Gut microbes are increasingly being viewed as hidden regulators of brain health, and a new study in Translational Psychiatry adds fuel to that idea by tracing a biochemical pipeline from the gut to stress-related mental disorders. The research, led by Yuan, Qin, Wu and colleagues, reports that metabolites produced—or shaped—by gut microbiota can influence developmental trajectories associated with anxiety- and stress-linked pathology.
Rather than focusing solely on which bacterial species are present, the team examined functional metabolic output. Using integrative approaches that combine microbiome profiling with metabolomic measurements, the researchers identified gut-derived compounds whose levels tracked with downstream markers relevant to stress vulnerability. The results suggest that microbial metabolism may act as an early biochemical “switch,” tuning how stress signals are processed later.
A key finding is that these microbial metabolites do not merely correlate with symptoms; they appear to modulate mechanisms tied to disorder emergence. The study proposes that certain metabolite patterns can reshape host signaling pathways implicated in stress reactivity, including processes that affect neurodevelopment and the maturation of stress-response circuits.
The work also highlights how microbial communities can influence the chemical environment of the gut, altering metabolite availability and thereby changing what reaches the systemic circulation. Once in contact with host tissues, these metabolites may interact with receptors or influence cellular pathways involved in inflammation control and neuronal function—two domains frequently linked to stress-related psychiatric conditions.
Importantly, the paper frames gut–brain communication as a developmentally time-sensitive phenomenon. By emphasizing “development of stress-related mental disorders,” the authors argue that microbial metabolite exposure during critical windows could bias the risk landscape long before clinical symptoms emerge.
Methodologically, the study leverages translational reasoning, connecting microbial metabolites to mechanistic readouts rather than stopping at taxonomic associations. This strategy strengthens the causal plausibility of a microbiota-driven metabolic model and provides candidate compounds that could be targeted in future interventions.
From a public-health perspective, the findings support the growing concept that dietary patterns, probiotics, or precision microbiome therapies might be designed to adjust metabolite production. Such interventions could potentially recalibrate stress susceptibility by shifting the gut’s chemical outputs toward more protective profiles.
As the field advances, the study’s DOI—10.1038/s41398-026-04154-8—marks another step toward metabolite-centered strategies for mental health, where gut chemistry becomes a lever for preventing stress-driven disorders.
Subject of Research: Gut microbiota-driven metabolites and stress-related mental disorders Article Title: Gut microbiota-driven metabolites modulate the development of stress-related mental disorders. Article References: Yuan, M., Qin, F., Wu, L. et al. (2026). Translational Psychiatry. https://doi.org/10.1038/s41398-026-04154-8 Image Credits: AI Generated DOI: https://doi.org/10.1038/s41398-026-04154-8
Older adults in China are not experiencing health improvements evenly, and a new study links part of that gap to how physical activity is distributed across communities. Researchers report that physical activity is associated with better health outcomes, but the benefits are not uniform—creating measurable health inequality rather than a single “one-size-fits-all” effect.
The findings focus on older adults and examine whether differences in activity levels translate into unequal health trajectories across demographic and socioeconomic conditions. Instead of treating “activity” as a simple yes-or-no behavior, the analysis considers how gradients of movement relate to disparities in health.
To quantify inequality, the study employs population-level metrics commonly used in health economics and epidemiology. These approaches can separate overall health variation into components that reflect group differences, allowing investigators to estimate how much of the inequality is potentially attributable to physical activity patterns.
The work also emphasizes that health inequality can widen even when average health improves. In this scenario, some groups may adopt or maintain activity more easily—because of access to safe spaces, health literacy, social support, or fewer mobility constraints—while other groups fall behind.
Technically, the authors analyze observational data and control for key confounders that might otherwise explain both activity and health status. This helps reduce the chance that the observed association is merely driven by factors such as baseline chronic disease burden, education, or geography.
Importantly, the results suggest that physical activity does not just correlate with health—it may act as a modifiable lever shaping how evenly health benefits are shared. If interventions can shift activity patterns among disadvantaged groups, the study implies that inequality could shrink, not just outcomes improve.
Because the paper is based on real-world evidence from China, it also highlights cultural and structural realities that shape behavior. Urban design, community programming, and the availability of age-friendly exercise options may influence who can be active consistently.
From a “viral science news” perspective, the headline takeaway is straightforward: getting older doesn’t have to mean accepting unequal health. The study frames physical activity as both a health tool and an inequality amplifier—meaning targeting participation gaps could deliver outsized public health impact.
