Category: 8. Health

  • Stress disrupts gut and brain barriers by reducing key microbial metabolites, study finds

    Stress disrupts gut and brain barriers by reducing key microbial metabolites, study finds

    A new study reports that a single, brief exposure to stress is associated with a rapid reduction of beneficial compounds produced by gut bacteria. The research, published in the journal Brain, Behavior, & Immunity – Health, also found that these same compounds, when tested in a laboratory setting, appear to protect the cellular barriers of both the gut and the brain from damage. The findings offer new insight into the immediate biological responses to stress, highlighting a potential mechanism through which even short-term stressors might influence our physiology.

    Scientists are increasingly interested in the gut-brain axis, the complex network of communication between the gastrointestinal system and the brain. This system includes not only nerves and hormones, but also immune signals and microbial products. One of the key components in this system is a group of substances called short-chain fatty acids, which are produced when gut bacteria digest dietary fiber.

    These fatty acids—mainly butyrate, acetate, and propionate—can influence gut health, inflammation, brain function, and even mood. While much attention has been paid to how stress affects the body over time, less is known about how the gut and brain respond to short bursts of stress. The current study set out to examine whether acute stress changes short-chain fatty acid production, and how those changes might influence the function of protective barriers in the body.

    “We have long been interested in the impact of stress on signalling in the gut-brain axis. This is a two-way street in that while gut microbes can tune the host stress response, stress exposures can also change the composition and function of the gut microbiota. Much less is known in this context about how acute stress, the building blocks of chronic stress, modify the gut. Essentially, we wanted to know what was going on in the stressed gut,” explained study author Gerard Clarke, a professor of neurobehavioral science at the University College Cork and co-author of Microbiota Brain Axis: A Neuroscience Primer.

    To explore this, the researchers exposed mice to a 15-minute period of restraint stress. They used both conventional mice, germ-free mice raised without any gut microbes, and germ-free mice that had been re-colonized with gut bacteria. After the stress exposure, the team measured the levels of various short-chain fatty acids and other related compounds in the animals’ lower intestines. The researchers also tested the effects of these compounds on cellular models that mimic the gut and blood-brain barriers, which are crucial in preventing harmful substances from entering sensitive tissues.

    In the animals exposed to stress, levels of butyrate and acetate dropped significantly in the lower intestinal contents, especially in conventional and colonized germ-free mice. These changes appeared quickly, within 45 minutes of the stress exposure. The researchers also found that stress reduced levels of dietary sugar breakdown products and other microbial metabolites. These findings suggest that acute stress disrupts the fermentation processes that gut bacteria use to produce beneficial compounds, which could have downstream effects on host health.

    “One of the intriguing findings here is that the consequences of an acute stress exposure is visible in the gut very quickly as alterations in microbial metabolites,” Clarke told PsyPost. “These results build on earlier work from our lab to potentially explain how an acute psychosocial stressor can impact intestinal permeability.”

    To understand whether these stress-induced reductions had functional consequences, the researchers turned to laboratory cell models. They applied varying concentrations of butyrate, acetate, and propionate to layers of gut and brain cells grown in the lab. The goal was to see whether these compounds could protect against barrier disruption triggered by lipopolysaccharide, a bacterial molecule known to increase permeability and inflammation.

    The results showed that certain concentrations of butyrate and acetate helped maintain barrier function, both in intestinal and brain cell models. For example, pretreatment with butyrate at 1 and 10 millimoles significantly prevented gut barrier damage, while acetate at 10 millimoles also had protective effects. Some concentrations of acetate, however, appeared to worsen permeability, indicating that its effects may vary depending on dose and context.

    The protective effects were linked to changes in tight junction proteins, which help hold the barrier cells together. One of these proteins, ZO-1, was reduced by the bacterial challenge, but this reduction was partially reversed by treatment with the short-chain fatty acids. Microscopy showed that butyrate and propionate increased both the abundance and structural complexity of ZO-1 proteins at the junctions between cells, forming wavy “ruffles” that may represent a more active or flexible barrier. In contrast, acetate did not increase ruffling but still helped restore overall protein levels.

    The researchers also looked at how these fatty acids influenced the activity of receptors known to respond to them. Specifically, butyrate increased the expression of FFAR2 and FFAR3, two receptors involved in immune and barrier regulation. These receptors are believed to play a role in maintaining the health of the gut lining, and mice lacking them show higher permeability and more inflammation. The current results suggest that short-chain fatty acids may help stabilize the gut barrier partly by activating these protective signaling pathways.

    In addition to looking at how fatty acids protect barrier function, the researchers also tried to understand why stress reduces their levels in the first place. By analyzing the breakdown products of dietary sugars in the intestines, they found that stress reduced the availability of key substrates that bacteria use to make short-chain fatty acids.

    The data also suggested that stress might shift microbial activity toward producing other compounds, such as polyols, or increase host absorption of fatty acids before they accumulate in the lower intestine. Some changes in microbial energy metabolism were also observed, depending on whether the animals had gut microbes or not. These findings point to a broad disturbance in the gut environment after stress, which could influence both microbial activity and the availability of beneficial compounds to the host.

    “Our gut microbes are like little factories, with production lines pumping out microbial metabolites,” Clarke said. “One of the key messages is that the experience of stress can also be felt by our gut microbes and one of the consequences of this is alterations in the production of these microbial metabolites, in this case a reduction in short-chain fatty acids. Our results using in vitro models show that these microbial metabolites, like butyrate, are important to maintain gut and blood-brain barrier function.”

    The findings offer new insight into how even short-term stress can alter gut-brain signaling, but the researchers acknowledge some limitations. The experiments used cell culture models to test barrier integrity, which cannot fully capture the complexity of living organisms.

    “We used in vitro studies to understand if short-chain fatty acids could be effectors of intestinal permeability alterations in the gut and the brain, but these are a very simple approximations of what is happening at these sites in the whole organism within the context of microbiota-gut-brain axis signalling,” Clarke explained. “We have recently noted that more sophisticated options like human induced pluripotent stem cells (hiPSCs) offer a more innovative model to advance these studies in the future.”

    The researchers emphasized that understanding how acute stress affects microbial metabolites like short-chain fatty acids may help explain how the gut-brain axis contributes to stress-related health problems. Since these metabolites are influenced by diet and microbial composition, they could become targets for new therapies aimed at supporting gut and brain barrier function during stress. For example, interventions that boost butyrate production or mimic its protective effects might help buffer against stress-induced damage.

    “We still need to understand what happens in the stressed gut when these acute stress exposures are experienced repeatedly and chronically, and if adaptive or maladaptive consequences emerge that will be important for stress-related disorders,” Clarke said.

    “This is all down to the great work of a really talented postdoctoral researcher, Dr. Cristina Rosell-Cardona,” he added. “Cristina is now an INSPIRE fellow at APC Microbiome Ireland and is going on to look at the impact of microbial metabolites in depression, a stress-related disorder with alterations in microbiota-gut-brain axis signalling.”

    The study, “Acute stress-induced alterations in short-chain fatty acids: Implications for the intestinal and blood brain barriers,” was authored by Cristina Rosell-Cardona, Sarah-Jane Leigh, Emily Knox, Emanuela Tirelli, Joshua M. Lyte, Michael S. Goodson, Nancy Kelley-Loughnane, Maria R. Aburto, John F. Cryan, and Gerard Clarke.

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  • 100 years ago, scientists predicted we’d live to 1,000 years old

    100 years ago, scientists predicted we’d live to 1,000 years old

    When Frederick Grant Banting discovered how to isolate insulin from animals in 1921, the young Canadian doctor—a WWI veteran and former farm boy—changed the calculus of diabetes forever. Prior to the 1920s, the disease killed more than 80 percent of preteen diabetic children. Banting’s breakthrough replaced the sometimes toxic remedy goat’s rue, or Galega officinalis, a flowering plant with glucose-lowering properties derived from guanidine. His discovery came during a wave of medical optimism fueled by new scientific tools and knowledge that were rapidly unlocking the mysteries of human anatomy, disease, and aging.

    The foundations for this optimism had been building for decades. Germs were first discovered in the 1880s, ushering in the golden age of bacteriology and numerous life-saving vaccines. Vitamins got their name in the early 1900s when London-based Polish biochemist Casimir Funk—one of many scientists seeking cures for common diseases by linking them to vital nutrient deficiencies—combined “vital” and “amines.” Rickets led to the discovery of vitamin D, scurvy to vitamin C, and vitamin B was tied to beriberi, a disease that causes weakness, weight loss, confusion, and, in extreme cases, death. Meanwhile, anesthesia transformed surgery from a grisly performing art with low survival rates to more precise procedures conducted in germ-free operating rooms. Bit by bit, medicine appeared to be conquering many of humanity’s most pernicious plagues and thereby extending our average lifespan.

    By July 1925, Popular Science writer John E. Lodge even suggested that humans might soon be able to extend their life expectancy to 1,000 years. “Thanks to the efforts of science in combatting the ravages of disease, the average span of life is increasing every year,” Lodge wrote. “Are we to expect, then, that in time science will succeed in prolonging the average life until, like Methuselah, we measure our lives by centuries instead of by years.” Lodge envisioned a world where aging could be halted by replacing worn-out enzymes, transplanting organs, or manipulating an elusive “vital spark.” Scientists, he claimed, might be on the verge of conquering death itself.

    The June 1925 issue of Popular Science questioned death. Image: Popular Science

    A hundred years later, we’re still not there, but we continue to chase immortality with the same zest. Just as a century ago, today that quest is fueled not by glamorous breakthroughs—even if history makes it seem so—but by painstaking, collaborative scientific research, yielding fresh medical insights. In place of insulin, vaccines, and vitamins, today we’re captivated by gene-editing, cellular reprogramming, and immunotherapy. From biohackers injecting stem cells in search of cellular youth to billionaires like Bryan Johnson leaning on wearable tech for preventative health, blood plasma exchanges, and caloric restriction, the goal of outsmarting death hasn’t diminished—the elixirs are just more sophisticated.

