In their study, published in the American Journal of Preventive Medicine, the researchers found that when simple but strategic digital features, such as colour-coded nutritional quality signals and a healthier alternative prompt, were added to an online grocery shopping platform, the nutritional quality of shoppers’ carts improved significantly.

The team’s findings verified the effectiveness of deploying front-of-package (FOP) labels — which only marginally improve diet quality when used alone — alongside other interventions.

In a randomised trial conducted on NUSMart, an online grocery store designed by the Duke-NUS team, study participants were randomly divided into two groups and asked to make a total of three orders over a period of three to six weeks. While those assigned to the control group used a standard version of the store, those assigned to the experimental group used a version of the store with additional digital features, including:

Signalling nutritional quality with a traffic light: FOP labels resembling traffic light signals alerted shoppers to food products’ nutritional quality using three colour bands — green (best), amber and red with an “X” mark — for easy identification of foods to avoid.

Items were sorted into the colour bands based on their Nutri-Score (NS)[1] points, which were assigned according to energy, sugar, sodium, saturated fat, fruit/vegetable, protein, and dietary fibre levels per 100g/ml. The points were then converted into grades on a five-letter grading system, with A being the healthiest and E being the least healthy.

Sorting groceries by nutritional value: Using food products’ NS points, the researchers presented the items by order of nutritional value, with the healthiest options appearing first. Items in the control version of NUSMart appeared in alphabetical order.

Showing real-time cart feedback: Participants could track the nutritional quality of their grocery carts via a pie chart that indicated the proportion of items in each colour band. They could also compare their carts with a reference cart for health grocery shopping, which the researchers had curated using past data.

Suggesting healthier options: Shoppers could also view up to four healthier alternatives with similar prices and characteristics to each selected food product and replace their chosen product with the healthier alternative at the click of a button.

With these interventions, the nutritional quality of participants’ grocery carts improved from NS grade C to NS grade B, which was more significant than results from previous studies involving standalone FOP labels. The interventions also reduced the amount of calories (12.86 kcal), total fat (1.21g), saturated fat (0.85g), sugar (0.82g) and sodium (156.64mg) purchased.

Notably, the researchers found that shopping with the four digital features led to healthier food choices across all three orders.

Assistant Professor Soye Shin from Duke-NUS’ Health Services and Systems Research Programme, the study’s first author, said of the findings: “As online grocery shopping is rapidly gaining ground, we wanted to see if we could design low cost, scalable online tools that could be used to nudge consumers toward healthier choices at the point of purchase. These results show the potential of these tools to improve diet and health outcomes.”

These findings underscore the advantages of introducing diet quality labels, such as Singapore’s Nutri-Grade labelling initiative, which currently only includes beverages but is planned to expand to other food categories.

Senior author Professor Eric Finkelstein, from Duke-NUS’ Health Services and Systems Research Programme, said: “These results are encouraging but the next step is to work with retailers to incorporate these features into existing online stores. Only then will the full value of this approach be realised.”

Next, the researchers will expand the study to include consumers of low socio-economic status and little nutritional knowledge. They will also investigate if the multi-pronged intervention strategy has the potential to positively impact consumers’ health in the long term.

Professor Patrick Tan, Senior Vice-Dean for Research at Duke-NUS, commented on the study’s impact: “This study reflects how smart, evidence-based interventions — when applied at the right moment — can empower people to make better everyday choices for their health. It also shows how research can lead to practical tools that improve not just individual choices, but population health outcomes.”

Duke-NUS is at the forefront of biomedical research and translational innovations. This new study is part of the School’s ongoing efforts to improve global health through systems research and scientific breakthroughs.

While organisms use energy for a very wide variety of processes — from growth to cognition — one activity is a major drain for many animals: movement. For highly mobile animals, movement is as such a powerful lens through which to estimate energy usage.

