After people stop taking GLP-1s, the effects also end, study finds
Researchers analyzed 11 different studies to understand weight outcomes after stopping anti-obesity medications called glucagon-like peptide-1 receptor agonists, or GLP-1s, which mimic the GLP-1 hormone that is produced in the gut after eating.
It can help produce more insulin, which reduces blood sugar and therefore helps control Type 2 diabetes. It can also interact with the brain and signal a person to feel full, which -- when coupled with diet and exercise -- can help reduce weight in those who are overweight or obese.
MORE: Compound versions of GLP-1 drugs for weight loss halted by FDA
The team, from Peking University People's Hospital in China, found that most began to regain weight within about two months of stopping treatment. In many cases, that weight gain continued for several months before leveling off.
The study was published Tuesday in the journal BMC Medicine.
However, Dr. Louis J. Aronne, founder and former chairman of the American Board of Obesity Medicine, told ABC News that doesn't mean the medications failed. In fact, they worked exactly as intended, he said.
Aronne, who is also a physician at Weill Cornell Medicine, said the findings are consistent with what happens when treatment ends for other chronic conditions.
"What happens after stopping an obesity medication is exactly what happens after stopping a diabetes, cholesterol-lowering, or a blood pressure medication," he told ABC News. "The effect of the medicine goes away, and people tend to go back to where they started."
Patients who had taken GLP-1s tended to lose more weight during treatment, which meant they had more weight to gain back afterward.
'It's not that the medicine didn't work,' Aronne said. 'It's that they lost more weight, so they had more weight to regain.'
Even participants who continued healthy eating and exercise habits after stopping medication experienced weight gain.
MORE: Compound versions of GLP-1 drugs for weight loss halted by FDA
That doesn't mean those efforts weren't worthwhile, Aronne further explained, but rather that obesity is a chronic disease with complex biological drivers.
"You wouldn't stop insulin and expect a person's blood sugar to stay low," he said.
The researchers noted several limitations, including a small number of included studies and a focus on weight and BMI without tracking other health markers like blood sugar or cholesterol.
These medications may not right for everyone, and decisions about starting or stopping should be made with your doctor, according to MedlinePlus.
People with certain medical conditions, including a history of pancreatitis or thyroid cancer, may not be good candidates, and should speak with their doctor to decide what management strategies are right for them.
Alexandra-Elise Dakaud Patterson, MD, MS, is a general surgery resident at University of Toledo Medical Center and a member of the ABC News Medical Unit.
Solve the daily Crossword
Hashtags
Try Our AI Features
Explore what Daily8 AI can do for you:
Comments
No comments yet...
Related Articles
Forbes
6 minutes ago
- Forbes
AI Training Gets 10x Faster, 95% Cheaper With Decentralized Strategy
A quiet shift in the foundations of artificial intelligence (AI) may be underway, and it is not happening in a hyperscale data center. 0G Labs, the first decentralized AI protocol (AIP), in collaboration with China Mobile, recently announced a technical breakthrough that could have sweeping implications for how businesses access and deploy large language models. Their innovation is a new method of training massive AI models with over 100 billion parameters, without needing the ultra-high-speed internet or expensive centralized infrastructure typically required. At first glance, this might sound like a win for the engineering world. But the real story is economic and strategic. What 0G Labs has achieved could lower the cost of building AI, put more control back into the hands of enterprises, and open the door for new players to enter the space. What It Means For AI Training To understand the shift, it helps to revisit how large-scale AI models are currently trained. Models like OpenAI's GPT-4 or Anthropic's Claude require vast computing power and network throughput. Traditionally, this means training them on powerful GPUs connected across high-speed, centralized data centers owned or rented from companies like Amazon Web Services, Google Cloud, or Microsoft Azure. As of early 2025, OpenAI's leadership, including Sam Altman, publicly stated that training GPT‑4 cost over $100 million. This is supported both by official statements and multiple cost models in recent AI analysis reportsIt is a model that demands capital, talent, and infrastructure that few organizations can afford. 0G Labs Is Challenging That Assumption For AI Training Their newly published framework, called DiLoCoX, introduces a low-communication training method that dramatically reduces the need for high-bandwidth connectivity. In practical terms, they successfully trained a 107 billion parameter model on a 1 Gbps network using decentralized clusters. This record is a 10x improvement of the previous record and the 300x speed-up breakthrough that made this possible for the first time. This is roughly the bandwidth of a typical office internet connection. Instead of building everything in one giant compute center, their approach links together smaller, distributed machines and optimizes how information is shared between them. The result is a highly scalable, cost-efficient way to train massive models outside the traditional cloud. In speaking with 0G labs founder and CEO Michael Heinrich, he said 'DiLoCoX marks a pivotal step in democratizing LLM training: bridging the gap between massive foundation models and decentralized clusters connected by slow, unreliable networks. By combining pipeline parallelism, delay‑tolerant communication overlap, and adaptive gradient compression, the framework delivers scale and speed previously thought exclusive to high‑bandwidth data centers. This will usher in a new era where large‑scale AI training is no longer tethered to centralized infrastructure.' Why Does AI Training Matter for Business At a time when every enterprise is under pressure to do more with AI, infrastructure is quickly becoming the bottleneck. Some businesses are starting to look at decentralized AI by design. Building large models remains expensive, exclusive, and largely confined to companies with deep resources or strategic cloud partnerships. 