Tag: Appetite and ingestive regulation

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A Text Messaging–Based Program to Transition From Basal Insulin to Glucagon-Like Peptide-1 Receptor Agonists in Safety-Net Diabetes Care: Pilot Quality Improvement Intervention Study

Background: Glucagon-like peptide-1 receptor agonists (GLP-1 RAs) and basal insulin both lower blood sugar, but while insulin puts people at risk of hypoglycemia and weight gain, GLP-1 RAs do not. In addition, GLP-1 RAs have added cardiometabolic and renal benefits. For these reasons, when possible, many primary care providers prefer their patients with type 2 diabetes to be from basal insulin to a GLP-1 RA. This transition process can be labor intensive, requiring multiple dosing adjustments and a watchful eye for hypoglycemia and hyperglycemia. The Mobile Insulin Titration Intervention (MITI)–GLP1 program uses SMS text messaging–based technology to support a streamlined and supervised transition process from basal insulin to a GLP-1 RA. This program takes place at a multilingual safety-net clinic. Objective: Our objectives were to assess program feasibility and acceptability to determine whether the intervention was doable, practical, and worthy of further investigation via a larger controlled trial. Preliminary clinical outcomes are also discussed in this paper. Methods: Patients were enrolled on a secure web platform that sent them a daily SMS text message asking the following: “What was your fasting blood sugar this morning?” Each weekday, texted responses containing patients’ fasting blood sugar levels were checked for alarm values, and once weekly, patients were called and advised on whether and how to lower their basal insulin and increase their GLP-1 RA dose. The program was co-run by general internal medicine physicians and nurses and continued until the patient had their insulin stopped completely and/or their GLP-1 RA dose reached the maximum, or 16 weeks elapsed. All enrolled patients were included in the analyses. Results: A total of 72 patients completed the pilot program. Feasibility and acceptability were high. Of 3671 SMS text messages sent by the program, 3520 (95.89%) received a response from patients. Of 719 cumulative weeks in which Thursday titration phone calls were attempted, successful connections with patients were made in 649 (90.26%) instances. Preliminary clinical outcomes were promising. Insulin doses were meaningfully reduced (55/72, 76.39% had their basal insulin reduced by at least 50%; 45/72, 62.5% had their insulin stopped completely). GLP-1 RA doses were meaningfully increased (64/72, 88.89% had their GLP-1 RA dose increased by ≥1 level; 45/72, 62.5% were discharged on the maximum dose of their GLP-1 RA). There was minimal hypoglycemia (5/3520, 0.14% of the SMS text messages reported a value of <80 mg/dL) and hyperglycemia (1/3520, 0.03% of the SMS text messages reported a value of >400 mg/dL). Conclusions: A general internal medicine–run MITI-GLP1 pilot program using SMS text messaging and interdisciplinary teamwork between internists and nurses is a feasible and acceptable intervention for safely and effectively transitioning people with well-controlled type 2 diabetes away from basal insulin and toward a GLP-1 RA.
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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 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 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.

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Mustafa Suleyman: AI development won’t hit a wall anytime soon—here’s why

We evolved for a linear world. If you walk for an hour, you cover a certain distance. Walk for two hours and you cover double that distance. This intuition served us well on the savannah. But it catastrophically fails when confronting AI and the core exponential trends at its heart.

From the time I began work on AI in 2010 to now, the amount of training data that goes into frontier AI models has grown by a staggering 1 trillion times—from roughly 10¹⁴ flops (floating-point operations‚ the core unit of computation) for early systems to over 10²⁶ flops for today’s largest models. This is an explosion. Everything else in AI follows from this fact.

The skeptics keep predicting walls. And they keep being wrong in the face of this epic generational compute ramp. Often, they point out that Moore’s Law is slowing. They also mention a lack of data, or they cite limitations on energy.

But when you look at the combined forces driving this revolution, the exponential trend seems quite predictable. To understand why, it’s worth looking at the complex and fast-moving reality beneath the headlines.

Think of AI training as a room full of people working calculators. For years, adding computational power meant adding more people with calculators to that room. Much of the time those workers sat idle, drumming their fingers on desks, waiting for the numbers to come through for their next calculation. Every pause was wasted potential. Today’s revolution goes beyond more and better calculators (although it delivers those); it is actually about ensuring that all those calculators never stop, and that they work together as one.

