Researchers at Cold Spring Harbor Laboratory (CSHL) believe they have discovered a much quicker way to guide crop development. Using the gene-editing tool CRISPR, plant biologists focused on goldenberry, a small fruit related to tomatoes. Their approach could make the plant easier to grow and manage, opening the door to large-scale farming in the U.S. and around the world. The same strategy could also speed the development of crops that can better withstand disease, pests, and drought.

“By using CRISPR, you open up paths to new and more resilient food options,” said Blaine Fitzgerald, the greenhouse technician in CSHL’s Zachary Lippman lab. “In an era of climate change and increasing population size, bringing innovation to agricultural production is going to be a huge path forward.”

Why Goldenberries Are Hard to Farm

The Lippman lab focuses on plants in the nightshade family, which includes staple crops like tomatoes, eggplants, and potatoes, along with lesser-known species such as goldenberries. Goldenberries are mostly grown in South America and are becoming more popular because of their nutrition and their balance of sweet and tart flavors. Some shoppers may already recognize them from supermarket shelves.

Despite their appeal, goldenberries remain difficult to cultivate on a large scale. Farmers still rely on plants that are “not really domesticated,” said Miguel Santo Domingo Martinez, a postdoctoral researcher in the Lippman lab who led the study.

“These massive, sprawling plants in an agricultural setting are cumbersome for harvest,” Fitzgerald explained.

Shrinking the Plant Without Losing the Flavor

Earlier work from the Lippman lab used CRISPR to modify tomatoes and another tomato relative called groundcherry, producing plants that were smaller and easier to grow in urban environments. Using that experience, the team edited similar genes in goldenberries. The modified plants were about 35% shorter, which made them easier to maintain and allowed farmers to plant them more densely.

The researchers then focused on taste. To identify the best fruit, they sampled goldenberries directly from the field. Fitzgerald described the process as eating “hundreds of them, walking a field, and trying fruit off every plant in the row.”

New Varieties and What Comes Next

After several generations of breeding, the team developed two promising goldenberry lines that combined compact growth with strong flavor. Although the fruits were slightly smaller, the researchers see room for improvement using the same gene-editing tools.

“We can try to target fruit size or disease resistance,” Santo Domingo said. “We can use these modern tools to domesticate undomesticated crops.”

The next step is regulatory approval, which would allow growers to access seeds and begin producing the newly developed goldenberry varieties on a wider scale.

The species has been named Diplolaimelloides woaabi and appears to live only in the Great Salt Lake. That makes it endemic to the lake and potentially an important, though still poorly understood, part of its ecosystem. To choose the name, the research team, led by University of Utah biology professor Michael Werner, worked with the Northwestern Band of the Shoshone Nation. Tribal elders suggested Wo’aabi, an Indigenous word meaning “worm.”

Why Nematodes Matter

Nematodes are among the most widespread animals on Earth. They are found in nearly every environment imaginable, including polar ice, deep-sea hydrothermal vents and ordinary backyard soil. Most are smaller than a millimeter, which is why they often go unnoticed.

Despite their size, nematodes are extraordinarily abundant. Scientists have identified more than 250,000 species so far, making them the most numerous animal phylum in both land and water ecosystems. Roughly 80% of animal life in terrestrial soils and about 90% of animals living on the ocean floor are nematodes.

The First Discovery in the Lake

Until recently, no nematodes had been definitively documented in the Great Salt Lake. That changed in 2022, when field expeditions led by Julie Jung uncovered nematodes living in the lake’s microbialites. These are hardened, mound-like structures formed by microbial communities on the lakebed.

Jung, who was a postdoctoral researcher in Werner’s lab at the time, collected samples while traveling across the lake by kayak and bicycle. The team reported that initial discovery in a scientific paper published last year.

“We thought that this was probably a new species of nematode from the beginning, but it took three years of additional work to taxonomically confirm that suspicion,” said Jung, now an assistant professor at Weber State University.

Only the Third Animal Known to Survive There

With this finding, nematodes became just the third group of animals known to live in the Great Salt Lake’s extremely salty water. The other two are brine shrimp and brine flies, which are crucial food sources for millions of migratory birds that stop at the lake each year.

