Clinical trial suggests semaglutide may help patients with alcohol use disorder and obesity.
Musk v. Altman week 1: Elon Musk says he was duped, warns AI could kill us all, and admits that xAI distills OpenAI’s models
In the first week of the landmark trial between Elon Musk and OpenAI, Musk took the stand in a crisp black suit and tie and argued that OpenAI CEO Sam Altman and president Greg Brockman had deceived him into bankrolling the company. Along the way, he warned that AI could destroy us all and sat through revelations that he had poached OpenAI employees for his own companies. He even confessed, to some audible gasps in the courtroom, that his own AI company, xAI, which makes the chatbot Grok, uses OpenAI’s models to train its own.
The federal courthouse in Oakland, California, was packed with armies of lawyers carrying boxes of exhibits, journalists typing away at their laptops, and a handful of concerned OpenAI employees. Outside, protesters lined the streets, carrying signs urging people to quit ChatGPT, boycott Tesla, or both. Musk looked calm and comfortable, slipping in the occasional quip in his distinct South African accent. But he also was full of remorse.
“I was a fool who provided them free funding to create a startup,” Musk told the jury. He said when he cofounded OpenAI in 2015 with Altman and Brockman, he was donating to a nonprofit developing AI for the benefit of humanity, not to make the executives rich. “I gave them $38 million of essentially free funding, which they then used to create what would become an $800 billion company,” he said.
Musk is asking the court to remove Altman and Brockman from their roles and to unwind the restructuring that allowed OpenAI to operate a for-profit subsidiary. The outcome of the trial could upend OpenAI’s race toward an IPO at a valuation approaching $1 trillion. Meanwhile, xAI is expected to go public as a part of Musk’s rocket company SpaceX as early as June, at a target valuation of $1.75 trillion.
This week’s testimony revolved around a central question of the trial: why Musk is suing OpenAI. Musk argued he was trying to save OpenAI’s mission to develop AI safely by restoring the company to its original nonprofit structure. OpenAI’s lawyer, William Savitt, who once represented Musk and his electric-car company Tesla, countered that Musk was “never committed to OpenAI being a nonprofit” and instead was suing to undermine his competitor.
Who is the steward of AI safety?
During his direct examination early in the week, Musk painted himself as a longtime advocate of AI safety. He said he cofounded OpenAI to create a “counterbalance to Google,” which was leading the AI race at the time. He said that when he asked Google cofounder Larry Page what happens if AI tries to wipe out humanity, Page told him, “That will be fine as long as artificial intelligence survives.”
“The worst-case scenario is a Terminator situation where AI kills us all,” Musk later told the jury.
Savitt stood at the lectern and argued that Musk was not a “paladin of safety and regulation.” As he cross-examined Musk in his sharp, surgical cadence, Savitt pointed out that xAI sued the state of Colorado in April over an AI law designed to prevent algorithmic discrimination.
Musk’s lawyer, Steven Molo, sprang to his feet to object. He asked the judge if he, too, could weigh in on ChatGPT’s safety record.
The lawyers then entered a heated debate about who was the true guardian of AI safety.
The sparring continued the next morning. “We all could die as a result of artificial intelligence!” said Molo, suggesting that OpenAI could not be trusted to build AI safely.
“Despite these risks, your client is creating a company that’s in the exact space,” Judge Yvonne Gonzalez Rogers said sternly, referring to xAI. “I suspect there’s plenty of people who don’t want to put the future of humanity in Mr. Musk’s hands.”
When the lawyers began talking over each other, the judge snapped. “This is not a trial on whether or not artificial intelligence has damaged humanity,” she said.
When did Musk think he was being duped?
As Savitt continued to cross-examine Musk, he pressed on the idea that Musk had never been committed to keeping OpenAI a nonprofit. He also claimed that Musk waited too long to sue OpenAI, filing after the statute of limitations ran out.
Musk explained why he sued in 2024 rather than earlier, describing “three phases” in his views of OpenAI. In phase one, he was “enthusiastically supportive” of the company.” In phase two, “I started to lose confidence that they were telling me the truth,” he said. In phase three, “I’m sure they’re looting the nonprofit.”