Three advances are now converging to enable this. First, the basic calculators got faster. Nvidia’s chips have delivered an over sevenfold increase in raw performance in just six years, from 312 teraflops in 2020 to 2,250 teraflops today. Our own Maia 200 chip, launched this January, delivers 30% better performance per dollar than any other hardware in our fleet. Second, the numbers arrive faster thanks to a technology called HBM, or high bandwidth memory, which stacks chips vertically like tiny skyscrapers; the latest generation, HBM3, triples the bandwidth of its predecessor, feeding data to processors fast enough to keep them busy all the time. Third, the room of people with calculators became an office and then a whole campus or city. Technologies like NVLink and InfiniBand connect hundreds of thousands of GPUs into warehouse-size supercomputers that function as single cognitive entities. A few years ago this was impossible.

These gains all come together to deliver dramatically more compute. Where training a language model took 167 minutes on eight GPUs in 2020, it now takes under four minutes on equivalent modern hardware. To put this in perspective: Moore’s Law would predict only about a 5x improvement over this period. We saw 50x. We’ve gone from two GPUs training AlexNet, the image recognition model that kicked off the modern boom in deep learning in 2012, to over 100,000 GPUs in today’s largest clusters, each one individually far more powerful than its predecessors.

Then there’s the revolution in software. Research from Epoch AI suggests that the compute required to reach a fixed performance level halves approximately every eight months, much faster than the traditional 18-to-24-month doubling of Moore’s Law. The costs of serving some recent models have collapsed by a factor of up to 900 on an annualized basis. AI is becoming radically cheaper to deploy.

The numbers for the near future are just as staggering. Consider that leading labs are growing capacity at nearly 4x annually. Since 2020, the compute used to train frontier models has grown 5x every year. Global AI-relevant compute is forecast to hit 100 million H100-equivalents by 2027, a tenfold increase in three years. Put all this together and we’re looking at something like another 1,000x in effective compute by the end of 2028. It’s plausible that by 2030 we’ll bring an additional 200 gigawatts of compute online every year—akin to the peak energy use of the UK, France, Germany, and Italy put together.

What does all this get us? I believe it will drive the transition from chatbots to nearly human-level agents—semiautonomous systems capable of writing code for days, carrying out weeks- and months-long projects, making calls, negotiating contracts, managing logistics. Forget basic assistants that answer questions. Think teams of AI workers that deliberate, collaborate, and execute. Right now we’re only in the foothills of this transition, and the implications stretch far beyond tech. Every industry built on cognitive work will be transformed.

The obvious constraint here is energy. A single refrigerator-size AI rack consumes 120 kilowatts, equivalent to 100 homes. But this hunger collides with another exponential: Solar costs have fallen by a factor of nearly 100 over 50 years; battery prices have dropped 97% over three decades. There is a pathway to clean scaling coming into view.

The capital is deployed. The engineering is delivering. The $100 billion clusters, the 10-gigawatt power draws, the warehouse-scale supercomputers … these are no longer science fiction. Ground is being broken for these projects now across the US and the world. As a result, we are heading toward true cognitive abundance. At Microsoft AI, this is the world our superintelligence lab is planning for and building.

Skeptics accustomed to a linear world will continue predicting diminishing returns. They will continue being surprised. The compute explosion is the technological story of our time, full stop. And it is still only just beginning.

Mustafa Suleyman is CEO of Microsoft AI.

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Neurocrine Grows in Endocrinology, Rare Disease with $2.9B Soleno Buyout

Neurocrine Biosciences has agreed to acquire Soleno Therapeutics for $2.9 billion, the companies said, in a deal designed to bolster the buyer’s portfolio of marketed endocrinology and rare disease therapies.

“This transaction will advance Neurocrine’s mission to deliver life-changing treatments while accelerating our revenue growth and portfolio diversification strategy,” Kyle W. Gano, PhD, Neurocrine’s CEO, said in a statement.