Further research suggests the story may not be finished. Genetic evidence indicates there could be a second, previously unknown nematode species among the samples collected. Thomas Murray, an undergraduate researcher and second author on the paper, has been helping sample different regions of the lake to investigate this possibility.

“It’s hard to tell distinguishing characteristics, but genetically we can see that there are at least two populations out there,” Werner said.

How Did the Worms Get There?

The discovery raises two major questions for scientists. First, how did these worms arrive in the Great Salt Lake? Second, what role do they play in the lake’s ecosystem?

From early on, the team suspected the nematodes belonged to the family Monhysteridae. This is an ancient group of nematodes known for surviving in extreme conditions, including very salty environments. Genetic and physical analyses confirmed that the species belongs to the genus Diplolaimelloides, a group typically found in coastal marine and brackish waters.

That makes the Great Salt Lake discovery especially puzzling. Only one other member of this genus is known to live outside coastal regions, and that species is found in eastern Mongolia. The Great Salt Lake, by contrast, sits about 4,200 feet above sea level and is roughly 800 miles from the nearest ocean.

“That begs some more interesting, intriguing questions that you wouldn’t have even known to think of until we figured out the alpha taxonomy,” Werner said. “There are two hypotheses, two models that are both kind of crazy for different reasons.”

Ancient Seas or Traveling Birds

One explanation comes from coauthor Byron Adams, a nematologist and biology professor at Brigham Young University. He suggests the worms may have been living in the region for millions of years. During the Cretaceous Period, much of what is now Utah was located along the shoreline of a vast inland sea that split North America in two.

“So we were on the beach here. This area was part of that seaway, and streams and rivers that drained into that beach would be great habitat for these kinds of organisms,” Adams said. “With the Colorado Plateau lifting up, you formed a great basin, and these animals were trapped here. That’s something that we have to test out and do more science on, but that’s my go-to. The null hypothesis is that they’re here because they’ve always kind of been here.”

Werner pointed out a major challenge to that idea. Northern Utah has not always been salty. Between 20,000 and 30,000 years ago, the region was covered by Lake Bonneville, a massive freshwater lake.

“If the nematode has been endemic since 100 million years ago, it has survived through these dramatic shifts in salinity at least once, probably a few times,” he said.

The alternative explanation, which Werner admits is even “crazier,” is that the worms were transported by migratory birds. In this scenario, nematodes could have clung to feathers after birds visited saline lakes in South America and were then carried thousands of miles north.

“So who knows. Maybe the birds are transporting small invertebrates, including nematodes, across huge distances,” Werner said. “Kind of hard to believe, but it seems like it has to be one of those two.”

A Potential Early Warning for Lake Health

Back in the lab, researchers noticed another unexpected pattern. Female nematodes were far more common than males in samples collected directly from the lake.

“That’s another confusing part of the story for us. When we sample out there on the lake and bring them back in the lab, we get less than 1% males. But when we have cultured them in the lab, the males make up about 50% of the sex ratio,” Werner said. “We’re super happy to be able to culture them in the lab, but there’s something about it that’s clearly different than the lake environment.”

The worms live within algal mats that coat the lake’s microbialites, feeding on bacteria that thrive there. Researchers found that the nematodes are concentrated in just the top few centimeters of these mats and are absent below that layer.

While scientists are still determining their exact position in the food web, nematodes are known to be ecologically important in many environments. Their presence in the Great Salt Lake suggests they likely play a meaningful role there as well.

Nematodes are also widely used as bioindicators. Changes in their populations, diversity or distribution can signal shifts in water quality, salinity or sediment chemistry. With the Great Salt Lake under increasing pressure from human activity, this newly identified species could become a valuable tool for monitoring environmental change.

“When you only have a handful of species that can persist in environments like that, and they’re really sensitive to change, those serve as really good sentinel taxa,” Adams said. “They tell you how healthy is your ecosystem.”

Because Diplolaimelloides woaabi appears to live exclusively on microbialites, it may have unique relationships with microbes or unusual survival strategies that scientists have yet to uncover. Since microbialites play a central role in producing energy and supporting life in the lake, any interactions involving these nematodes could have effects that spread throughout the ecosystem.

