AimsA machine learning approach to identify a multidimensional signature associated with relapse and long-term outcome in alcohol dependence treatment.DesignIn this observational naturalistic study, inpatients with alcohol dependence received qualified detoxification plus CBT (Cognitive Behavioral Therapy) and were followed up 6-months after discharge to assess abstinence and drinking behavior. Cross-validated multivariate sparse partial least squares analysis (SPLS) was used to investigate the relationship between clinical features and four long-term outcome variables.SettingGermany.Participants152 patients (on average 47.8 years old, 72% male) with alcohol dependence, who received inpatient qualified detoxification plus CBT.Measurements35 clinical features were used to cover all three phases of inpatient treatment (pre-, within-, post-treatment). Among these, sociodemographic characteristics, ICD-10 psychiatric diagnoses, previous detoxification treatments, and somatic measurements as well as inpatient treatment setting such as withdrawal medication, liver ultrasound, further information about the patients´ stay, and post-inpatient care were assessed. The four outcome dimensions included: continuous abstinence, abstinence at follow up, daily alcohol consumption, and days of abstinence after discharge.FindingsSix months after withdrawal treatment 46% of the patients achieved continuous abstinence. Socioeconomic, clinical and somatic features across the treatment timeline were analyzed and summarized into a multivariate signature associated with long-term treatment outcome. Thereby, the SPLS algorithm identified regular completion of withdrawal treatment, higher education, and employment status to be most strongly associated with a positive outcome. Alcohol-related hepatic and hematopoietic damage, number of previous withdrawal treatments and living in a shelter were most profoundly associated with a negative outcome.ConclusionConceiving treatment outcome as a multidimensional signature and moving beyond simple binary classifications of relapse versus abstinence may improve the understanding of relapse pathways and support more individualized treatment strategies.
Brain Circuits Underlying Placebo Pain Relief Identified in Mice
Though the placebo effect is a well documented phenomenon, the neurological mechanisms that underlie the process are still not fully understood. Now scientists from multiple institutions led by a team at the University of California San Diego (UCSD) have pinpointed the brain circuitry in mice that they believe is responsible for placebo pain relief. Details of their findings are published in a new paper in the journal Neuron. In it, they describe brain regions that support placebo effects and highlight sites where endogenous opioid neuropeptides send signals that are important for placebo pain relief.
The paper is titled “Top-down control of the descending pain modulatory system drives multimodal placebo analgesia.” According to the team, theirs is the first study to establish placebo mechanisms by adapting a protocol used for humans to work in mice. Working alongside labs at the University of Pennsylvania, University of California Irvine, and elsewhere, the UCSD team detected activity in parts of the mouse brain that correspond to those previously implicated in human studies. Furthermore, by precisely mapping neural pathways and brain activity in the mice, the team identified essential roles for neural circuits that link the cortex to the brainstem and spinal cord during placebo pain relief.
They also found that training mice to exhibit a placebo effect with one type of pain results in relief from several different types of pain including pain from injuries. That is particularly notable because it has “direct implications for how placebo training in humans might be used to produce resilience to future pain that results from injury,” explained Matthew Banghart, PhD, an associate professor in UCSD’s neurobiology department and lead author on the study. The findings also open a door to “expectancy-driven” placebo effects as a substitute for addictive painkillers, he noted, meaning that it might be possible to use placebo conditioning to train patients to build preemptive resilience to pain.
Full details of the findings and methods used are provided in the paper. In it, the teams explain that they used sensor technology and a light-activated drug developed in the Banghart lab to study the role of naturally-occurring opioid peptides in the brain. Specifically, they used the sensors to detect opioid peptide signaling in the ventrolateral periaqueductal gray (vlPAG) region, a known hub for pain signaling, during placebo trials. They then used the light-activated drug called photoactivatable naloxone, or PhNX, to establish that these opioid peptides actually drive pain relief in a manner similar to drugs like morphine. The light allowed the scientists control and timing of the opioid signaling interference. Using PhNX, they confirmed that both morphine-induced pain relief and placebo pain relief use the same opioid signaling pathway in the vlPAG region of the brain.