Strong methods do exist for measuring animal movement in the context of energy expenditure, but these are limited by the physical size of the equipment used. Now, in a paper published in the Journal of Experimental Biology, researchers from the Marine Biophysics Unit at the Okinawa Institute of Science and Technology (OIST), in collaboration with Professor Amatzia Genin from the Hebrew University of Jerusalem, describe an innovative method for measuring energy usage during movement with video and 3D-tracking via deep learning. “The best method without space limitations has until now been ruled out in the study of about half of the world’s species, due to the reliance on wearable equipment,” says Dr. Kota Ishikawa, first author of the study. “With video, we now have a more inclusive method for studying energy usage in the context of animal behavior and ecology.”

Dynamic Body Acceleration (DBA) has long been the state-of-the-art method for estimating energy usage during movement. In essence, DBA involves measuring the oxygen consumption of a given species performing a given behavior in the lab, by simply measuring the amount of oxygen consumed through the activity. Oxygen is a good indicator of energy, as it is consumed as part of aerobic respiration to produce ATP — the ‘fuel’ providing energy for most bodily processes, including muscle contraction. The acceleration of the animal is measured simultaneously with an accelerometer, and in most cases, because the correlation between acceleration and oxygen consumption during the behavior is very strong, DBA provides a reliable estimate of energy consumption.

With the standard set in the lab, DBA is then measured in the wild, where reliably measuring oxygen consumption is impossible, through a wearable accelerometer. However, relying on physical equipment presents a major barrier. “To ensure accurate measurements without influencing the behavior during observation, researchers have used equipment that weighs at least ten times less than the animal. Given that the accelerometer and battery pack weigh 10-20 grams, this rules out the study of any animals below 100 grams — about half the world’s vertebrate species. It can also affect movement, especially when it depends on drag efficiency, such as swimming or flight,” says Dr. Ishikawa.

Their solution to this problem is elegantly simple. Instead of using a physical accelerometer to measure the movements, two cameras capture video footage of the behavior — in this case, a damselfish swimming in a fishtank — from multiple angles to reconstruct the behavior in 3D space. A few frames of the videos are then used to train a deep learning neural network to track the position of body features such as eyes, which allows researchers to subsequently measure the movement-related acceleration.

Both in the field and in the lab, once cameras are set up to capture animal movements, energy consumption can be estimated. For example, video-based DBA can be used in the context of collective behavior: “Energy expenditure during schooling of small fish has long remained mysterious,” explains Dr. Ishikawa. “For example, do the leading fish use more energy, and is schooling an energy-efficient form of movement? And what can that tell us about the ecology and evolution of fish schooling?”

With video-based DBA, the accurate measurement of energy usage during free-ranging animal behavior has been opened to the smaller half of the world’s vertebrate species, potentially enabling many new research avenues into the breadth of life on our planet.

A recent study, led by Professor Zi Yang MENG and co-authored by his PhD student Menghan SONG of HKU Department of Physics, in collaboration with researchers from the Chinese University of Hong Kong, Yale University, University of California, Santa Barbara, Ruhr-University Bochum and TU Dresden, has unravelled some of the secrets concealed within the entangled web of quantum systems.

Their findings, recently published in the journal Science Advances, push the boundaries of modern physics and offer a fresh perspective on how quantum matter operates at these enigmatic junctures. The study not only deepens our understanding of quantum mechanics but also paves the way for future discoveries that could revolutionise technology, materials science, and even our understanding of the cosmos.

What are Deconfined Quantum Critical Points?

In everyday life, we are familiar with phase transitions, such as water freezing into ice or boiling into steam. These transitions are well-understood and explained by thermodynamics. However, in the realm of quantum physics, phase transitions can occur at absolute zero temperature (-273.15 °C), driven not by thermal energy but by quantum fluctuations — tiny, unpredictable movements of particles at the smallest scales. These are known as quantum critical points.

Traditional quantum critical points act as boundaries between two distinct states: a symmetry-broken phase (ordered phase), where particles are neatly arranged, and a disordered phase, where particles are jumbled and chaotic. This kind of transition is well-described by the Landau theory, a framework that has been the foundation of our understanding of phase transitions for decades.

But deconfined quantum critical points (DQCPs) break this mould. Instead of a sharp boundary separating an ordered phase from a disordered phase, DQCPs lie between two different ordered phases, each with its own unique symmetry-breaking pattern, meaning the way particles are arranged or interact in one phase is fundamentally different from the other. This is unusual because, traditionally, phase transitions involve moving from an ordered state to a disorder one, not from one type of order to another. This distinction makes DQCPs fundamentally different and highly intriguing.