Subject of Research: Health inequality among older adults; physical activity effects Article Title: The impact of physical activity on health inequality among older adults: evidence from China Article References: Dang, X., Pan, J., Xing, X. et al. The impact of physical activity on health inequality among older adults: evidence from China. BMC Geriatr (2026). https://doi.org/10.1186/s12877-026-08008-2 Image Credits: AI Generated DOI: https://doi.org/10.1186/s12877-026-08008-2 Keywords: physical activity; health inequality; older adults; China; public health; epidemiology
A common culprit behind severe gum disease may also stiffen and narrow the heart's aortic valve, preliminary research suggests. This may help explain links between gum problems and poor heart health.
Now, researchers suggest that Porphyromonas gingivalis, one of the main bacteria involved in severe gum disease, may also contribute to calcific aortic valve disease (CAVD), a condition in which calcium builds up in the heart's aortic valve. The findings were presented in July at a meeting of the American Heart Association in Boston and have not been peer-reviewed yet.
Dr. Elena Aikawa, a professor of medicine at Harvard Medical School who was not involved in the study, said the findings "fit well with our current understanding of calcific aortic valve disease as an active, inflammation-driven disease rather than simply wear and tear of the valve."
Periodontal disease is likely just one contributor among many, rather than the sole cause of CAVD, she added. But the study does identify a biologically plausible pathway linking oral health to heart health that could open new opportunities for prevention and treatment.
How mouth bacteria harm the heart
CAVD affects millions of people worldwide, and there are no approved drugs that slow or stop its progression. As the disease worsens, people may develop fatigue, shortness of breath and chest pain, and in severe cases, they often require valve-replacement surgery.
Meanwhile, prior to the new research, it was known that bacteria and inflammatory signals from the mouth can sometimes enter the bloodstream and affect distant tissues, said Richard Lamont, chair of oral immunology and infectious diseases at the University of Louisville School of Dentistry, who was not involved in the study. Over time, these effects can accrue and gradually reshape how the immune system behaves, he said.
In CAVD, calcium builds up in the aortic valve, which connects the left side of the heart to the large artery that carries oxygen-rich blood to the body. (Image credit: Noctiluxx via Getty Images)
P. gingivalis is known to be particularly destructive to gum tissue, causing lots of inflammation, and it's been tied to cardiovascular disease in the past. To see if the bacterium might be directly impacting heart health, researchers at Fuwai Hospital in Beijing looked for P. gingivalis in aortic valve tissue removed during valve-replacement surgery. They compared this diseased tissue to valves that weren't calcified.
The calcified valves contained much higher levels of P. gingivalis DNA and proteins made by the bacterium. "P. gingivalis was about 30 times more abundant in calcified valves than in non-calcified valves," study first author Dr. Chenyang Li, a doctoral candidate at Fuwai Hospital, told Live Science in an email.
To test whether the bacteria might directly contribute to valve damage, Li and colleagues injected live P. gingivalis into the bloodstreams of healthy lab mice. The bacteria reached the animals' aortic valves, triggering inflammation, calcium buildup and narrowing of the valve.
"We were surprised to see valve calcification even in mice without high cholesterol," Li said. "This suggests P. gingivalis may directly contribute to valve disease, beyond traditional cardiovascular risk factors."
A comparison group of mice was given antibiotics before the injection and had less bacteria in their aortic valves and slower disease progression. Meanwhile, mice injected with dead P. gingivalis bacteria did not show any changes in their aortic valves.
The researchers traced the changes in the mice's hearts to interleukin-1 beta, a signalling molecule that normally helps the body fight infections. Exposure to P. gingivalis ramped up the production of this molecule, and in turn, the immune signal switched on genes that pushed healthy valve cells to behave more like bone-building cells, which is a hallmark of CAVD. This switch causes the cells to lay down calcium in the surrounding valve tissue.
When the scientists blocked interleukin-1 beta in some mice, the animals developed much less valve calcification even when the bacteria reached the valve. Lamont said that the mouse experiments strengthen the case that there's a biological mechanism directly linking gum and heart diseases. However, he cautioned that mice have very different oral microbiomes than humans, making it difficult to know whether the exact same process occurs in people.
That said, the team also saw the same pattern in human valve cells grown in the lab. Exposure to P. gingivalis increased inflammation and calcium buildup among the cells, while blocking interleukin-1 beta blunted those effects.
Both Aikawa and Lamont said more research is needed to confirm the relationship in humans. The findings give researchers a new pathway to explore for future treatments, raising questions of whether targeting inflammatory signals like interleukin-1 beta could help slow the progression of this widespread heart disease.