Essentially, “we trained a mouse brain to create its own broad-spectrum painkillers on demand, precisely where they are needed to treat pain, without the off-target effects of opioid-based painkillers,” said Janie Chang-Weinberg, a PhD student in the biological sciences graduate program at UCSD and one of the first authors on the study.
Future studies planned by the team will dig more deeply into how placebo learning unfolds in the brain and evaluate different placebo training strategies in mice with an eye towards developing protocols that readily translate to produce placebo pain resilience in people living with chronic pain.
The post Brain Circuits Underlying Placebo Pain Relief Identified in Mice appeared first on GEN – Genetic Engineering and Biotechnology News.
<![CDATA[High-potency cannabis surges; psychiatry confronts psychosis risk, dependence, and data gaps—why clinicians must guide safer use now.]]>
Editorial: Assessment and management of addictive disorders in sexual and racial minorities
AACR Warns Congress of Cancer Care Setbacks from Proposed NIH Cuts, Again
Cancer researchers in the United States are once again bracing for a high-stakes funding battle in Washington, as a proposed $6 billion cut to the National Institutes of Health (NIH) for fiscal year 2027 threatens to derail years of scientific progress.
For advocates like Jon Retzlaff, Chief Policy Officer and Vice President for Science Policy and Government Affairs at the American Association for Cancer Research (AACR), the situation feels strikingly familiar and deeply consequential. That sense of déjà vu is shaping the response from the cancer research community, which is now urging Congress to once again reject the administration’s proposal just as it did last year.
To understand the urgency of the current moment, Retzlaff points back to the turmoil of the previous budget cycle. “A year ago, the president had proposed a 40% cut to NIH,” Retzlaff told Inside Precision Medicine. “Things looked pretty bleak.” The consequences were immediate and unsettling: grants were stuck and there were cutbacks on committees and staff.
But Congress ultimately intervened decisively. “We engaged with Congress, who has the power of the purse,” Retzlaff said. “They summarily rejected the president’s proposal for the 40% cuts and instead provided a $450 million increase for NIH.” Lawmakers also delivered a significant boost to the National Cancer Institute (NCI), reinforcing what Retzlaff described as a clear signal of bipartisan support for biomedical research. “What we saw for the current fiscal year… is they summarily rejected the president’s proposal,” Retzlaff said. “So now we are going through the exercise all over again.”
Despite the renewed threat, Retzlaff sees reasons for hope rooted in last year’s outcome. “People asked, ‘How can you be so optimistic?’” He recalled the earlier funding fight. “At least this year, I’m going to be able to tell them why I can be optimistic,” he said. “Because it was Congress that stood up.”
Still, he cautioned against complacency. “We can’t rest on our laurels. We can’t take it for granted,” Retzlaff said. “We will be continuing to press the issue.”
Holding down the precision oncology fort
For AACR, the renewed funding fight underscores a central truth: cancer research depends on long-term, uninterrupted investment. “You need this sustained funding over time,” Retzlaff said. “You go where the science is showing opportunities and also where there might not be opportunities right now.”
He emphasized that scientific progress is rarely linear or predictable. “Even though people can’t necessarily say it’s clear-cut that if we do research in this, we’re going to make some progress,” he said, “for some of the cancers, we just need to do research to try to have that knowledge discovery going on.”
Retzlaff added, “Basic biology is so important,” stressing that foundational science underpins every future breakthrough and the continued growth of precision medicine as the new standard of healthcare. “It’s about identifying the biomarkers that are important,” Retzlaff explained.
Meanwhile, emerging areas such as cancer vaccines are generating both excitement and urgency. “Cancer vaccines are now a big issue,” he said. “AACR is very interested in pushing that kind of research forward.”
Yet all of this progress depends on stable funding. Without it, Retzlaff warned, research priorities could narrow dangerously. “If you start cutting back, the next thing you know, we’re just funding breast cancer and lung cancer,” he said, “whereas the rare cancers need to be investigated. We need to give those people hope.”
Sustained national commitment to health
Funding cuts would also ripple through the clinical research pipeline. Retzlaff, who has become more involved in clinical trials in recent years, noted their complexity and cost.
While pharmaceutical companies often support later-stage development, early and exploratory studies depend heavily on NIH funding. “We rely on pharmaceutical companies… once you get into the translational part,” he explained, but without federal investment at the front end, fewer discoveries will ever reach that stage.