Scientists have debated for decades whether DQCPs represent continuous phase transitions (which are smooth and gradual) or first-order transitions (which are sudden and abrupt). Understanding DQCPs could provide new insights into how particles interact and how exotic states of matter emerge.

The Key to the Mystery: Entanglement Entropy

At the heart of this new study lies the concept of entanglement entropy, a measure of how particles in quantum systems are interrelated. It provides a way to quantify the amount of information shared between different parts of a system. Entanglement entropy offers a glimpse into the hidden structure of quantum systems, serving as a fundamental tool for probing quantum matter and understanding the nature of complex interactions that emerge at critical points.

Using advanced quantum Monte Carlo simulations (a computational method for modelling quantum systems) and rigorous theoretical analysis, researchers examine the behaviour of entanglement entropy in square-lattice SU(N) spin models — a theoretical framework designed to capture the essence of DQCPs.

Their meticulous computations revealed something extraordinary: at small value N (a parameter that determines the symmetry of the system), the behaviour of entanglement entropy deviated from expectations for smooth, continuous phase transitions. Instead, they found that DQCPs exhibit anomalous logarithmic behaviors, defyingthe theoretical constraints typically associated with continuous phase transitions.

The Breakthrough: A Critical Threshold and Conformal Fixed Points

One of the most striking revelations of the study was the identification of a critical threshold value of N. When N exceeds this threshold, DQCPs exhibit behaviours consistent with conformal fixed points — a mathematical framework that describes smooth, continuous phase transitions. This discovery is significant because it suggests that, under certain conditions, DQCPs can resemble continuous phase transitions. At these critical points, the system aligns with conformal fixed points, revealing a hidden structure in the quantum world where the boundaries between distinct phases dissolve, and matter exists in a state of extraordinary fluidity, defying the usual rules of physics.

Why This Matters

The implications of these findings are profound. DQCPs provide a unique testing ground for exploring the interplay of quantum mechanics, symmetry, and critical phenomena. Understanding their nature could unlock new insights into:

1. Exotic States of Matter: DQCPs are believed to be connected to the emergence of exotic phases, such as quantum spin liquids, which have potential applications in quantum computing and other advanced technologies.
2. Fundamental Physics: By challenging the traditional Landau paradigm, DQCPs force us to rethink the principles that govern phase transitions, potentially leading to new theoretical frameworks.
3. Technological Innovation: Insights gained from studying DQCPs could inform the design of novel materials with unique quantum properties, such as high-temperature superconductors or quantum magnets.

Conclusion The enigmatic world of deconfined quantum critical points stands at the frontier of modern physics, offering a glimpse into the uncharted territory of quantum mechanics. Through their meticulous investigation of entanglement entropy and SU(N) spin models, researchers have made significant strides in unravelling the mysteries of these critical phenomena.

This study was conducted in collaboration with Dr Jiarui ZHAO from the Chinese University of Hong Kong, Professor Meng CHENG from Yale University, Professor Cenke XU from the University of California, Santa Barbara, Professor Michael M. SCHERER from Ruhr-University Bochum, and Professor Lukas JANSSEN from TU Dresden.

Thyroid cancer is among the most prevalent cancers in Hong Kong and globally. Precision management of the disease often rely on two systems: (1) the 8th edition of the American Joint Committee on Cancer (AJCC) or Tumour-Node-Metastasis (TNM) cancer staging system to determine the cancer stage; and (2) the American Thyroid Association (ATA) risk classification system to categorise cancer risk. These systems are crucial for predicting patient survival and guiding treatment decisions. However, the manual integration of complex clinical information into these systems can be time-consuming and lack efficiency.

The research team developed an AI assistant that leverages large language models (LLMs), like ChatGPT and DeepSeek, which are designed to understand and process human language, to analyse clinical documents and enhance the accuracy and efficiency of thyroid cancer staging and risk classification.