    And yet, we’ve come a long way in a century. In 1925, the average American lifespan was 58 years; today, it’s 78.4 years, according to the US Centers for Disease Control. Such progress might seem meager compared to our grandiose early 20th century expectations, but the trend suggests that by the next century the average American would live to be a centenarian. There’s even reason to believe—as there was in 1925—that current promising research might yield treatments as soon as the next few decades that significantly extend our lifespans while improving disease resistance.

    vintage graphic showing average lifespans in 1600, 1750, and 1925

    Longevity increased greatly over 300 years. Image: Popular Science

    Consider how researchers in Singapore have extended the lives of mice 25 percent by blocking the protein interleukin-11. Scientists at the University of Rochester have successfully transferred a longevity gene to mice from naked mole rats, which live ten times longer than similar rodents. The gene, known for producing high molecular weight hyaluronic acid, or HMW-HA, extended mouse lives by 4.4% and improved their overall health. The researchers now aim to transfer these benefits to humans.

    In an ironic twist, a century after Banting’s insulin discovery displaced goat’s rue, a derivative of the pink-and-white flowering plant is back in favor. Metformin, a biguanide medication, has become one of the leading drugs for managing type 2 diabetes. Like its medieval predecessor, which was used for everything from increasing milk flow in livestock to alleviating plague symptoms, metformin has been similarly used or tested in myriad applications: as an antimalarial drug,  influenza treatment, lactation enhancer, arthritis remedy, and cardiovascular medicine. Now, scientists have begun to piece together the mystery of metformin’s versatility by mapping how it works at a cellular level. Recent research has shown that it may slow or inhibit cellular changes leading to inflammation and age-related diseases, extending lifespan.

    The cellular aging story stretches back to the late 19th century. As scientists were discovering germs, developing vaccines, uncovering the link between vital nutrients and common diseases, and improving surgery, evolutionary biologist August Weismann theorized that human cells had replication limits, which explained why the ability to heal diminished with age. By the 1960s, scientists had proven Weismann correct. Today, researchers are learning to halt and reverse cellular aging through reprogramming, an idea first attempted in the 1980s and advanced by Nobel Prize recipient Shinya Yamanaka, who discovered how to revert mature, specialized cells back to their embryonic, or pluripotent state, enabling them to regenerate into new tissue like liver cells or teeth.

    Read more ‘What a Difference a Century Makes—Or Not’ series

    But none of this means we’re approaching thousand-year lifespans. Most longevity interventions work only in tightly controlled laboratory settings or short-lived animals. Translating them into humans presents entirely different—and enormously complex—challenges. Even if we managed to double or triple the human lifespan, equally complex social challenges would follow: Who would get access to life-extending therapies? How do we support a society where most people live into their third or fourth century? What psychological toll does such extreme longevity take?

    The optimism of 1925 wasn’t misplaced; it was simply premature. It still might be, but today’s longevity researchers are armed with more sophisticated tools and a deeper understanding of biological processes. Whether today’s tools and knowledge will finally enable us to defy death remains to be seen. If there’s a lesson to draw from the past hundred years, however, it’s that life extension is incremental, fragile, and often humbling. We’ve added decades to average life expectancy, transformed once-fatal diseases into manageable conditions, and dramatically improved the quality of life in later years. That’s no small feat—but it’s not immortality.

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  • Air Pollution ‘Strongly Associated’ With DNA Mutations Tied to Lung Cancer : ScienceAlert

    Air Pollution ‘Strongly Associated’ With DNA Mutations Tied to Lung Cancer : ScienceAlert

    Lung cancer cases are on the rise in non-smokers around the world, and air pollution could be an insidious, contributing factor.

    A genome study has now found that outdoor smog and soot are strongly associated with DNA mutations related to lung cancer – including known drivers seen in smokers, and new ones unique to non-smokers.

    The more pollution someone was exposed to, the more mutations scientists found in their lung tumors.

    The findings don’t mean that air pollution is directly causing lung cancer, but they do contribute to evidence suggesting that possibility.

    Related: Geneticists Just Got Closer to The Sources of Lung Cancer in People Who Never Smoked

    “We’re seeing this problematic trend that never-smokers are increasingly getting lung cancer, but we haven’t understood why,” explains biomolecular scientist Ludmil Alexandrov from the University of California San Diego (UCSD).

    “Our research shows that air pollution is strongly associated with the same types of DNA mutations we typically associate with smoking.”

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    The extensive international analysis examined the cancer genomes of 871 individuals from four continents, all of whom had lung cancer despite never having smoked and who had not yet received cancer treatment.

    Those who lived in regions with high levels of air pollution were significantly more likely to have TP53 mutations, EGFR mutations, and shorter telomeres.

    Abnormal TP53 and EGFR genes are hallmarks of lung cancers, especially those driven by the SBS4 DNA mutation, and shorter telomeres are linked to accelerated aging.

    In the current study, non-smokers who lived in areas with higher air pollution were nearly four times more likely to exhibit SBS4 signatures as those who lived in regions with cleaner air.

    By contrast, exposure to secondhand smoke, which is a known cancer risk, showed only a slight increase in genetic mutations.

    “If there is a mutagenic effect of secondhand smoke, it may be too weak for our current tools to detect,” says geneticist Tongwu Zhang from the US National Cancer Institute (NCI).

    Not so for air pollution or tobacco smoking: both were strongly linked to DNA mutations.

    Today in the United States, people who have never smoked or who have smoked fewer than 100 cigarettes in their lives make up about 10 to 20 percent of lung cancer cases.

    Scientists have long suspected that air pollution could be a contributing factor, but exactly how fine particulate matter in the air compares to tobacco smoking or secondhand smoke exposure remains unclear.

    Some studies suggest that breathing polluted air is on par with smoking a pack a day, and yet these conclusions are mostly based on observational analyses.

    The current study digs further by looking at some of the molecular mechanisms that may be at play. It compared the lung cancer genomes of the 871 non-smokers with tumors from 345 smokers, to find similarities and differences.

    The majority of non-smokers with lung cancer had adenocarcinomas (the most common type of lung cancer), and nearly 5 percent of those tumors showed the SBS4 mutational signature.

    In addition, 28 percent of non-smokers showed a new signature called SBS40a, which wasn’t found in tobacco smokers. Strangely, the cause of this particular mutational driver was unknown, but doesn’t seem to be environmental in nature.

    “We see it in a majority of cases in this study, but we don’t yet know what’s driving it,” says Alexandrov. “This is something entirely different, and it opens up a whole new area of investigation.”

    The current research relied only on regional air pollution levels, which means it can’t say how much any one individual was directly exposed to fine particulate matter in the air. Participants who said they had never smoked may have also smoked more than reported.

    These limitations notwithstanding, the overall findings align with other evidence indicating that soot or smog may trigger tumor growth in a similar way to cigarette chemicals.

    “This is an urgent and growing global problem that we are working to understand regarding never-smokers,” says epidemiologist Maria Teresa Landi from the NCI.

    The team now hopes to expand their study to include cancer genomes from a more diverse, global cohort.

    The study was published in Nature.

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  • Move over microbiome, time for the human gut’s “virome” to shine

    Move over microbiome, time for the human gut’s “virome” to shine

    For years, bacteria dominated gut health research. Now, the gut virome is gaining attention as a major player in health and disease. Bacteriophages, which are viruses that infect bacteria, make up around 90% of the gut virome.

    Though small in biomass, they outnumber bacteria by up to tenfold. These tiny viruses shape microbial communities, influence immunity, and affect gut health.


    A landmark review published recently in Precision Clinical Medicine reveals how the gut virome links to diseases like inflammatory bowel disease (IBD), colorectal cancer, and Clostridium difficile infection (CDI).

    Gut’s virome changes across life stages

    The gut virome changes constantly throughout life. Infants show high diversity in gut bacteriophages (also called phages), which is shaped by birth method, diet, and antibiotic exposure.

    As individuals grow, diet and hormones refine the virome. In adults, phages and bacteria form a stable, balanced community.

    However, aging shifts this balance. Older adults show more lysogenic phages, which integrate into bacterial genomes and potentially alter metabolism and immune interactions.

    Diet and environment shape the virome

    Diet greatly influences the virome. High-fiber diets support beneficial phages by encouraging helpful bacteria. Western diets, rich in fat and sugar, promote harmful phages.

    Urbanization reduces exposure to natural viruses. Studies show that rural diets which are rich in fiber boost gut virome diversity.

    Host genetics and immunity also play roles. Genetic variants can affect viral detection and clearance. Immune defenses, like interferons and IgA, keep the virome balanced.

    However, immune dysfunction can trigger viral imbalances, worsening conditions like HIV and gut inflammation.

    Viruses and bacteria interacting

    Viruses, including phages, along with bacteria and fungi form a complex and dynamic ecosystem in the gut. The components interact in positive and negative ways, thus affecting host metabolism, immune regulation and resistance to disease.

    The gut virome plays a crucial regulatory role in these intricate interactions. Phages interact with bacteria through lytic, lysogenic, and budding cycles, which regulate bacterial populations and gene flow.

    In a form of microbial arms race, the bacteria defend against phages using CRISPR systems and the phages evolve escape mechanisms.

    Some phages even boost bacterial metabolism and biofilm formation, affecting disease risk. For instance, phages can transfer genes that enhance bacterial survival in acidic conditions.

    Virome’s influence on immunity

    The virome affects gut immunity deeply. Phages can anchor to gut mucus and form a protective barrier. They also shape our own T and B cell activity and regulate macrophages. In addition, viruses that infect eukaryotic cells affect immune pathways and maintain gut balance.