For AACR, protecting NIH funding is about more than preserving scientific momentum; it’s about sustaining a national commitment to health. “We’ve got 50,000 members,” Retzlaff said. “Two-thirds of them are from the U.S., and probably two-thirds of them are completely reliant in many ways on NIH funding.” That dependence drives the organization’s advocacy efforts. “Our number one priority is inspiring excitement on Capitol Hill and from lawmakers for robust, sustained and predictable funding for the NIH,” he said.
AACR’s outreach spans everything from congressional briefings to large-scale advocacy events. “It’s working with the entire community,” Retzlaff said, noting collaborations with hundreds of organizations and initiatives, such as Medical Research Hill Day. “We’re constantly looking at drum[ming] up conversations with the media,” he added. “It’s things like that—briefings, reports, letters—you name it.”
At the same time, AACR is navigating broader policy and public health challenges. Retzlaff highlighted ongoing engagement with the Food and Drug Administration (FDA) on issues ranging from clinical trial efficiency to tobacco regulation.
Backing cancer vaccines
Prevention, too, remains a critical priority. “HPV prevention is very important,” he said, though he acknowledged that misinformation has slowed progress. “The anti-vaccine movement is a huge concern.”
Retzlaff said that the cancer vaccine issue is rooted in communication and not the regulators. According to Retzlaff, the director of the National Cancer Institute has had some meetings with Secretary Kennedy, who was supportive of moving cancer vaccines forward. “We have to figure out what it is that people will accept about cancer vaccines that they’re not accepting about vaccines overall,” Retzlaff said. “That’s a communication issue… trying to combat the misinformation out there.”
AACR has even debated trying new names for the modality. Retzlaff elaborated, “There was some discussion about whether we can change the name of this from ‘cancer vaccines’ to something else.”
As Congress weighs the proposed cuts, AACR is calling on researchers, patients, and advocates to speak out once again. The message, Retzlaff said, is simple but urgent: “We definitely want to get the information out… about the importance of NIH medical research… and inspire people to take action.”
The outcome will determine not only the trajectory of cancer research but also the pace at which new discoveries can translate into treatments and, ultimately, save lives.
The post AACR Warns Congress of Cancer Care Setbacks from Proposed NIH Cuts, Again appeared first on Inside Precision Medicine.
Prediction of Relapse Using Digital Technology in People in Recovery From Substance Use Disorders: Early Economic Evaluation With a Case Study of the Subreal App
Background: Many people relapse after achieving abstinence in substance use disorders. Health care providers may scan the horizon for new technologies to predict response that allow interventions to be targeted rather than routine. Currently, no such predictive technologies are available in the United Kingdom. The Subreal app is available for use in research contexts, but no clinical data specific to the app are yet available. Early health economic modeling can use data from the literature to explore characteristics essential for the new technology to be cost-effective. This information can guide developers in setting performance targets and pricing and estimating potential cost savings and/or cost-effectiveness for health care providers. Objective: This study was supported by a UK industry funding body to explore the potential of digital technologies such as the Subreal app to offer cost savings or cost-effectiveness for health care providers. We explored the threshold price and clinical effectiveness required to deliver cost savings and cost-effectiveness in 2 subpopulations with substance use disorders in a UK setting. Methods: Deterministic models were used to estimate costs per relapse and quality-adjusted life years over 1-, 5-, and 20-year time horizons for people who have achieved abstinence after treatment for alcohol or opioid misuse. The intervention was a digital technology predicting relapse, provided—in addition to standard care—for 1 year post achievement of abstinence. In Subreal, biomarker data are collected daily through the app, and artificial intelligence–enhanced risk assessment flags patients who require additional support. The comparator was event-driven, reactive response to relapse. Costs and quality-of-life estimates were calculated using Markov models with data from existing published sources. The base-case estimate of 15% reduction in first-year relapse rates was based on a previous study on a similar but simpler digital technology. Results: Digital technologies such as the Subreal app have the potential to be cost-saving from a UK health and social care perspective, especially when used over a longer time horizon. Assuming a reduction of 15% in first-year relapse rates, digital technologies have the potential to be cost-saving, provided that they do not cost more than £300 (US $400.09) and £460 (US $613.47) per patient per annum for alcohol and opioid use disorders, respectively. No cost was included for postalert care, as it was assumed that this could be met within existing resources. Cost savings would be achieved predominantly through a reduction in treatment requirements as fewer people relapse. Price thresholds would reduce correspondingly if a <15% reduction in relapse rates were achieved. Conclusions: Developers of digital technologies that aim to reduce relapse need to focus on the generation of evidence of clinical effectiveness and develop a commercially sustainable pricing model that allows health care providers to benefit from cost savings.
