technology, from genealogy to precision medicine to genetic engineering. He names a handful of companies providing these services, with a combined market cap in the billions. “Nucleus is collapsing all five of these companies into one,” he says. “We are not an IVF testing company. We are a genetic stack.”


This spring, I elbowed my way into a packed hotel bar in the Flatiron district, where over a hundred people had gathered to hear a talk called “How to create SUPERBABIES.” The event was part of New York’s Deep Tech Week, so I expected to meet a smattering of biotech professionals and investors. Instead, I was surprised to encounter a diverse and curious group of creatives, software engineers, students, and prospective parents—many of whom had come with no previous knowledge of the subject.

The speaker that evening was Jonathan Anomaly, a soft-spoken political philosopher whose didactic tone betrays his years as a university professor.

Some of Anomaly’s academic work has focused on developing theories of rational behavior. At Duke and the University of Pennsylvania, he led introductory courses on game theory, ethics, and collective action problems as well as bioethics, digging into thorny questions about abortion, vaccines, and euthanasia. But perhaps no topic has interested him so much as the emerging field of genetic enhancement. 

In 2018, in a bioethics journal, Anomaly published a paper with the intentionally provocative title “Defending Eugenics.” He sought to distinguish what he called “positive eugenics”—noncoercive methods aimed at increasing traits that “promote individual and social welfare”—from the so-called “negative eugenics” we know from our history books.

Anomaly likes to argue that embryo selection isn’t all that different from practices we already take for granted. Don’t believe two cousins should be allowed to have children? Perhaps you’re a eugenicist, he contends. Your friend who picked out a six-foot-two Harvard grad from a binder of potential sperm donors? Same logic.

His hiring at the University of Pennsylvania in 2019 caused outrage among some students, who accused him of “racial essentialism.” In 2020, Anomaly left academia, lamenting that “American universities had become an intellectual prison.”

A few years later, Anomaly joined a nascent PGT-P company named Herasight, which was promising to screen for IQ.

At the end of July, the company officially emerged from stealth mode. A representative told me that most of the money raised so far is from angel investors, including Srinivasan, who also invested in Orchid and Nucleus. According to the launch announcement on X, Herasight has screened “hundreds of embryos” for private customers and is beginning to offer its first publicly available consumer product, a polygenic assessment that claims to detect an embryo’s likelihood of developing 17 diseases.

Their marketing materials boast predictive abilities 122% better than Orchid’s and 193% better than Genomic Prediction’s for this set of diseases. (“Herasight is comparing their current predictor to models we published over five years ago,” Genomic Prediction responded in a statement. “Our team is confident our predictors are world-class and are not exceeded in quality by any other lab.”) 

The company did not include comparisons with Nucleus, pointing to the “absence of published performance validations” by that company and claiming it represented a case where “marketing outpaces science.” (“Nucleus is known for world-class science and marketing, and we understand why that’s frustrating to our competitors,” a representative from the company responded in a comment.) 

Herasight also emphasized new advances in “within-family validation” (making sure that the scores are not merely picking up shared environmental factors by comparing their performance between unrelated people to their performance between siblings) and “cross-­ancestry accuracy” (improving the accuracy of scores for people outside the European ancestry groups where most of the biobank data is concentrated). The representative explained that pricing varies by customer and the number of embryos tested, but it can reach $50,000.

When it comes to traits that Jonathan Anomaly believes are genetically encoded, intelligence is just the tip of the iceberg. He has also spoken about the heritability of empathy, violence, religiosity, and political leanings.

Herasight tests for just one non-disease-related trait: intelligence. For a couple who produce 10 embryos, it claims it can detect an IQ spread of about 15 points, from the lowest-scoring embryo to the highest. The representative says the company plans to release a detailed white paper on its IQ predictor in the future.

The day of Herasight’s launch, Musk responded to the company announcement: “Cool.” Meanwhile, a Danish researcher named Emil Kirkegaard, whose research has largely focused on IQ differences between racial groups, boosted the company to his nearly 45,000 followers on X (as well as in a Substack blog), writing, “Proper embryo selection just landed.” Kirkegaard has in fact supported Anomaly’s work for years; he’s posted about him on X and recommended his 2020 book Creating Future People, which he called a “biotech eugenics advocacy book,” adding: “Naturally, I agree with this stuff!”

When it comes to traits that Anomaly believes are genetically encoded, intelligence—which he claimed in his talk is about 75% heritable—is just the tip of the iceberg. He has also spoken about the heritability of empathy, impulse control, violence, passivity, religiosity, and political leanings.

Anomaly concedes there are limitations to the kinds of relative predictions that can be made from a small batch of embryos. But he believes we’re only at the dawn of what he likes to call the “reproductive revolution.” At his talk, he pointed to a technology currently in development at a handful of startups: in vitro gametogenesis. IVG aims to create sperm or egg cells in a laboratory using adult stem cells, genetically reprogrammed from cells found in a sample of skin or blood. In theory, this process could allow a couple to quickly produce a practically unlimited number of embryos to analyze for preferred traits. Anomaly predicted this technology could be ready to use on humans within eight years.

SELMAN DESIGN

“I doubt the FDA will allow it immediately. That’s what places like Próspera are for,” he said, referring to the so-called “startup city” in Honduras, where scientists and entrepreneurs can conduct medical experiments free from the kinds of regulatory oversight they’d encounter in the US.

“You might have a moral intuition that this is wrong,” said Anomaly, “but when it’s discovered that elites are doing it privately … the dominoes are going to fall very, very quickly.” The coming “evolutionary arms race,” he claimed, will “change the moral landscape.”

He added that some of those elites are his own customers: “I could already name names, but I won’t do it.”

After Anomaly’s talk was over, I spoke with a young photographer who told me he was hoping to pursue a master’s degree in theology. He came to the event, he told me, to reckon with the ethical implications of playing God. “Technology is sending us toward an Old-to-New-Testament transition moment, where we have to decide what parts of religion still serve us,” he said soberly.


Criticisms of polygenic testing tend to fall into two camps: skepticism about the tests’ effectiveness and concerns about their ethics. “On one hand,” says Turley from the Social Science Genetic Association Consortium, “you have arguments saying ‘This isn’t going to work anyway, and the reason it’s bad is because we’re tricking parents, which would be a problem.’ And on the other hand, they say, ‘Oh, this is going to work so well that it’s going to lead to enormous inequalities in society.’ It’s just funny to see. Sometimes these arguments are being made by the same people.”

One of those people is Sasha Gusev, who runs a quantitative genetics lab at the Dana-Farber Cancer Institute. A vocal critic of PGT-P for embryo selection, he also often engages in online debates with the far-right accounts promoting race science on X.

Gusev is one of many professionals in his field who believe that because of numerous confounding socioeconomic factors—for example, childhood nutrition, geography, personal networks, and parenting styles—there isn’t much point in trying to trace outcomes like educational attainment back to genetics, particularly not as a way to prove that there’s a genetic basis for IQ.

He adds, “I think there’s a real risk in moving toward a society where you see genetics and ‘genetic endowments’ as the drivers of people’s behavior and as a ceiling on their outcomes and their capabilities.”

Gusev thinks there is real promise for this technology in clinical settings among specific adult populations. For adults identified as having high polygenic risk scores for cancer and cardiovascular disease, he argues, a combination of early screening and intervention could be lifesaving. But when it comes to the preimplantation testing currently on the market, he thinks there are significant limitations—and few regulatory measures or long-term validation methods to check the promises companies are making. He fears that giving these services too much attention could backfire.

“These reckless, overpromised, and oftentimes just straight-up manipulative embryo selection applications are a risk for the credibility and the utility of these clinical tools,” he says.

Many IVF patients have also had strong reactions to publicity around PGT-P. When the New York Times published an opinion piece about Orchid in the spring, angry parents took to Reddit to rant. One user posted, “For people who dont [sic] know why other types of testing are necessary or needed this just makes IVF people sound like we want to create ‘perfect’ babies, while we just want (our) healthy babies.”

Still, others defended the need for a conversation. “When could technologies like this change the mission from helping infertile people have healthy babies to eugenics?” one Redditor posted. “It’s a fine line to walk and an important discussion to have.”

Some PGT-P proponents, like Kirkegaard and Anomaly, have argued that policy decisions should more explicitly account for genetic differences. In a series of blog posts following the 2024 presidential election, under the header “Make science great again,” Kirkegaard called for ending affirmative action laws, legalizing race-based hiring discrimination, and removing restrictions on data sets like the NIH’s All of Us biobank that prevent researchers like him from using the data for race science. Anomaly has criticized social welfare policies for putting a finger on the scale to “punish the high-IQ people.”

