As millions flock to GLP-1s, doctors warn the drugs can cause rapid and significant muscle loss.
5 min
Chanel Robinson achieved exactly what the gold rush of blockbuster weight-loss drugs promised: She lost nearly 100 pounds, lowered her cholesterol to normal levels and reined in her polycystic ovary syndrome.
Yet, nearly three years into her journey on Mounjaro, the 30-year-old from Atlanta, Ga., is discovering the hidden costs of the slimmed-down life.
Robinson experiences muscle fatigue daily, feeling physically weak, frail and often cold. Robinson said she experiences bursts of sluggishness sporadically during the day, and has trouble with basic tasks like opening a jar. “It shouldn’t be this difficult,” she said.
GLP-1 drugs like Ozempic, Mounjaro and Zepbound have been a success for public health and the pharmaceutical companies that make them. Obesity rates are falling, the volume of food consumed in America is declining and retailers report a slump in sales of plus-size apparel. It has improved health and happiness for millions of people.
But for at least some of the 13 million Americans taking them, losing muscle along with fat is an unexpected downside that isn’t broadly discussed or immediately apparent.
The drugs can cause rapid and significant loss of lean muscle mass, up to 10%, comparable to a decade or more of aging, according to an analysis published by the American Diabetes Association.
The loss of lean tissue is similar to weight loss from dieting, but the magnitude over a short period can lead to frailty, instability and lack of coordination, doctors and researchers say. Another concern is that losing muscle could slow down patients’ metabolism, leading to weight regain.
“We are curing obesity by encouraging frailty,” said Daniel Green, principal research fellow at the University of Western Australia, who contributed to the analysis. Many taking weight-loss medications initially lose fat and feel great, but quickly start to feel weak and lethargic, he said.
Green’s research showed that the rate of muscle loss could be slowed significantly by regular strength workouts. “It should say ‘must be taken with resistance training’ on the box,” he said.
Drugmakers say weight-loss drugs should be taken only on the advice of a physician and as part of a long-term plan that includes diet and exercise.
A spokesperson for Eli Lilly, maker of Zepbound, said Food and Drug Administration guidelines say it should be used “with increased physical activity.” The spokesperson added: “Sustainable weight loss is about more than a number on a scale.”
Both Eli Lilly and Novo Nordisk said clinical trials showed users did lose some lean muscle tissue, though at far lower rates than fat. Liz Skrbkova, a spokeswoman for Novo Nordisk, said that trials for its drug Wegovy showed changes in muscle mass didn’t “significantly differ” from patients who took a placebo. Eli Lilly said users lost three times more fat weight than lean tissue.
Rayna Kingston, 30, from Denver, said her injections of Zepbound left her feeling so tired the following day that she struggled to complete anything other than basic tasks. She said she shifted her dose to a Sunday because Mondays were her least busy day. Her partner would bring her meals in bed because she felt so weak.
She stopped exercising, and said her doctor didn’t give her any guidance on strength training or muscle maintenance. “I was relying on Reddit forums to understand what was happening to my body,” she said. She got so frustrated with the fatigue she came off the medication just under two months later.
Experts say that losing muscle at such a rate can be especially dangerous for those over 50 or with osteoporosis or limited mobility as it could lead to an increased risk of injury. “Loss of muscle mass is detrimental to moving around and quality of life, but it is also not safe,” said Katsu Funai, associate professor at the University of Utah.
Elderly Americans are set to be able to get GLP-1s from Medicare from July.
There is also pushback from doctors and regulators against using weight-loss drugs as a “quick fix” to lose a bit of weight.
People who take GLP-1s regain weight four times faster than those who lose weight through lifestyle interventions, and weight regained is often mostly fat, according to a recent analysis published in the British Medical Journal. There currently are few, if any, guidelines or studies on de-prescribing the drugs, researchers say.
The nurse practitioner who prescribed Robinson the medication didn’t warn her that resistance training is essential to maintaining muscle mass, Robinson said. She said she regrets not exercising and now does Pilates once a week.
In the haste to disrupt the obesity epidemic, weight loss has been treated as the singular, undisputed metric of success, which experts say is problematic.
“People worship body weight as an outcome measure because it’s simple, quick and inexpensive,” said Green. “But what matters is fat and muscle mass, which is more expensive to measure as it requires an MRI.”
Grace Parkin, 34, a property manager from Sheffield, England, has lost 125 pounds after she started taking Mounjaro in 2024. “I don’t care about my muscle mass as long as I’m a healthy weight,” she said.