    This infographic illustrates four major therapeutic approaches—fecal microbial transplantation (FMT), phage therapy, dietary interventions, and probiotics/prebiotics—that aim to restore gut health by modulating the microbiome and virome. Each strategy offers unique methods, benefits, and challenges in managing gastrointestinal disorders such as IBD, CRC, and CDI, highlighting the growing potential of virome-informed precision medicine. Credit: Precision Clinical Medicine
    This infographic illustrates four major therapeutic approaches—fecal microbial transplantation (FMT), phage therapy, dietary interventions, and probiotics/prebiotics—that aim to restore gut health by modulating the microbiome and virome. Each strategy offers unique methods, benefits, and challenges in managing gastrointestinal disorders such as IBD, CRC, and CDI, highlighting the growing potential of virome-informed precision medicine. Click image to enlarge. Credit: Precision Clinical Medicine

    However, infections and antibiotics can disrupt this harmony and lead to inflammation. Phages can trigger TLR9 pathways, causing excessive immune responses such as those linked to IBD.

    Virome’s role in disease

    Imbalances in the gut virome have been linked to several gut diseases. In IBD, specific phages become abundant and worsen inflammation.

    Certain groups of phages (such as Caudovirales) amplify immune responses, while norovirus and rotavirus can damage the gut lining. Viral imbalances are also common in IBS and CDI.

    In colorectal cancer, phages and eukaryotic viruses like HPV and JC virus (polyomavirus) appear more often. They may promote tumor growth by altering bacterial communities and gene expression.

    Studies suggest that some phages directly influence cancer risk by promoting biofilms and transferring harmful genes.

    Virus-based treatments

    Several therapies now explore the virome’s potential to treat certain diseases or conditions. Fecal microbiota transplantation (FMT) restores gut balance and reduces inflammation in IBD and CDI.

    FMT works not only by shifting bacteria but also by transferring beneficial phages. Studies show that donor phages often align quickly with recipients and promote recovery.

    Phage therapy, which uses viruses to kill specific bacteria, is another approach that is gaining traction. It can reduce harmful microbes while preserving beneficial ones.

    Researchers have created phage cocktails that target specific gut pathogens like E. coli and Klebsiella pneumoniae, and this approach has had promising results in animal models.

    Dietary strategies also reshape the virome. High-fiber diets boost beneficial phages and support bacterial diversity.

    Whey protein has shown potential in managing Crohn’s disease by improving phage-bacteria interactions.

    Other interventions, such as exclusive enteral nutrition and low-FODMAP diets, also show benefits in gut diseases.

    Probiotics, prebiotics, and phages

    Probiotics introduce beneficial bacteria, while prebiotics feed them. Combining them, known as symbiotics, shows promise for restoring and maintaining gut health.

    Recent research suggests that adding phages can enhance these effects. Phage-based products, such as PreforPro, have already shown benefits in clinical trials.

    These products can reduce populations of harmful bacteria and improve probiotic performance, thereby creating a more stable gut environment.

    Using gut viruses to detect diseases early

    The gut virome is now being studied as a diagnostic tool. Viral patterns are more stable than bacterial ones, making them reliable biomarkers.

    Distinct virome signatures can predict IBD and colorectal cancer. Machine learning models already use virome data to detect these diseases early.

    Virome profiling may also track treatment responses, such as FMT success in IBD and CDI.

    Challenges and future prospects

    Despite the excitement over potential new treatments, many hurdles remain. Current methods struggle to capture the full virome due to technical limits.

    Many phages are still unculturable, and their roles remain unclear. Researchers are working to improve viral databases and develop targeted therapies. Once these gaps close, virome-based therapies could change how we treat gut diseases.

    The study is published in the journal Precision Clinical Medicine.

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  • Development and validation of a risk prediction model for acute biliar

    Development and validation of a risk prediction model for acute biliar

    Introduction

    The onset of acute pancreatitis is primarily attributed to biliary system stones and alcohol consumption.1 Among these, acute biliary pancreatitis (ABP) is a severe inflammatory condition of the pancreas induced by biliary stones. Epidemiological data suggest that the mortality rate in ABP patients ranges from 20% to 40%, indicating considerable variability in disease progression.2 Biliary stones not only serve as a major trigger for acute pancreatitis but also significantly influence treatment outcomes and the prognosis of ABP.3 Therefore, treatment strategies for ABP should encompass both the removal of the underlying cause and the management of the inflammatory response, aiming to reduce recurrence risk and improve overall survival rates.

    For patients experiencing their first episode of acute biliary pancreatitis (ABP), treatment options may include conservative management, surgical intervention, or interventional therapy.4 Clinical studies have shown that gallstones are a major factor contributing to ABP recurrence; as a result, cholecystectomy is widely regarded as an effective approach to reduce recurrence rates.5,6 Laparoscopic cholecystectomy (LC), considered the “gold standard” for treating gallstones, has become the preferred treatment due to its minimally invasive nature, quicker recovery, and shorter hospital stays.7 While LC has yielded favorable outcomes in the treatment of gallstones, certain postoperative complications, including acute pancreatitis, may still arise. When biliary system stones induce pancreatitis, it leads to ABP, which not only exacerbates postoperative discomfort but also prolongs hospital stays, diminishes the overall benefits of surgery, and, in severe cases, increases the risk of mortality.8 Furthermore, given the complexity of treating pancreatitis, its often-prolonged course, and its association with a relatively poor prognosis, early prediction of the risk of pancreatitis following LC in patients with gallstones is crucial.9 Timely and effective interventions to mitigate this risk represent an important area of research aimed at reducing the incidence of postoperative pancreatitis and improving patient outcomes.

    This study aims to develop and validate a predictive model for assessing the risk of post-laparoscopic cholecystectomy (LC) pancreatitis in patients with gallstones, utilizing demographic and clinical characteristics. By identifying key risk factors and providing a reliable risk assessment tool, our findings contribute to the advancement of early prevention strategies and the optimization of clinical management for gallstone-related ABP.

    Methods

    Study Population

    This study was designed as a retrospective cohort study, collecting demographic data and clinical characteristics of patients who underwent laparoscopic cholecystectomy at Henan Province Hospital of Traditional Chinese Medicine from June 2021 to December 2023. This dataset was considered as training set (n=871). We then collected the patient’s data from March 2024 to October 2024 at the same hospital, and this dataset was considered as external validation set (n=160).

    The inclusion criteria are as follows: (1) Patients diagnosed with gallstones according to the Chinese Consensus on the Diagnosis and Treatment of Chronic Cholecystitis and Gallstones (2018),10 confirmed by ultrasound, magnetic resonance imaging (MRI), or abdominal CT; (2) No history of jaundice; (3) First-time laparoscopic cholecystectomy treatment. The following patients were excluded: (1) Age <18 years; (2) History of pancreatic diseases, such as acute pancreatitis (AP), chronic pancreatitis, or pancreatic cancer; (3) Presence of obstructive cholecystitis or acute cholecystitis; (4) Severe dysfunction of vital organs, including the heart, liver, or kidneys; (5) Severe coagulation disorders or bleeding disorders; (6) History of malignancies; (7) Presence of infectious diseases or systemic inflammatory response syndrome; (8) Women in special physiological stages, such as pregnancy or lactation; (9) Recent use of antibiotics, nonsteroidal anti-inflammatory drugs (NSAIDs), glucocorticoids, or other immunosuppressants; (10) Incomplete clinical data. Finally, 968 patients were included in the study.

    Laparoscopic Cholecystectomy

    Briefly, the laparoscopic cholecystectomy surgical procedure is as follows: Preoperative routine disinfection and draping are performed. After satisfactory anesthesia, the patient is positioned in a head-up, foot-down position with a left tilt of approximately 20°C. Pneumoperitoneum is established, maintaining an intra-abdominal pressure of 8–12 mmHg. The four-port technique is used to enter the abdomen, and laparoscopy is performed to explore the abdominal cavity, confirm the presence of gallstones, and assess the morphology, size, and surrounding structures of the gallbladder. Normal tissues and organs are carefully separated. The cystic artery and cystic duct are clipped with titanium clips and then severed. Hemostasis is achieved through electrocautery, and the gallbladder is removed using a sterile glove. Postoperatively, patients receive routine fluid replacement, anti-infective therapy, and nutritional support.

    Diagnosis of Acute Biliary Pancreatitis

    According to the Chinese Guidelines for the Diagnosis and Treatment of Acute Pancreatitis (2021),11 a diagnosis of ABP can be made if any two of the following three criteria are met at one month after operation: (1) sudden onset of upper abdominal pain (persistent and severe, often radiating to the back); (2) serum amylase and/or lipase levels ≥ three times the upper limit of normal; (3) typical imaging findings of acute pancreatitis. ABP refers to acute pancreatitis patients in whom biliary stones have been confirmed by examinations such as ultrasound, computed Tomography, magnetic resonance cholangiopancreatography, or endoscopic retrograde cholangiopancreatography.

    Data Collection and Definition

    The collected data included demographic information and clinical characteristics: age, sex, body mass index (BMI) is equal to weight (kg) divided by the square of height (m), smoking is defined as someone who has smoked continuously or cumulatively for six months or more in their lifetime,12 alcohol consumption was defined as drinking at least once per week during the past year, duration of disease,13 diabetes: fasting plasma glucose (FPG) ≥7.0 mmol/L or 2-hour plasma glucose ≥11.1 mmol/L during an oral glucose tolerance test or HbA1c ≥ 6.5% (48 mmol/mol),14 hypertension: systolic blood pressure ≥140 mmHg and/or diastolic blood pressure≥90 mmHg,15 hyperlipidemia (total Cholesterol (TC)≥5.2 mmol/L or low-density lipoprotein cholesterol≥3.4 mmol/L or high-density lipoprotein cholesterol < (1.0 mmol/L in men or <50 1.3 mmol/L in women or triglycerides≥1.7 mmol/L),16 and choledocholithiasis. The following biochemical data were collected: alanine aminotransferase (ALT), aspartate aminotransferase (AST), total bilirubin, TG, TC, total protein, albumin, FBG, blood urea nitrogen, creatinine, C-reactive protein (CRP), white blood cell (WBC), hemoglobin, Hematocrit (HCT), mean corpuscular volume, red cell distribution width (RDW), neutrophil, monocyte, lymphocyte, platelet.