The model leverages four offline open-source LLMs — Mistral (Mistral AI), Llama (Meta), Gemma (Google), and Qwen (Alibaba) — to analyse free-text clinical documents. The AI model was trained with a United States based open-access data with pathology reports of 50 thyroid cancer patients from The Cancer Genome Atlas Programme (TCGA), with subsequent validation against pathology reports from 289 TCGA patients and 35 pseudo cases created by endocrine surgeons.

By combining the output of all four LLMs, the team improved the overall performance of the AI model, achieving overall accuracy of 88.5% to 100% in ATA risk classification and 92.9% to 98.1% in AJCC cancer staging. Compared to traditional manual document reviews, this advancement is expected to halve the time clinicians spend on pre-consultation preparation.

Professor Joseph T Wu, Sir Kotewall Professor in Public Health and Managing Director of InnoHK D24H at HKUMed, emphasised the model’s remarkable performance. ‘Our model achieves more than 90% accuracy in classifying AJCC cancer stages and ATA risk category’, he said. ‘A significant advantage of this model is its offline capability, which would allow local deployment without the need to share or upload sensitive patient information, thereby providing maximum patient privacy.’

‘In view of the recent debut of DeepSeek, we conducted further comparative tests with a “zero-shot approach” against the latest versions of DeepSeek — R1 and V3 — as well as GPT-4o. We were pleased to find that our model performed on par with these powerful online LLMs,’ added Professor Wu.

Dr Matrix Fung Man-him, Clinical Assistant Professor and Chief of Endocrine Surgery, Department of Surgery, School of Clinical Medicine, HKUMed, stated, ‘In addition to providing high accuracy in extracting and analysing information from complex pathology reports, operation records and clinical notes, our AI model also dramatically reduces doctors’ preparation time by almost half compared to human interpretation. It could simultaneously provide cancer staging and clinical risk stratification based on two internationally recognised clinical systems.’

‘The AI model is versatile and could be readily integrated into various settings in the public and private sectors, and both local and international healthcare and research institutes,’ said Dr Fung. ‘We are optimistic that the real-world implementation of this AI model could enhance the efficiency of frontline clinicians and improve the quality of care. In addition, doctors will have more time to counsel with their patients.’

‘In line with government’s strong advocacy of AI adoption in healthcare, as exemplified by the recent launch of LLM-based medical report writing system in the Hospital Authority, our next step is to evaluate the performance of this AI assistant with a large amount of real-world patient data. Once validated, the AI model can be readily deployed in real clinical settings and hospitals to help clinicians improve operational and treatment efficiency,’ explained Dr Carlos Wong, Honorary Associate Professor in the Department of Family Medicine and Primary Care, School of Clinical Medicine, HKUMed.

The study was led by Professor Joseph Wu Tsz-kei, Sir Robert Kotewall Professor in Public Health in the School of Public Health, and Managing Director & Lead Scientist of InnoHK D24H; Dr Matrix Fung Man-him, Clinical Assistant Professor and Chief of Endocrine Surgery in the Department of Surgery, School of Clinical Medicine; and Dr Carlos Wong King-ho, Honorary Associate Professor in the Department of Family Medicine and Primary Care, School of Clinical Medicine, and Senior Research Director in InnoHK D24H; all under HKUMed. The first authors were Dr Eric Tang Ho-man and Dr Tingting Wu from InnoHK D24H.

The phase 3b, double-blind, randomised, placebo-controlled trial enrolled 1,457 influenza-positive index patients and 2,681 household contacts across 15 countries from 2019 to 2024. The index patients, aged 5 to 64, were assigned to receive either baloxavir or a placebo within 48 hours of symptom onset. The primary endpoint was laboratory-confirmed influenza transmission to household contacts by day 5.

Key Findings:

• Treatment with baloxavir reduced the odds of untreated household members contracting the virus by 32%.
• Transmission resulting in symptomatic influenza was lower with baloxavir (5.8% vs. 7.6%), though the difference was not statistically significant (P=0.16).
• Baloxavir led to a faster reduction in viral titers, with a mean reduction of 2.22 log₁₀ TCID₅₀/mL by day 3 compared to 1.85 log₁₀ TCID₅₀/mL for placebo.
• Drug-resistant viruses emerged in 7.2% of baloxavir-treated index patients but were not detected in household contacts, suggesting limited transmission risk.
• No new safety concerns were identified, with adverse events reported in 4.6% of baloxavir-treated patients compared to 7.0% in the placebo group.