Indeed, the notion of genetic determinism has gained some traction among loyalists to President Donald Trump. 

In October 2024, Trump himself made a campaign stop on the conservative radio program The Hugh Hewitt Show. He began a rambling answer about immigration and homicide statistics. “A murderer, I believe this, it’s in their genes. And we got a lot of bad genes in our country right now,” he told the host.

Gusev believes that while embryo selection won’t have much impact on individual outcomes, the intellectual framework endorsed by many PGT-P advocates could have dire social consequences.

“If you just think of the differences that we observe in society as being cultural, then you help people out. You give them better schooling, you give them better nutrition and education, and they’re able to excel,” he says. “If you think of these differences as being strongly innate, then you can fool yourself into thinking that there’s nothing that can be done and people just are what they are at birth.”

For the time being, there are no plans for longitudinal studies to track actual outcomes for the humans these companies have helped bring into the world. Harden, the behavioral geneticist from UT Austin, suspects that 25 years down the line, adults who were once embryos selected on the basis of polygenic risk scores are “going to end up with the same question that we all have.” They will look at their life and wonder, “What would’ve had to change for it to be different?”

Julia Black is a Brooklyn-based features writer and a reporter in residence at Omidyar Network. She has previously worked for Business Insider, Vox, The Information, and Esquire.

Take our quiz: How much do you know about antimicrobial resistance?

This week we had some terrifying news from the World Health Organization: Antibiotics are failing us. A growing number of bacterial infections aren’t responding to these medicines—including common ones that affect the blood, gut, and urinary tract. Get infected with one of these bugs, and there’s a fair chance antibiotics won’t help. 

The scary truth is that a growing number of harmful bacteria and fungi are becoming resistant to drugs. Just a few weeks ago, the US Centers for Disease Control and Prevention published a report finding a sharp rise in infections caused by a dangerous type of bacteria that are resistant to some of the strongest antibiotics. Now, the WHO report shows that the problem is surging around the world.

In this week’s Checkup, we’re trying something a bit different—a little quiz. You’ve probably heard about antimicrobial resistance (AMR) before, but how much do you know about microbes, antibiotics, and the scale of the problem? Here’s our attempt to put the “fun” in “fundamental threat to modern medicine.” Test your knowledge below!

This article first appeared in The Checkup, MIT Technology Review’s weekly biotech newsletter. To receive it in your inbox every Thursday, and read articles like this first, sign up here.

The quest to find out how our bodies react to extreme temperatures

It’s the 25th of June and I’m shivering in my lab-issued underwear in Fort Worth, Texas. Libby Cowgill, an anthropologist in a furry parka, has wheeled me and my cot into a metal-walled room set to 40 °F. A loud fan pummels me from above and siphons the dregs of my body heat through the cot’s mesh from below. A large respirator fits snug over my nose and mouth. The device tracks carbon dioxide in my exhales—a proxy for how my metabolism speeds up or slows down throughout the experiment. Eventually Cowgill will remove my respirator to slip a wire-thin metal temperature probe several pointy inches into my nose.

Cowgill and a graduate student quietly observe me from the corner of their so-called “climate chamber. Just a few hours earlier I’d sat beside them to observe as another volunteer, a 24-year-old personal trainer, endured the cold. Every few minutes, they measured his skin temperature with a thermal camera, his core temperature with a wireless pill, and his blood pressure and other metrics that hinted at how his body handles extreme cold. He lasted almost an hour without shivering; when my turn comes, I shiver aggressively on the cot for nearly an hour straight.

I’m visiting Texas to learn about this experiment on how different bodies respond to extreme climates. “What’s the record for fastest to shiver so far?” I jokingly ask Cowgill as she tapes biosensing devices to my chest and legs. After I exit the cold, she surprises me: “You, believe it or not, were not the worst person we’ve ever seen.”

Climate change forces us to reckon with the knotty science of how our bodies interact with the environment.

Cowgill is a 40-something anthropologist at the University of Missouri who powerlifts and teaches CrossFit in her spare time. She’s small and strong, with dark bangs and geometric tattoos. Since 2022, she’s spent the summers at the University of North Texas Health Science Center tending to these uncomfortable experiments. Her team hopes to revamp the science of thermoregulation. 

While we know in broad strokes how people thermoregulate, the science of keeping warm or cool is mottled with blind spots. “We have the general picture. We don’t have a lot of the specifics for vulnerable groups,” says Kristie Ebi, an epidemiologist with the University of Washington who has studied heat and health for over 30 years. “How does thermoregulation work if you’ve got heart disease?” 

“Epidemiologists have particular tools that they’re applying for this question,” Ebi continues. “But we do need more answers from other disciplines.”

Climate change is subjecting vulnerable people to temperatures that push their limits. In 2023, about 47,000 heat-related deaths are believed to have occurred in Europe. Researchers estimate that climate change could add an extra 2.3 million European heat deaths this century. That’s heightened the stakes for solving the mystery of just what happens to bodies in extreme conditions. 

Extreme temperatures already threaten large stretches of the world. Populations across the Middle East, Asia, and sub-­Saharan Africa regularly face highs beyond widely accepted levels of human heat tolerance. Swaths of the southern US, northern Europe, and Asia now also endure unprecedented lows: The 2021 Texas freeze killed at least 246 people, and a 2023 polar vortex sank temperatures in China’s northernmost city to a hypothermic record of –63.4 °F. 

This change is here, and more is coming. Climate scientists predict that limiting emissions can prevent lethal extremes from encroaching elsewhere. But if emissions keep course, fierce heat and even cold will reach deeper into every continent. About 2.5 billion people in the world’s hottest places don’t have air-­conditioning. When people do, it can make outdoor temperatures even worse, intensifying the heat island effect in dense cities. And neither AC nor radiators are much help when heat waves and cold snaps capsize the power grid.

A thermal image shows a human male holding up peace signs during a test of extreme temperatures.

COURTESY OF MAX G. LEVY
A thermal image shows a human hand during a test of extreme temperatures.

COURTESY OF MAX G. LEVY
A thermal image shows a human foot during a test of extreme temperatures.

COURTESY OF MAX G. LEVY

“You, believe it or not, were not the worst person we’ve ever seen,” the author was told after enduring Cowgill’s “climate chamber.”

Through experiments like Cowgill’s, researchers around the world are revising rules about when extremes veer from uncomfortable to deadly. Their findings change how we should think about the limits of hot and cold—and how to survive in a new world. 

Embodied change

Archaeologists have known for some time that we once braved colder temperatures than anyone previously imagined. Humans pushed into Eurasia and North America well before the last glacial period ended about 11,700 years ago. We were the only hominins to make it out of this era. Neanderthals, Denisovans, and Homo floresiensis all went extinct. We don’t know for certain what killed those species. But we do know that humans survived thanks to protection from clothing, large social networks, and physiological flexibility. Human resilience to extreme temperature is baked into our bodies, behavior, and genetic code. We wouldn’t be here without it. 

“Our bodies are constantly in communication with the environment,” says Cara Ocobock, an anthropologist at the University of Notre Dame who studies how we expend energy in extreme conditions. She has worked closely with Finnish reindeer herders and Wyoming mountaineers. 

But the relationship between bodies and temperature is surprisingly still a mystery to scientists. In 1847, the anatomist Carl Bergmann observed that animal species grow larger in cold climates. The zoologist Joel Asaph Allen noted in 1877 that cold-dwellers had shorter appendages. Then there’s the nose thing: In the 1920s, the British anthropologist Arthur Thomson theorized that people in cold places have relatively long, narrow noses, the better to heat and humidify the air they take in. These theories stemmed from observations of animals like bears and foxes, and others that followed stemmed from studies comparing the bodies of cold-accustomed Indigenous populations with white male control groups. Some, like those having to do with optimization of surface area, do make sense: It seems reasonable that a tall, thin body increases the amount of skin available to dump excess heat. The problem is, scientists have never actually tested this stuff in humans. 

“Our bodies are constantly in communication with the environment.”

Cara Ocobock, anthropologist, University of Notre Dame

Some of what we know about temperature tolerance thus far comes from century-old race science or assumptions that anatomy controls everything. But science has evolved. Biology has matured. Childhood experiences, lifestyles, fat cells, and wonky biochemical feedback loops can contribute to a picture of the body as more malleable than anything imagined before. And that’s prompting researchers to change how they study it.