    The clinical characteristics: gallbladder size (determined by ultrasound), gallbladder wall thickness (determined by B-mode ultrasound), stone diameter, number of stones (single, >3 stones, determined by ultrasound), stone characteristics (determined by MRCP or MRI), history of pancreatic disease, choledocholithiasis (confirmed by MRCP and endoscopic ultrasound), operation time, intraoperative blood loss, time to pain relief, duration of hospitalization, and number of intubations, somatostatin usage, incisional infection, timing of cholecystectomy (early: within 14 days, delayed: more than 14 days).2

    Statistical Analysis

    In this study, multiple measures were implemented to control potential biases inherent in retrospective research. To ensure data quality, a dual-entry process was conducted independently by two researchers, followed by third-party verification. Outcome assessments were performed in a blinded manner, and strict adherence to predefined inclusion and exclusion criteria was maintained. Regarding data completeness, multiple imputations were applied using the “mice” package to handle missing values, and variables with a missing rate exceeding 10% were excluded from the analysis.

    Statistical analyses were conducted using IBM SPSS 23.0 and R 4.4.0. Categorical variables were expressed as frequencies (percentages), and group comparisons were performed using the chi-square (χ²) test. Continuous variables were presented as mean ± standard deviation (for normally distributed data, analyzed using the independent samples t-test) or median [interquartile range] (for non-normally distributed data, analyzed using the Wilcoxon rank-sum test). Univariate and multivariate logistic regression analyses were performed to identify independent risk factors for acute biliary pancreatitis (ABP), with odds ratios (ORs) and 95% confidence intervals (CIs) calculated. Based on the least absolute shrinkage and selection operator (LASSO) and multivariate regression results, a nomogram model was developed to facilitate individualized risk prediction. Internal validation was conducted using a five-fold cross-validation approach, while external validation was performed using datasets from different time periods.

    The predictive performance of the model was assessed using a comprehensive evaluation framework. Discriminative ability was quantified by the concordance statistic (C-statistic) based on the area under the receiver operating characteristic (ROC) curve. The SHAP value was used for evaluating the importance of features. Calibration was evaluated using calibration curves to assess the agreement between predicted and observed outcomes. The clinical utility of the model was determined through decision curve analysis (DCA). All statistical tests were two-tailed, with a significance threshold set at P < 0.05.

    Results

    Baseline Characteristics for Training and Validation Set

    Based on the inclusion and exclusion criteria, we identified a total of 871 patients in the training set and 97 patients in the validation set who underwent laparoscopic cholecystectomy. The incidences of acute biliary pancreatitis (ABP) were 9.07% and 8.75%, respectively. No significant differences were observed in the ABP incidences between the training set and validation set (P = 0.897). The mean age of the training set was 54.27 ± 8.17 years, with 45.01% of patients being female. Among all patients, 19.8% had a history of alcohol consumption, and 36.97% had a history of smoking. The prevalence of hypertension, diabetes, and hyperlipidemia was 33.52%, 24.57%, and 33.64%, respectively. The mean age of the validation set was 54.98 ± 8.45 years, with 38.75% of patients being female. The proportions of patients with a history of smoking and drinking were slightly higher in the validation set, but no significant differences were found. The rates of hypertension, diabetes, and hyperlipidemia exhibited similar trends in both sets. There were no significant differences in demographic characteristics, clinical features, treatment factors, or laboratory results between the training and validation sets (P > 0.05). Detailed results for both groups can be found in Table 1.

    Table 1 General Characteristics of Training and Validation Set

    Baseline Characteristics Between ABP and Non-ABP in Training Set

    Our results indicate that there were no significant differences between the ABP and non-ABP groups in terms of age (P = 0.238), sex (P = 0.292), BMI (P = 0.572), smoking status (P = 0.304), drinking (P = 0.103), hypertension (P = 0.897), diabetes (P = 0.872), or disease duration (P = 0.067). However, the prevalence of hyperlipidemia was significantly higher in the ABP group (44.30%) compared to the non-ABP group (32.58%) (P = 0.035). Additionally, the baseline APACHE II score was significantly higher in the ABP group than in the non-ABP group (P < 0.001). Regarding clinical characteristics, the prevalence of choledocholithiasis was significantly higher in the ABP group than in the non-ABP group (P = 0.004). No significant differences were observed in gallbladder wall thickness, diameter, size, number, or shape (P > 0.05). During the operation, there were no significant differences in operation time, intraoperative blood loss, or contrast imaging times between the ABP and non-ABP groups (P > 0.05). However, the number of intubation attempts was significantly higher in the ABP group than in the non-ABP group (P < 0.001). No significant differences were found in time to pain relief, duration of hospitalization, rates of balloon dilation or somatostatin use, or the incidence of incisional infections (P > 0.05). The ABP group tended to have a delayed timing of cholecystectomy compared to the non-ABP group (P < 0.001). Biochemical parameters revealed that the ABP group had higher levels of triglycerides (TG), C-reactive protein (CRP), white blood cells (WBC), red cell distribution width (RDW), and neutrophils compared to the non-ABP group (P < 0.05). There were no significant differences in other biochemical parameters between the two groups (P > 0.05). Detailed data for both groups are presented in Table 2.

    Table 2 Comparisons of Clinical Characteristics Between ABP and Non-ABP in Training Set

    Developing of Model Predicting ABP in the Training Set

    Univariate logistic regression analysis revealed that hyperlipidemia, baseline APACHE II score, choledocholithiasis, number of intubation attempts, timing of cholecystectomy, levels of D-Dimer, triglycerides (TG), C-reactive protein (CRP), white blood cells (WBC), neutrophils, and red cell distribution width (RDW) were significantly associated with the occurrence of acute biliary pancreatitis (ABP) (P < 0.001). To refine the model, a LASSO regression was performed prior to the multivariate logistic regression (Figure 1A and B). In the final multivariate logistic regression model, ten variables were identified as significant predictors of ABP: baseline APACHE II (OR: 1.30, 95% CI: 1.10–1.52, P < 0.001), choledocholithiasis (OR: 2.49, 95% CI: 1.25–4.95, P = 0.010), number of intubation attempts (OR: 3.17, 95% CI: 1.70–59.1, P < 0.001), timing of cholecystectomy (OR: 3.17, 95% CI: 1.63–6.15, P < 0.001), D-Dimer (OR: 1.99, 95% CI: 1.02–3.85, P = 0.042), TG (OR: 1.21, 95% CI: 1.06–1.37, P = 0.003), CRP (OR: 1.06, 95% CI: 1.04–1.08, P < 0.001), WBC (OR: 1.62, 95% CI: 1.37–1.93, P < 0.001), neutrophils (OR: 1.91, 95% CI: 1.01–3.61, P = 0.047), and RDW (OR: 1.24, 95% CI: 1.12–1.37, P < 0.001). Further details are presented in Table 3. We also performed the ROC analysis using these variables. The results were presented in the Supplementary material 1. The results suggested that the AUCs were 0.617 for APACHE II, 0.585 for choledocholithiasis, 0.620 for times of intubations, 0.617 for timing of cholecystector, 0.613 for TG, 0.750 for WBC, 0.845 for CRP, 0.619 for neutrophil, 0.654 for RDW and 0.554 for D-dimer.

    Table 3 Univariate and Multivariate Logistic Regression for ABP in the Training Set

    Figure 1 Development and validation of the predict model for ABP after LC. (A and B) LASSO regression identified the relevant risk factors. (C) Receiver operating characteristics curve (ROC) of predict model in training set. (D) ROCs of five samples using five-fold cross validation. (E) ROC of predicting model in external validation set. (F) SHAP analyses identified the importance of features in the model.

    Validation and Assessment of Model Predicting ABP

    The predictive ability of the established model was assessed using the training set. The model’s ROC curve in the training set was 0.949 (95% CI: 0.930–0.969, Figure 1C), indicating a relatively high predictive capability. We then performed internal validation using 5-fold cross-validation, which demonstrated high and stable predictability across the five random samples. The ROC values for folds 1–5 ranged from 0.855 to 0.962 (Figure 1D). In the external validation set, the ROC value was 0.924 (95% CI: 0.874–0.973, Figure 1E). SHAP analysis revealed that CRP had the highest feature importance, followed by WBC, RDW, timing of cholecystectomy, and baseline APACHE II. D-Dimer ranked last in importance (Figure 1F). Based on these variables, we developed an individualized risk scoring system (Figure 2A).

    Figure 2 Assessment of predicting model for ABP. (A) Nomogram using identified risk factors for ABP after LC. (B and C) Calibration plots of predicting model in training and validation sets. (D and E) Unoptimized decision curves of training and validation sets. (F and G) Optimized decision curves of training and validation sets.

    Calibration analyses were performed for both the training and validation sets. The training set showed stable prediction performance for ABP (Figure 2B). Although the validation set exhibited some fluctuations, it remained stable with a predicted probability greater than 0.35 (Figure 2C). Decision curve analysis (DCA) further demonstrated that the model provided high net benefit across a range of threshold probabilities in both the training and validation sets (Figure 2D and E). The optimized DCA yielded similar results (Figure 2F and G). A threshold effect analysis revealed a significant dose-response relationship between the risk score and ABP occurrence. Specifically, for risk scores <0.032, the association was marginally significant (P = 0.035), while for risk scores ≥0.032, the association was highly significant (P < 0.001) (Figure 3).

    Figure 3 Dose-response between risk score and ABP in training set.

    Discussion

    Laparoscopic cholecystectomy (LC) is considered the “gold standard” for treating gallstones, as it effectively alleviates the patient’s condition. However, LC necessitates gallbladder removal, involves a certain degree of surgical trauma, and still carries a relatively high risk of postoperative complications. Among these, acute biliary pancreatitis (ABP) is one of the most common and severe, often manifesting with multiple symptoms that can compromise surgical outcomes and prolong hospital stays. Currently, no effective drugs are available for the treatment or prevention of pancreatitis. Previous studies have reported that the incidence of post-LC pancreatitis in gallstone patients ranges from approximately 2% to 9%.17,18 In this study, the incidence of postoperative pancreatitis was 9.07%, consistent with previous findings. ABP can present with symptoms such as fever, nausea, vomiting, and abdominal pain; in severe cases, it may lead to respiratory distress, shock, or even sudden death. Although systematic treatment can alleviate primary symptoms, pancreatitis still impacts overall therapeutic outcomes, underscoring the importance of early prevention.