In 2017, Musk and other OpenAI cofounders discussed creating a for-profit subsidiary to raise enough capital to build artificial general intelligence—powerful AI that can compete with humans on most cognitive tasks. Musk wanted a majority interest in the subsidiary and the right to choose a majority of the board members. He also pitched having Tesla acquire OpenAI. (He left OpenAI in 2018.)
“I was not opposed to there being a small for-profit that provides funding to the nonprofit,” he told the jury, “as long as the tail didn’t wag the dog.”
But it was only in late 2022, Musk testified, that he “lost trust in Altman” and his commitment to keeping the company a nonprofit. The key moment came, he said, when he learned that Microsoft would invest $10 billion in OpenAI.
“I texted Sam Altman, ‘What the hell is going on? This is a bait and switch,’” he told the jury. Microsoft would give $10 billion only if it expected “a very big financial return,” he said.
Is Musk just trying to kill competition?
But Savitt argued that Musk was really suing to undermine OpenAI as a competitor to his empire of tech companies. While he was on the board of OpenAI, Musk was also running Tesla and his brain-implant company, Neuralink. He founded xAI in 2023.
Savitt pulled up an email that Musk had sent to a Tesla vice president in 2017 after hiring Andrej Karpathy, a founding member of OpenAI, to work at Tesla.“The OpenAI guys are gonna want to kill me. But it had to be done,” he wrote.
When asked about it, Musk was flustered. He claimed Karpathy had already decided to leave OpenAI when he recruited him to work at Tesla. “I believe it’s a free world,” he said.
Savitt pulled up another email that Musk sent to a cofounder at Neuralink in 2017. He wrote that they could “hire independently or directly from OpenAI.” When pressed about it, he sounded frazzled. “It’s a free country,” he said. “I can’t restrict their ability to hire people from other companies.”
Savitt also pointed out that Tesla, SpaceX, Neuralink, and X were socially beneficial for-profit companies, like OpenAI. He stressed that xAI was also a closed-source, for-profit company.
But Musk claimed that xAI was not a real competitor to OpenAI. “We’re not currently tracking to reach AGI first,” he told the jury.
In fact, Musk admitted that xAI uses OpenAI’s technology. In response to Savitt’s relentless questioning, he said xAI “partly” distills OpenAI’s models. Some people in the courtroom gasped.
Distillation is a technique where a smaller AI model is trained to mimic the behavior of larger, more capable models, so it can run faster and more cheaply while performing nearly as well. But OpenAI and other AI companies have pushed back against the practice. In February, OpenAI accused the Chinese AI company DeepSeek of distilling its AI models. In August 2025, Wired reported that Anthropic had blocked OpenAI’s access to Claude for violating the company’s terms of service, which prohibit, among other things, reverse-engineering its services and building competing products.
“It is standard practice to use other AIs to validate your AI,” argued Musk.
Next week, Stuart Russell, a computer scientist at UC Berkeley, will testify about AI safety. Brockman, who has been taking notes during Musk’s testimony, will also testify.
This story is part of MIT Technology Review’s ongoing coverage of the Musk v. Altman trial. Follow @techreview or @michelletomkim on X for up-to-the-minute reporting.