The acquisition would bolster Neurocrine’s offerings to include three treatments that have already reached the market:

  • Crenessity® (crinecerfont), a treatment of classic congenital adrenal hyperplasia (CAH) due to 21-hydroxylase deficiency that received FDA approval in December 2024
  • Ingrezza® (valbenazine), a vesicular monoamine transmitter 2 (VMAT2) drug approved in 2017 as a treatment for tardive dyskinesia and the chorea associated with Huntington’s disease
  • Vykat™ XR (diazoxide choline), approved last year as the first and only therapy indicated to treat hyperphagia in patients ages four and older with Prader-Willi syndrome (PWS).

“Neurocrine is the right strategic partner to expand the reach of Vykat XR in the Prader-Willi syndrome community given their experience in endocrinology and rare disease and their proven ability to execute successful commercial launches,” stated Anish Bhatnagar, MD, Soleno’s chairman and CEO. “We are excited to accelerate Vykat XR’s impact for PWS patients following completion of the transaction by leveraging Neurocrine’s strong commercial capabilities.”

Soleno finished 2025 with $190.4 million in net revenue from sales of Vykat XR—including $91.7 million generated during the fourth quarter, pushing the company to profitability with positive net income of $20.9 million.

‘A little surprising’

Stifel analysts Paul Matteis and James Condulis called the planned acquisition “a little surprising” since Vykat XR is projected to garner approximately $400 million in annual net revenue, he commented in a note reported by Bloomberg News.

Vykat XR won FDA approval in March 2025. From then through December 31, 859 active patients were prescribed the drug by 630 unique prescribers (136 of them in Q4), while the company received 1,250 patient start forms (207 in Q4).

Neurocrine expects Vykat XR’s numbers to improve in coming years, since the drug is positioned as a foundational first-line therapy for PWS and is supported by a patent portfolio that is expected to protect the drug’s exclusivity into the mid-2040s.

Vykat XR would join Neurocrine’s marketed portfolio which includes Ingrezza and Crenessity. Ingrazza racked up blockbuster net revenue numbers of $2.51 billion up 9% year-over-year (including $657.5 million during Q4, up 7% from the year-ago quarter). Neurocrine has credited double-digit prescription volume growth in total prescriptions and new (first-time) prescriptions, partially offset by a lower net price that the company called new “formulary access investments” designed to support long-term growth.

Crenessity generated $301.2 million in net product sales last year for Neurocrine, including $135.3 million in the fourth quarter, reflecting 2,048 total new patient enrollment start forms, 431 of them in Q4 2025.

Neurocrine reasons that the three drugs will deliver sustained revenue growth for the combined company through the end of the decade.

Also for Neurocrine, a buyout of Soleno presents a “more sensible way into metabolic disease” than by developing its own pipeline candidates, which are in preclinical phases, and risking competitive and regulatory challenges, BMO Capital Markets analyst Evan Seigerman observed in a research note reported by Reuters.

Neurocrine has disclosed plans to begin Phase I studies this year for two preclinical obesity candidates: NBIP-‘2118, a CRF2 agonist; and ‘NBIP-‘1968, a combination of ‘2118 and the company’s own GIP (glucose-dependent insulinotropic polypeptide)/ GLP-1 (glucagon-like peptide-1) preferring triple agonist, which Neurocrine calls “light” on glucagon activity.

News of a potential buyout of Soleno by Neurocrine was first reported Sunday by the Financial Times.

Soleno investors signaled approval of the buyout Monday by sending shares to $52.25, up 32% from Thursday’s close of $39.49 (Markets were closed Friday for Good Friday). However, Neurocrine’s investors weren’t as supportive of the deal as that company’s shares barely budged, closing at $132.48, up 0.67% from $131.60 on Thursday.

Second thoughts?

A potential reason: Neurocrine investors may have second thoughts about a deal that would add to its pipeline Vykat XR, whose prescribing label includes warnings and precautions about past reports of hyperglycemia and fluid retention/edema, as Sumant Kulkarni, a senior analyst covering biotechnology with Canaccord Genuity, commented in a research note.

“We believe NBIX would have to articulate its plans very well for investors to display enthusiasm from the get-go,” Kulkarni wrote.