    To address this, we developed a logistic regression-based predictive model using diverse clinical and laboratory parameters, with thorough validation to ensure reliability. Univariate analysis identified significant risk factors for ABP, including hyperlipidemia, APACHE II score, choledocholithiasis, intubation, cholecystectomy timing, and inflammatory markers (D-dimer, TG, CRP, WBC, neutrophils, RDW). LASSO regression was applied to prevent overfitting, yielding ten key predictors for the final multivariate model. These combined clinical and biochemical variables demonstrated strong predictive performance, with an AUC of 0.949 in the training set and 0.924 in external validation. Five-fold cross-validation (AUC: 0.855–0.962) confirmed model stability. SHAP analysis highlighted CRP, WBC, RDW, cholecystectomy timing, and APACHE II score as top contributors, underscoring the importance of inflammation and disease severity. Threshold effect and decision curve analyses further supported the model’s clinical utility. Despite minor calibration fluctuations, overall performance was consistent, affirming its robustness.

    Among the identified risk factors, the APACHE II score was significantly associated with ABP occurrence. Although the APACHE II score primarily reflects systemic physiological changes rather than localized disease status, it is widely regarded as an effective early diagnostic and prognostic tool for pancreatitis.19,20 Our statistical analysis showed that the APACHE II score was significantly higher in the ABP group than in the non-ABP group. Moreover, ROC analysis suggested that the APACHE II score could help differentiate ABP from non-ABP cases, highlighting the need for comprehensive assessments in LC patients to improve ABP prediction. Choledocholithiasis also emerged as a critical risk factor for ABP after LC. When gallstones are present in the common bile duct, they can cause obstruction, impair bile drainage, and lead to bile reflux into the pancreatic duct. This process can activate pancreatic enzymes such as trypsin, chymotrypsin, and elastase, triggering pancreatitis.21 Additionally, the increased bile duct pressure resulting from obstruction further exacerbates bile reflux into the pancreatic duct, worsening pancreatic injury.22 The number of intubations during surgery was another key factor influencing ABP risk. Overfilling of the pancreatic duct with contrast agents can lead to reflux into the interstitial space and venous circulation, causing pancreatic duct visualization. This phenomenon is often associated with acinar clouding in the pancreas, which can induce chemical damage and increase ABP risk.23 To minimize this risk, LC procedures should avoid unnecessary pancreatic duct imaging, limit multiple intubations, and employ soft guidewires to reduce pancreatic juice reflux. The timing of cholecystectomy is also related to the occurrence of post-operative ABP. Studies show that if gallstones are left untreated, the recurrence rate of ABP is 32–61%.24 Early LC in patients has a lower incidence and recurrence rate. Regardless of laboratory test results and pain status, laparoscopic cholecystectomy can be safely performed within the first 48 hours for patients with gallstone-induced pancreatitis.25 It was suggested that performing laparoscopic cholecystectomy within the first 48 hours on approximately half of the patients with acute pancreatitis due to biliary causes, and the results showed significant reductions in both the occurrence of ABP and hospital stay.26

    This study also confirms that multiple biochemical markers are closely related to the pathological process of ABP. In ABP patients, TG levels are significantly elevated. The free fatty acids released by lipoprotein hydrolysis by lipase form micelle structures that directly damage pancreatic cells, leading to local ischemia and acidosis, which in turn activate proenzymes, triggering pancreatic autodigestion. The damage to acinar cells also triggers an inflammatory cascade, and unsaturated fatty acids further promote the release of inflammatory mediators.27 The study also found that D-dimer levels are significantly elevated in ABP patients, reflecting hypercoagulability and a tendency toward thrombosis. D-dimer promotes inflammatory cell infiltration and cytokine release, forming a coagulation-inflammation vicious cycle, which exacerbates pancreatic microcirculation disorder.28 Additionally, CRP, WBC, neutrophils, and RDW are all associated with ABP. CRP, as an acute-phase protein, rises rapidly within 2–12 hours after inflammation onset, playing a dual role in regulating the inflammatory response and protecting the body. WBC elevation is primarily driven by neutrophils, and their overactivation may worsen tissue damage. RDW elevation is associated with the suppression of erythrocyte maturation by pro-inflammatory factors and erythrocyte membrane damage caused by reactive oxygen species.29–31 These markers provide important basis for the diagnosis and assessment of ABP.

    Our study has several limitations that should be acknowledged. First, it is a single-center, retrospective study, which may introduce information bias and limit the ability to infer causal relationships. Second, the sample size of the validation cohort is relatively small; future studies with larger sample sizes and prospective cohort data are needed. Third, the study population consisted exclusively of patients who underwent LC, which may limit the generalizability of the predictive model to other populations. Furthermore, future research should extend the follow-up period to evaluate the long-term predictive performance of the model.

    In conclusion, the predictive model developed in this study effectively estimates the risk of post-LC pancreatitis in patients with gallstones. Calibration curves and decision curve analyses demonstrated the model’s robust predictive performance and considerable net clinical benefit. In addition, this study highlights the multifactorial nature of acute biliary pancreatitis (ABP) following LC and emphasizes the value of a predictive model that integrates both clinical and biochemical parameters. By identifying key risk factors and providing a reliable risk assessment tool, our findings contribute to the advancement of early prevention strategies and the optimization of clinical management for gallstone-related ABP. Nonetheless, further studies with larger sample sizes and prospective designs are warranted to validate the model and enhance its generalizability.

    Data Sharing Statement

    All original data can be available from the corresponding author upon request.

    Ethical Approval and Consent to Participate

    This study adhered to the principles of the Declaration of Helsinki and was approved by the Institutional Review Board of the Ethics Committee of Henan Province Hospital of Traditional Chinese Medicine (HNSZYYWZ-20241105030). The informed consent is waived by the ethics committee because this is a retrospective design study. Patient confidentiality and data privacy were strictly safeguarded throughout the study.

    Funding

    This study was supported in Special Project for Scientific Research of Traditional Chinese Medicine in Henan Province (2019DJZX054, 2023ZY1014).

    Disclosure

    The authors have no conflicts of interest.

    References

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    2. Di Martino M, Ielpo B, Pata F. Timing of cholecystectomy after moderate and severe acute biliary pancreatitis. JAMA Surg. 2023;158(10):e233660. doi:10.1001/jamasurg.2023.3660

    3. Benatta MA, Barthet M, Desjeux A, Grimaud JC. Endoscopic extraction of biliary stones and a migrated endoclip for acute pancreatitis. Hepatobiliary Surg Nutr. 2015;4(3):216–217. doi:10.3978/j.issn.2304-3881.2015.01.09

    4. Colak E, Ciftci AB. Acute biliary pancreatitis management during the coronavirus disease 2019 pandemic. Healthcare. 2022;10(7):1284. doi:10.3390/healthcare10071284

    5. Tang D, Gu J, Ao Y, Zhao L. Clinical efficacy of endoscopic retrograde cholangiopancreatography in the treatment of acute biliary pancreatitis: a meta-analysis. Wideochir Inne Tech Maloinwazyjne. 2022;17(4):561–578. doi:10.5114/wiitm.2022.119902

    6. Chu BK, Gnyawali B, Cloyd JM. Early unplanned readmissions following same-admission cholecystectomy for acute biliary pancreatitis. Surg Endosc. 2022;36(5):3001–3010. doi:10.1007/s00464-021-08595-8

    7. Salati SA, Alfehaid M, Alsuwaydani S, AlSulaim L. Spilled gallstones after laparoscopic cholecystectomy: a systematic review. Pol Przegl Chir. 2022;95(2):1–20. doi:10.5604/01.3001.0015.8571

    8. Wang L, Chen Z. Comparison of laparoscopic common bile duct exploration and endoscopic retrograde cholangiopancreatography in the treatment of bile duct stones and analysis of risk factors for postoperative acute pancreatitis. Altern Ther Health Med. 2023;29(6):358–363.

    9. Zver T, Calame P, Koch S, Aubry S, Vuitton L, Delabrousse E. Early prediction of acute biliary pancreatitis using clinical and abdominal CT features. Radiology. 2022;302(1):118–126. doi:10.1148/radiol.2021210607

    10. Editorial Borad of Chinese Journal of DigestionCooperation Group of Hepatobiliary Disease of Chinese Society of Gastroenterology. Consensus on diagnosis and treatment of chronic cholecystitis and gallstones in China (2018). J Clin Hepatol. 2019;35(6):1231–1236. doi:10.3969/j.issn.1001-5256.2019.06.011

    11. Chinese Pancreatic Surgery Association. Chinese Society of Surgery, Chinese Medical Association. Guidelines for diagnosis and treatment of acute pancreatitis in China (2021). Chin J Surg. 2021;59(7):578–587. doi:10.3760/cma.j.cn112139-20210416-00172

    12. Streck JM, Rigotti NA, Livingstone-Banks J. Interventions for smoking cessation in hospitalised patients. Cochrane Database Syst Rev. 2024;5(5):D1837. doi:10.1002/14651858.CD001837.pub4

    13. Ma H, Liu F, Li J. Sex differences in associations between socioeconomic status and incident hypertension among Chinese adults. Hypertension. 2023;80(4):783–791. doi:10.1161/HYPERTENSIONAHA.122.20061

    14. Zimmet P, Shi Z, El-Osta A, Ji L. Chinese famine and the diabetes mellitus epidemic. Nat Rev Endocrinol. 2020;16(2):123. doi:10.1038/s41574-019-0300-9