‘These results highlight baloxavir’s potential not only to treat influenza but also to reduce its spread within communities,’ said Professor Benjamin Cowling, co-author of the study and Helen and Francis Zimmern Professor in Population Health, Chair Professor of Epidemiology, and Head of the Division of Epidemiology and Biostatistics, School of Public Health, HKUMed. ‘This dual effect could transform how we manage seasonal influenza and prepare for future pandemics.’

The study underscores the complementary role of antiviral drugs alongside vaccination, particularly in unvaccinated populations or during pandemics when vaccines may not be immediately available.

Professor Seyoung Kim and Dr. Hyunjeong Kwak from the Departments of Materials Science & Engineering and Semiconductor Engineering at POSTECH, in collaboration with Dr. Oki Gunawan from the IBM T.J. Watson Research Center, have become the first to uncover the hidden operating mechanisms of Electrochemical Random-Access Memory (ECRAM), a promising next-generation technology for AI. This groundbreaking study has been published in the journal, Nature Communications.

As AI technologies advance, data processing demands have exponentially increased. Current computing systems, however, separate data storage (‘memory’) from data processing (‘processors’), resulting in significant time and energy consumption due to data transfers between these units. To address this issue, researchers developed the concept of ‘In-Memory Computing.’

‘In-Memory Computing’ enables calculations directly within memory, eliminating data movement and achieving faster, more efficient operations. ECRAM is a critical technology for implementing this concept. ECRAM devices store and process information using ionic movements, allowing for continuous analog-type data storage. However, understanding their complex structure and high-resistive oxide materials has remained challenging, significantly hindering commercialization.

To address this, the research team developed a multi-terminal structured ECRAM device using tungsten oxide and applied the ‘Parallel Dipole Line Hall System’, enabling observation of internal electron dynamics from ultra-low temperatures (-223°C, 50K) to room temperature (300K). They observed, for the first time, that oxygen vacancies inside the ECRAM create shallow donor states (~0.1 eV), effectively forming ‘shortcuts’ through which electrons move freely. Rather than simply increasing electron quantity, the ECRAM inherently creates an environment facilitating easier electron transport. Crucially, this mechanism remained stable even at extremely low temperatures, demonstrating the robustness and durability of the ECRAM device..

Prof. Seyoung Kim from POSTECH emphasized, “This research is significant as it experimentally clarified the switching mechanism of ECRAM across various temperatures. Commercializing this technology could lead to faster AI performance and extended battery life in devices such as smartphones, tablets, and laptops.”

This work was supported by K-CHIPS(Korea Collaborative & High-tech Initiative for Prospective Semiconductor Research)funded by the Ministry of Trade, Industry & Energy of Korea (MOTIE).

Notes:

1. ECRAM(Electrochemical Random-Access Memory): An electrochemical memory device whose channel conductivity varies according to the concentration of ions within the channel. This behavior allows for the expression of analog memory states. The device features a three-terminal structure consisting of a source, drain, and gate. By applying voltage to the gate, ion movement is controlled, and the channel conductivity is read through the source and drain.

2. Parallel Dipole Line Hall System, PDL Hall System: A Hall measurement system composed of two cylindrical dipole magnets. When one magnet is rotated, the other rotates automatically, enabling the generation of a strong, superimposed magnetic field. This configuration allows for enhanced sensitivity in observing internal electron behaviors.

They are the building blocks of life, serving as essential components in nearly all cellular processes. They facilitate communication between cells and ensure that biological systems function properly.

Put simply, life wouldn’t exist without proteins. That’s why researchers around the world are dedicated to understanding them.

Now, a new study from the University of Copenhagen highlights how protein research could revolutionize multiple areas within biology and medicine. The study, published in the journal Cell, was led by scientists at the University of Copenhagen’s Novo Nordisk Foundation Center for Protein Research.