Applicable Scenarios, Desired Features, and Risks of AI Psychotherapists in Depression Treatment From the Patient’s Perspective: Exploratory Qualitative Study
Background: Depression is a pervasive global mental health issue, yet access to trained professionals remains severely limited. With the rapid advancement of artificial intelligence (AI), digital tools are increasingly seen as a viable way to address this shortage. However, questions remain about how digital platforms for mental health care can be effectively designed. Objective: This study aimed to investigate, from an end user’s (patient’s) perspective, the potential use scenarios, desired features, and perceived risks of AI psychotherapists in depression treatment, providing design guidelines for their development. Methods: A grounded theory approach was applied to analyze qualitative responses from 452 individuals recruited via Amazon Mechanical Turk. Data were collected through a scenario-based online survey on AI-assisted depression treatment administered between March 2023 and May 2023. Participants responded to 3 open-ended questions regarding the potential use of AI in treating depression, the characteristics expected from an AI psychotherapist, and the associated perceived risks, along with demographic, control, and contextual measures. The open-ended responses were inductively coded into themes, with intercoder reliability established (Cohen κ=0.80). In addition, variations in themes were further examined across participant profiles, including social stigma, current depression severity, trust in an AI psychotherapist, and privacy awareness. Results: Participants envisioned AI psychotherapists across 5 primary scenarios: diagnosis, treatment, consultation, self-management, and companionship. Key desired features include professionalism, warmth, precision care, empathy, remote services, active listener, personalization, flexible treatment options, patience, trustworthiness, and basic treatment alternative, while critical concerns include diagnostic inaccuracy, treatment errors, privacy breach, lack of human interaction, technical malfunctions, and lack of emotional engagement. Based on these findings, a general MoSCoW (must have, should have, could have, and won’t have) prioritization framework was proposed to serve as a conceptual starting point for future AI system design and empirical validation in mental health care. Notably, feature prioritization varied across user profiles: individuals with higher stigma placed greater emphasis on privacy protection, those with more severe depression prioritized precision care and timely access, low-trust users de-emphasized remote services, and privacy-sensitive individuals showed reduced preference for features requiring extensive data disclosure. These patterns highlight the need for context-sensitive design. Conclusions: This study provides a patient-centered framework for designing AI psychotherapists and complements the existing literature by highlighting the importance of balancing clinical effectiveness with relational considerations. The findings offer actionable guidelines for designing AI mental health care tools that are aligned with user expectations and sensitive to individual differences.
<img src="https://jmir-production.s3.us-east-2.amazonaws.com/thumbs/f0fc77a313314d9cc27d237b4dc786f1" />
An Ecological Momentary Assessment Smartphone App for High-Risk HIV Populations: Development and Usability Study
Background: HIV incidence has continued to increase among men who have sex with men (MSM) in Peru, despite intervention efforts. Addressing stigma, risky behaviors, and low medication adherence is key to reducing incidence rates. Ecological momentary assessment (EMA) allows for collection of discrete, real-time data on stigmatized, risky behaviors while reducing recall bias. Objective: The aim of this study was to develop and assess the usability of an EMA smartphone app among MSM with HIV in Peru, which tracks daily health risk behaviors to determine ease of use, usefulness, and satisfaction with the app. Methods: A mixed-method 3-phase study was conducted with 10 MSM with HIV, which included a usability test, 10-day field testing, and a debriefing focus group. Quantitative survey data and user analytics allowed for assessments of acceptability and user compliance. Qualitative interview and focus group data were thematically analyzed for in-depth assessments of user satisfaction. Results: Acceptability of the EMA app was high, with a mean usability rating of 6.4 of 7.0 (SD 0.62), indicating high user satisfaction, ease of use, and usefulness. A 10-day field test demonstrated a high average compliance rate of 93% (93/100), which suggests high feasibility of the app for daily tracking of health risk behaviors among MSM with HIV. Interview and focus group findings indicated that the app was navigable, time-efficient, and holds promise for long-term use, particularly with the inclusion of daily reminders and incentives for prolonged use. Conclusions: EMA apps can provide valuable real-time data while protecting users’ privacy. This formative work lays the foundation for future larger-scale EMAs of substance use and sexual risk behaviors among high-risk HIV populations, and for the development of just-in-time interventions to address stigma, improve medication adherence, and reduce risky behaviors.