    15. Ma S, Yang L, Zhao M, Magnussen CG, Xi B. Trends in hypertension prevalence, awareness, treatment and control rates among Chinese adults, 1991-2015. J Hypertens. 2021;39(4):740–748. doi:10.1097/HJH.0000000000002698

    16. Wang J, Shi T, Xu L. Correlation between hyperlipidemia and serum vitamin D levels in an adult Chinese cohort. Front Nutr. 2024;11:1302260. doi:10.3389/fnut.2024.1302260

    17. Yuan X, Xu B, Wong M. The safety, feasibility, and cost-effectiveness of early laparoscopic cholecystectomy for patients with mild acute biliary pancreatitis: a meta-analysis. Surgeon. 2021;19(5):287–296. doi:10.1016/j.surge.2020.06.014

    18. Lopez-Lopez V, Kuemmerli C, Maupoey J. Textbook outcome in patients with biliary duct injury during cholecystectomy. J Gastrointest Surg. 2024;28(5):725–730. doi:10.1016/j.gassur.2024.02.027

    19. Neitzel E, Salahudeen O, Mueller PR. Part 2: current concepts in radiologic imaging & intervention in acute biliary tract diseases. J Intensive Care Med. 2024;1364588444. doi:10.1177/08850666241259420

    20. Xu C, Wang J, Jin X, Yuan Y, Lu G. Establishment of a predictive model for outcomes in patients with severe acute pancreatitis by nucleated red blood cells combined with Charlson complication index and APACHE II score. Turk J Gastroenterol. 2020;31(12):936–941. doi:10.5152/tjg.2020.19954

    21. Chen SH, Wang WQ, Fei X. Risk factors of negative diagnosis of magnetic resonance cholangiopancreatography in acute biliary pancreatitis patients with choledocholithiasis. Pancreas. 2025;54(1):e45–e50. doi:10.1097/MPA.0000000000002395

    22. Celik A, Ertekin C, Ercan LD. Might be over-evaluated: predicting choledocholithiasis in patients with acute biliary pancreatitis. Ulus Travma Acil Cerrahi Derg. 2025;31(3):249–258. doi:10.14744/tjtes.2024.36114

    23. Banerjee A. Different contrast agents and development of pancreatitis after endoscopic retrograde pancreatography. Am J Gastroenterol. 1992;87(5):683–684, 684–685.

    24. Sekimoto M, Takada T, Kawarada Y. JPN Guidelines for the management of acute pancreatitis: epidemiology, etiology, natural history, and outcome predictors in acute pancreatitis. J Hepatobiliary Pancreat Surg. 2006;13(1):10–24. doi:10.1007/s00534-005-1047-3

    25. Zhong FP, Wang K, Tan XQ, Nie J, Huang W-F, Wang X-F. The optimal timing of laparoscopic cholecystectomy in patients with mild gallstone pancreatitis: a meta-analysis. Medicine. 2019;98(40):e17429. doi:10.1097/MD.0000000000017429

    26. Demir U, Yazici P, Bostanci O. Timing of cholecystectomy in biliary pancreatitis treatment. Ulus Cerrahi Derg. 2014;30(1):10–13. doi:10.5152/UCD.2014.2401

    27. Berberich AJ, Hegele RA. Rapidly lowering triglyceride levels by plasma exchange in acute pancreatitis: what’s the point? J Clin Apher. 2022;37(3):194–196. doi:10.1002/jca.21972

    28. Newton MV. D-dimer as a marker of severity and prognosis in acute pancreatitis. Int J Appl Basic Med Res. 2024;14(2):101–107. doi:10.4103/ijabmr.ijabmr_483_23

    29. Huang L, Chen C, Yang L, Wan R, Hu G. Neutrophil-to-lymphocyte ratio can specifically predict the severity of hypertriglyceridemia-induced acute pancreatitis compared with white blood cell. J Clin Lab Anal. 2019;33(4):e22839. doi:10.1002/jcla.22839

    30. Panek J, Kusnierz-Cabala B, Dolecki M, Pietron J. Serum proinflammatory cytokine levels and white blood cell differential count in patients with different degrees of severity of acute alcoholic pancreatitis. Pol Przegl Chir. 2012;84(5):230–237. doi:10.2478/v10035-012-0038-8

    31. Jagodic EA, Ejubovic M, Jahic R. The role of Red Cell Distribution Width (RDW), RDW/platelet ratio, and mean platelet volume as prognostic markers in acute pancreatitis severity and complications based on the bedside index for severity in acute pancreatitis score. Cureus. 2024;16(8):e66193. doi:10.7759/cureus.66193

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  • Bladder Erosion and stone formation: a rare complication of laparoscopic cervical cerclage | BMC Women’s Health

    Bladder Erosion and stone formation: a rare complication of laparoscopic cervical cerclage | BMC Women’s Health

    In this case, the Mersilene tape caused penetration of the cervix resulting in erosion of the bladder and subsequent calculi formation, leading to symptoms of urinary tract infection. However, the site of penetration was not associated with bladder rupture or fistula formation. In previous case reports, there have been reports of the cerclage line eroding organs or forming genital fistulas after vaginal or abdominal cerclage. The vaginal cerclage methods include the McDonald method and the Shirodkar method. The latter requires a higher suture position and requires stripping the overlying bladder peritoneum to avoid the Mersilene tape adhering to the bladder. Based on the surgeon’s customary practices, the placement of the knot of the cerclage line in front or behind the cervix is determined. Theoretically, anterior positioning of the knot may result in bladder erosion, whether posterior positioning may lead to rectal erosion. To date, there have been no documented cases of rectal erosion caused by the cerclage line. The potential impact of positioning the knot of the cerclage line behind the uterus on the reduction of significant erosive complications necessitates further investigation. Laveaux et al. [6] reported a case of intermittent vaginal bleeding following Shirodkar cerclage, in which the cerclage line was cut inside the cervical canal and a bladder-cervix fistula was formed. The symptoms disappeared following surgical intervention and removal of the sutures. The cause of this rare complication may be related to the Mersilene tape buried in the cervix, gradually eroding the cervix, and then eroding the urinary tract epithelium. Similarly, Golomb et al. [7] reported a case of bladder-cervix fistula formation after Shirodkar cerclage, with the primary clinical manifestation being urinary incontinence. After removing the cerclage line, bladder drainage was performed to promote the healing process of the fistula, and the patient’s condition improved. Bladder-cervix fistula was previously mistakenly thought to be a complication unique to Shirodkar cerclage, and there have been reports of two cases of bladder-cervix fistula formation after McDonald cerclage [9]. In one of the cases, the cerclage line was removed as scheduled one year after the surgery, and the remaining cerclage fragments were removed urgently ten years later due to repeated symptoms such as difficulty urinating, hematuria, and frequent urination during which bladder calculi were found. Multiple sutures of lines were found at the bottom of the bladder. It is speculated that even after the second attempt to remove the cerclage line, the suture material was still retained in the tissue between the vagina and the bladder. There may also have been multiple suture lines placed during the initial cerclage, and over time, the retained suture lines migrated, ultimately eroding the bladder and causing the formation of calculi [8]. Togas et al. [4] also described a case of recurrent urinary tract infections and hematuria caused by complete penetration of the Mersilene tape through the bladder during laparoscopic surgery. Similarly, the suture in this case also eroded the bladder and led to the formation of calculi. The reason may be related to the lack of surgical experience of doctors in primary hospitals, which caused the Mersilene tape to be too close to the bladder at the first time or the incorrect separation of anatomical layers during operation. The Mersilene tape may also penetrate the cervical canal, emphasizing the importance of using hysteroscopy to check the integrity of the cervical canal at the end of routine surgery [5]. Therefore, it is also important to screen for relevant cerclage in patients with complaints related to the urinary system. In this case, the symptoms of the urinary system occurred two months after the operation and the corresponding treatment was not given until nearly one year after the operation, which is worth pondering. This alerts us that it is equally important to carry out related screening for cerclage patients with complaints of urinary tract discomfort.

    In conclusion, for patients with cervical incompetence, the implementation of laparoscopic cervical cerclage prior to pregnancy can significantly enhance the success rate of the surgical procedure. However, it is crucial to monitor for potential long-term complications associated with the use of Mersilene tape, particularly the erosion of adjacent organs and the formation of bladder calculi.

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  • A clinical-metabolic prediction model for suicidal behaviors risk stra

    A clinical-metabolic prediction model for suicidal behaviors risk stra

    Introduction

    Suicide is a critical global public health issue. The World Health Organization estimates over 800,000 annual suicide deaths worldwide.1,2 It ranks as the 18th leading cause of death across all ages, but is the second leading cause among those aged 15–29, surpassed only by unintentional injuries.3 Alarmingly, one suicide occurs approximately every 40 seconds.3 Suicide rates are high in many nations.4 The United States Centers for Disease Control and Prevention reported in 2018 that the US age-adjusted suicide rate rose 33% from 1999 to 2017.5 Critically, global rates are likely significantly underestimated. Some suicides may be misclassified (eg, as undetermined causes), potentially making actual figures 10–50% higher than reported.6,7 Suicide deaths represent merely the tip of the iceberg: non-fatal attempts are estimated to be 10–20 times more frequent, and suicidal ideation without action is vastly more common than completed suicide.8–10

    Suicidal behaviors (SB) arises from complex interactions between psychiatric illness, environmental stressors, and sociocultural determinants.11 Among psychiatric disorders, Major Depressive Disorder (MDD) emerges as the most potent predictor, implicated in >90% of suicide fatalities.12,13 MDD, characterized by profound disability and high recurrence rates,14–16 diminishes quality of life,17 disrupts occupational functioning,18 and exacerbates socioeconomic burdens,19 and also significantly elevates the risk of suicide.20 Despite therapeutic advances, persistent SB vulnerability during antidepressant treatment reveals critical shortcomings in current risk stratification paradigms.21 Specifically, the inability to identify subgroups at high risk of SB that are resistant to conventional interventions highlights the need to develop refined predictive models that integrate biomarkers.