“We hope our findings will help explore how drugs influence protein turnover and contribute to the development of better medicines. Our research could also reveal how protein stability changes with age and how we might promote healthy aging,” says Professor Jesper Velgaard Olsen.

“In short, we have developed a cutting-edge technology that allows us to analyse and quantify proteins in individual cells with unprecedented depth. We can now identify exactly which proteins are present and in what quantities.”

With this new approach, researchers can measure how individual cells produce and break down proteins — a process known as ‘protein turnover’. The technique, called SC-pSILAC, enables scientists to track both the abundance of proteins and the rate at which they are turned over in single cells. These insights could have significant implications for cancer research, drug development, and personalized medicine.

Mapping the impact of cancer treatments

Despite their fundamental importance, there is still much we don’t know about proteins — including how many exist in a human cell.

SC-pSILAC is a breakthrough since it can distinguish between dividing and non-dividing cells. A prime example is cancer cells, which divide rapidly and are typically targeted by chemotherapy.

However, some cancer cells do not divide, allowing them to evade chemotherapy. The new method helps identify these treatment-resistant cells, leading to better therapies.

“We can now observe that non-dividing cells remain metabolically active and continue to affect their surroundings — something previous methods couldn’t detect,” explains Olsen.

The researchers have also used this technique to examine how specific drugs impact protein turnover in individual cells, including the cancer medication bortezomib. Their findings uncovered specific proteins and previously unknown biological processes influenced by the treatment.

“This method represents a significant leap in protein research,” Olsen states.

“In my field, we have worked for years to analyze proteins within cells. Only recently has technological progress enabled us to do so at the single-cell level.”

Thanks to this innovation, scientists now have a far more detailed understanding of how proteins operate at the molecular level. The hope is that this knowledge will drive advancements in disease diagnostics and treatment strategies.

By studying light anomalies made by the newly found planet’s host star and combining their results with a larger sample from a KMTNet microlensing survey, the team found that super-Earths can exist as far from their host star as our gas giants are from the sun, said Andrew Gould, co-author of the study and professor emeritus of astronomy at The Ohio State University.

“Scientists knew there were more small planets than big planets, but in this study, we were able to show that within this overall pattern, there are excesses and deficits,” he said. “It’s very interesting.”

While it can be relatively easy to locate worlds that orbit close to their star, planets with wider paths can be difficult to detect. Still, researchers further estimated that for every three stars, there should be at least one super-Earth present with a Jupiter-like orbital period, suggesting these massive worlds are extremely prevalent across the universe, said Gould, whose early theoretical research helped develop the field of planetary microlensing.

The findings in this study were made via microlensing, an observational effect that occurs when the presence of mass warps the fabric of space-time to a detectable degree. When a foreground object, such as a star or planet, passes between an observer and a more distant star, light is curved from the source, causing an apparent increase in the object’s brightness that can last anywhere from a few hours to several months.

Astronomers can use these fluctuations, or bumps, in brightness to help locate alien worlds unlike our own. In this case, microlensing signals were used to locate OGLE-2016-BLG-0007, a super-Earth with a mass ratio roughly double that of Earth’s and an orbit wider than Saturn’s.

These observations allowed the team to divide exoplanets into two groups, one that consists of super-Earths and Neptune-like planets and the other comprising gas giants like Jupiter or Saturn. This discovery opens new doors for planetary system science: Having a better understanding of exoplanet distribution can reveal new insights about the processes by which they form and evolve.

The study, led by researchers in China, Korea and at Harvard University and the Smithsonian Institution in the United States, was recently published in the journal Science.

To explain their results, researchers also compared their findings to predictions made from theoretical simulations of planet formation. Their results showed that while exoplanets can be separated into groups by mass and makeup, the mechanisms that may produce them can vary.

“The dominant theory of gas-giant formation is through runaway gas accretion, but other people have said that it could be both accretion and gravitational instability,” said Gould. “We’re saying we can’t distinguish between those two yet.”

Doing so will likely require greater swaths of long-term data from specialized systems such as KMTNet and other microlensing instruments like it, said Richard Pogge, another co-author of the study and a professor of astronomy at Ohio State.