<img src="https://jmir-production.s3.us-east-2.amazonaws.com/thumbs/d5969f95bbfd7056b7362eebeea780e8" />
A Bilingual AI-Based Chatbot for Nutrition Education in a Food Is Medicine Intervention for High-Risk Pregnant Women: Design and Development Study
Background: Conversational agents (artificial intelligence [AI]–based chatbots) offer a novel approach to health interventions by providing personalized, adaptive interactions that improve over time based on user engagement. In nutrition education, given the wide variation in knowledge, skills, and abilities across participants, AI-based chatbots have the potential to enhance accessibility, engagement, and behavior change. Food is Medicine (FIM) interventions, which aim to improve food security and diet quality among multicultural, at-risk populations, often face challenges related to sustained engagement and use. Objective: This paper describes the design, development, and iterative refinement of a bilingual AI-driven nutrition chatbot integrated into an FIM intervention for high-risk pregnant women receiving care at obstetric clinics in Houston, Texas. Methods: The chatbot was developed using an iterative process informed by behavioral theory, human-centered design (HCD), and plan-do-study-act (PDSA) quality improvement cycles. The conversational agent was embedded within an ongoing 2-arm randomized controlled trial (N=200) comparing standard FIM nutrition education to FIM plus AI-driven nutrition chatbot support. HCD activities took place prior to deployment and involved community advisory group members and implementation stakeholders. Postdeployment refinements were guided by 2 PDSA cycles and informal question-and-answer sessions conducted with intervention arm participants. Qualitative feedback was collected using structured scripts to identify facilitators of and barriers to chatbot engagement. Results: The chatbot was developed using the GPT-3.5 Turbo application programming interface. An initial prototype built in Python using Gradio enabled rapid testing but lacked flexibility for modifications. To improve scalability and logging capabilities, the system was rebuilt using PHP, HTML, JavaScript, and SQL. To further understand usage patterns, participants who interacted with the chatbot at least once or not at all (classified as low users; n=32) were engaged in question-and-answer sessions. Of these participants, all were female (32/32, 100%), 88% (28/32) identified as Hispanic or Latino, and 90% (29/32) preferred Spanish. Two PDSA cycles guided iterative refinements. Cycle 1 identified low initial engagement, whereas cycle 2 focused on improving content clarity and cultural relevance through physical reminder prompts. Qualitative findings identified key barriers to engagement, including high cooking self-efficacy with perceived lack of need for support, low technology self-efficacy, and low urgency due to competing priorities. Conclusions: Embedding a bilingual AI-driven nutrition chatbot within an FIM intervention was feasible and featured critical design and implementation considerations for engaging high-risk pregnant populations. Findings show the importance of HCD and iterative refinement to address engagement barriers. This work provides actionable guidance for integrating conversational agents into FIM programs, with implications for future evaluation of clinical outcomes, long-term engagement, and scalability. Trial Registration: ClinicalTrials.gov NCT07165990; https://clinicaltrials.gov/study/NCT07165990
<img src="https://jmir-production.s3.us-east-2.amazonaws.com/thumbs/a63127d3ad7741a792dfe16a916ed09f" />
DNA-Containing Extracellular Vesicles Boost Antitumor Responses in Mice
A study led by investigators at Weill Cornell Medicine has found that activated T cells secrete extracellular vesicles (EVs) containing DNA, which can enter other immune and tumor cells to stimulate the body’s defense systems. Preclinical experiments showed that this vesicle-associated DNA could be useful therapeutically, boosting T cell attacks against tumors that otherwise evoke little or no immune response.
Studies in live mice showed that these activated T cell-derived-EVs (AT-EVs) enhanced antigen processing and presentation (APP) in tumor cells and dendritic cells (DCs) across different immunologically cold tumors. The ATEVs also synergized with immune checkpoint inhibitors (ICIs) to trigger antitumor immunity and hold back tumor growth.
The discovery extends the scientific understanding of the immune system, identifies a new strategy for boosting immunity against cancers, and potentially offers a new tool for delivering genetic payloads to other cells. “These findings reveal a natural mechanism for treating immunologically silent tumors and other diseases that stem from insufficient immune surveillance,” said David Lyden, MD, PhD, the Stavros S. Niarchos professor in pediatric cardiology and a member of the Gale and Ira Drukier Institute for Children’s Health and the Sandra and Edward Meyer Cancer Center at Weill Cornell Medicine.
Lyden is co-senior author of the researchers’ published paper in Cancer Cell, titled “Activated T cell extracellular vesicle DNA transfer enhances antigen presentation and anti-tumor immunity,” in which they stated, “We uncover a mechanism whereby activated T cell-derived extracellular vesicles (ATEVs) drive a positive feedback loop that enhances antigen presentation and immune responses in normal physiology and cancer … Notably, ATEVs hold promise as an acellular immunotherapy, restoring APP and synergizing with checkpoint blockade in immunotherapy-refractory tumors.”