“If you take someone who’s super long and lanky and lean and put them in a cold climate, are they gonna burn more calories to stay warm than somebody who’s short and broad?” Ocobock says. “No one’s looked at that.”

Ocobock and Cowgill teamed up with Scott Maddux and Elizabeth Cho at the Center for Anatomical Sciences at the University of North Texas Health Fort Worth. All four are biological anthropologists who have also puzzled over whether the rules Bergmann, Allen, and Thomson proposed are actually true. 

For the past four years, the team has been studying how factors like metabolism, fat, sweat, blood flow, and personal history control thermoregulation. 

Your native climate, for example, may influence how you handle temperature extremes. In a unique study of mortality statistics from 1980s Milan, Italians raised in warm southern Italy were more likely to survive heat waves in the northern part of the country. 

Similar trends have appeared in cold climes. Researchers often measure cold tolerance by a person’s “brown adipose,” a type of fat that is specialized for generating heat (unlike white fat, which primarily stores energy). Brown fat is a cold adaptation because it delivers heat without the mechanism of shivering. Studies have linked it to living in cold climates, particularly at young ages. Wouter van Marken Lichtenbelt, the physiologist at Maastricht University who with colleagues discovered brown fat in adults, has shown that this tissue can further activate with cold exposure and even help regulate blood sugar and influence how the body burns other fat. 

That adaptability served as an early clue for the Texas team. They want to know how a person’s response to hot and cold correlates with height, weight, and body shape. What is the difference, Maddux asks, between “a male who’s 6 foot 6 and weighs 240 pounds” and someone else in the same environment “who’s 4 foot 10 and weighs 89 pounds”? But the team also wondered if shape was only part of the story. 

Their multi-year experiment uses tools that anthropologists couldn’t have imagined a century ago—devices that track metabolism in real time and analyze genetics. Each participant gets a CT scan (measuring body shape), a DEXA scan (estimating percentages of fat and muscle), high-resolution 3D scans, and DNA analysis from saliva to examine ancestry genetically. 

Volunteers lie on a cot in underwear, as I did, for about 45 minutes in each climate condition, all on separate days. There’s dry cold, around 40 °F, akin to braving a walk-in refrigerator. Then dry heat and humid heat: 112 °F with 15% humidity and 98 °F with 85% humidity. They call it “going to Vegas” and “going to Houston,” says Cowgill. The chamber session is long enough to measure an effect, but short enough to be safe. 

Before I traveled to Texas, Cowgill told me she suspected the old rules would fall. Studies linking temperature tolerance to race and ethnicity, for example, seemed tenuous because biological anthropologists today reject the concept of distinct races. It’s a false premise, she told me: “No one in biological anthropology would argue that human beings do not vary across the globe—that’s obvious to anyone with eyes. [But] you can’t draw sharp borders around populations.” 

She added, “I think there’s a substantial possibility that we spend four years testing this and find out that really, limb length, body mass, surface area […] are not the primary things that are predicting how well you do in cold and heat.” 

Adaptable to a degree

In July 1995, a week-long heat wave pushed Chicago above 100 °F, killing roughly 500 people. Thirty years later, Ollie Jay, a physiologist at the University of Sydney, can duplicate the conditions of that exceptionally humid heat wave in a climate chamber at his laboratory. 

“We can simulate the Chicago heat wave of ’95. The Paris heat wave of 2003. The heat wave [in early July of this year]  in Europe,” Jay says. “As long as we’ve got the temperature and humidity information, we can re-create those conditions.”

“Everybody has quite an intimate experience of feeling hot, so we’ve got 8 billion experts on how to keep cool,” he says. Yet our internal sense of when heat turns deadly is unreliable. Even professional athletes overseen by experienced medics have died after missing dangerous warning signs. And little research has been done to explore how vulnerable populations such as elderly people, those with heart disease, and low-income communities with limited access to cooling respond to extreme heat. 

Jay’s team researches the most effective strategies for surviving it. He lambastes air-conditioning, saying it demands so much energy that it can aggravate climate change in “a vicious cycle.” Instead, he has monitored people’s vital signs while they use fans and skin mists to endure three hours in humid and dry heat. In results published last year, his research found that fans reduced cardiovascular strain by 86% for people with heart disease in the type of humid heat familiar in Chicago. 

Dry heat was a different story. In that simulation, fans not only didn’t help but actually doubled the rate at which core temperatures rose in healthy older people.

Heat kills. But not without a fight. Your body must keep its internal temperature in a narrow window flanking 98 °F by less than two degrees. The simple fact that you’re alive means you are producing heat. Your body needs to export that heat without amassing much more. The nervous system relaxes narrow blood vessels along your skin. Your heart rate increases, propelling more warm blood to your extremities and away from your organs. You sweat. And when that sweat evaporates, it carries a torrent of body heat away with it. 

This thermoregulatory response can be trained. Studies by van Marken Lichtenbelt have shown that exposure to mild heat increases sweat capacity, decreases blood pressure, and drops resting heart rate. Long-term studies based on Finnish saunas suggest similar correlations

The body may adapt protectively to cold, too. In this case, body heat is your lifeline. Shivering and exercise help keep bodies warm. So can clothing. Cardiovascular deaths are thought to spike in cold weather. But people more adapted to cold seem better able to reroute their blood flow in ways that keep their organs warm without dropping their temperature too many degrees in their extremities. 

Earlier this year, the biological anthropologist Stephanie B. Levy (no relation) reported that New Yorkers who experienced lower average temperatures had more productive brown fat, adding evidence for the idea that the inner workings of our bodies adjust to the climate throughout the year and perhaps even throughout our lives. “Do our bodies hold a biological memory of past seasons?” Levy wonders. “That’s still an open question. There’s some work in rodent models to suggest that that’s the case.”

Although people clearly acclimatize with enough strenuous exposures to either cold or heat, Jay says, “you reach a ceiling.” Consider sweat: Heat exposure can increase the amount you sweat only until your skin is completely saturated. It’s a non­negotiable physical limit. Any additional sweat just means leaking water without carrying away any more heat. “I’ve heard people say we’ll just find a way of evolving out of this—we’ll biologically adapt,” Jay says. “Unless we’re completely changing our body shape, then that’s not going to happen.”

And body shape may not even sway thermoregulation as much as previously believed. The subject I observed, a personal trainer, appeared outwardly adapted for cold: his broad shoulders didn’t even fit in a single CT scan image. Cowgill supposed that this muscle mass insulated him. When he emerged from his session in the 40 °F environment, though, he had finally started shivering—intensely. The researchers covered him in a heated blanket. He continued shivering. Driving to lunch over an hour later in a hot car, he still mentioned feeling cold. An hour after that, a finger prick drew no blood, a sign that blood vessels in his extremities remained constricted. His body temperature fell about half a degree C in the cold session—a significant drop—and his wider build did not appear to shield him from the cold as well as my involuntary shivering protected me. 

I asked Cowgill if perhaps there is no such thing as being uniquely predisposed to hot or cold. “Absolutely,” she said. 

A hot mess

So if body shape doesn’t tell us much about how a person maintains body temperature, and acclimation also runs into limits, then how do we determine how hot is too hot? 

In 2010 two climate change researchers, Steven Sherwood and Matthew Huber, argued that regions around the world become uninhabitable at wet-bulb temperatures of 35 °C, or 95 °F. (Wet-bulb measurements are a way to combine air temperature and relative humidity.) Above 35 °C, a person simply wouldn’t be able to dissipate heat quickly enough. But it turns out that their estimate was too optimistic. 

Researchers “ran with” that number for a decade, says Daniel Vecellio, a bioclimatologist at the University of Nebraska, Omaha. “But the number had never been actually empirically tested.” In 2021 a Pennsylvania State University physiologist, W. Larry Kenney, worked with Vecellio and others to test wet-bulb limits in a climate chamber. Kenney’s lab investigates which combinations of temperature, humidity, and time push a person’s body over the edge. 

Not long after, the researchers came up with their own wet-bulb limit of human tolerance: below 31 °C in warm, humid conditions for the youngest cohort, people in their thermoregulatory prime. Their research suggests that a day reaching 98 °F and 65% humidity, for example, poses danger in a matter of hours, even for healthy people. 

JUSTIN CLEMONS

JUSTIN CLEMONS
three medical team members make preparations around a person on a gurney

JUSTIN CLEMONS

Cowgill and her colleagues Elizabeth Cho (top) and Scott Maddux prepare graduate student Joanna Bui for a “room-temperature test.”