This article is for informational purposes only and is not meant to offer medical advice.
What do you know about the body's hardest-working muscle? Find out with our heart quiz!
Genetic prediction models are poised to revolutionize medicine. But because they have been trained on European DNA, they threaten to widen health care disparities.
In the weeks around the 2026 annual meeting of the American Society of Clinical Oncology, my phone kept buzzing with alerts about GLP-1 drugs and cancer. The headlines were everywhere — from NPR and The Washington Post to Substack and heated exchanges on social media — all circling the same claim: Ozempic might lower the risk of cancer.
Behind those headlines is a real wave of studies, involving millions of patients. I'm a physician and clinical epidemiologist, and my team and I design and interpret these same kinds of studies that test what widely used drugs actually do.
The excitement around the idea that GLP-1 drugs can prevent cancer is running well ahead of the evidence. Not wrong, necessarily. Just not yet earned.
Cancer is among the hardest things to study, because it is not one disease but more than a hundred. Breast cancer, lung cancer and blood cancers are not variations of the same disease; each has its own distinct biology and its own mix of genetic, environmental and behavioral risks.
A single drug never gets one verdict on how it affects cancer. It gets a hundred, raising the risk of some, lowering it for others and leaving most untouched.
The early cancer concerns
Before the current buzz that GLP-1 drugs could reduce cancer risk, the worry ran in the opposite direction: that they might raise cancer risk.
Researchers were originally concerned about thyroid cancer. Studies in rodents found that Ozempic caused thyroid C cell tumors, which is why U.S. regulators added a black box warning in June 2026 to the drug that advises against its use among those with a personal or family history of related conditions.
But rodents are not people. Human thyroid C cells are less sensitive to GLP-1 drugs compared to rodent C cells because they have far fewer GLP-1 receptors. Long-term studies in monkeys did not show the same abnormal thyroid cell growth seen in rodents.
A 2025 analysis of data from 93 clinical trials found no clear link between taking certain GLP-1 drugs and thyroid cancer. European regulators reached the same conclusion in 2023 after reviewing available research on GLP-1s.
Pancreatic cancer was another concern. Here, too, researchers found no consistent increased risk from taking GLP-1 drugs in a 2025 analysis of data from 62 studies.
A 2025 study of about 87,000 adults found that overall cancer rates among those who started a GLP-1 drug were roughly 17% lower, with the clearest reductions in endometrial and ovarian cancers as well as a type of brain cancer called meningioma. The risk of kidney cancer was 38% higher among patients taking GLP-1 drugs, though larger studies are needed to confirm its significance.
A 2026 study of more than 110,000 women undergoing breast imaging found a roughly 30% lower risk of breast cancer among those who use GLP-1 drugs compared to those who do not.
Researchers are also studying how GLP-1 drugs affect a patient's chances of surviving cancer. Among more than 6,800 colon cancer patients in a 2024 study, around 16% of the 103 on a GLP-1 drug died within five years compared to 37% of those who were not taking one. A study presented at the 2026 American Society of Clinical Oncology reported a 34% lower risk of death across six cancer types for those taking GLP-1 drugs.
Why the evidence is so easy to misread
While these findings are exciting, studies on the effects of GLP-1 drugs on cancer risk have three features that make them especially easy to misread.
The first is healthy user bias. People who start a GLP-1 drug tend to be healthier and wealthier than those who do not. A person with obesity who has insurance, sees doctors regularly and can afford Ozempic is far more likely to start the drug than someone with the same height and weight who lacks those advantages.
Healthy user bias is very difficult to scrub out of observational studies, which compare people as they are treated in real life rather than assigning the drug at random. The very advantages that made it possible to receive a GLP-1 prescription, not the drug itself, may be what lowered a patient's cancer risk.
The second is the choice of the comparison drug. To understand the effect of a drug, it has to be measured against something else. What it's measured against will shape the results of the study. The 2024 study reported that patients taking GLP-1 drugs saw large reductions in cancer risk compared to those taking insulin. But comparing patients on GLP-1 drugs with patients taking the diabetes drug metformin showed no clear reduction in cancer risk.
Insulin is reserved for patients with more advanced diabetes, a condition that is itself a risk factor for cancer. In these studies, patients on insulin start out with a higher risk of cancer. Measured against a group at higher risk of developing cancer, almost anything can look protective. The apparent benefit came from the comparison drug, not the GLP-1 drug.