Most animal cells secrete extracellular vesicles which can contain cargo including proteins, snippets of DNA, and other molecules. “Extracellular vesicles (EVs) are nanoparticles naturally released by all living cells, containing proteins, lipids, and genetic material, that facilitate intercellular communication,” the investigators wrote.
The Lyden lab in recent years has made seminal discoveries about extracellular vesicles and their functions, finding for example that vesicles secreted by tumor cells can influence the immune system’s anti-tumor response. Their findings, they noted, “… raised the possibility that EVDNA from immune cells, such as T cells, may also have immune-related functions.” For their new study the team examined the roles of vesicles secreted by immune cells, and specifically T cells, which are the immune system’s principal tumor-fighters.
In their initial experiments, the scientists found that under physiological conditions, T cell-secreted vesicles tend to home to lymph nodes, spleen and other centers of immune activity. There the vesicles are preferentially taken up by antigen-presenting immune cells, including dendritic cells, which assist in T cell activation, a critical process in the immune response. The researchers found that the overall effect of these vesicles released by activated T cells is to boost the antigen-presenting process, thus promoting T cell priming and broader immune activation. The key payloads in these immune-boosting vesicles turned out to be snippets of T cell DNA.
“These surprisingly abundant DNA fragments are mostly on the surfaces of the vesicles, and are not just random—they are enriched for immune-related genes, including genes that help cells display antigens to the immune system,” said co-senior author Haiying Zhang, PhD, an assistant professor of cell and developmental biology in pediatrics and member of the Lyden lab. “We also found that these vesicles have, attached to their surfaces, a special enzyme that acts as a molecular drill, enabling the transfer of vesicle-carried DNA into the nucleus of the recipient cell where they can be expressed transiently,” added study co-first author Diao Liu, PhD, a postdoctoral research associate in the Lyden Lab.
Infusing DNA-carrying vesicles from activated T cells into mice with tumors, the researchers found that the vesicles were taken up not only by antigen-presenting cells but also by tumor cells themselves. The treated tumors grew more slowly and were better infiltrated by T cells and other immune cells, indicating that the vesicles induced a stronger anti-tumor response. “Our work reveals an EV-mediated mechanism through which activated T cells enhance APP across diverse recipient cells—from DCs in physiological conditions to cancer cells across tumor types,” the authors noted. Although cancers—and viruses—frequently suppress the antigen-presenting process to make malignant or infected cells “invisible” to the immune system, the main effect of the extracellular vesicular DNA was to reverse this process, restoring tumor cells’ visibility.
The team demonstrated the effectiveness of this approach, alone and in combination with existing immunotherapy, in preclinical models of three different immunologically silent cancers: glioblastoma, pancreatic and triple-negative breast cancer. “By boosting APP machinery, ATEVs enhance tumor immunogenicity and elicit robust anti-tumor responses, particularly when combined with ICIs in otherwise resistant tumors, including pancreatic, breast, and brain cancers,” they stated. “These findings reveal the translational potential of activated T cell-derived extracellular vesicles (ATEVs) by exploiting a naturally occurring immune-boosting process to overcome immune evasion, particularly in immunologically silent cancers.”
Co-senior author Irina Matei, PhD, an assistant professor of immunology research in pediatrics and member of the Lyden lab, stated, “There seems to be a positive-feedback loop, in which the DNA-carrying vesicles from activated T cells amplify the immune response by acting on both antigen-presenting cells, which increase expression of the machinery processing tumor antigens, and tumor cells, promoting their recognition by the immune system as well as their own production of DNA-laden vesicles.”
The researchers are now working to translate their findings into a new, vesicle-based cancer treatment, which could be used on its own or in conjunction with standard immunotherapies or other cancer treatments. “The surprising ability of these vesicles to transfer DNA from donor T cells into the nuclei of recipient cells suggests their potential as a natural, non-viral platform for transient gene delivery,” said co-first author Mengying Hu, PhD, a postdoctoral research associate in the Lyden Lab who led the research and is now an assistant professor of pharmaceutical sciences at the Ohio State University. “The results point to a broadly applicable gene-transfer strategy that may offer improved safety and efficiency compared with current gene therapy approaches.”