    Current literature predominantly investigates SB prevalence in mixed outpatient/inpatient cohorts or recurrent MDD populations,22,23 with limited focus on first-hospitalized patients—a high-risk subgroup requiring urgent intervention. This gap is significant because the initial hospitalization often represents the first major clinical presentation and intervention point for severe MDD. Individuals experiencing their first severe depressive episode necessitating hospitalization may present distinct clinical profiles, biological correlates, and vulnerability to SB compared to those with chronic or recurrent illness.24–26 Factors such as the acute onset of severe symptoms, potential treatment naivety, and the profound psychological impact of a first psychiatric hospitalization could uniquely shape SB risk trajectories.26,27 Understanding SB determinants at this pivotal juncture is crucial for developing effective early intervention strategies. Furthermore, while metabolic dysregulation and thyroid dysfunction are increasingly recognized as SB correlates,28 potential neurobiological mechanisms include: dyslipidemia and visceral adiposity may promote neuroinflammation,28,29 serotonergic dysfunction,30 and HPA axis hyperactivity;31 elevated TSH may impair monoaminergic neurotransmission,32 reduce GABAergic inhibition,33 and diminish neurotrophic support.34 These disturbances likely synergize with psychopathology to exacerbate suicide risk through convergent effects on prefrontal-limbic circuitry.35,36 Nevertheless, clinically applicable biomarkers remain elusive despite compelling pathophysiological links.28,37

    This study addresses these gaps through three primary objectives: 1) establishing SB prevalence in first-admission MDD patients within China’s Han population; 2) identifying clinical and metabolic correlates of SB occurrence and severity; 3) constructing a multidimensional prediction model integrating psychometric and biological markers. By focusing on treatment-naïve inpatients during acute-phase MDD, our findings aim to enhance early risk detection and inform targeted prevention strategies.

    Study Design and Participants

    The sample size was determined using the formula:


    In this equation, n stands for the sample size, Z refers to the Z-score associated with the desired confidence level (1.96 for a 95% confidence interval), P indicates the estimated prevalence or proportion (chosen as 0.2 based on the rate of dyslipidemia in the general Chinese population), and d is the acceptable margin of error (set at 0.05). Based on these values, the calculated sample size came to 246 individuals.

    Participants were consecutively recruited from all first-admission MDD inpatients meeting inclusion criteria at Wuhan Mental Health Center (It is the largest public institution with psychiatric specialty in central China, visited by the patient population throughout the region) between July 2017 and August 2022. All eligible patients during this period were approached for enrollment, and those providing informed consent were included in the study. Diagnosis of MDD was confirmed through structured clinical interviews aligned with the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) criteria (Figure 1: Participant Flow Diagram).

    Figure 1 Study Flowchart.

    Inclusion required: (1) no prior psychiatric hospitalization history; (2) age 18–60 years; (3) male or female; (4) Han Chinese ethnicity; (5) acute-phase depressive severity (HAMD-17 score ≥24).

    Exclusion criteria encompassed: (1) pregnancy/lactation; (2) substance use disorders; (3) severe medical comorbidities or personality disorders; (4) diabetes mellitus diagnosis; (5) current use of psychotropic medications or drugs affecting metabolic/endocrine parameters; (6) cognitive/behavioral impairment precluding assessment compliance.

    The study was approved by the Ethics Committee of Wuhan Mental Health Center. All participants provided written informed consent prior to their involvement in the research, in accordance with the principles of the Declaration of Helsinki.

    Clinical and Biochemical Assessments

    Demographic profiles (age, sex, marital status), illness characteristics (age of onset, disease duration), and treatment history were systematically recorded. Within 24 hours post-admission, certified psychiatrists administered validated instruments:

    Depressive Severity

    Assessed using the 17-item Hamilton Depression Rating Scale (HAMD-17), quantifying symptoms via clinician-administered ratings (0–4/0–2 per item; total range 0–52).

    Anxiety Severity

    Measured with the 14-item Hamilton Anxiety Rating Scale (HAMA-14) evaluating somatic and psychic symptoms on 0–4 scales (total range 0–56).

    Psychotic Features

    Evaluated by the Positive subscale (P1–P7) of the PANSS (PSS) scoring seven psychotic symptoms on 1–7 severity dimensions (subscale range 7–49).

    Global Illness Severity

    Rated via the Clinical Global Impression–Severity Index (CGI-SI), a clinician-determined 7-point global metric (1=normal to 7=extremely ill).

    Fasting venous blood samples collected on Day 2 were analyzed for:

    Thyroid Function

    Thyroid-stimulating hormone (TSH), free triiodothyronine (FT3), free thyroxine (FT4).

    Metabolic Indices

    Total cholesterol (TC), triglycerides (TG), high-/low-density lipoprotein cholesterol (HDL-C/LDL-C), fasting blood glucose (FBG).

    Anthropometrics

    Waist circumference (WC), body mass index (BMI), systolic/diastolic blood pressure (SBP/DBP).

    Suicidal Behaviors Evaluation

    Current SB (past 30 days) was ascertained through semi-structured interviews with patients and corroborated by family members/guardians. The clinician-administered Columbia Suicide Severity Rating Scale (C-SSRS) quantified SB severity through six ordinal levels (passive ideation to lethal attempts) in SB-positive cases. The C-SSRS was administered by raters trained to reliability standards (kappa>0.80) through the official Chinese C-SSRS certification program. To ensure validity: All interviews followed the standardized C-SSRS structured guide.38

    Trained psychiatrists (≥5 years experience) administered all instruments following standardized protocols, with inter-rater reliability maintained at κ > 0.80 through monthly calibration sessions.

    Data Analysis

    Categorical variables were expressed as frequencies (%), continuous variables as mean±SD or median (IQR) based on distribution normality (Shapiro–Wilk test). Between-group comparisons employed χ²-tests for categorical data, independent t-tests for normally distributed variables, and Mann–Whitney U-tests for nonparametric measures. Variables with p < 0.10 in univariate analyses were entered into binary logistic regression (backward elimination) to identify SB correlates. Model discrimination was evaluated via receiver operating characteristic (ROC) curves, with area under the curve (AUC) >0.70 considered clinically informative.39,40 SB severity correlates were analyzed through multiple linear regression. Significance was set at two-tailed p<0.05. Analyses utilized SPSS 27 and GraphPad Prism 8.4.3.

    Results

    Clinical and Metabolic Profile of SB Subgroups

    A total of 132 cases of MDD accompanied by SB were recorded, accounting for 13.46% (132/981) of the total. Comparative analysis revealed substantial disparities between MDD patients with SB (n = 132) and non-SB counterparts (n = 849). The SB cohort demonstrated elevated psychopathological severity across multiple domains: PANSS positive symptom scores were markedly higher (Z = −14.49, p < 0.001), as were anxiety symptoms (HAMA: Z = −12.43, p < 0.001) and global illness severity (CGI-SI: Z = −11.76, p<0.001). Metabolic dysregulation was prominent in SB patients, evidenced by increased TSH (Z = −6.59, p < 0.001), WC (Z = −2.15, p = 0.032), FBG (t = −3.98, p < 0.001), and TC (Z = −7.35, p < 0.001). Notably, SB patients exhibited shorter median disease duration (9.5 vs 10.5 months, p=0.004) (Table 1).

    Table 1 The Demographic and General Clinical Data in Different Clinical Subgroups

    Multivariate Predictors of Suicidal Behaviors

    Binary logistic regression identified six factors independently associated with SB (Table 2). Anxiety severity (HAMA: OR=1.37, 95% CI=1.25–1.51) and psychotic features (PSS: OR=1.08, 95% CI=1.01–1.14) showed dose-dependent associations with SB risk. Clinical global severity (CGI-SI: OR=3.52, 95% CI=2.38–5.22) emerged as the strongest predictor, while metabolic parameters including WC (OR=1.04, 95% CI=1.01–1.07), DBP (OR=1.04, 95% CI=1.01–1.08), and TC (OR=1.51, 95% CI=1.07–2.12) contributed additively. Paradoxically, higher LDL-C levels reduced SB likelihood (OR=0.58, 95% CI=0.40–0.84).

    Table 2 Binary Logistic Regression Analyses of Determinants of SB in MDD Patients

    Discriminative Capacity and Severity Associations

    ROC analysis demonstrated differential discriminatory capacity among SB-associated factors (Table 3). The HAMA scale achieved superior performance (AUC=0.83, 95% CI=0.80–0.87), followed by CGI-SI (AUC=0.79) and PSS (AUC=0.76). A composite model integrating these three clinical measures yielded exceptional classification accuracy (AUC=0.87, 95% CI=0.83–0.91), significantly outperforming isolated metabolic parameters (AUC range: 0.56–0.69) (Figure 2). Linear regression of SB severity (C-SSRS scores) identified HAMA as a positive contributor (β=0.21, p=0.029) and TC as a mitigating factor (β=−0.98, p=0.032), accounting for 18.7% of severity variance (adjusted R²=0.187) (Table 4).

    Table 3 ROC Analysis of Factors Influencing SB

    Table 4 Multiple Linear Regression Analysis of Correlates of SB Severity

    Figure 2 The discriminatory capacity of related factors for distinguishing between patients with and without SB in MDD patients. The area under the curve of PSS score, HAMA score, CGI-SI score, and the combination of these three factors were 0.76, 0.83, 0.79, and 0.87, respectively.

    Discussion

    This investigation provides novel insights into SB among first-hospitalized MDD patients, addressing critical gaps in characterizing this high-risk population. Four principal findings emerge: (1) SB prevalence of 13.46% in treatment-naïve inpatients; (2) distinct psychopathological and metabolic profiles in SB subgroups; (3) validated discriminative utility of a multidimensional clinical model; (4) anxiety severity as an independent correlate of SB intensity. These findings could inform risk stratification for SB and support targeted prevention strategies in high-risk clinical populations.