In 2023, Vecellio and Huber teamed up, combining the growing arsenal of lab data with state-of-the-art climate simulations to predict where heat and humidity most threatened global populations: first the Middle East and South Asia, then sub-Saharan Africa and eastern China. And assuming that warming reaches 3 to 4 °C over preindustrial levels this century, as predicted, parts of North America, South America, and northern and central Australia will be next. 

Last June, Vecellio, Huber, and Kenney co-published an article revising the limits that Huber had proposed in 2010. “Why not 35 °C?” explained why the human limits have turned out to be lower than expected. Those initial estimates overlooked the fact that our skin temperature can quickly jump above 101 °F in hot weather, for example, making it harder to dump internal heat.

The Penn State team has published deep dives on how heat tolerance changes with sex and age. Older participants’ wet-bulb limits wound up being even lower—between 27 and 28 °C in warm, humid conditions—and varied more from person to person than they did in young people. “The conditions that we experience now—especially here in North America and Europe, places like that—are well below the limits that we found in our research,” Vecellio says. “We know that heat kills now.”  

What this fast-growing body of research suggests, Vecellio stresses, is that you can’t define heat risk by just one or two numbers. Last year, he and researchers at Arizona State University pulled up the hottest 10% of hours between 2005 and 2020 for each of 96 US cities. They wanted to compare recent heat-health research with historical weather data for a new perspective: How frequently is it so hot that people’s bodies can’t compensate for it? Over 88% of those “hot hours” met that criterion for people in full sun. In the shade, most of those heat waves became meaningfully less dangerous. 

“There’s really almost no one who ‘needs’ to die in a heat wave,” says Ebi, the epidemiologist. “We have the tools. We have the understanding. Essentially all [those] deaths are preventable.”

More than a number

A year after visiting Texas, I called Cowgill to hear what she was thinking after four summers of chamber experiments. She told me that the only rule about hot and cold she currently stands behind is … well, none.

She recalled a recent participant—the smallest man in the study, weighing 114 pounds. “He shivered like a leaf on a tree,” Cowgill says. Normally, a strong shiverer warms up quickly. Core temperature may even climb a little. “This [guy] was just shivering and shivering and shivering and not getting any warmer,” she says. She doesn’t know why this happened. “Every time I think I get a picture of what’s going on in there, we’ll have one person come in and just kind of be a complete exception to the rule,” she says, adding that you can’t just gloss over how much human bodies vary inside and out.

The same messiness complicates physiology studies. 

Jay looks to embrace bodily complexities by improving physiological simulations of heat and the human strain it causes. He’s piloted studies that input a person’s activity level and type of clothing to predict core temperature, dehydration, and cardiovascular strain based on the particular level of heat. One can then estimate the person’s risk on the basis of factors like age and health. He’s also working on physiological models to identify vulnerable groups, inform early-warning systems ahead of heat waves, and possibly advise cities on whether interventions like fans and mists can help protect residents. “Heat is an all-of-­society issue,” Ebi says. Officials could better prepare the public for cold snaps this way too.

“Death is not the only thing we’re concerned about,” Jay adds.  Extreme temperatures bring morbidity and sickness and strain hospital systems: “There’s all these community-level impacts that we’re just completely missing.”

Climate change forces us to reckon with the knotty science of how our bodies interact with the environment. Predicting the health effects is a big and messy matter. 

The first wave of answers from Fort Worth will materialize next year. The researchers will analyze thermal images to crunch data on brown fat. They’ll resolve whether, as Cowgill suspects, your body shape may not sway temperature tolerance as much as previously assumed. “Human variation is the rule,” she says, “not the exception.” 

Max G. Levy is an independent journalist who writes about chemistry, public health, and the environment.

AI is changing how we quantify pain

For years at Orchard Care Homes, a 23‑facility dementia-care chain in northern England, Cheryl Baird watched nurses fill out the Abbey Pain Scale, an observational methodology used to evaluate pain in those who can’t communicate verbally. Baird, a former nurse who was then the facility’s director of quality, describes it as “a tick‑box exercise where people weren’t truly considering pain indicators.”

As a result, agitated residents were assumed to have behavioral issues, since the scale does not always differentiate well between pain and other forms of suffering or distress. They were often prescribed psychotropic sedatives, while the pain itself went untreated.

Then, in January 2021, Orchard Care Homes began a trial of PainChek, a smartphone app that scans a resident’s face for microscopic muscle movements and uses artificial intelligence to output an expected pain score. Within weeks, the pilot unit saw fewer prescriptions and had calmer corridors. “We immediately saw the benefits: ease of use, accuracy, and identifying pain that wouldn’t have been spotted using the old scale,” Baird recalls.

In nursing homes, neonatal units, and ICU wards, researchers are racing to turn pain into something a camera or sensor can score as reliably as blood pressure.

This kind of technology-assisted diagnosis hints at a bigger trend. In nursing homes, neonatal units, and ICU wards, researchers are racing to turn pain—medicine’s most subjective vital sign—into something a camera or sensor can score as reliably as blood pressure. The push has already produced PainChek, which has been cleared by regulators on three continents and has logged more than 10 million pain assessments. Other startups are beginning to make similar inroads in care settings.

The way we assess pain may finally be shifting, but when algorithms measure our suffering, does that change the way we understand and treat it?

Science already understands certain aspects of pain. We know that when you stub your toe, for example, microscopic alarm bells called nociceptors send electrical impulses toward your spinal cord on “express” wires, delivering the first stab of pain, while a slower convoy follows with the dull throb that lingers. At the spinal cord, the signal meets a microscopic switchboard scientists call the gate. Flood that gate with friendly touches—say, by rubbing the bruise—or let the brain return an instruction born of panic or calm, and the gate might muffle or magnify the message before you even become aware of it.

The gate can either let pain signals pass through or block them, depending on other nerve activity and instructions from your brain. Only the signals that succeed in getting past this gate travel up to your brain’s sensory map to help locate the damage, while others branch out to emotion centers that decide how bad it feels. Within milliseconds, those same hubs in the brain shoot fresh orders back down the line, releasing built-in painkillers or stoking the alarm. In other words, pain isn’t a straightforward translation of damage or sensation but a live negotiation between the body and the brain.

But much of how that negotiation plays out is still a mystery. For instance, scientists cannot predict what causes someone to slip from a routine injury into years-long hypersensitivity; the molecular shift from acute to chronic pain is still largely unknown. Phantom-limb pain remains equally puzzling: About two-thirds of amputees feel agony in a part of their body that no longer exists, yet competing theories—cortical remapping, peripheral neuromas, body-schema mismatch—do not explain why they suffer while the other third feel nothing.

The first serious attempt at a system for quantifying pain was introduced in 1921. Patients marked their degree of pain as a point on a blank 10‑centimeter line and clinicians scored the distance in millimeters, converting lived experience into a 0–100 ladder. By 1975, psychologist Ronald Melzack’s McGill Pain Questionnaire offered 78 adjectives like “burning,” “stabbing,” and “throbbing,” so that pain’s texture could join intensity in the chart. Over the past few decades, hospitals have ultimately settled on the 0–10 Numeric Rating Scale.

Yet pain is stubbornly subjective. Feedback from the brain in the form of your reaction can send instructions back down the spinal cord, meaning that expectation and emotion can change how much the same injury hurts. In one trial, volunteers who believed they had received a pain relief cream reported a stimulus as 22% less painful than those who knew the cream was inactive—and a functional magnetic resonance image of their brains showed that the drop corresponded with decreased activity in the parts of the brain that report pain, meaning they really did feel less hurt.

What’s more, pain can also be affected by a slew of external factors. In one study, experimenters applied the same calibrated electrical stimulus to volunteers from Italy, Sweden, and Saudi Arabia, and the ratings varied dramatically. Italian women recorded the highest scores on the 0–10 scale, while Swedish and Saudi participants judged the identical burn several points lower, implying that culture can amplify or dampen the felt intensity of the same experience.

Bias inside the clinic can drive different responses even to the same pain score. A 2024 analysis of discharge notes found that women’s scores were recorded 10% less often than men’s. At a large pediatric emergency department, Black children presenting with limb fractures were roughly 39% less likely to receive an opioid analgesic than their white non-Hispanic peers, even after the researchers controlled for pain score and other clinical factors. Together these studies make clear that an “8 out of 10” does not always result in the same reaction or treatment. And many patients cannot self-report their pain at all—for example, a review of bedside studies concludes that about 70% of intensive-care patients have pain that goes unrecognized or undertreated, a problem the authors link to their impaired communication due to sedation or intubation.