0G's breakthrough opens up a third path. This is not just a story of cost savings. It is a story of optionality and control. 1. Lowering the Barrier to Entry DiLoCoX's approach reduces the infrastructure by up to 95% required to participate in the LLM race. For startups, this means the ability to experiment and scale without burning through venture capital on GPU spend. For mid-sized enterprises, it offers the possibility of training models in-house without making large cloud commitments. For governments and research labs, it means more accessible and sovereign development of AI capabilities. 2. Strategic Independence from Hyperscalers Most AI training today depends on three cloud providers. That concentration carries risk in terms of cost escalation, vendor lock-in, and compliance. If your business depends on AI but also operates in a sensitive sector like healthcare, defense, or finance, the ability to train or fine-tune models independently becomes a powerful strategic lever. Decentralized AI offers a route toward digital autonomy. By breaking the assumption that cutting-edge AI must be trained inside centralized cloud platforms, 0G's model creates new room for competition and for innovation. 3. Aligning with Data Privacy and Compliance Needs Many companies are cautious about uploading proprietary data to cloud-based models or training environments. With decentralized training, it becomes possible to keep data local within jurisdiction, within the firewall, or even on edge devices while still participating in large-scale AI development. This is particularly attractive in regions with strict data sovereignty laws such as the European Union or countries building their own AI ecosystems. The 0G network never sees any of the private data 4. Accelerating Innovation in Underserved Markets The high cost of entry has kept many countries and industries on the sidelines of advanced AI development. DiLoCoX lowers that threshold. A university in Kenya, a telecom provider in Southeast Asia, or a regional bank in Latin America may not have access to the same compute as Silicon Valley, but they may soon have the tools to train and deploy their intelligent systems on existing infrastructure. 5. Geopolitical and Regulatory Risks While the technical achievement is impressive, the involvement of China Mobile raises questions. As tensions between the United States and China continue to escalate over technology leadership and national security, businesses must weigh the potential regulatory scrutiny, data governance concerns, and reputational risks associated with partnerships involving Chinese state-affiliated entities. For companies based in the United States or operating in allied markets, any integration of infrastructure or research tied to China could face export controls, legal restrictions, or public backlash. Organizations exploring decentralized AI solutions will need to consider not just performance and cost, but also political alignment, compliance frameworks, and long-term viability. However, having DiLoCoX on a decentralized infrastructure where the network is trustless, this is not a concern because China Mobile never sees your data, and the system doesn't rely on them for results. Reframing the Business Model of AI If DiLoCoX is widely adopted, it could create ripple effects across the broader AI ecosystem. Cloud revenue models, currently boosted by AI workloads, could face new pricing pressure. AI-as-a-service platforms may need to re-architect to support hybrid or decentralized deployments. Open-source frameworks might grow in influence as decentralization emphasizes interoperability and local control. Enterprise software vendors may need to rethink their AI strategies to reflect a more distributed compute landscape. This shift also aligns with the broader trend of AI for everyone. From low-code agent builders to edge-based inferencing, the movement is toward more accessible, modular, and customizable AI stacks. Decentralized training is the natural extension of that philosophy. An AI Signal for CIOs and CTOs For enterprise leaders, 0G's work serves as a signal not of immediate disruption, but of near-future opportunity. AI is evolving from its critical beginning. Now is the time to reevaluate infrastructure strategy. Should your organization continue investing in cloud-based model hosting, or begin exploring decentralized alternatives? Could your internal data center serve as a node in a distributed training system? Decentralized federated learning is a great way of tapping into private data from different parties on a network, like hospitals training a cancer diagnostic model. Might you partner with others in your sector to co-develop models using decentralized protocols? Even if the answer is not yes today, the emergence of frameworks like DiLoCoX should push AI infrastructure planning higher on the strategic agenda. Businesses that prepare for this shift by building internal capacity, evaluating partners, and understanding the technical stack will be best positioned to move when the economics tip in their favor. A Future Where AI is Built Differently What 0G Labs and China Mobile have demonstrated is more than just a technical proof of concept. It is a new way of thinking about how intelligence is built, trained, and distributed. By showing that it is possible to train 100 billion parameter models without centralized supercomputers, they are not just pushing the boundaries of scale. They are expanding access. For business, that means AI may soon be less about who owns the biggest data center and more about who can build the smartest systems with the most flexibility. That is an AI future worth preparing for.