The third is timing. It is not so much another source of bias as a clue that other biases may be at work. Cancer can take decades to develop, yet most GLP-1 studies follow people for only a handful of years. In some studies, the apparent cancer benefits of GLP-1 drugs show almost immediately. But prevention doesn't work that quickly. A drug that truly lowered the risk of developing cancer would show its effect gradually as fewer tumors surface over time, not within the first few months of taking a drug.
When cancer risk appears to drop almost as soon as a patient starts treatment, the speed is not a magical triumph of GLP-1 drugs. Rather, in my view, it is the tell: People who start these drugs were already at lower cancer risk, and the GLP-1 drugs are getting credit for that preexisting advantage.
Moreover, almost all of this research comes from a handful of high-income countries, even as uptake of these drugs is increasing globally and the burden of cancer has disproportionately grown in lower-income countries. The idea that GLP-1s may reduce cancer risk for much of the world is being inferred from data generated almost entirely in a few rich ones.
What do randomized trials show?
The cleanest way around these study biases is randomized trials, which by design make comparison groups alike from the start.
Available clinical trials tell a quieter story than the headlines. Two 2025 meta-analyses, which pool data from multiple studies — one covering 50 trials and a second covering 48 — found little evidence that GLP-1 drugs either raise or lower cancer risk.
There are many GLP-1 brands available for consumers. (Image credit: Iuliia Burmistrova via Getty Images)
The findings of these meta-analyses are generally more reliable than those of observational studies because their total amount of data increases their statistical power. Despite their strengths, these analyses inherit the limitations of the trials they include. Most of the trials in these 2025 studies had a short follow-up of a year or two and recorded too few cancer cases to settle the question.
Randomized clinical trials designed to answer the question of whether GLP-1 drugs affect cancer risk would need to enroll tens of thousands of people and follow them for many years. Short of that, the next best evidence will come from observational studies designed to emulate randomized trials.
Until then, the idea that GLP-1 drugs lower cancer risk is a hypothesis worth testing but not a conclusion to act on.
Based on the available evidence, the clearest conclusion is the reassuring one: GLP-1 drugs do not appear to raise overall cancer risk. However, more ambitious claims that GLP-1s actively prevent cancer or improve survival after diagnosis remain unproven.
The surest thing I can say is also the least satisfying: It's still too early. These drugs are far younger than the cancers they are being credited with preventing.
Researchers have discovered that children's brains can contain cells with their mother's DNA and that these cells can persist for decades.
The findings, which were posted to the preprint database bioRxiv June 10 but have not been peer-reviewed yet, are part of a growing body of work showing that a mother and fetus exchange cells during pregnancy — a phenomenon known as "microchimerism." Previously, scientists had found that a mother's brain harbors cells with her children's DNA.
The findings are important for several reasons, said Amy Boddy, co-director of the Microchimerism, Human Health and Evolution Project at the University of California, Santa Barbara, who was not involved in the study. Past work mostly found evidence of maternal microchimerism in infancy, and in blood samples, she told Live Science in an email. "What's exciting here is that it's tissue, not blood; it's real human data, not an animal model; and the methods are cutting-edge."
More broadly, the work reinforces the idea that microchimerism is "a normal process of mammalian biology," Boddy said.
Hunting down maternal cells in the brain
Before this study, there was sparse evidence for maternal microchimeric cells in brains, mostly because it is hard for researchers to get samples of human brain tissue and DNA from both parents and their children.
To overcome this challenge, a team led by Sami Kanaan, a staff scientist at the Fred Hutchinson Cancer Center in Seattle, analyzed brain tissue that had been surgically removed from dozens of children with severe epilepsy as part of their treatment. The patients ranged in age from 28 days to 19 years at the time of their surgery, and their mothers provided DNA samples through cheek swabs.
Kanaan's team used a tool called quantitative PCR to identify and count maternal cells hiding among millions of cells in the children's brains.
Out of 37 mother-child pairs, 26 children — 70% — had their mother's cells in their brains. These maternal cells were distributed across multiple regions of the brain, including the frontal, temporal and parietal lobes, which sit on the brain's outer surface, and the hippocampus, which is buried deep inside. Each sample had, on average, about 2.2 maternal cells per 100,000, though one sample from the hippocampus had 459 maternal cells per 100,000 and 11 children had no evidence of maternal DNA in their brains.
That prevalence is probably underestimated, Boddy said, "due to the limits of detecting rare cells at low frequency with this method."
Being a firstborn child seemed to increase the odds of having maternal cells in the brain. Of the children who carried their mother's cells, 14 were firstborns and 12 were later-born. In contrast, among the children who didn't have these cells, only one was a firstborn and 10 were later-born.