Study Details and Funding

The research appears in the November 2025 issue of the Journal of Nematology under the title, “Diplolaimelloides woaabi sp. n. (Nematoda: Monhysteridae): A Novel Species of Free-Living Nematode from the Great Salt Lake, Utah.”

The study’s authors include Solinus Farrer, Abigail Borgmeier and Byron J. Adams of Brigham Young University; Jon Wang and Morgan Marcus of the University of Utah; Gustavo Fonseca of the Federal University of São Paulo; and Thomas Powers of the University of Nebraska. Funding was provided by the National Institutes of Health, the Society of Systematic Biologists, the National Science Foundation and the Conselho Nacional de Desenvolvimento Científico e Tecnológico.

The research was led by PhD student Matilda Boyce and involved scientists from UWA’s School of Earth and Oceans, the University of Bristol, the Geological Survey of Western Australia, and Curtin University. Their findings were published in Nature Communications.

Studying Some of the Oldest Rocks on Earth

The team focused on anorthosites that formed about 3.7 billion years ago and were collected from the Murchison region of Western Australia. These rocks are the oldest known on the Australian continent and rank among the most ancient rocks ever discovered on Earth.

“The timing and rate of early crustal growth on Earth remains contentious due to the scarcity of very ancient rocks,” Ms Boyce said.

To overcome this challenge, the researchers used high-precision techniques to examine untouched portions of plagioclase feldspar crystals. These areas preserve the isotopic “fingerprint” of Earth’s ancient mantle, offering a rare glimpse into conditions on the early planet.

When Earth’s Continents Began to Grow

The chemical evidence suggests that Earth’s continents did not start forming immediately after the planet took shape. Instead, significant continental growth appears to have begun around 3.5 billion years ago, roughly one billion years after Earth formed.

This timeline challenges long-standing assumptions about how quickly Earth developed its continents and provides new context for understanding the planet’s early evolution.

Linking Earth and the Moon’s Origins

The researchers also compared their results with data from lunar anorthosites brought back to Earth during NASA’s Apollo missions.

“Anorthosites are rare rocks on Earth but very common on the Moon,” Ms. Boyce said.

“Our comparison was consistent with the Earth and Moon having the same starting composition of around 4.5 billion years ago.

“This supports the theory that a planet collided with early Earth and the high-energy impact resulted in the formation of the Moon.”

The study was supported by funding from the Australian Research Council.

The findings were announced at a news conference during the 247th meeting of the American Astronomical Society in Phoenix and have been accepted for publication in The Astrophysical Journal.

Eight Years of Observations Reveal a Stellar Wake

Researchers identified the influence of Siwarha by carefully measuring subtle changes in Betelgeuse’s light over nearly eight years. These long-term observations revealed the effects of a companion star that had previously been suspected but not confirmed. As the companion moves through Betelgeuse’s outer layers, it disrupts the surrounding gas, producing a trail of denser material.

This detection resolves one of the most enduring puzzles surrounding Betelgeuse. By confirming the companion’s presence, astronomers can now better explain how the star behaves and changes over time. The discovery also provides valuable insight into the later stages of evolution for other massive stars approaching the ends of their lives.

Betelgeuse is located about 650 light-years from Earth in the constellation Orion. It is a red supergiant of extraordinary size, large enough to contain more than 400 million Suns. Because it is both enormous and relatively close to Earth, Betelgeuse is one of the few stars whose surface and surrounding atmosphere can be directly studied, making it a key target for understanding how giant stars grow older, lose material, and eventually explode as supernovae.

Multiple Telescopes Confirm the Companion’s Impact

By combining data from Hubble with observations from the Fred Lawrence Whipple Observatory and the Roque de Los Muchachos Observatory, the team identified repeating patterns in Betelgeuse’s behavior. These patterns provided strong evidence of the long-suspected companion star and revealed how it affects the red supergiant’s outer atmosphere.

Scientists observed changes in the star’s spectrum, meaning the specific colors of light produced by different elements, along with shifts in the motion of gas in the outer atmosphere. These changes are linked to a dense wake formed by the companion star. The wake appears shortly after the companion passes in front of Betelgeuse approximately every six years, or about 2,100 days, in agreement with earlier theoretical predictions.