These issues have prompted a search for a better, more objective way to understand and assess pain. Progress in artificial intelligence has brought a new dimension to that hunt.

Research groups are pursuing two broad routes. The first listens underneath the skin. Electrophysiologists strap electrode nets to volunteers and look for neural signatures that rise and fall with administered stimuli. A 2024 machine-learning study reported that one such algorithm could tell with over 80% accuracy, using a few minutes of resting-state EEG, which subjects experienced chronic pain and which were pain-free control participants. Other researchers combine EEG with galvanic skin response and heart-rate variability, hoping a multisignal “pain fingerprint” will provide more robust measurements.

One example of this method is the PMD-200 patient monitor from Medasense, which uses AI-based tools to output pain scores. The device uses physiological patterns like heart rate, sweating, or peripheral temperature changes as the input and focuses on surgical patients, with the goal of helping anesthesiologists adjust doses during operations. In a 2022 study of 75 patients undergoing major abdominal surgery, use of the monitor resulted in lower self-reported pain scores after the operation—a median score of 3 out of 10, versus 5 out of 10 in controls—without an increase in opioid use. The device is authorized by the US Food and Drug Administration and is in use in the United States, the European Union, Canada, and elsewhere.

The second path is behavioral. A grimace, a guarded posture, or a sharp intake of breath correlates with various levels of pain. Computer-vision teams have fed high-speed video of patients’ changing expressions into neural networks trained on the Face Action Coding System (FACS), which was introduced in the late 1970s with the goal of creating an objective and universal system to analyze such expressions—it’s the Rosetta stone of 44 facial micro-movements. In lab tests, those models can flag frames indicating pain from the data set with over 90% accuracy, edging close to the consistency of expert human assessors. Similar approaches mine posture and even sentence fragments in clinical notes, using natural-language processing, to spot phrases like “curling knees to chest” that often correlate with high pain.

PainChek is one of these behavioral models, and it acts like a camera‑based thermometer, but for pain: A care worker opens the app and holds a phone 30 centimeters from a person’s face. For three seconds, a neural network looks for nine particular microscopic movements—upper‑lip raise, brow pinch, cheek tension, and so on—that research has linked most strongly to pain. Then the screen flashes a score of 0 to 42. “There’s a catalogue of ‘action‑unit codes’—facial expressions common to all humans. Nine of those are associated with pain,” explains Kreshnik Hoti, a senior research scientist with PainChek and a co-inventor of the device. This system is built directly on the foundation of FACS. After the scan, the app walks the user through a yes‑or‑no checklist of other signs, like groaning, “guarding,” and sleep disruption, and stores the result on a cloud dashboard that can show trends.

Linking the scan to a human‑filled checklist was, Hoti admits, a late design choice. “Initially, we thought AI should automate everything, but now we see [that] hybrid use—AI plus human input—is our major strength,” he says. Care aides, not nurses, complete most assessments, freeing clinicians to act on the data rather than gather it.

PainChek was cleared by Australia’s Therapeutic Goods Administration in 2017, and national rollout funding from Canberra helped embed it in hundreds of nursing homes in the country. The system has also won authorization in the UK—where expansion began just before covid-19 started spreading and resumed as lockdowns eased—and in Canada and New Zealand, which are running pilot programs. In the US, it’s currently awaiting an FDA decision. Company‑wide data show “about a 25% drop in anti­psychotic use and, in Scotland, a 42% reduction in falls,” Hoti says.

a person holding a phone up in front of an elderly person, whose face is visible on the screen
PainChek is a mobile app that estimates pain scores by applying artificial intelligence to facial scans.
COURTESY OF PAINCHEK

Orchard Care Homes is one of its early adopters. Baird, then the facility’s director of quality, remembers the pre‑AI routine that was largely done “to prove compliance,” she says.

PainChek added an algorithm to that workflow, and the hybrid approach has paid off. Orchard’s internal study of four care homes tracked monthly pain scores, behavioral incidents, and prescriptions. Within weeks, psychotropic scripts fell and residents’ behavior calmed. The ripple effects went beyond pharmacy tallies. Residents who had skipped meals because of undetected dental pain “began eating again,” Baird notes, and “those who were isolated due to pain began socializing.”

Inside Orchard facilities, a cultural shift is underway. When Baird trained new staff, she likened pain “to measuring blood pressure or oxygen,” she says. “We wouldn’t guess those, so why guess pain?” The analogy lands, but getting people fully on board is still a slog. Some nurses insist their clinical judgment is enough; others balk at another login and audit trail. “The sector has been slow to adopt technology, but it’s changing,” Baird says. That’s helped by the fact that administering a full Abbey Pain Scale takes 20 minutes, while a PainChek scan and checklist take less than five.

Engineers at PainChek are now adapting the code for the very youngest patients. PainChek Infant targets babies under one year, whose grimaces flicker faster than adults’. The algorithm, retrained on neonatal faces, detects six validated facial action units based on the well-established Baby Facial Action Coding System. PainChek Infant is starting limited testing in Australia while the company pursues a separate regulatory pathway.

Skeptics raise familiar red flags about these devices. Facial‑analysis AI has a history of skin‑tone bias, for example. Facial analysis may also misread grimaces stemming from nausea or fear. The tool is only as good as the yes‑or‑no answers that follow the scan; sloppy data entry can skew results in either direction. Results lack the broader clinical and interpersonal context a caregiver is likely to have from interacting with individual patients regularly and understanding their medical history. It’s also possible that clinicians might defer too strongly to the algorithm, over-relying on outside judgment and eroding their own.

If PainChek is approved by the FDA this fall, it will be part of a broader effort to create a system of new pain measurement technology. Other startups are pitching EEG headbands for neuropathic pain, galvanic skin sensors that flag breakthrough cancer pain, and even language models that comb nursing notes for evidence of hidden distress. Still, quantifying pain with an external device could be rife with hidden issues, like bias or inaccuracies, that we will uncover only after significant use.

For Baird, the issue is fairly straightforward nonetheless. “I’ve lived with chronic pain and had a hard time getting people to believe me. [PainChek] would have made a huge difference,” she says. If artificial intelligence can give silent sufferers a numerical voice—and make clinicians listen—then adding one more line to the vital‑sign chart might be worth the screen time.

Deena Mousa is a researcher, grantmaker, and journalist focused on global health, economic development, and scientific and technological progress.

Mousa is employed as lead researcher by Open Philanthropy, a funder and adviser focused on high-impact causes, including global health and the potential risks posed by AI. The research team investigates new causes of focus and is not involved in work related to pain management. Mousa has not been involved with any grants related to pain management, although Open Philanthropy has funded research in this area in the past.

How aging clocks can help us understand why we age—and if we can reverse it

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. 

Doctors have long used functional tests that measure their patients’ strength or the distance they can walk, for example, or simply “eyeball” them to guess whether they look fit enough to survive some treatment regimen, says Tamir Chandra, who studies aging at the Mayo Clinic. 

But over the past decade, scientists have been uncovering new methods of looking at the hidden ways our bodies are aging. What they’ve found is changing our understanding of aging itself. 

“Aging clocks” are new scientific tools that can measure how our organs are wearing out, giving us insight into our mortality and health. They hint at our biological age. While chronological age is simply how many birthdays we’ve had, biological age is meant to reflect something deeper. It measures how our bodies are handling the passing of time and—perhaps—lets us know how much more of it we have left. And while you can’t change your chronological age, you just might be able to influence your biological age.

It’s not just scientists who are using these clocks. Longevity influencers like Bryan Johnson often use them to make the case that they are aging backwards. “My telomeres say I’m 10 years old,” Johnson posted on X in April. The Kardashians have tried them too (Khloé was told on TV that her biological age was 12 years below her chronological age). Even my local health-food store offers biological age testing. Some are pushing the use of clocks even further, using them to sell unproven “anti-aging” supplements.

The science is still new, and few experts in the field—some of whom affectionately refer to it as “clock world”—would argue that an aging clock can definitively reveal an individual’s biological age. 

But their work is revealing that aging clocks can offer so much more than an insta-brag, a snake-oil pitch—or even just an eye-catching number. In fact, they are helping scientists unravel some of the deepest mysteries in biology: Why do we age? How do we age? When does aging begin? What does it even mean to age?

Ultimately, and most importantly, they might soon tell us whether we can reverse the whole process.