Using a technique called single nucleus RNA sequencing, which reveals what a cell is doing by showing which genes within the cells are switched on, the researchers found that the maternal cells had transformed into several types of brain cells. Likely, these cells were originally leukocytes and stem cells and had been transferred to the fetus via the placenta or during pregnancy or breastfeeding.
The transformed cells included neurons; oligodendrocytes, which produce the protective sheath around neurons; astrocytes, which support many brain functions and help fuel neurons; microglia, the brain's immune cells; and endothelial cells, which line blood vessels.
"Being able to use single-nucleus RNA sequencing to identify what type of cells the maternal microchimeric cells actually are is amazing," Boddy said. "We've been so limited in understanding the function of these cells, and papers like this, with these methods, are getting us closer."
During pregnancy, the mother and fetus exchange cells — a phenomenon called "microchimerism." (Image credit: Rhenizara S via Getty Images)
Checking healthy brains
To see whether these findings also applied to people without epilepsy, the researchers examined brain autopsy data from 29 individuals with no known neurodevelopmental conditions, ranging in age from 22 weeks of gestation to 40 years. They also analyzed brain tissue from three men in their late 80s and early 90s without any known brain conditions originally collected as part of an Alzheimer's disease study.
In total, the researchers found foreign cells in 25 of 32 people — about 78% — including in the brain of a man in his 90s.
The researchers suspect these foreign cells are maternal, but they couldn't confirm it because they didn't have DNA from the mothers. It is possible that the foreign cells came from a twin; an older sibling; a past pregnancy, miscarriage or abortion (in females); or, rarely, from a maternal grandmother, the team wrote in their paper.
As in the epilepsy patients, these cells had matured into several types of working brain cells. In younger brains, they most often became a specific kind of neuron, a layer 2/3 neuron, while in older brains, maternal cells were more likely to be microglia.
Given that these microchimeric cells take on very different roles, it would be interesting to know whether that diversity reflects their origins — for example, whether they came from a mother, an older biological sibling or a maternal grandmother, said Dr. Sing Sing Way, a microchimerism researcher at Cincinnati Children's Hospital Medical Center who was not involved in the study.
The number of maternal cells declined with age, but they never completely disappeared.
Boddy said the findings mostly raise new questions. "Do we maybe need microchimeric cells to 'help out'?" she asked. "Is diversity of cells in the brain important, or is it just a byproduct of being a placental mammal?”
Because microchimeric cells appear to be common, Boddy suspects they "are doing an important job in the brain, so understanding their function could be very important for understanding healthy brain development."
By using new analytical tools to pinpoint microchimeric cells, the study "pushes the boundaries" of previous research, but future studies would benefit from larger and more uniform datasets, Way said. That would mean obtaining more brain biopsies, analyzing more cells from each sample, collecting specimens at different ages, and sampling similar brain regions across individuals to better compare the results.
This article is for informational purposes only and is not meant to offer medical advice.
What it means: Neuroplasticity is the ability of the nervous system to adapt to the environment or to injuries by forming new connections and modifying and strengthening existing connections. Neuroplasticity can happen after an injury, such as a stroke or trauma to the brain, but it also occurs during ordinary learning. Just by reading this definition, you may be making new, sustainable brain connections.
How to use it in a sentence: Scientists used to think neuroplasticity was an ability only the the young had, but researchers are increasingly learning that aging brains can adapt throughout the lifespan, much to everyone's relief.
Can you crack our science word of the day puzzle, Chain Word?
Scientists long thought that when RNA kick-started life on Earth 4 billion years ago, it could form only small, simple structures. But new research shows that naturally occurring RNA molecules can also adopt large, sophisticated geometries, like filaments and cages. Now, scientists wonder whether the structures were present at life's beginning.
According to an idea known as the RNA world hypothesis, RNA-based life-forms preceded modern ones that use DNA and protein. RNA, a molecular cousin of DNA, still plays roles in modern cells but does not serve as the primary genetic material. By comparison, primordial species used RNA to store genetic information and to catalyze reactions as stand-in enzymes.
Proteins eventually dominated as enzymes, perhaps because they can fold into more diverse figures than RNA can. That's because proteins are composed of 20 types of subunits, called amino acids, each with a unique structure, whereas RNA is composed of only four subunits, called nucleotides, that all adopt similar shapes.
Scientists originally thought that only proteins were varied enough to assemble into large structures, but a new paper has demonstrated that RNA — though more limited in its variety — also has the capacity to form these large configurations. The research was posted to the preprint server bioRxiv July 1 and has not been peer-reviewed yet.