“It’s a bit like a boat moving through water. The companion star creates a ripple effect in Betelgeuse’s atmosphere that we can actually see in the data,” said Andrea Dupree, an astronomer at the CfA and lead author of the study. “For the first time, we’re seeing direct signs of this wake, or trail of gas, confirming that Betelgeuse really does have a hidden companion shaping its appearance and behavior.”

Decades of Strange Variability Explained

Astronomers have monitored Betelgeuse for decades, tracking changes in its brightness and surface features in an effort to understand its unpredictable behavior. Interest surged in 2020 when the star unexpectedly dimmed after what was described as a stellar “sneeze.” Scientists identified two major cycles in Betelgeuse’s variability: a shorter 400-day period linked to pulsations inside the star, and a much longer cycle lasting about 2,100 days.

Before this discovery, scientists explored many explanations for Betelgeuse’s long-term changes. These included massive convection cells, clouds of dust, magnetic activity, and the potential influence of a hidden companion. Recent studies suggested that the longer cycle was best explained by a low-mass star orbiting deep within Betelgeuse’s atmosphere. Although one group reported a possible detection, there was no definitive evidence until now.

The newly detected wake provides the strongest proof yet that a companion star is actively disturbing the atmosphere of this red supergiant.

“The idea that Betelgeuse had an undetected companion has been gaining in popularity for the past several years, but without direct evidence, it was an unproven theory,” said Dupree. “With this new direct evidence, Betelgeuse gives us a front-row seat to watch how a giant star changes over time. Finding the wake from its companion means we can now understand how stars like this evolve, shed material, and eventually explode as supernovae.”

Looking Ahead to Future Observations

From Earth’s perspective, Betelgeuse is currently eclipsing its companion star. Astronomers are planning additional observations when the companion becomes visible again in 2027. Researchers say this discovery could also help solve similar mysteries involving other giant and supergiant stars.

Hubble’s Continuing Contributions

The Hubble Space Telescope has been operating for more than 30 years and continues to produce discoveries that deepen our understanding of the universe. Hubble is a collaborative project between NASA and ESA (European Space Agency). NASA’s Goddard Space Flight Center in Greenbelt, Maryland, oversees mission operations, with additional support from Lockheed Martin Space in Denver. The Space Telescope Science Institute in Baltimore, operated by the Association of Universities for Research in Astronomy, manages Hubble’s scientific operations for NASA.

“This is a tale of a failed galaxy,” said the program’s principal investigator, Alejandro Benitez-Llambay of the Milano-Bicocca University in Milan, Italy. “In science, we usually learn more from the failures than from the successes. In this case, seeing no stars is what proves the theory right. It tells us that we have found in the local Universe a primordial building block of a galaxy that hasn’t formed.”

A Rare Glimpse of the Dark Universe

“This cloud is a window into the dark Universe,” explained team member Andrew Fox of AURA/STScI for the European Space Agency. “We know from theory that most of the mass in the Universe is expected to be dark matter, but it’s difficult to detect this dark material because it doesn’t emit light. Cloud-9 gives us a rare look at a dark-matter-dominated cloud.”

Cloud-9 belongs to a category known as Reionization-Limited H I Clouds, or “RELHICs.” The term “H I” refers to neutral hydrogen, while “RELHIC” describes a hydrogen cloud formed in the early Universe that never progressed to form stars. Scientists had predicted the existence of such objects for years, but direct confirmation remained elusive. Only after observing Cloud-9 with Hubble were researchers able to verify that it truly contains no stars.

Ruling Out a Hidden Dwarf Galaxy

“Before we used Hubble, you could argue that this is a faint dwarf galaxy that we could not see with ground-based telescopes. They just didn’t go deep enough in sensitivity to uncover stars,” explained lead author Gagandeep Anand of the Space Telescope Science Institute (STScI), Baltimore, USA. “But with Hubble’s Advanced Camera for Surveys, we’re able to nail down that there’s nothing there.”

The discovery itself caught researchers off guard. “Among our galactic neighbors, there might be a few abandoned houses out there,” said STScI’s Rachael Beaton, who is also on the research team.