Clocks kick off

The way your genes work can change. Molecules called methyl groups can attach to DNA, controlling the way genes make proteins. This process is called methylation, and it can potentially occur at millions of points along the genome. These epigenetic markers, as they are known, can switch genes on or off, or increase or decrease how much protein they make. They’re not part of our DNA, but they influence how it works.

In 2011, Steve Horvath, then a biostatistician at the University of California, Los Angeles, took part in a study that was looking for links between sexual orientation and these epigenetic markers. Steve is straight; he says his twin brother, Markus, who also volunteered, is gay.

That study didn’t find a link between DNA methyl­ation and sexual orientation. But when Horvath looked at the data, he noticed a different trend—a very strong link between age and methylation at around 88 points on the genome. He once told me he fell off his chair when he saw it

Many of the affected genes had already been linked to age-related brain and cardiovascular diseases, but it wasn’t clear how methylation might be related to those diseases. 

If a model could work out what average aging looks like, it could potentially estimate whether someone was aging unusually fast or slowly. It could transform medicine and fast-track the search for an anti-aging drug. It could help us understand what aging is, and why it happens at all.

In 2013, Horvath collected methylation data from 8,000 tissue and cell samples to create what he called the Horvath clock—essentially a mathematical model that could estimate age on the basis of DNA methylation at 353 points on the genome. From a tissue sample, it was able to detect a person’s age within a range of 2.9 years.

That clock changed everything. Its publication in 2013 marked the birth of “clock world.” To some, the possibilities were almost endless. If a model could work out what average aging looks like, it could potentially estimate whether someone was aging unusually fast or slowly. It could transform medicine and fast-track the search for an anti-aging drug. It could help us understand what aging is, and why it happens at all.

The epigenetic clock was a success story in “a field that, frankly, doesn’t have a lot of success stories,” says João Pedro de Magalhães, who researches aging at the University of Birmingham, UK.

It took a few years, but as more aging researchers heard about the clock, they began incorporating it into their research and even developing their own clocks. Horvath became a bit of a celebrity. Scientists started asking for selfies with him at conferences, he says. Some researchers even made T-shirts bearing the front page of his 2013 paper.

Some of the many other aging clocks developed since have become notable in their own right. Examples include the PhenoAge clock, which incorporates health data such as blood cell counts and signs of inflammation along with methyl­ation, and the Dunedin Pace of Aging clock, which tells you how quickly or slowly a person is aging rather than pointing to a specific age. Many of the clocks measure methylation, but some look at other variables, such as proteins in blood or certain carbohydrate molecules that attach to such proteins.

Today, there are hundreds or even thousands of clocks out there, says Chiara Herzog, who researches aging at King’s College London and is a member of the Biomarkers of Aging Consortium. Everyone has a favorite. Horvath himself favors his GrimAge clock, which was named after the Grim Reaper because it is designed to predict time to death.

That clock was trained on data collected from people who were monitored for decades, many of whom died in that period. Horvath won’t use it to tell people when they might die of old age, he stresses, saying that it wouldn’t be ethical. Instead, it can be used to deliver a biological age that hints at how long a person might expect to live. Someone who is 50 but has a GrimAge of 60 can assume that, compared with the average 50-year-old, they might be a bit closer to the end.

GrimAge is not perfect. While it can strongly predict time to death given the health trajectory someone is on, no aging clock can predict if someone will start smoking or get a divorce (which generally speeds aging) or suddenly take up running (which can generally slow it). “People are complicated,” Horvath tells MIT Technology Review. “There’s a huge error bar.”

On the whole, the clocks are pretty good at making predictions about health and lifespan. They’ve been able to predict that people over the age of 105 have lower biological ages, which tracks given how rare it is for people to make it past that age. A higher epigenetic age has been linked to declining cognitive function and signs of Alzheimer’s disease, while better physical and cognitive fitness has been linked to a lower epigenetic age.

Black-box clocks

But accuracy is a challenge for all aging clocks. Part of the problem lies in how they were designed. Most of the clocks were trained to link age with methylation. The best clocks will deliver an estimate that reflects how far a person’s biology deviates from the average. Aging clocks are still judged on how well they can predict a person’s chronological age, but you don’t want them to be too close, says Lucas Paulo de Lima Camillo, head of machine learning at Shift Bioscience, who was awarded $10,000 by the Biomarkers of Aging Consortium for developing a clock that could estimate age within a range of 2.55 years.

a cartoon alarm clock shrugging
None of the clocks are precise enough to predict the biological age of a single person. Putting the same biological sample through five different clocks will give you five wildly different results.
LEON EDLER

“There’s this paradox,” says Camillo. If a clock is really good at predicting chronological age, that’s all it will tell you—and it probably won’t reveal much about your biological age. No one needs an aging clock to tell them how many birthdays they’ve had. Camillo says he’s noticed that when the clocks get too close to “perfect” age prediction, they actually become less accurate at predicting mortality.

Therein lies the other central issue for scientists who develop and use aging clocks: What is the thing they are really measuring? It is a difficult question for a field whose members notoriously fail to agree on the basics. (Everything from the definition of aging to how it occurs and why is up for debate among the experts.)

They do agree that aging is incredibly complex. A methylation-based aging clock might tell you about how that collection of chemical markers compares across individuals, but at best, it’s only giving you an idea of their “epigenetic age,” says Chandra. There are probably plenty of other biological markers that might reveal other aspects of aging, he says: “None of the clocks measure everything.” 

We don’t know why some methyl groups appear or disappear with age, either. Are these changes causing damage? Or are they a by-product of it? Are the epigenetic patterns seen in a 90-year-old a sign of deterioration? Or have they been responsible for keeping that person alive into very old age?

To make matters even more complicated, two different clocks can give similar answers by measuring methylation at entirely different regions of the genome. No one knows why, or which regions might be the best ones to focus on.

“The biomarkers have this black-box quality,” says Jesse Poganik at Brigham and Women’s Hospital in Boston. “Some of them are probably causal, some of them may be adaptive … and some of them may just be neutral”: either “there’s no reason for them not to happen” or “they just happen by random chance.”

What we know is that, as things stand, none of the clocks are precise enough to predict the biological age of a single person (sorry, Khloé). Putting the same biological sample through five different clocks will give you five wildly different results.

Even the same clock can give you different answers if you put a sample through it more than once. “They’re not yet individually predictive,” says Herzog. “We don’t know what [a clock result] means for a person, [or if] they’re more or less likely to develop disease.”

And it’s why plenty of aging researchers—even those who regularly use the clocks in their work—haven’t bothered to measure their own epigenetic age. “Let’s say I do a clock and it says that my biological age … is five years older than it should be,” says Magalhães. “So what?” He shrugs. “I don’t see much point in it.”

You might think this lack of clarity would make aging clocks pretty useless in a clinical setting. But plenty of clinics are offering them anyway. Some longevity clinics are more careful, and will regularly test their patients with a range of clocks, noting their results and tracking them over time. Others will simply offer an estimate of biological age as part of a longevity treatment package.

And then there are the people who use aging clocks to sell supplements. While no drug or supplement has been definitively shown to make people live longer, that hasn’t stopped the lightly regulated wellness industry from pushing a range of “treatments” that range from lotions to herbal pills all the way through to stem-cell injections.

Some of these people come to aging meetings. I was in the audience at an event when one CEO took to the stage to claim he had reversed his own biological age by 18 years—thanks to the supplement he was selling. Tom Weldon of Ponce de Leon Health told us his gray hair was turning brown. His biological age was supposedly reversing so rapidly that he had reached “longevity escape velocity.”

But if the people who buy his supplements expect some kind of Benjamin Button effect, they might be disappointed. His company hasn’t yet conducted a randomized controlled trial to demonstrate any anti-aging effects of that supplement, called Rejuvant. Weldon says that such a trial would take years and cost millions of dollars, and that he’d “have to increase the price of our product more than four times” to pay for one. (The company has so far tested the active ingredient in mice and carried out a provisional trial in people.)

More generally, Horvath says he “gets a bad taste in [his] mouth” when people use the clocks to sell products and “make a quick buck.” But he thinks that most of those sellers have genuine faith in both the clocks and their products. “People truly believe their own nonsense,” he says. “They are so passionate about what they discovered, they fall into this trap of believing [their] own prejudices.” 

The accuracy of the clocks is at a level that makes them useful for research, but not for individual predictions. Even if a clock did tell someone they were five years younger than their chronological age, that wouldn’t necessarily mean the person could expect to live five years longer, says Magalhães. “The field of aging has long been a rich ground for snake-oil salesmen and hype,” he says. “It comes with the territory.” (Weldon, for his part, says Rejuvant is the only product that has “clinically meaningful” claims.) 