In their paper the authors concluded, “Overall, ATEVs emerge as an acellular immunotherapy and delivery modality that can prime antitumor immunity, synergize with existing therapies, and serve as a vaccine adjuvant,” they concluded. “Our findings provide a foundation for the therapeutic application of ATEVs through a deeper understanding of the biological role of AT-EVDNA.”
The post DNA-Containing Extracellular Vesicles Boost Antitumor Responses in Mice appeared first on GEN – Genetic Engineering and Biotechnology News.
OTX-202 Smartphone App to Reduce Suicidal Ideation Among High-Risk Transition-Age Youth: Open-Label, Single-Arm, Phase 1 Clinical Trial
<strong>Background:</strong> The transition from adolescence to adulthood (18 to 25 years) is associated with an increased risk of suicidal ideation and behaviors. Suicide-focused cognitive behavioral therapies (CBTs) have been shown to significantly reduce suicidal ideation and behaviors but are not widely available to high-risk individuals. Digital therapeutics could improve access to these treatments. <strong>Objective:</strong> This study aimed to evaluate the acceptability, safety, and potential efficacy of OTX-202 among transition-age youth (18 to 25 years) receiving mental health care outside an inpatient hospital setting. <strong>Methods:</strong> In this phase 1 single-arm clinical trial, 59 transition-age youth with recent suicidal ideation or suicide attempts used OTX-202, a smartphone app designed to deliver suicide-focused CBT, concurrently with usual outpatient mental health care. After baseline, eligible patients completed 12 weekly assessments of suicidal ideation, depression, and anxiety. <strong>Results:</strong> From baseline to week 12, participants reported statistically significant, large reductions in suicidal ideation (mean difference –5.1, 95% CI –6.5 to –3.7; <i>d</i>=0.95). In total, 3 (5.1%; 95% CI 0%-11.2%) participants reported suicide attempts. Reductions in suicidal ideation and suicide attempt rates were consistent with results from previously published randomized clinical trials of suicide-focused CBTs. Participants rated OTX-202 in the 97th percentile of usability and completed a mean of 9.0 (SD 3.5) of 12 app modules, supporting the app’s acceptability. There were no patient deaths, device-related events, or severe adverse events, supporting the app’s safety. <strong>Conclusions:</strong> Results support the safety, acceptability, and potential efficacy of OTX-202 for reducing suicide risk among transition-age youth. <strong>Trial Registration:</strong> ClinicalTrials.gov NCT06008132; https://clinicaltrials.gov/study/NCT06008132
Restoring Protein Recycling Reverses T-Cell Exhaustion in Mice
New research published by scientists at the University of California, San Diego (UCSD), describes an unexpected factor underlying T-cell exhaustion. The details of their work in mice are published in a new Cell paper titled “Proteostasis sustains T-cell differentiation potential and tumor-infiltrating lymphocyte function.”
T cells are critical members of the immune system but there are limits to their defensive capabilities. When fighting cancer cells, T cells often burn out and become dysfunctional. A major focus of current cancer immunotherapy efforts is rescuing T cells from this state and getting them back into cancer-fighting shape. The new Cell study led by scientists in the lab of Ananda Goldrath, PhD, a professor of molecular biology at UCSD, and their collaborators elsewhere, suggests that a potential solution to T-cell exhaustion might have to do with protein recycling.
Specifically, their finding has to do with proteostasis, the network of cellular processes that orchestrates the proper construction, movement, and destruction of proteins in cells. A component of this network features a type of recycling function where healthy cells continuously dismantle old and damaged proteins to preserve energy and reuse building blocks to make new proteins. According to the paper, the scientists uncovered an impaired protein recycling function as the surprise culprit in T-cell exhaustion.
“We found that exhausted T cells’ recycling programs are falling apart, leading to damaged and misfolded proteins that pile up with nowhere to go,” said Nicole Scharping, PhD, a post-doctoral fellow in the Goldrath lab and lead author on the paper. Additionally, the scientists also uncovered a way to reverse the accumulation of misfolded proteins by fixing the broken recycling function and restoring normal proteostasis. As Scharping explained, the issue can be resolved with a “tag and sort” fix. This is accomplished using E3 ligase enzymes which act as labelers at a recycling facility, tagging worn-out proteins so the cell knows to break them down.