RELHICs are believed to be dark matter structures that never gathered enough gas to trigger star formation. Because of this, they preserve conditions from the early Universe. Cloud-9 points to the likely existence of many other small, dark matter-dominated objects, often described as failed galaxies. Studying them offers new insight into parts of the Universe that remain difficult to observe because most telescopes focus on bright stars and galaxies.

Measuring an Invisible Giant

Hydrogen clouds near the Milky Way have been studied for decades, but most are far larger and more irregular in shape than Cloud-9. By contrast, Cloud-9 is smaller, denser, and nearly spherical, giving it a distinctly different appearance from other known gas clouds.

At its center, Cloud-9 contains neutral hydrogen spanning roughly 4900 light-years. The hydrogen gas alone has a mass about 1 million times that of the Sun. If the gas pressure is indeed balanced by the gravitational pull of the surrounding dark matter, then dark matter must account for most of the object’s mass. Based on this balance, Cloud-9 is estimated to contain roughly 5 billion solar masses.

Why Starless Objects Matter

Cloud-9 highlights how much of the Universe exists beyond stars. Observing starlight alone does not reveal the full picture. By examining gas and dark matter together, scientists can better understand systems that would otherwise remain hidden.

Finding failed galaxies like Cloud-9 is difficult because nearby bright objects often overpower their faint signals. These systems are also sensitive to environmental effects such as ram-pressure stripping, which can remove gas as they move through intergalactic space. These challenges help explain why such objects appear to be rare.

Discovery Through Radio Telescopes

Cloud-9 was first detected three years ago during a radio survey conducted with the Five-hundred-meter Aperture Spherical Telescope (FAST) in Guizhou, China. The discovery was later confirmed using the Green Bank Telescope and the Very Large Array in the United States. The name “Cloud-9” carries no cultural meaning in China and was assigned simply because it was the ninth gas cloud identified near the outer regions of the spiral galaxy Messier 94 (M94).

The cloud lies close to M94 and appears to be physically connected to the galaxy. High-resolution radio observations reveal slight distortions in the gas, which may be evidence of interaction between Cloud-9 and its larger neighbor.

A Galaxy That Might Still Form

Whether Cloud-9 will eventually become a galaxy depends on whether it gains additional mass. If it had been much larger, gravity would have caused it to collapse and form stars long ago. If it were much smaller, its gas might have dispersed and become ionized, leaving little behind. Instead, it exists in a narrow range that allows it to persist as a RELHIC.

This discovery advances understanding of how galaxies form, how the early Universe evolved, and how dark matter behaves. Because Cloud-9 contains no stars, it allows scientists to study the properties of dark matter clouds without interference from starlight. As future surveys improve, researchers expect to uncover more of these rare relics, offering deeper insight into the Universe’s hidden structure and the physics of dark matter.

Expanded DNA repeats are responsible for more than 60 inherited disorders. These conditions develop when repeating genetic sequences lengthen beyond normal limits and interfere with healthy cell function. Examples include Huntington’s disease, myotonic dystrophy, and certain forms of ALS.

Although most people carry DNA repeats that slowly expand throughout life, scientists had not previously examined how widespread this instability is or which genes control it using large biobank datasets. This research shows that repeat expansion is far more common than previously recognized. It also identifies dozens of genes involved in regulating the process, creating new opportunities to develop treatments that could slow disease progression.

How Researchers Studied Nearly a Million Genomes

The research team, which included scientists from UCLA, the Broad Institute, and Harvard Medical School, analyzed whole genome sequencing data from 490,416 participants in the UK Biobank and 414,830 participants in the All of Us Research Program. To carry out the analysis, they developed new computational approaches capable of measuring DNA repeat length and instability using standard sequencing data.

Using these tools, the team examined 356,131 variable repeat sites across the human genome. They tracked how repeat lengths changed with age in blood cells and identified inherited genetic variants that affected the speed of expansion. The researchers also searched for associations between repeat expansion and thousands of disease outcomes in order to uncover previously unknown links to human illness.

Key Findings on DNA Repeat Instability

The study found that common DNA repeats in blood cells consistently expand as people get older. Researchers identified 29 regions of the genome where inherited genetic variants altered repeat expansion rates, with differences of up to fourfold between individuals with the highest and lowest genetic risk scores.