In any case, Magalhães adds that he thinks any publicity is better than no publicity.

And there’s the rub. Most people in the longevity field seem to have mixed feelings about the trendiness of aging clocks and how they are being used. They’ll agree that the clocks aren’t ready for consumer prime time, but they tend to appreciate the attention. Longevity research is expensive, after all. With a surge in funding and an explosion in the number of biotech companies working on longevity, aging scientists are hopeful that innovation and progress will follow. 

So they want to be sure that the reputation of aging clocks doesn’t end up being tarnished by association. Because while influencers and supplement sellers are using their “biological ages” to garner attention, scientists are now using these clocks to make some remarkable discoveries. Discoveries that are changing the way we think about aging.

How to be young again

Two little mice lie side by side, anesthetized and unconscious, as Jim White prepares his scalpel. The animals are of the same breed but look decidedly different. One is a youthful three-month-old, its fur thick, black, and glossy. By comparison, the second mouse, a 20-month-old, looks a little the worse for wear. Its fur is graying and patchy. Its whiskers are short, and it generally looks kind of frail.

But the two mice are about to have a lot more in common. White, with some help from a colleague, makes incisions along the side of each mouse’s body and into the upper part of an arm and leg on the same side. He then carefully stitches the two animals together—membranes, fascia, and skin. 

The procedure takes around an hour, and the mice are then roused from their anesthesia. At first, the two still-groggy animals pull away from each other. But within a few days, they seem to have accepted that they now share their bodies. Soon their circulatory systems will fuse, and the animals will share a blood flow too.

cartoon man in profile with a stick of a wrist watch around a lit stick of dynamite in his mouth
“People are complicated. There’s a huge error bar.” — Steve Horvath, former biostatistician at the University of California, Los Angeles
LEON EDLER

White, who studies aging at Duke University, has been stitching mice together for years; he has performed this strange procedure, known as heterochronic parabiosis, more than a hundred times. And he’s seen a curious phenomenon occur. The older mice appear to benefit from the arrangement. They seem to get younger.

Experiments with heterochronic parabiosis have been performed for decades, but typically scientists keep the mice attached to each other for only a few weeks, says White. In their experiment, he and his colleagues left the mice attached for three months—equivalent to around 10 human years. The team then carefully separated the animals to assess how each of them had fared. “You’d think that they’d want to separate immediately,” says White. “But when you detach them … they kind of follow each other around.”

The most striking result of that experiment was that the older mice who had been attached to a younger mouse ended up living longer than other mice of a similar age. “[They lived] around 10% longer, but [they] also maintained a lot of [their] function,” says White. They were more active and maintained their strength for longer, he adds.

When his colleagues, including Poganik, applied aging clocks to the mice, they found that their epigenetic ages were lower than expected. “The young circulation slowed aging in the old mice,” says White. The effect seemed to last, too—at least for a little while. “It preserved that youthful state for longer than we expected,” he says.

The young mice went the other way and appeared biologically older, both while they were attached to the old mice and shortly after they were detached. But in their case, the effect seemed to be short-lived, says White: “The young mice went back to being young again.” 

To White, this suggests that something about the “youthful state” might be programmed in some way. That perhaps it is written into our DNA. Maybe we don’t have to go through the biological process of aging. 

This gets at a central debate in the aging field: What is aging, and why does it happen? Some believe it’s simply a result of accumulated damage. Some believe that the aging process is programmed; just as we grow limbs, develop a brain, reach puberty, and experience menopause, we are destined to deteriorate. Others think programs that play an important role in our early development just turn out to be harmful later in life by chance. And there are some scientists who agree with all of the above.

White’s theory is that being old is just “a loss of youth,” he says. If that’s the case, there’s a silver lining: Knowing how youth is lost might point toward a way to somehow regain it, perhaps by restoring those youthful programs in some way. 

Dogs and dolphins

Horvath’s eponymous clock was developed by measuring methylation in DNA samples taken from tissues around the body. It seems to represent aging in all these tissues, which is why Horvath calls it a pan-tissue clock. Given that our organs are thought to age differently, it was remarkable that a single clock could measure aging in so many of them.

But Horvath had ambitious plans for an even more universal clock: a pan-species model that could measure aging in all mammals. He started out, in 2017, with an email campaign that involved asking hundreds of scientists around the world to share samples of tissues from animals they had worked with. He tried zoos, too.   

The pan-mammalian clock suggests that there is something universal about aging—not just that all mammals experience it in a similar way, but that a similar set of genetic or epigenetic factors might be responsible for it.

“I learned that people had spent careers collecting [animal] tissues,” he says. “They had freezers full of [them].” Amenable scientists would ship those frozen tissues, or just DNA, to Horvath’s lab in California, where he would use them to train a new model.

Horvath says he initially set out to profile 30 different species. But he ended up receiving around 15,000 samples from 200 scientists, representing 348 species—including everything from dogs to dolphins. Could a single clock really predict age in all of them?

“I truly felt it would fail,” says Horvath. “But it turned out that I was completely wrong.” He and his colleagues developed a clock that assessed methylation at 36,000 locations on the genome. The result, which was published in 2023 as the pan-mammalian clock, can estimate the age of any mammal and even the maximum lifespan of the species. The data set is open to anyone who wants to download it, he adds: “I hope people will mine the data to find the secret of how to extend a healthy lifespan.”

The pan-mammalian clock suggests that there is something universal about aging—not just that all mammals experience it in a similar way, but that a similar set of genetic or epigenetic factors might be responsible for it.

Comparisons between mammals also support the idea that the slower methylation changes occur, the longer the lifespan of the animal, says Nelly Olova, an epigeneticist who researches aging at the University of Edinburgh in the UK. “DNA methylation slowly erodes with age,” she says. “We still have the instructions in place, but they become a little messier.” The research in different mammals suggests that cells can take only so much change before they stop functioning.

“There’s a finite amount of change that the cell can tolerate,” she says. “If the instructions become too messy and noisy … it cannot support life.”

Olova has been investigating exactly when aging clocks first begin to tick—in other words, the point at which aging starts. Clocks can be trained on data from volunteers, and by matching the patterns of methylation on their DNA to their chronological age. The trained clocks are then typically used to estimate the biological age of adults. But they can also be used on samples from children. Or babies. They can be used to work out the biological age of cells that make up embryos. 

In her research, Olova used adult skin cells, which—thanks to Nobel Prize–winning research in the 2000s—can be “reprogrammed” back to a state resembling that of the pluripotent stem cells found in embryos. When Olova and her colleagues used a “partial reprogramming” approach to take cells close to that state, they found that the closer they got to the entirely reprogrammed state, the “younger” the cells were. 

It was around 20 days after the cells had been reprogrammed into stem cells that they reached the biological age of zero according to the clock used, says Olova. “It was a bit surreal,” she says. “The pluripotent cells measure as minus 0.5; they’re slightly below zero.”

Vadim Gladyshev, a prominent aging researcher at Harvard University, has since proposed that the same negative level of aging might apply to embryos. After all, some kind of rejuvenation happens during the early stages of embryo formation—an aged egg cell and an aged sperm cell somehow create a brand-new cell. The slate is wiped clean.

Gladyshev calls this point “ground zero.” He posits that it’s reached sometime during the “mid-embryonic state.” At this point, aging begins. And so does “organismal life,” he argues. “It’s interesting how this coincides with philosophical questions about when life starts,” says Olova. 

Some have argued that life begins when sperm meets egg, while others have suggested that the point when embryonic cells start to form some kind of unified structure is what counts. The ground zero point is when the body plan is set out and cells begin to organize accordingly, she says. “Before that, it’s just a bunch of cells.”

This doesn’t mean that life begins at the embryonic state, but it does suggest that this is when aging begins—perhaps as the result of “a generational clearance of damage,” says Poganik.

It is early days—no pun intended—for this research, and the science is far from settled. But knowing when aging begins could help inform attempts to rewind the clock. If scientists can pinpoint an ideal biological age for cells, perhaps they can find ways to get old cells back to that state. There might be a way to slow aging once cells reach a certain biological age, too. 

“Presumably, there may be opportunities for targeting aging before … you’re full of gray hair,” says Poganik. “It could mean that there is an ideal window for intervention which is much earlier than our current geriatrics-based approach.”