The doctor who prescribed the drug didn’t tell her to exercise, though the pharmacy that sold the medication gave her information on exercise and protein intake, she said.
She didn’t exercise and said she soon felt side effects: a “deathly cold, from the inside” likely because of the drug. Still, she vowed to keep going, saying the weight loss was worth it.
In response to some of the side effects, drug companies are hoping to develop weight-loss treatments aimed at preserving or even building lean muscle mass.
German drugmaker Boehringer Ingelheim recently said it had promising results from one such drug. Eli Lilly last September halted a trial of a similar drug.
While weight-loss medications are designed as lifelong treatments for chronic diseases, namely obesity and Type 2 diabetes, they are increasingly marketed as lifestyle fixes.
Tennis superstar Serena Williams, who used GLP-1s to slim down after having children, was featured in this year’s Super Bowl commercial promoting telehealth company Ro’s weight-loss medication.
Serena Williams poses for an ad campaign for a weight-loss drug. Ro/Handout/Reuters
Women may be particularly vulnerable to the drugs’s side effects, which can also include nausea, diarrhea, migraines and rarer cases of pancreatitis.
A study last year from a university hospital in Turin, Italy, showed that women are more prone to adverse reactions to weight-loss drugs than men, including muscle loss.
Green, the researcher, said the issue is of particular concern to those taking GLP-1s recreationally and who don’t have much muscle mass to begin with. Others say a lack of oversight is compounding the issue.
“Patients are self-reporting, and telehealth companies don’t have the patient in front of them to conduct a proper medical assessment,” said Rupal Mathur, an internist in Houston whose practice specializes in weight loss.
She said medical spas are prescribing off-label drugs that don’t meet the criteria set out by the FDA that justify a prescription.
The number of people taking weight-loss drugs who are not living with obesity or Type 2 diabetes is difficult to track since it is unregulated.
However, an analysis by the FDA from 2023 found that more than half of new Ozempic and Mounjaro users didn’t have Type 2 diabetes.
Scientists are calling for more clinical trials to pin down the full effects of weight-loss drugs on muscle loss in different demographics.
“The only studies that have been done have looked at people living with obesity or Type 2 diabetes,” said Green. “That makes it all the more concerning for those using weight-loss drugs in an ad hoc or unregistered way.”
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Could fears of an AI apocalypse be distracting us from the dangers already here? Experts debate whether regulation should focus on speculative existential threats or present-day harms, including cyberattacks, weapons and unsafe autonomous agents. Read more via the link in bio.
5 min
There is ample and alarming evidence that artificial intelligence can help humans do bad things, such as committing cyberattacks, building weapons and even killing themselves or others. The Hugging Face hacking episode—and a growing list of others by poorly constrained swarms of agents—indicate just how powerful and potentially dangerous AI has quickly become.
Yet many inside the U.S. AI industry insist far worse is coming, on account of the imminent arrival of AI with superhuman and self-improving abilities.
Plenty of experts, including many who study AI harms for a living, are skeptical. The so-called doomers’ assertion that AI might decide to wipe out all of humanity—or even “just” topple human civilization—is contingent on it achieving a pace of development not yet seen.
And if the assumptions behind this global-doomsday scenario are wrong, it could lead us to curb or regulate AI in ways that don’t address its real harms.
At the center of this debate is the claim that current AI systems might take over the job of training their next versions, a process called “recursive self-improvement.” Think of it like evolution on steroids—billions of years happening at light speed within vast AI supercomputers. Anthropic Chief Executive Dario Amodei recently proposed a global agreement to slow down the pace of releasing new AI models, with the goal of delaying the arrival of recursive self-improvement.
“I don’t think we’re anywhere near ‘artificial general intelligence,’” says Melanie Mitchell, a professor at the nonprofit research group Santa Fe Institute who studies AI. She said AI is making impressive strides but thinks claims by engineers that they’ve achieved recursive self-improvement don’t stand up to scrutiny.
She is hardly alone. A recent paper by two dozen academics at Princeton, Stanford and other institutions found that even the most cutting-edge AIs are incapable of doing the original research required to advance the AI frontier.
Two of the authors involved in that paper also threw cold water on the idea that the Hugging Face swarm hack by OpenAI agents occurred because of a breakthrough in intelligence. The attack succeeded primarily because of a lack of basic technical guardrails, not an unmanageable explosion in AI capability, they wrote.