"We show RNA can do things which we have never seen before," said study co-author Lin Huang, an RNA biologist at Sun Yat-Sen University in China. "It suggests that at the origin of life RNA could assemble into all kinds of shapes," he told Live Science.
Huang and his colleagues had hypothesized that RNA molecules could link together if they possessed sequences that fold into "kissing stem loops." This occurs when an RNA strand folds over on itself, forming a structure that resembles a loop in a shoelace. If loops from different RNAs bond together, or "kiss," the molecules could link up and form larger complexes, the researchers proposed.
After sifting through a bevy of RNA sequences, the researchers found a family of RNA molecules encoded by bacteriophages — viruses that infect bacteria — that form these loops. They purified several of these RNA molecules in the lab, allowed them to assemble in a dish, and then captured their structures using cryo-electron microscopy.
They discovered that some of the RNA molecules formed long filaments. These resembled protein-based filaments such as the cellular cytoskeleton, a scaffold that participates in many functions, including shaping and moving the cell.
Other RNA molecules assembled into cages as large as common viruses. Some of these took the form of an icosahedron — a 3D shape that is built from 20 equilateral triangles and resembles a soccer ball. Many viruses, including herpesviruses, package their genome into protein-based icosahedra called capsids. This raises a question: Could RNA-based capsids have packaged genomes in the RNA world?
RNA structures assemble into icosahedra as large as protein-based virus capsids. (Image credit: Lin Huang)
This work demonstrates that RNA had the capacity to assemble into these elaborate structures during the RNA world, Huang said, but that doesn't prove it actually happened.
"I definitely think that environmental parameters are a question," Anna Medvegy, an evolutionary biologist at Eötvös Loránd University in Hungary, told Live Science in an email. "Can these structures form in the environment in which the hypothetical RNA World existed?" said Medvegy, who was not involved in the new work.
If scientists could recreate these environmental conditions at the dawn of life, such as high temperatures and low pH, and still observe that these structures take shape, that would strengthen the theory that they could have been present in the RNA world, she said.
Although the RNA cages and filaments were large, Huang's team generated them using only short RNA strands, each no longer than 200 subunits. Medvegy said long RNAs are susceptible to breaking, so if short strands can assemble into these structures, that provides more promise that these multi-tiered molecules could have formed in the RNA world.
Another question is whether these elaborate RNA complexes currently assemble inside the bacteriophage-infected bacteria from which they were derived. So far, Huang's team has only seen these structures form in a lab dish, so they need to determine if factors inside bacteria, such as proteins, would either disrupt or enable their formation inside cells.
Beyond providing insight into life's beginnings, these RNA cages could have potential applications in biotechnology, Huang thinks. Efforts are underway to use DNA folded into "DNA origami" to deliver drugs into cells, and Huang thinks DNA's older cousin, RNA, could one day play a similar role in medicine.
U.S. health officials have identified shredded iceberg lettuce served at Taco Bell as one source of contamination behind an outbreak of parasitic infections affecting over 1,000 people.
The outbreak has been caused by the protozoan parasite Cyclospora cayetanensis, which can enter the body when someone consumes food or water contaminated with the parasite. Since May 1, there have been 1,644 confirmed cases across Indiana, Kentucky, Ohio, West Virginia and Michigan, which is currently the hardest-hit state.
(Health officials are also investigating additional cases of Cyclospora infection in the U.S. that have yet to be linked to this specific lettuce-related outbreak. Those other potential cases remain under investigation and total over 5,100, per the Centers of Disease Control and Prevention (CDC).)
In the five-state outbreak, there have been 94 hospitalizations and no deaths to date.
Through an investigation, health officials found that all 1,644 of the sickened people with confirmed cases of Cyclospora reported eating Taco Bell in Indiana, Kentucky, Michigan, Ohio or West Virginia before becoming ill. Interviews with the affected individuals suggested that shredded lettuce might be a common source of exposure.
The Food and Drug Administration (FDA) then investigated the source of the lettuce, identifying a single supplier of iceberg lettuce from Mexico that was used by Taco Bell locations where the people ate before becoming ill. News reports have named California-based Taylor Farms as the supplier.
"FDA is working directly with the identified supplier to determine if potentially contaminated shredded iceberg lettuce remains on the market," an FDA notice states. "As part of this investigation, FDA and state partners have initiated collection of product samples for testing and analysis."
The investigation is ongoing, and additional sources of contamination or affected restaurants could possibly be identified later, the FDA noted. In the meantime, Taco Bell has said it will stop using lettuce from the implicated supplier.