One surprising result was that the same DNA repair genes did not behave uniformly. Genetic variants that helped stabilize some repeats made other repeats more unstable. The researchers also identified a newly recognized repeat expansion disorder involving the GLS gene. Expansions in this gene, which occur in about 0.03% of people, were linked to a 14-fold increase in the risk of severe kidney disease and a 3-fold increase in the risk of liver diseases.

What the Findings Mean for Future Research

The results suggest that measuring DNA repeat expansion in blood could serve as a useful biomarker for evaluating future treatments designed to slow repeat growth in diseases such as Huntington’s. The computational tools developed for this study can now be applied to other large biobank datasets to identify additional unstable DNA repeats and related disease risks.

Researchers note that further mechanistic studies will be needed to understand why the same genetic modifiers can have opposite effects on different repeats. These efforts will focus on how DNA repair processes differ across cell types and genetic contexts. The discovery of kidney and liver disease linked to GLS repeat expansion also suggests that additional, previously unrecognized repeat expansion disorders may be hidden within existing genetic data.

Expert Perspective on the Findings

“We found that most human genomes contain repeat elements that expand as we age,” said Margaux L. A. Hujoel, PhD, lead author of the study and assistant professor in the Departments of Human Genetics and Computational Medicine at the David Geffen School of Medicine at UCLA. “The strong genetic control of this expansion, with some individuals’ repeats expanding four times faster than others, points to opportunities for therapeutic intervention. These naturally occurring genetic modifiers show us which molecular pathways could be targeted to slow repeat expansion in disease.”

Margaux L. A. Hujoel (UCLA and Brigham and Women’s Hospital/Harvard Medical School), Robert E. Handsaker (Broad Institute and Harvard Medical School), David Tang (Brigham and Women’s Hospital/Harvard Medical School), Nolan Kamitaki (Brigham and Women’s Hospital/Harvard Medical School), Ronen E. Mukamel (Brigham and Women’s Hospital/Harvard Medical School), Simone Rubinacci (Brigham and Women’s Hospital/Harvard Medical School and Institute for Molecular Medicine Finland), Pier Francesco Palamara (University of Oxford), Steven A. McCarroll (Broad Institute and Harvard Medical School), Po-Ru Loh (Brigham and Women’s Hospital/Harvard Medical School and Broad Institute)

M.L.A.H. was supported by US NIH fellowship F32 HL160061; R.E.H. and S.A.M. by US NIH grant R01 HG006855; D.T. by US NIH training grant T32 HG002295; N.K. by US NIH training grant T32 HG002295 and fellowship F31 DE034283; R.E.M. by US NIH grant K25 HL150334; S.R. by a Swiss National Science Foundation Postdoc. Mobility fellowship; P.F.P. by ERC Starting Grant no. 850869; and P.-R.L. by US NIH grants R56 HG012698, R01 HG013110 and UM1 DA058230 and a Burroughs Wellcome Fund Career Award. The All of Us Research Program is supported by the NIH. The authors declare no competing interests.

Preservatives are part of the broader category of food additives and are widely used throughout the global food supply. Their presence is extensive. In 2024, the Open Food Facts World database listed around three and a half million food and beverage products. More than 700,000 of those products contained at least one preservative.

Two Major Types of Preservative Additives

In their analysis, Inserm researchers divided preservative additives into two main groups. The first group includes non-antioxidant preservatives, which slow spoilage by limiting microbial growth or slowing chemical reactions in food. The second group consists of antioxidant additives, which help preserve foods by reducing or controlling exposure to oxygen in packaging.

On ingredient labels, these additives typically appear under European codes between E200 and E299 (for preservatives in the strict sense) and between E300 and E399 (for antioxidant additives).

Why Researchers Are Investigating Preservatives

Earlier experimental research has raised concerns that some preservatives may harm cells or DNA and interfere with normal metabolic processes. However, direct evidence linking preservative intake to type 2 diabetes in large human populations has been limited until now.

To better understand this potential connection, a research team led by Mathilde Touvier, Inserm Research Director, examined long-term exposure to food preservatives and the incidence of type 2 diabetes using detailed data from the NutriNet-Santé study.