    Current literature extensively documents the prevalence of SB in patients with MDD. A large-scale meta-analysis reveals a lifetime SB prevalence of 23.7% among MDD patients.41 For individuals experiencing their first MDD episode and receiving outpatient care, SB detection rates range from 17.3% to 20.1%,42,43 comparable to hospitalization-based SB detection rates (17.3%),41 but notably lower than those observed in chronic/recurrent MDD cohorts (20–36%).24,25 Intriguingly, our study identified a significantly reduced SB detection rate of 13.46%, which markedly deviates from the previously reported benchmarks. This observed pattern suggests that illness chronicity, rather than treatment setting alone, may be a more significant modulator of SB vulnerability. The coexistence of heightened psychotic symptoms (PSS), anxiety severity (HAMA), and metabolic abnormalities in SB patients aligns with integrative “brain-body” models of suicidality, wherein neuroendocrine-metabolic dysregulation synergizes with psychopathological processes was associated with increased SB risk.28,35 Notably, thyroid dysfunction and lipid anomalies may impair prefrontal-limbic circuitry through neuroinflammatory pathways,35 while visceral adiposity (reflected by elevated WC) co-occurred with insulin resistance, potentially reflecting a shared pathway underlying mood dysregulation.37 The shorter disease duration in SB subgroups further suggests acute biopsychosocial decompensation—rather than illness chronicity—may drive SB emergence, urging reevaluation of duration-based risk paradigms.

    Secondly, comparative analyses of sociodemographic and clinical features between MDD patients with and without SB revealed significantly heightened severity of psychopathology, psychological symptoms, and metabolic disturbances in the SB subgroup. This aligns with findings from large-scale studies in Chinese populations, which have consistently reported similar clinical and metabolic abnormalities associated with SB in MDD.26,28,37,40,44,45 While the precise mechanisms, including lipid dysregulation, HPA axis dysfunction, neuroplasticity alterations, and inflammation, remain to be fully elucidated,35 our findings suggest that SB in MDD co-occurs with characterized by a more adverse psychophysical state.

    Thirdly, we identified several key factors associated with SB in MDD patients. These factors are multifaceted, encompassing both clinical symptoms (eg, PSS, HAMA, CGI-SI scores) and metabolic parameters. Prior research has underscored the roles of elevated anxiety symptoms, psychotic features, and specific lipid markers as factors associated with SB in MDD.27,45,46 However, it has to be emphasized that LDL-C levels were considered by this study yet as an inverse predictor of SB in patients with MDD, contrary to the vast majority of other metabolic parameters addressed in this study. A large-scale meta-analysis yielded similar conclusions and was not confounded by ethnicity,47,48 which emphasizes the special status of LDL-C in distinguishing it from other metabolic parameters in terms of their association with SB. While the specific clinical parameters identified in our study may differ, these findings collectively emphasize the significant association value of clinical and biological indicators in assessing SB risk in MDD.

    Fourthly, we developed discriminative models for characterizing SB in MDD patients. Our analyses demonstrated robust discriminative ability of PSS, HAMA, and CGI-SI scores in distinguishing patients with and without SB. Previous studies have also explored similar approaches, achieving success using peripheral blood inflammatory cytokines and some other serum indicators.49–51 Among these, the combination of IL-17C and TNF-β, and the combination of IL-1α, IL-5, and ICAM-1 demonstrated accuracy in distinguishing SB with AUC values of 0.848 and 0.850, respectively. However, the combination of α1-antitrypsin, transferrin, HDL-C, and apolipoprotein A1 demonstrated higher discriminatory ability (AUC = 0.928).49–51 Our model, with a combined AUC of 0.87, also demonstrates strong capacity to differentiate SB subgroups, highlighting the efficacy of traditional clinical indicators even in the absence of advanced biomarkers.

    Finally, by assessing SB severity as a continuous variable, we found HAMA scores to be predictive of more severe SB. The detrimental impact of anxiety symptoms on SB is well-established, with studies in both general university populations and MDD patients highlighting the role of anxiety in increasing the risk of suicidal ideation and behaviors.52–54 Consequently, MDD patients with comorbid anxiety may require augmented treatment and care to mitigate the potential for SB.

    Study limitations include: (1) inherent cross-sectional causality constraints; (2) acute-phase sample homogeneity, limiting generalizability to chronic MDD; (3) undocumented antipsychotics and antidepressants exposures potentially confounding metabolic findings; and (4) potential circularity in CGI-SI assessment, as clinicians’ awareness of suicide risk may inflate severity ratings. Although collinearity tests showed no critical bias, CGI-SI likely captures global severity context rather than SB-specific pathways. (5) some key psychosocial determinants of SB—including recent life stressors, substance use patterns, and socioeconomic status—were not systematically assessed. Future longitudinal designs tracking SB trajectories from first-admission through maintenance phases—while incorporating blinded CGI ratings—could elucidate dynamic risk interactions and reduce assessment bias.

    Conclusion

    This study establishes the clinical and metabolic signatures of SB in first-hospitalized MDD patients. The operationalized discriminative model, leveraging clinical and metabolic variables, shows utility in distinguishing high-risk subgroups.

    Data Sharing Statement

    All relevant data are within the manuscript.

    Acknowledgments

    We express our gratitude to all the medical staff and patients who participated in our study, as well as to those who contributed to the diagnosis and clinical evaluation of the subjects.

    Funding

    The authors received no specific funding for this work.

    Disclosure

    The authors report no conflicts of interest in this work.

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  • New study reveals simple food choice that can have major impact on your health: ‘Helps to lower cholesterol’

    New study reveals simple food choice that can have major impact on your health: ‘Helps to lower cholesterol’

    “Beans, beans … good for the heart” is part of a popular saying, and now there’s evidence they can help prediabetic people manage their cholesterol and inflammation too.

    Medical News Today summarized the study, which investigated the metabolic benefits of consuming legumes among people with prediabetes. The 12-week study found that, compared to eating white rice, consuming chickpeas or black beans daily instead had measurable impacts on inflammation. Eating chickpeas was linked with reduced blood cholesterol.

    Prediabetes occurs when an individual has higher-than-normal blood glucose levels but not high enough to be diagnosed as Type 2 diabetes. While prediabetes can lead to Type 2 diabetes, it can also be reversed. This study is one of a handful that investigates dietary interventions that could help prevent prediabetic people from developing Type 2 diabetes.

    “The soluble fiber in these legumes helps to lower cholesterol by reducing how much is absorbed into the bloodstream,” Maddie Gallivan, a registered dietitian who was not involved in the study, told MNT.

    She also highlighted some of the other benefits of consuming these legumes.

    “Beans and chickpeas are excellent examples of plant-based protein sources that are also packed with fiber,” she said. “They also help you keep you fuller for longer.”

    Watch now: How bad is a gas stove for your home’s indoor air quality?

    Plus, beans can support a healthy gut microbiome, supporting overall health, Gallivan added.

    The benefits don’t stop there, though.

    Eating more beans and other plant-based food options can also help you save money at the cash register. Plus, eating a plant-based diet is good for the planet. In fact, one study found that if we replaced half of our meat products with plant-based alternatives, we could reduce pollution caused by global agriculture by as much as a third by 2050, compared to 2020 levels.

    “There are a lot of ways to incorporate beans into your regular diet as a cost-effective way to support overall health and reduce the risk of chronic diseases,” the study’s presenting author, Morganne Smith, said, according to MNT. “You can blend them to add some thickness to a soup base, add them as a salad topping, or pair them with other grains like rice or quinoa.”

    “They are also good for the environment,” added Federica Amati, head nutritionist at ZOE, a science and nutrition company. “Eat more of them.”

    Join our free newsletter for easy tips to save more and waste less, and don’t miss this cool list of easy ways to help yourself while helping the planet.

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  • Scientists confirm adult human brain continues to generate new neurons

    Scientists confirm adult human brain continues to generate new neurons

    Swedish researchers have found that even as people age, the hippocampus, the part of the brain responsible for memory, continues to produce new cells. The team identified the early-stage cells that eventually develop into neurons by analyzing brain samples from individuals of all ages using sophisticated instruments.These discoveries support the idea that our brains are still more flexible than previously thought, which may lead to new therapies for conditions affecting the brain and memory loss.The journal Science has published the study. It offers strong new evidence that neurons in the hippocampus, the brain’s memory center, continue to develop well into late adulthood. Scientists from Sweden’s Karolinska Institutet conducted the study.One area of the brain that is crucial for memory and learning as well as emotion control is the hippocampus.In a well-known study conducted back in 2013, Jonas Frisen’s team at Karolinska Institute shown that mature humans’ hippocampal regions are capable of producing new neurons.The time at which the cells were generated was subsequently ascertained by the researchers by measuring the amount of carbon-14 in DNA extracted from brain tissue. Identifying cells of originThe scope and importance of this neurogenesis—the creation of new neurons—are still up for discussion, though. The existence and division of neural progenitor cells—the cells that come before new neurons—in adult humans has not been conclusively demonstrated.“We have now been able to identify these cells of origin, which confirms that there is an ongoing formation of neurons in the hippocampus of the adult brain,” says the study’s lead researcher, Jonas Frisen, a professor of stem cell research at the Karolinska Institute’s Department of Cell and Molecular Biology. From 0 to 78 years of ageIn the new study, the researchers used a variety of cutting-edge techniques to analyze brain tissue from international biobanks belonging to individuals ranging in age from 0 to 78.They employed flow cytometry to examine cell characteristics and single-nucleus RNA sequencing, which examines gene activity in individual cell nuclei.They were able to distinguish between several stages of neuronal development—from stem cells to immature neurons, many of which were in the division phase—by fusing this with machine learning. The researchers employed RNAscope and Xenium, two tools that indicate the location of active genes in tissue, to locate these cells.These techniques verified that the newly generated cells were situated in the dentate gyrus, a particular region of the hippocampal region. Learning, memory development, and cognitive flexibility all depend on this region.The findings indicate that while adult neurons’ progenitors resemble those of mice, pigs, and monkeys, there are some variations in the genes that are active.Individual differences were also significant; although some adult people had a high number of brain progenitor cells, others had almost none.


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