"Taco Bell is working to stop use of all lettuce implicated by this investigation. Not all Taco Bell locations in these states received implicated product," the FDA statement says.
Currently, the CDC is warning consumers not to eat shredded iceberg lettuce served at Taco Bell locations in Indiana, Kentucky, Michigan, Ohio or West Virginia.
Case counts reported by individual states may differ from the FDA's and CDC's counts, because states may include both probable and confirmed cases. The FDA and CDC will update their totals once they can confirm additional cases reported to them.
Symptoms of Cyclospora infection typically emerge two to 14 days after a person consumes the parasite. These symptoms include watery diarrhea, fatigue, cramping, bloating, gas and loss of appetite, and without treatment, they can last between a few days to a month or more. The infection is treated with an antibiotic, which blocks the parasite's ability to multiply.
This article is for informational purposes only and is not meant to offer medical advice.
Scientists have identified a brain protein that may help Alzheimer’s spread, revealing a potential new target for slowing the disease’s progression. Alzheimer’s disease is closely linked to the accumulation of a toxic form of the protein Tau inside the brain. As Tau damage reaches additional brain regions, more neurons are harmed, symptoms intensify, and the [...]
Biological defenses make losing weight and keeping it off unusually difficult. Better treatments, healthier environments, and sustainable habits may support long-term health more effectively than willpower alone. For years, weight loss has been framed as a simple test of discipline: consume fewer calories and exercise more. Modern research, however, shows that body weight is regulated [...]
Even moderate amounts of walking may help protect brain function and slow changes linked to Alzheimer’s disease. New research reveals a modest amount of daily movement may help protect the brain from Alzheimer’s disease, but the apparent benefit does not involve the biological target researchers expected. Using pedometers, researchers followed nearly 300 older adults with [...]
Sleep tracking on Apple Watch can make an invisible part of daily health easier to understand. Each morning, the watch may show sleep duration, stages, interruptions, overnight vitals and a score intended to summarize the night.
That information can be useful. It can also create a new habit of judging the entire day before getting out of bed.
One low score may make a person feel tired before noticing how they actually feel. A few minutes less deep sleep can become a source of concern. Going to bed can even start to feel like a test that must be passed rather than a normal biological process.
Apple Watch sleep data works better as a long-term observation tool than as a nightly verdict. The goal is not to collect perfect scores. It is to notice repeatable patterns and make small changes when those patterns match real-life problems.
What Apple Watch Measures During Sleep
Apple Watch estimates sleep using movement and signals collected while it is worn overnight. Depending on the model and available features, the Health and Sleep apps can show total sleep time, awake periods and estimates for core, deep and REM sleep.
The watch may also collect sleeping heart rate, respiratory rate, wrist temperature and other overnight metrics. The Vitals app compares several of these measurements with the user’s established typical range.
A sleep score compresses parts of the night into one number or category. Apple’s score considers factors including sleep duration, consistency and time spent awake. That makes it convenient, but convenience should not be confused with a complete medical interpretation.
The watch cannot know whether a stressful conversation kept someone awake, whether a child interrupted the night or whether the wearer woke feeling unexpectedly refreshed after fewer hours. It estimates physiological and behavioral signals. The person still supplies the context.
Sleep Tracking Apple Watch Data Works Best as a Trend
One night can be distorted by almost anything: travel, alcohol, illness, noise, a late meal, room temperature or a loose watch band. Even the position of the arm can affect sensor collection.
A weekly or monthly view is more useful because it reduces the influence of isolated nights. Patterns begin to emerge around bedtime consistency, total duration and changes in overnight vitals.
To review longer sleep trends on iPhone:
Health > Search > Sleep > select the weekly, monthly or six-month view
Look first at sleep duration and timing. These measurements are generally easier to interpret than small changes in individual sleep stages.
A person who repeatedly sleeps less on work nights may have a scheduling issue. Someone whose sleep becomes more fragmented after late caffeine may have found a useful connection. A consistently irregular bedtime may explain more than whether deep sleep moved by 12 minutes on Tuesday.
The data becomes valuable when it supports an observation that can lead to a realistic adjustment.
Image Credit: Apple Inc.
Do Not Treat Sleep Stages as Exact Measurements
Core, deep and REM sleep are presented as distinct colored sections, which can make them appear more precise than they are. Apple Watch estimates these stages from signals available at the wrist. A clinical sleep study uses additional equipment and is designed for medical evaluation.
That does not make the stage information worthless. It means small nightly differences should not be overinterpreted.
A person does not need to increase deep sleep manually or achieve a perfect proportion of REM sleep. Sleep architecture changes naturally across age, stress levels, exercise, illness and different parts of the night.