Tracking Diet and Health Over More Than a Decade

The study followed more than 100,000 French adults between 2009 and 2023. Participants regularly provided information about their medical history, socio-demographic background, physical activity, lifestyle habits, and overall health.

They also submitted detailed food records covering multiple 24-hour periods. These records included the names and brands of industrial food products they consumed. Researchers cross-referenced this information with several databases (Open Food Facts, Oqali, EFSA) and combined it with measurements of additives in foods and beverages. This allowed the team to estimate each participant’s long-term exposure to preservatives.

Measuring Preservative Consumption

Across all food records, researchers identified a total of 58 preservative-related additives. This included 33 preservatives in the strict sense and 27 antioxidant additives. From this group, 17 preservatives were analyzed individually because they were consumed by at least 10% of the study participants.

The analysis accounted for many factors that could influence diabetes risk, including age, sex, education, smoking, alcohol use, and overall diet quality (calories, sugar, salt, saturated fats, fibre, etc.).

Diabetes Cases and Risk Increases

Over the study period, 1,131 cases of type 2 diabetes were identified among the 108,723 participants.

Compared with people who consumed the lowest levels of preservatives, those with higher intake showed a markedly increased risk of developing type 2 diabetes. Overall preservative consumption was linked to a 47% higher risk. Non-antioxidant preservatives were associated with a 49% increase, while antioxidant additives were linked to a 40% higher risk.

Specific Preservatives Associated With Risk

Among the 17 preservatives examined individually, higher intake of 12 was associated with an increased risk of type 2 diabetes. These included widely used non-antioxidant preservatives (potassium sorbate (E202), potassium metabisulphite (E224), sodium nitrite (E250), acetic acid (E260), sodium acetates (E262) and calcium propionate (E282)) as well as antioxidant additives (sodium ascorbate (E301), alpha-tocopherol (E307), sodium erythorbate (E316), citric acid (E330), phosphoric acid (E338) and rosemary extracts (E392)).

What the Researchers Say

“This is the first study in the world on the links between preservative additives and the incidence of type 2 diabetes. Although the results need to be confirmed, they are consistent with experimental data suggesting the harmful effects of several of these compounds,” explains Mathilde Touvier, Inserm research director and coordinator of this work.

“More broadly, these new data add to others in favor of a reassessment of the regulations governing the general use of food additives by the food industry in order to improve consumer protection,” adds Anaïs Hasenböhler, a doctoral student at EREN who conducted these studies.

“This work once again justifies the recommendations made by the National Nutrition and Health Programme to consumers to favor fresh, minimally processed foods and to limit unnecessary additives as much as possible,” concludes Mathilde Touvier.

This work was funded by the European Research Council (ERC ADDITIVES), the National Cancer Institute, and the French Ministry of Health.

The system, called SleepFM, was trained using almost 600,000 hours of sleep recordings from 65,000 individuals. These recordings came from polysomnography, an in-depth sleep test that uses multiple sensors to track brain activity, heart function, breathing patterns, eye movement, leg motion, and other physical signals during sleep.

Sleep Studies Hold Untapped Health Data

Polysomnography is considered the gold standard for evaluating sleep and is typically performed overnight in a laboratory setting. While it is widely used to diagnose sleep disorders, researchers realized it also captures a vast amount of physiological information that has rarely been fully analyzed.

“We record an amazing number of signals when we study sleep,” said Emmanual Mignot, MD, PhD, the Craig Reynolds Professor in Sleep Medicine and co-senior author of the new study, which will publish Jan. 6 in Nature Medicine. “It’s a kind of general physiology that we study for eight hours in a subject who’s completely captive. It’s very data rich.”

In routine clinical practice, only a small portion of this information is examined. Recent advances in artificial intelligence now allow researchers to analyze these large and complex datasets more thoroughly. According to the team, this work is the first to apply AI to sleep data on such a massive scale.

“From an AI perspective, sleep is relatively understudied. There’s a lot of other AI work that’s looking at pathology or cardiology, but relatively little looking at sleep, despite sleep being such an important part of life,” said James Zou, PhD, associate professor of biomedical data science and co-senior author of the study.