When young meets old

When White first started stitching mice together, he would sit and watch them for hours. “I was like, look at them go! They’re together, and they don’t even care!” he says. Since then, he’s learned a few tricks. He tends to work with female mice, for instance—the males tend to bicker and nip at each other, he says. The females, on the other hand, seem to get on well. 

The effect their partnership appears to have on their biological ages, if only temporarily, is among the ways aging clocks are helping us understand that biological age is plastic to some degree. White and his colleagues have also found, for instance, that stress seems to increase biological age, but that the effect can be reversed once the stress stops. Both pregnancy and covid-19 infections have a similar reversible effect.

Poganik wonders if this finding might have applications for human organ transplants. Perhaps there’s a way to measure the biological age of an organ before it is transplanted and somehow rejuvenate organs before surgery. 

But new data from aging clocks suggests that this might be more complicated than it sounds. Poganik and his colleagues have been using methylation clocks to measure the biological age of samples taken from recently transplanted hearts in living people. 

If being old is simply a case of losing our youthfulness, then that might give us a clue to how we can somehow regain it.

Young hearts do well in older bodies, but the biological age of these organs eventually creeps up to match that of their recipient. The same is true for older hearts in younger bodies, says Poganik, who has not yet published his findings. “After a few months, the tissue may assimilate the biological age of the organism,” he says. 

If that’s the case, the benefits of young organs might be short-lived. It also suggests that scientists working on ways to rejuvenate individual organs may need to focus their anti-aging efforts on more systemic means of rejuvenation—for example, stem cells that repopulate the blood. Reprogramming these cells to a youthful state, perhaps one a little closer to “ground zero,” might be the way to go.

Whole-body rejuvenation might be some way off, but scientists are still hopeful that aging clocks might help them find a way to reverse aging in people.

“We have the machinery to reset our epigenetic clock to a more youthful state,” says White. “That means we have the ability to turn the clock backwards.” 

This test could reveal the health of your immune system

Attentive readers might have noticed my absence over the last couple of weeks. I’ve been trying to recover from a bout of illness.

It got me thinking about the immune system, and how little I know about my own immune health. The vast array of cells, proteins, and biomolecules that works to defend us from disease is mind-bogglingly complicated. Immunologists are still getting to grips with how it all works.

Those of us who aren’t immunologists are even more in the dark. I had my flu jab last week and have no idea how my immune system responded. Will it protect me from the flu virus this winter? Is it “stressed” from whatever other bugs it has encountered in the last few months? And since my husband had his shot at the same time, I can’t help wondering how our responses will compare. 

So I was intrigued to hear about a new test that is being developed to measure immune health. One that even gives you a score.

Writer David Ewing Duncan hoped that the test would reveal more about his health than any other he’d ever taken. He described the experience in a piece published jointly by MIT Technology Review and Aventine.

The test David took was developed by John Tsang at Yale University and his colleagues. The team wanted to work out a way of measuring how healthy a person’s immune system might be.

It’s a difficult thing to do, for several reasons. First, there’s the definition of “healthy.” I find it’s a loose concept that becomes more complicated the more you think about it. Yes, we all have a general sense of what it means to be in good health. But is it just the absence of disease? Is it about resilience? Does it have something to do with withstanding the impact of aging?

Tsang and his colleagues wanted to measure “deviation from health.” They looked at blood samples from 228 people who had immune diseases that were caused by single-gene mutations, as well as 42 other people who were free from disease. All those individuals could be considered along a health spectrum.

Another major challenge lies in trying to capture the complexity of the immune system, which involves hundreds of proteins and cells interacting in various ways. (Side note: Last year, MIT Technology Review recognized Ang Cui at Harvard University as one of our Innovators under 35 for her attempts to make sense of it all using machine learning. She created the Immune Dictionary to describe how hundreds of proteins affect immune cells—something she likens to a “periodic table” for the immune system.)

Tsang and his colleagues tackled this by running a series of tests on those blood samples. The vast scope of these tests is what sets them apart from the blood tests you might get during a visit to the doctor. The team looked at how genes were expressed by cells in the blood. They measured a range of immune cells and more than 1,300 proteins.

The team members used machine learning to find correlations between these measurements and health, allowing them to create an immune health score for each of the volunteers. They call it the immune health metric, or IHM.

When they used this approach to find the immune scores of people who had already volunteered in other studies, they found that the IHM seemed to align with other measures of health, such as how people respond to diseases, treatments, and vaccines. The study was published in the journal Nature Medicine last year.

The researchers behind it hope that a test like this could one day help identify people who are at risk of cancer and other diseases, or explain why some people respond differently to treatments or immunizations.

But the test isn’t ready for clinical use. If, like me, you’re finding yourself curious to know your own IHM, you’ll just have to wait.

This article first appeared in The Checkup, MIT Technology Review’s weekly biotech newsletter. To receive it in your inbox every Thursday, and read articles like this first, sign up here.

How do our bodies remember?

MIT Technology Review Explains: Let our writers untangle the complex, messy world of technology to help you understand what’s coming next. You can read more from the series here.

“Like riding a bike” is shorthand for the remarkable way that our bodies remember how to move. Most of the time when we talk about muscle memory, we’re not talking about the muscles themselves but about the memory of a coordinated movement pattern that lives in the motor neurons, which control our muscles. 

Yet in recent years, scientists have discovered that our muscles themselves have a memory for movement and exercise.

When we move a muscle, the movement may appear to begin and end, but all these little changes are actually continuing to happen inside our muscle cells. And the more we move, as with riding a bike or other kinds of exercise, the more those cells begin to make a memory of that exercise.

When we move a muscle, the movement may appear to begin and end, but all these little changes are actually continuing to happen inside our muscle cells.

We all know from experience that a muscle gets bigger and stronger with repeated work. As the pioneering muscle scientist Adam Sharples—a professor at the Norwegian School of Sport Sciences in Oslo and a former professional rugby player in the UK—explained to me, skeletal muscle cells are unique in the human body: They’re long and skinny, like fibers, and have multiple nuclei. The fibers grow larger not by dividing but by recruiting muscle satellite cells—stem cells specific to muscle that are dormant until activated in response to stress or injury—to contribute their own nuclei and support muscle growth and regeneration. Those nuclei often stick around for a while in the muscle fibers, even after periods of inactivity, and there is evidence that they may help accelerate the return to growth once you start training again. 

Sharples’s research focuses on what’s called epigenetic muscle memory.Epigenetic” refers to changes in gene expression that are caused by behavior and environment—the genes themselves don’t change, but the way they work does. In general, exercise switches on genes that help make muscles grow more easily. When you lift weights, for example, small molecules called methyl groups detach from the outside of certain genes, making them more likely to turn on and produce proteins that affect muscle growth (also known as hypertrophy). Those changes persist; if you start lifting weights again, you’ll add muscle mass more quickly than before.

In 2018, Sharples’s muscle lab was the first to show that human skeletal muscle has an epigenetic memory of muscle growth after exercise: Muscle cells are primed to respond more rapidly to exercise in the future, even after a monthslong (and maybe even yearslong) pause. In other words: Your muscles remember how to do it.

Subsequent studies from Sharples and others have replicated similar findings in mice and older humans, offering further supporting evidence of epigenetic muscle memory across species and into later life. Even aging muscles have the capacity to remember when you work out.

At the same time, Sharples points to intriguing new evidence that muscles also remember periods of atrophy—and that young and old muscles remember this differently. While young human muscle seems to have what he calls a “positive” memory of wasting—“in that it recovers well after a first period of atrophy and doesn’t experience greater loss in a repeated atrophy period,” he explains—aged muscle in rats seems to have a more pronounced “negative” memory of atrophy, in which it appears “more susceptible to greater loss and a more exaggerated molecular response when muscle wasting is repeated.” Basically, young muscle tends to bounce back from periods of muscle loss—“ignoring” it, in a sense—while older muscle is more sensitive to it and might be more susceptible to further loss in the future. 

Illness can also lead to this kind of “negative” muscle memory; in a study of breast cancer survivors more than a decade after diagnosis and treatment, participants showed an epigenetic muscle profile of people much older than their chronological age. But get this: After five months of aerobic exercise training, participants were able to reset the epigenetic profile of their muscle back toward that of muscle seen in an age-matched control group of healthy women.  

What this shows is that “positive” muscle memories can help counteract “negative” ones. The takeaway? Your muscles have their own kind of intelligence. The more you use them, the more they can harness it to become a lasting beneficial resource for your body in the future. 

Bonnie Tsui is the author of On Muscle: The Stuff That Moves Us and Why It Matters (Algonquin Books, 2025).