Yet two things could work in Neurocrine’s favor, Kulkarni added: The company’s solid track record of commercialization as seen with Ingrezza and Crenessity, and the prospect of adding to the portfolio Vykat XR given its approval for a rare form of obesity.

San Diego-based Neurocrine reported approximately 2,000 employees as of December 31, 2025, with plans during the first quarter to complete the expansion of sales teams for Ingrezza and Crenessity “to maximize our commercial momentum.” Soleno is based in Redwood City, CA, and reported a workforce of 182 full-time employees as of the end of 2025.

At $53 per share cash, the purchase price represents a premium of about 34% above Soleno’s closing share price Thursday, and a premium of 51% to Soleno’s 30-day volume-weighted average price (VWAP).

The boards of both Neurocrine and Soleno have approved the transaction, which is expected to close within 90 days subject to satisfying customary closing conditions that include receipt of regulatory approvals.

Neurocrine will acquire Soleno by launching a tender offer for that company’s outstanding shares. Following a successful completion of the tender offer, a wholly owned subsidiary of Neurocrine will merge with Soleno, and the outstanding Soleno shares not tendered in the offer will be converted into the right to receive the same $53 per share in cash paid in the tender offer.

Consummation of the tender offer is subject to the tender of at least a majority of the outstanding shares of Soleno, the expiration or termination of the waiting period under the Hart-Scott-Rodino Antitrust Improvements Act of 1976, and other customary conditions.

Neurocine said it will fund its acquisition of Soleno using a “modest amount” of pre-payable debt plus cash on hand. Neurocrine reported $1.48 billion in cash, cash equivalents, and marketable securities as of December 31, 2025—up 37.5% from $1.076 billion a year earlier.

The post Neurocrine Grows in Endocrinology, Rare Disease with $2.9B Soleno Buyout appeared first on GEN – Genetic Engineering and Biotechnology News.

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STAT+: Novo Nordisk launches high-dose Wegovy

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Good morning. Here in Chicago, cherry blossoms are blooming, rat birth control is working, and it finally feels like spring may be upon us.

Budget proposals aim to boost U.S. drugmaking

As part of President Trump’s 2027 budget blueprint, the FDA has proposed policies aimed at encouraging domestic development and manufacturing of drugs, such as making it easier for drugmakers to move into clinical testing in the U.S. and giving an “exclusivity” period to U.S.-based generics manufacturers.

Continue to STAT+ to read the full story…

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Analysis of the prevalence of dyslipidemia in early-onset schizophrenia patients and its correlation with clinical characteristics

ObjectiveTo analyze the prevalence of dyslipidemia and related influencing factors in patients with early-onset schizophrenia (EOS).MethodsWe recruited 289 pediatric and adolescent EOS patients from October 2021 to June 2024 in the Third People’s Hospital of Fuyang. Researchers gathered comprehensive demographic and clinical records. Utilizing the 2023 Chinese Guidelines for Lipid Management, they calculated dyslipidemia prevalence and the incidence of irregularities in total cholesterol, triglycerides, LDL cholesterol, HDL cholesterol, and non-HDL cholesterol. Subsequently, differences in dyslipidemia among different genders, body mass index, and antipsychotic medication groups were analyzed. Finally, independent influencing factors of dyslipidemia in EOS patients were explored.ResultsThe overall prevalence of dyslipidemia was 24.9% (72/289), with abnormal rates of TG, TC, HDL-C, LDL-C, and non-HDL-C being 15.9%, 6.6%, 6.6%, 4.2%, and 7.3%, respectively. Male patients, those who were overweight or obese, and those taking two antipsychotic drugs had significantly higher rates of dyslipidemia. Regression analysis showed that male gender (OR = 2.04, P = 0.016), overweight/obesity (OR = 4.55, P < 0.001), body roundness index (OR = 1.53, P = 0.005), and the use of two antipsychotic drugs (OR = 1.90, P = 0.030) were risk factors for dyslipidemia in EOS patients.ConclusionThe prevalence of dyslipidemia in EOS patients is relatively high. When monitoring lipid levels in clinical practice, particular attention should be paid to male patients, those who are overweight or obese, and those receiving combined drug therapy.