“In exhausted T cells, many of these enzymes get switched off, and recycling grinds to a halt,” said Scharping. After examining thousands of proteins, the scientists honed in on NEURL3, RNF149 and WSB1 as the E3 ligases responsible for rescuing T cell recycling functions. “When we restored specific E3 ligases, the buildup cleared, and the T cells regained their function and worked better at clearing tumors.” While the new study was conducted in mice, the researchers indicate that similar strategies could be employed for immunotherapy treatments in human cancer.
Importantly, the findings may have implications in other diseases as impaired protein processing is not unique to exhausted T cells. “We think this loss of proteostasis resembles what occurs in neurons in other protein aggregate diseases such as Parkinson’s and Alzheimer’s,” said Goldrath. “Rescuing these cells from exhaustion could improve the ability of T cells to respond to both chronic infection as well as tumors.”
The post Restoring Protein Recycling Reverses T-Cell Exhaustion in Mice appeared first on GEN – Genetic Engineering and Biotechnology News.
Data-Driven Tool Identifies Individuals at Highest Risk of Obesity-Related Disease
A new clinical risk model may transform how obesity is managed, by identifying which individuals are most likely to develop serious complications, regardless of their body mass index (BMI).
Developed by researchers at Queen Mary University of London and the Berlin Institute of Health, the tool, called OBSCORE, uses just 20 routinely collected clinical variables to predict the future risk of 18 obesity-related conditions, ranging from type 2 diabetes to cardiovascular disease.
Published in Nature Medicine, the study challenges the long-standing reliance on BMI as the primary metric for assessing obesity-related health risk.
Moving beyond BMI
BMI has long served as a simple proxy for obesity, but it fails to capture the biological heterogeneity of patients. Two individuals with similar BMI can have vastly different risks of developing complications.
The new model addresses this limitation directly. As described in the study, it “provides information beyond BMI” by integrating multiple dimensions of health into a unified risk score.
These include demographic data, clinical biomarkers, disease history, and lifestyle factors, variables already commonly available in healthcare settings.
The findings show that BMI alone is a poor discriminator of risk. The model consistently outperformed BMI-based approaches across all tested outcomes.
Large-scale data enables precision risk prediction
To build the model, researchers analyzed health data from nearly 200,000 individuals with overweight or obesity from the UK Biobank.
Using an interpretable machine learning framework, they screened more than 2,000 potential predictors and distilled them into a core set of 20 features that best predicted long-term health outcomes.
The resulting OBSCORE model estimates the 10-year risk of developing 18 conditions, including cardiovascular disease, kidney disease, sleep apnea, and metabolic disorders.
The model demonstrated strong predictive performance, with median concordance indices around 0.75 across outcomes, indicating robust discrimination between high- and low-risk individuals.
Hidden high-risk individuals
One of the most striking findings is that high-risk individuals are not always those with the highest BMI.
A substantial proportion of individuals classified as high risk fell into the “overweight” category (BMI 27–30 kg/m²), rather than obesity. In some outcomes, up to ~40% of those in the highest risk group had BMI below the obesity threshold.
This reveals a critical gap in current clinical practice: individuals who may benefit from intervention could be overlooked simply because they do not meet BMI-based criteria.
On the other hand, some individuals with obesity may have relatively low risk and may not require intensive intervention.
Strong risk stratification across diseases
Beyond prediction, the scientists believe that OBSCORE enables meaningful risk stratification. Individuals in the highest risk group showed dramatically higher rates of disease compared to those in the lowest group.
For example, the study reports:
- Up to 89-fold higher risk for chronic kidney disease
- 42-fold higher risk for type 2 diabetes
- 47-fold higher risk for cardiovascular mortality
These differences exceed those observed when comparing individuals based solely on BMI categories, underscoring the added value of multidimensional risk assessment.
Clinical and healthcare implications
The implications of these findings are significant, particularly in the context of emerging obesity therapies.