Teaching AI the Patterns of Sleep

To unlock insights from the data, the researchers built a foundation model, a type of AI designed to learn broad patterns from very large datasets and then apply that knowledge to many tasks. Large language models like ChatGPT use a similar approach, though they are trained on text rather than biological signals.

SleepFM was trained on 585,000 hours of polysomnography data collected from patients evaluated at sleep clinics. Each sleep recording was divided into five-second segments, which function much like words used to train language-based AI systems.

“SleepFM is essentially learning the language of sleep,” Zou said.

The model integrates multiple streams of information, including brain signals, heart rhythms, muscle activity, pulse measurements, and airflow during breathing, and learns how these signals interact. To help the system understand these relationships, the researchers developed a training method called leave-one-out contrastive learning. This approach removes one type of signal at a time and asks the model to reconstruct it using the remaining data.

“One of the technical advances that we made in this work is to figure out how to harmonize all these different data modalities so they can come together to learn the same language,” Zou said.

Predicting Future Disease From Sleep

After training, the researchers adapted the model for specific tasks. They first tested it on standard sleep assessments, such as identifying sleep stages and evaluating sleep apnea severity. In these tests, SleepFM matched or exceeded the performance of leading models currently in use.

The team then pursued a more ambitious objective: determining whether sleep data could predict future disease. To do this, they linked polysomnography records with long-term health outcomes from the same individuals. This was possible because the researchers had access to decades of medical records from a single sleep clinic.

The Stanford Sleep Medicine Center was founded in 1970 by the late William Dement, MD, PhD, who is widely regarded as the father of sleep medicine. The largest group used to train SleepFM included about 35,000 patients between the ages of 2 and 96. Their sleep studies were recorded at the clinic between 1999 and 2024 and paired with electronic health records that followed some patients for as long as 25 years.

(The clinic’s polysomnography recordings go back even further, but only on paper, said Mignot, who directed the sleep center from 2010 to 2019.)

Using this combined dataset, SleepFM reviewed more than 1,000 disease categories and identified 130 conditions that could be predicted with reasonable accuracy using sleep data alone. The strongest results were seen for cancers, pregnancy complications, circulatory diseases, and mental health disorders, with prediction scores above a C-index of 0.8.

How Prediction Accuracy Is Measured

The C-index, or concordance index, measures how well a model can rank people by risk. It reflects how often the model correctly predicts which of two individuals will experience a health event first.

“For all possible pairs of individuals, the model gives a ranking of who’s more likely to experience an event — a heart attack, for instance — earlier. A C-index of 0.8 means that 80% of the time, the model’s prediction is concordant with what actually happened,” Zou said.

SleepFM performed especially well when predicting Parkinson’s disease (C-index 0.89), dementia (0.85), hypertensive heart disease (0.84), heart attack (0.81), prostate cancer (0.89), breast cancer (0.87), and death (0.84).

“We were pleasantly surprised that for a pretty diverse set of conditions, the model is able to make informative predictions,” Zou said.

Zou also noted that models with lower accuracy, often around a C-index of 0.7, are already used in medical practice, such as tools that help predict how patients might respond to certain cancer treatments.

Understanding What the AI Sees

The researchers are now working to improve SleepFM’s predictions and better understand how the system reaches its conclusions. Future versions may incorporate data from wearable devices to expand the range of physiological signals.

“It doesn’t explain that to us in English,” Zou said. “But we have developed different interpretation techniques to figure out what the model is looking at when it’s making a specific disease prediction.”

The team found that while heart-related signals were more influential in predicting cardiovascular disease and brain-related signals played a larger role in mental health predictions, the most accurate results came from combining all types of data.

“The most information we got for predicting disease was by contrasting the different channels,” Mignot said. Body constituents that were out of sync — a brain that looks asleep but a heart that looks awake, for example — seemed to spell trouble.

Rahul Thapa, a PhD student in biomedical data science, and Magnus Ruud Kjaer, a PhD student at Technical University of Denmark, are co-lead authors of the study.

Researchers from the Technical University of Denmark, Copenhagen University Hospital -Rigshospitalet, BioSerenity, University of Copenhagen and Harvard Medical School contributed to the work.

The study received funding from the National Institutes of Health (grant R01HL161253), Knight-Hennessy Scholars and Chan-Zuckerberg Biohub.