<![CDATA[New ASAM youth criteria redefine SUD care with brain-stage levels, chronic monitoring, detox safety, and family-centered support.]]>
Therapeutic Potential of GLP-1 Receptor Agonists for Smoking Cessation
Glucagon-like peptide-1 (GLP-1) therapies are under investigation for a growing number of neuropsychiatric conditions, including substance use disorders. Cigarette smoking accounts for the largest proportion of substance use-related morbidity and mortality, in part reflecting increased risk for cardiometabolic disease among people who smoke. Given modest quit rates with approved smoking cessation therapies, medications with novel mechanisms of action are needed to expand the available monotherapy and combination treatment options.
How sports betting apps hook users
For most of the last 80 years, sports betting was limited to Las Vegas. But after a 2018 Supreme Court decision loosened regulations on professional sports wagers, it became possible to place bets on games 24/7 — with nothing more than a smartphone and a bank account.
In 2013, just five years prior to the landmark SCOTUS case, gambling was classified in the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) in a new category called “Substance-Related and Addictive Disorders.” This grouped gambling with alcohol use disorder and other addictions. Gambling is also known to have the highest suicide rate of any addiction.
23andMe Reports Genetic Predictors of Response to GLP-1 Drugs for Obesity
On any given morning, skyrocketing numbers of people reach for a small injection pen (and soon a pill) that, just a few years ago, was barely available outside of diabetes clinics. Drugs like semaglutide and tirzepatide have become cultural phenomena, reshaping not only medicine but also public discourse and the advertising industry around weight, metabolism, and obesity. Today, it is impossible to open a magazine, turn on the TV or radio, or walk down the grocery aisle without encountering some form of advertisement for these GLP-1 receptor agonists (GLP-1RAs).
Almost any individual in the United States can obtain a subscription to a GLP-1RA without having to visit a doctor’s office. Just visit Hims/Hers, Ro, or Noom and answer a few questions about weight, height, goals, and concerns to get a prescription. (One such site claims it is taking weight and height data and “combining with clinical data,” whatever that means, before presenting a plan and steps for ordering a prescription.)
But there are some major problems, one being that these drugs don’t work uniformly. Some patients respond to GLP-1RAs almost immediately, reporting diminished cravings within days. Others see little change. Side effects, too, can vary dramatically, from mild discomfort to debilitating nausea and vomiting. The spread of outcomes is wide and not fully understood.
Before blindly beginning to take a drug that, on the one hand, has seemingly miraculous effects and, on the other hand, might cause serious side effects like pancreatitis, gallbladder disease, and kidney failure, wouldn’t prescribers and prescription seekers want to know this information?
A study published in Nature Medicine by the 23andMe Research Institute—the new nonprofit entity founded by the company’s co-founder, Anne Wojcicki, for $305 million to replace the bankrupt biotechnology company—suggests the answer may be found, at least in part, in something far more fundamental than diet or willpower: our genes.
In speaking with Inside Precision Medicine for the first time since the company filed for bankruptcy and was resold to the nonprofit public benefit corporation, Adam Auton, PhD, vice president of Human Genetics at the 23andMe Research Institute, said, “The ‘GLP-1s’ have completely transformed weight loss management. A huge fraction of the population is benefiting. It’s a very natural question: Are people’s experiences on GLP-1s modulated by genetics?”
The short answer is, yes. Auton and 23andMe Research Institute scientists have provided genetic evidence that variation in drug target genes contributes to variability in response among individuals, laying the groundwork for consumer-based precision medicine approaches to obesity treatment and beyond.