Yann LeCun, former chief AI scientist at Meta, posted that this analysis was “a welcome dose of sanity in an otherwise insane debate.”
OpenAI and Anthropic didn’t respond to several requests for comment.
The people who disagree with the doomers still consider AI to be dangerous, and point out that such systems don’t have to be particularly capable to be powerful. Some argue that AI should undergo regular evaluation by outsiders and that the companies that make it should be held responsible when their systems do harm. AI should also be treated the same as airplanes and elevators, and should be designed to do the least harm possible, they say.
“If you believe that technology is powerful enough to create novel, dangerous viruses, or to essentially take over the whole planet for some reason, then it must also be strong enough to create cures for cancer, to cure aging, to fix socio-economic or political problems,” says Christopher Canal, CEO of EquiStamp, a company that helps companies and governments evaluate AIs.
AI has shown an ability to rapidly advance because it is matching the abilities of humans who are constantly feeding it their knowledge. Sometimes it can recombine that knowledge and exceed what people have been capable of, through a kind of post-training known as reinforcement learning, as we’ve seen in mathematics.
Today’s LLMs are “models of knowledge” rather than actually intelligent, wrote Yi Ma, professor of AI at Hong Kong University.
Researchers at universities and commercial AI research labs in China wrote in a recent paper that autonomous, self-improving AI is likely to be a long way off, due to the sheer number of breakthroughs required. They also argue that humans will probably remain in the loop, supervising that process—and gating how fast it can occur.
Vals AI, a company that evaluates today’s AI models, maintains an RSI Index that benchmarks whether models can “do the research that builds the next model.” So far, no publicly released model is even close.
Yet Rayan Krishnan, CEO of Vals AI, says his team projects models will exceed humans’ ability to improve the next generation of AIs by August 2027, or sooner, and at that point could start building their successors all on their own.
“Once we get to recursive self-improvement, the fear is that all bets are off,” he says. “You could end up with a ‘fast takeoff’ situation, where the models quickly acquire skills and eclipse humans across every possible domain.”
In a reply to the resignation tweet heard round the world from Jacob Coxon, another Anthropic engineer declared his belief that those odds were at least 10% over the next decade. Many others in the industry chimed in to say they thought the percentage was even higher.
Some who argue the end is nigh say they calculate their personal p(doom) based on a chain of conditional probabilities—a bit like the Drake equation for calculating the likelihood of intelligent alien life. Since all those probabilities are based on speculation, estimates range from 0% to nearly 100%. Many land around 10%.
“The weird thing is that if you go back and look at the predictions on this over the last 10 years or more, it’s always been 10%—it’s just a nice round number,” says Mitchell. “I think it’s all vibes, and there’s no actual evidence or calculation.”
One argument against worrying about superintelligent AI is that the world is full of unlikely humanity-ending disasters, and trying to avert them all can make it impossible to prioritize, says Canal.
This has led AI experts and the policymakers who listen to them to propose remedies that don’t get at its real and present dangers.
AI companies’ proposals to “pace the frontier” aren’t addressing the problem in the right way, argues Stuart Russell, a computer-science professor at the University of California, Berkeley, and the president of the International Association for Safe and Ethical Artificial Intelligence.
“It’s like saying we’re driving toward the cliff at 60 miles per hour and we’re going to drive toward it at 40 miles per hour instead, and everything will be OK,” he says.
Russell and his peers have proposed that AI companies should have to meet the same standards that govern other areas of everyday life, from air travel and buildings to food and drugs. They highlight the “behavioral red lines” AI should not be allowed to cross. Breaking into other computer systems, stealing information or advising terrorists on how to build biological weapons are all illegal for a human to do, and should be illegal for companies’ AIs as well, he argues.
The challenge for AI companies in such a proposal, he adds, is that it would be a de facto ban on today’s advanced AI systems, since the companies behind them don’t know how to make them respect such boundaries all of the time.
Others have proposed something like the Food and Drug Administration, but for AI, but setting up a new agency has so far been a nonstarter in Congress. And some prominent voices in tech have said such a structure would give up America’s AI edge to China.
Given the bipartisan groundswell of support for curbing AI companies and their creations, however, that may soon change.
“The tech industry has had this mantra for decades that regulation is bad,” says Russell. “They don’t accept the liability for any harm, and they hide behind free speech. That, I think, has to change.”