Highly effective drugs such as GLP-1 receptor agonists and dual incretin therapies have transformed treatment options, but their high cost and limited availability make patient prioritization essential.
As the authors note, current systems lack robust frameworks to identify which patients should receive treatment.
OBSCORE offers a potential solution by enabling risk-based allocation of interventions, ensuring that treatment is directed toward those most likely to benefit.
This could improve clinical outcomes while optimizing healthcare resource use.
Toward implementation in clinical practice
One of the key strengths of OBSCORE is its practicality. Unlike many predictive models, it relies on a small number of variables that are already routinely collected, making it suitable for integration into electronic health records.
The researchers envision the model being used as a decision-support tool in clinical settings, complementing rather than replacing existing frameworks.
External validation in independent cohorts—including populations of different ancestry, demonstrated strong generalizability, further supporting its potential for real-world deployment.
Limitations and next steps
Despite its promise, the model requires further validation in broader populations, including younger individuals and more diverse healthcare settings.
Additionally, while OBSCORE effectively stratifies risk, translating these predictions into actionable treatment thresholds will require clinical consensus and cost-effectiveness analyses.
The authors also emphasize that the model identifies predictive, not necessarily causal, factors, and should be interpreted accordingly.
Taken together, the findings mark a shift toward precision medicine in obesity, moving from simplistic metrics like BMI to data-driven, individualized risk assessment.
By capturing the complex interplay of metabolic, clinical, and behavioral factors, OBSCORE could enable earlier intervention, better targeting of therapies, and improved long-term outcomes for patients living with overweight and obesity.
The post Data-Driven Tool Identifies Individuals at Highest Risk of Obesity-Related Disease appeared first on Inside Precision Medicine.
Machine Learning Tool Helps Improve Type 1 Diabetes Prediction
A machine learning model can improve genetic prediction of type 1 diabetes by as much as 10%, show results from a University of California, San Diego study.
The researchers used the machine‑learning model T1GRS to improve on a gold standard polygenic genetic risk score used to predict who is likely to develop the condition called GRS2.
Type 1 diabetes is an autoimmune condition that impacts around 2 million people in the U.S. While it is a multifactorial condition, genetics plays a big role and around 50% of a person’s susceptibility comes from genetics.
“The natural history of type 1 diabetes suggests that the disease occurs in genetically susceptible individuals exposed to environmental triggers, leading to the development of islet-specific autoantibodies and autoreactive T cells and progressive loss of insulin secretory function, although the underlying etiology is not fully understood,” write lead author Kyle Gaulton, PhD, associate professor of pediatrics at UC San Diego School of Medicine, and colleagues in Nature Genetics.
The GRS2 polygenic risk score has been widely tested and can be used to predict newborns who are at high risk of developing type 1 diabetes. While early prediction can’t necessarily stop the disease it can help to prevent emergencies like diabetic ketoacidosis at diagnosis, allow families time to prepare and could allow use of therapies to delay onset of the condition.
In this study, Gaulton and colleagues carried out a genome‑wide association study in 20,355 people with type 1 diabetes and 797,363 non‑diabetic Europeans, as well as a further analysis around the MHC region in 10,107 diabetic and 19,639 nondiabetic individuals.
“The MHC has ‘blocks’ of co-inherited genetic information that are very highly enriched in individuals with type 1 diabetes,” said co-first author Emily Griffin, PhD, a postdoctoral fellow in Gaulton’s lab. “If you have them, it doesn’t mean that you’re going to get diabetes, but if you don’t have them, it means you have a very low chance of getting diabetes.”
Overall 160 risk signals were identified, and the team trained their T1GRS model to predict who was likely to develop type 1 diabetes based on their genetics. The model was able to improve on the GRS2 model predictions by up to 10% in both populations of European and African American ancestry.
Overall the new score correctly flagged about 89 of 100 people with type 1 diabetes while correctly reassuring about 84 of 100 people without the disease.
“Our results highlight the value of combining the results of genetic association studies with machine learning methods to improve the prediction of complex diseases,” conclude the authors.
The post Machine Learning Tool Helps Improve Type 1 Diabetes Prediction appeared first on Inside Precision Medicine.