Crowd-sourcing GLP-1 genetics
To better understand why responses to GLP-1 receptor agonists vary so widely, the 23andMe Research Institute team leveraged its uniquely large and engaged research cohort. Over the past decade, the company has assembled genetic data from more than 15 million participants who consented to research, enabling analyses that would be difficult in traditional clinical trials. Immediately following the company’s filing for bankruptcy in March 2025, 23andMe reported that over 1.9 million users requested for their data to be deleted. Auton told Inside Precision Medicine that the current number of consented customers is around 11 million.
Building on this resource, Auton and colleagues launched a targeted survey asking participants detailed questions about their GLP-1 drug use, including medication type, duration, dosage, weight loss, and side effects. More than 27,885 customers responded, providing a rich, real-world dataset. “That’s the power of having a large, engaged cohort,” said Auton. “You can ask a question and very rapidly get meaningful data back.”
Using these data, Auton and colleagues conducted a genome-wide association study (GWAS), scanning millions of genetic variants to identify those associated with treatment outcomes. “You’re starting with the entire genome,” Auton explained. “You’re testing every variant for correlation with the trait of interest. And when you see a signal, it tends to be overwhelming.”
The team focused on two primary traits: weight loss and the presence of side effects. The strongest association emerged in GLP1R, the gene encoding the GLP-1 receptor—the direct target of these drugs. A missense variant, rs10305420, was linked to significantly greater weight loss, with each copy associated with an additional 0.76 kilograms lost.
“It made very clear biological sense,” Auton said. “This is the receptor that the drug is acting on.” The missense variant may affect how much receptor is expressed on the cell surface, meaning individuals with more receptors could experience a stronger response to the same dose.
A second key finding involved a substitution in GIPR (rs1800437; p.Glu354Gln), which encodes the receptor for glucose-dependent insulinotropic polypeptide and is targeted by dual agonists such as tirzepatide. Unlike the GLP1R result, this association was not related to weight loss but to drug tolerability. Carriers of the variant were more likely to report nausea and vomiting—but only when taking medications that act on the GIP receptor. No such effect was observed among users of semaglutide, which does not target GIPR.
“It was very, very clean,” Auton said. “We saw this effect specifically in people taking the medications that actually target that receptor.”
Together, these findings underscore a central principle of pharmacogenetics: genetic variation can shape not only whether a drug works, but also how it is experienced, often in highly drug-specific ways.
Who is represented
One of the study’s more unconventional aspects is its reliance on self-reported data, a method sometimes viewed with skepticism in clinical research given the limits of memory and potential inaccuracies in reporting weight loss or medication use. Anticipating this concern, scientists at the 23andMe Research Institute validated their findings using a subset of participants who also shared electronic health records (EHRs), enabling direct comparison between self-reported and clinically recorded data.
The results were reassuring: survey-reported weight loss closely tracked with medical records, and medication histories aligned well across both sources. Although participants tended to slightly overestimate weight loss, they also reported longer treatment durations, effects that largely offset each other. Importantly, the genetic associations remained robust under independent scrutiny, with replication in the All of Us Research Program, a large, federally funded dataset based on clinical records rather than self-report.
While weight loss is the headline feature of GLP-1RAs, side effects often determine whether patients persist with treatment. Nausea, vomiting, and gastrointestinal discomfort are among the most common reasons for discontinuation, yet they are frequently underreported in traditional clinical datasets. EHRs may document when a medication is stopped but rarely capture why. Self-reported data addresses this gap by directly capturing patient experience.
“We were able to ask people directly about their experiences,” Auton said. “That’s something that’s often missing from clinical datasets.” By linking these experiences to genetic variation, the study enables a more refined understanding of drug tolerability, moving beyond population averages to individualized risk profiles.
As with many large-scale genetic studies, statistical power was greatest among individuals of European ancestry, reflecting broader imbalances in genomic datasets. However, the key findings were consistent across multiple ancestral groups, supporting their generalizability.
“We’re not seeing fundamentally different genetic effects across populations,” Auton said. Still, increasing diversity in genetic research remains essential to ensure equitable advances in precision medicine. As digital tools continue to integrate genetic, clinical, and self-reported data, this participant-driven model may play an increasingly central role in biomedical discovery.
Putting pharmacogenomics in patients’ hands
Identifying genetic variants is only the first step, of course. The larger goal is to translate those discoveries into tools that can guide real-world decisions. To that end, the 23andMe Research Institute scientists developed predictive models that combine genetic information with clinical factors to estimate treatment outcomes.