Medscape
an hour ago
- Medscape
Body Composition, Not Just BMI, Matters in Psoriatic Disease
BOGOTÁ, Colombia — While obesity has been studied extensively in psoriasis and psoriatic arthritis (PsA), little is known about the role of body composition, or the specific location of fat deposition in and around organs and muscle, in these diseases. At the Group for Research and Assessment of Psoriasis and Psoriatic Arthritis (GRAPPA) 2025 Annual Meeting and Trainee Symposium, two groups of researchers working with MRI and clinical data from the UK Biobank presented detailed findings that shed light on how different body composition profiles might influence or be influenced by psoriatic disease. Visceral, Liver, and Muscle Fat Predict Disease In a poster, Rheumatologist Jean-Guillaume Letarouilly, MD, PhD, of Lille University, Lille, France, and Oxford University, Oxford, England, showed results from a cross-sectional study of 841 patients with psoriasis, 125 patients with PsA, and about 34,000 nonpsoriatic control individuals. Patients across groups had a mean age of 64 years, were nearly all White, approximately half were women, and only a minority (17%-22% across groups) had a BMI ≥ 30. All patients underwent abdominal MRI scan, and participants with psoriasis or PsA were identified by self-report and primary care and hospital records. In multivariate models, Letarouilly and his colleagues found that patients diagnosed with psoriasis tended to have significantly lower muscle volume, higher liver fat, higher visceral fat, higher muscle fat infiltration, and higher total abdominal fat than control individuals without psoriasis. Patients diagnosed with PsA, meanwhile, tended to have significantly elevated liver fat, higher muscle fat infiltration, and higher abdominal fat (but not visceral fat) than control individuals without PsA. The results were suggestive of 'a potential impact of the inflammatory burden of the psoriatic disease on the body composition,' the investigators concluded. In an interview at the meeting, Letarouilly cautioned that his findings 'are for the moment just descriptive. What we need to do now is determine in a longitudinal way whether these body composition changes appear before the disease itself and what role these different types of body fat play in the [pathologic] process.' Body Composition Profiles Predict Comorbidities At the same meeting, Lyn Ferguson, MBChB, PhD, of the University of Glasgow, Glasgow, Scotland, presented results from a different body composition study using UK Biobank data. Ferguson compared results for 236 patients with psoriasis and 1180 control individuals matched for age (mean of about 66 years), sex (about 51% women), and BMI (about 27) — all variables that can affect body fat distribution. She also identified 61 patients with PsA and 305 matched control individuals, both of whom had a mean age of about 63 years, 49% were women, and BMI of about 27. None of the control individuals had metabolic or cardiovascular disease at the time of their baseline scans. Ferguson aimed to determine not only whether people with psoriasis and PsA are more likely to have certain body composition profiles but also whether the same measures of body composition — liver fat, visceral fat, and muscle quality — could also predict diabetes and coronary heart disease in these patients. Ferguson found that people with psoriasis had significantly more fat around the organs (visceral), in the liver, and in muscle, whereas people with PsA had significantly more fat in the liver and muscle than age-, sex-, and BMI-matched control individuals. These body composition profiles were significantly associated with greater propensity to type 2 diabetes. 'We know from the clinic that patients with psoriatic disease have a greater prevalence of diabetes,' Ferguson said in an interview at the meeting. 'This could be in part related to the fat distribution pattern in psoriasis and PsA.' The Surprising Importance of Muscle Ferguson noted that about half of people with psoriasis or PsA had either low muscle volume, high muscle fat, or both — a state known as adverse muscle composition. In individuals with psoriatic disease, adverse muscle composition was associated with over double the propensity to coronary heart disease and three times the propensity to type 2 diabetes compared with a normal muscle composition. Ferguson said a provocative finding from her study was that poor muscle quality was also associated with more than double the risk for new-onset psoriasis (adjusted hazard ratio, 2.12; 95% CI, 1.29-3.50). 'Having more fat stored in the muscle and lower muscle volume appears to be associated with greater risk of developing psoriasis. Whether this may relate to muscle fat being potentially more pro-inflammatory, or whether immune-metabolic dysregulation is already present and contributing to more fat in the muscle and muscle loss, is unknown and warrants further study.' At a meeting intensely focused on the potential benefits of GLP-1 receptor agonists (RAs) in psoriatic disease, both Ferguson and Letarouilly said the muscle-quality findings were of special concern to them as physicians. 'It's really important that people stay physically active to maintain muscle quality, especially if you're losing a lot of weight' because of using GLP-1 RAs, Ferguson said. With the profound weight loss from this drug class, 'a significant amount of weight loss could be muscle. And we've identified a group that has lower thigh muscle volume. That's something we have to watch.' Letarouilly added that stopping GLP-1 drugs has been reported to result in sarcopenic obesity, or concurrent muscle loss and fat gain. 'That's why it is important to advise people to do more exercise,' he said, adding that maintaining bone health is another, related concern. 'We already see this with obesity surgery. With GLP-1s, we would need to monitor patients in the same way.'
Bloomberg
2 hours ago
- Bloomberg
How Novo Nordisk Gave Up Its Weight-Loss Gains
For a while, Danish drugmaker Novo Nordisk A/S looked unstoppable. It was at the forefront of the weight-loss injection boom as people discovered that its Ozempic shot, originally developed to treat diabetes, could help trim their waistlines.