Use stage data to notice substantial or persistent changes, not to compete with yesterday’s chart. Spending 15 minutes less in one estimated stage is not automatically a health problem.
Start With How You Feel
Before opening the Sleep app, notice how the morning feels.
Was waking difficult? Is concentration normal? Is there unusual daytime sleepiness? Does the body feel recovered after exercise? Was the night interrupted for an obvious reason?
This brief self-check prevents the score from defining the experience. Someone may feel fine after a lower-rated night, while another person may feel exhausted despite receiving a favorable score.
The most useful interpretation combines both sources: subjective experience and device data. When they repeatedly agree, the pattern deserves attention. When they conflict for one night, there may be no reason to react.
Use Vitals as Context, Not a Diagnosis
The Vitals app can show whether overnight measurements such as heart rate, respiratory rate, wrist temperature and sleep duration fall within a typical range. It becomes more useful after the watch has collected enough nights to establish a personal baseline.
An outlier may reflect alcohol, medication, elevation, intense training or the beginning of an illness. It may also be an ordinary variation or imperfect reading.
Several unusual metrics appearing together may provide more context than one isolated number. Even then, Apple Watch data should not be used to diagnose a condition independently.
Persistent symptoms, breathing concerns, extreme daytime sleepiness or major changes in sleep should be discussed with a qualified healthcare professional. Bring the trend data if it helps describe what has been happening, but do not let the watch replace an evaluation.
Build a Routine Around Behavior, Not Scores
The healthiest use of sleep tracking is to connect the data with actions a person can actually control.
A consistent bedtime may improve the pattern. Charging the watch before the evening makes tracking more reliable. Reducing late caffeine, adjusting the bedroom temperature or moving an intense workout earlier may also produce noticeable changes.
To set a sleep schedule:
Health > Search > Sleep > Full Schedule & Options > Add Schedule
A schedule can provide Wind Down reminders and help keep bedtime more regular. It should remain a helpful structure rather than a rigid obligation. Life will occasionally interrupt it.
Choose one adjustment and observe it for a week or two. Changing five habits after one poor night makes it difficult to identify what helped.
Image Credit: Apple Inc.
When Sleep Tracking Creates More Stress
Some people become preoccupied with improving sleep data, repeatedly checking results or worrying that a low score guarantees a bad day. The anxiety can itself make falling asleep more difficult.
That is a sign to reduce exposure to the numbers.
Sleep tracking can be turned off temporarily, or the data can be reviewed only once per week. Another option is to avoid checking the score in the morning and examine trends at a neutral time later.
To change sleep tracking settings:
Watch app on iPhone > My Watch > Sleep
A break does not erase the knowledge already gained. A person who has learned that regular timing and less late caffeine improve sleep may no longer need a daily chart to confirm it.
The device should support rest, not supervise it.
A Better Way to Read the Morning Report
A practical morning review can remain simple. Check whether the watch recorded the full night. Notice total duration. Consider whether the bedtime was typical. Look for any major change in overnight vitals, then move on.
There is rarely a need to analyze every stage, compare several apps or search for explanations after one ordinary variation.
Sleep tracking on Apple Watch is most useful when it reveals something that was difficult to see without data: a repeated shortfall in duration, an inconsistent schedule or a pattern linked to habits and recovery.
The score can summarize one night, but it cannot grade the person who slept through it. A healthier measure is whether the information helps create calmer nights, better days and fewer reasons to think about the watch after waking.
If you’re like me, you or a loved one has struggled through the process of gaining pre-approval for the medical care that your physician has recommended. Personal stories abound regarding the tribulations of patients as they go through hoops to get their health insurer to pay for certain prescription medications, medical procedures, and more.
When used judiciously, this process—known as prior authorization—serves as a check on overuse and spending on services or technologies for which there are less costly alternatives. But a large majority of physicians voice concerns about care delays, which can cause patients to abandon recommended treatments while waiting for the insurance company to verify their eligibility and confirm that the treatment is, indeed, medically necessary. Patients who are denied care may submit an appeal, but that requires more time.
AI might be able to help. With its ability to efficiently sort through vast reams of information, artificial intelligence could theoretically expedite approval of unambiguously allowable claims, thereby reducing care delays. However, AI-driven prior authorization is facing resistance, as it may increase wrongful denials of health insurance coverage. A 2025 American Medical Association survey of physicians revealed significant concern about the application of AI tools, with 61 percent of doctors worrying that AI will exacerbate denials of what they deem are necessary treatments.
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