The vision is straightforward: before starting a GLP-1 drug, a patient could receive a personalized profile indicating likely weight loss and risk of side effects. “People are making decisions about whether these medications are right for them,” Auton said. “Can we give them information to help with that decision?”
Such tools could have immediate clinical applications. A patient with a high predicted risk of nausea, for example, might start at a lower dose or follow a slower titration schedule. Another with a favorable genetic profile might be reassured about expected benefits.
For now, these findings are unlikely to immediately change prescribing practices, as clinical guidelines will require further validation through prospective studies. However, the trajectory is clear. In the near future, patients considering GLP-1 therapies may undergo genetic testing as part of routine care, with treatment decisions—such as drug choice, dosing, and expectations—guided in part by their DNA. For a class of drugs already transforming millions of lives, this approach could further enhance both efficacy and tolerability, underscoring that responses to GLP-1 therapies are shaped not only by pharmacology but also by the subtle variations of the human genome.
The broader significance of the study lies in its contribution to precision medicine: the idea that treatments should be tailored to individual biology rather than applied uniformly. In fields like oncology, this approach is already standard. But precision obesity treatment is in far earlier stages.
Auton is quick to re-emphasize that genetics is only one piece of the puzzle. Lifestyle, environment, treatment adherence, and underlying health conditions all shape outcomes. Still, even a partial predictive signal could be transformative in a field where trial-and-error prescribing is common.
As researchers continue to study GLP-1RAs, their potential appears to extend far beyond weight and blood sugar. Early evidence suggests benefits in cardiovascular health, inflammation, and even neurological conditions. Some studies are exploring their role in addiction and compulsive behaviors. “There’s an increasing literature that they’re beneficial in multiple areas,” Auton said.
This expanding scope makes understanding variability even more important. If GLP-1 drugs are to be used to treat a wide range of conditions, predicting who will benefit and who may be at risk becomes one of the most important, if not the most important, challenges.
What about sequencing?
Throughout our conversation, there was at least one elephant in the room. One is that this is not the first study to identify genetic variants influencing responses to GLP-1 drugs, as prior research has also implicated rs10305420. Slovenian researchers showed that genetic variability in GLP1R is associated with inter-individual differences in the weight-lowering-lowering potential of GLP-1 drugs in obese women with polycystic ovary syndrome (PCOS) in 2015, at a time when the main GLP-1 drug was liraglutide, which required daily injection.
More provocative is that the directionality of the variants’ effect reported in the Nature Medicine paper is the opposite of these previous studies. Auton’s team writes that such discrepancies may stem from differences in disease context, smaller sample sizes, limited statistical power, and variations in drug type, cohorts, and analytical methods.
Additionally, the GIPR variant rs1800437 (p.Glu354Gln) is already a known partial loss-of-function mutation, previously identified in a study of Chinese type 2 diabetes patients in 2019.
Perhaps the more significant issue is the question of sequencing. It’s not a space that 23andMe has completely avoided, as their premier consumer kit employs exome sequencing. But the cost of whole genome sequencing (WGS) direct-to-consumer products is now often priced lower than 23andMe’s premier kit, which goes for $499.
When asked about employing WGS, Auton revealed little of the calculus behind why 23andMe hasn’t added WGS to its arsenal of tools for interrogating genomes. “We’re very excited about that space,” Auton said. “Our focus has always been on what we can do in a direct consumer framework. There’s always been a price question there for WGS. It’s great. But when it was $1,000, it wasn’t obvious that that was going to be a compelling consumer offering. The pricing has reached its current level. It’s an area we’re very excited about and we’ll continue to look at.”
With studies like this, 23andMe 2.0 is making a case, perhaps its strongest yet, that its true value lies in something far more consequential: the ability to predict how individuals will respond to medicine before they ever take it. If that vision holds, the implications extend well beyond GLP-1 drugs. It suggests a future where prescribing a medication without first consulting a patient’s genetic profile feels incomplete, even irresponsible.
The post 23andMe Reports Genetic Predictors of Response to GLP-1 Drugs for Obesity appeared first on Inside Precision Medicine.