“Finding a microlensing star event is hard. Finding a microlensing star with a planet is hard-squared,” he said. “We have to look at hundreds of millions of stars to find even a hundred of these things.”

These alignments are so rare that only 237 out of the more than 5,000 exoplanets ever discovered have been identified using the microlensing method. Now, with the help of three powerful custom-built telescopes located in South Africa, Chile and Australia, the KMTNet system routinely allows scientists to scour the cosmos for these amazing events, said Pogge.

Most notably, it was scientists in Ohio State’s Imaging Sciences Laboratory who designed and built the Korean Microlensing Telescope Network Cameras (KMTCam) that the system relies on to identify exoplanets. And as technology continues to evolve, having dedicated, global collaborations like this one will turn visions of scientific theory into real discoveries, said Pogge.

“We’re like paleontologists reconstructing not only the history of the universe we live in but the processes that govern it,” he said. “So helping to bring both of those pieces together into one picture has been enormously satisfying.”

Other members of Ohio State’s ISL team include Bruce Atwood, Tom O’Brien, Mark Johnson, Mark Derwent, Chris Colarosa, Jerry Mason, Daniel Pappalardo and Skip Shaller. This work was supported by the National Science Foundation, Tsinghua University, the National Natural Science Foundation of China, the Harvard-Smithsonian Center for Astrophysics, the China Manned Space Project, Polish National Agency for Academic Exchange and the National Research Foundation of Korea.

Now preclinical research by City of Hope^®, one of the largest and most advanced cancer research and treatment organizations in the United States and a leading research center for diabetes and other life-threatening illnesses, has uncovered the cellular culprit behind age-related abdominal fat, providing new insights into why our midsections widen with middle age. Published today in Science, the findings suggest a novel target for future therapies to prevent belly flab and extend our healthy lifespans.

“People often lose muscle and gain body fat as they age — even when their body weight remains the same,” saidQiong (Annabel) Wang, Ph.D., the study’s co-corresponding author and an associate professor of molecular and cellular endocrinology at City of Hope’sArthur Riggs Diabetes & Metabolism Research Institute, one of the world’s foremost scientific organizations dedicated to investigating the biology and treatment of diabetes. “We discovered aging triggers the arrival of a new type of adult stem cell and enhances the body’s massive production of new fat cells, especially around the belly.”

In collaboration with the UCLA laboratory co-corresponding author Xia Yang, Ph.D., the scientists conducted a series of mouse experiments later validated on human cells. Wang and her colleagues focused on white adipose tissue (WAT), the fatty tissue responsible for age-related weight gain.

While it’s well-known that fat cells grow larger with age, the scientists suspected that WAT also expanded by producing new fat cells, meaning it may have an unlimited potential to grow.

To test their hypothesis, the researchers focused on adipocyte progenitor cells (APCs), a group of stem cells in WAT that evolve into fat cells.

The City of Hope team first transplanted APCs from young and older mice into a second group of young mice. The APCs from the older animals rapidly generated a colossal amount of fat cells.

When the team transplanted APCs from young mice into the older mice, however, the stem cells did not manufacture many new fat cells. The results confirmed that older APCs are equipped to independently make new fat cells, regardless of their host’s age.

Using single-cell RNA sequencing, the scientists next compared APC gene activity in young and older mice. While barely active in young mice, APCs woke up with a vengeance in middle-aged mice and began pumping out new fat cells.

“While most adult stem cells’ capacity to grow wanes with age, the opposite holds true with APCs — aging unlocks these cells’ power to evolve and spread,” said Adolfo Garcia-Ocana, Ph.D., the Ruth B. & Robert K. Lanman Endowed Chair in Gene Regulation & Drug Discovery Research and chair of the Department of Molecular & Cellular Endocrinology at City of Hope. “This is the first evidence that our bellies expand with age due to the APCs’ high output of new fat cells.”

Aging also transformed the APCs into a new type of stem cell called committed preadipocytes, age-specific (CP-As). Arising in middle age, CP-A cells actively churn out new fat cells, explaining why older mice gain more weight.

A signaling pathway called leukemia inhibitory factor receptor (LIFR) proved critical for promoting these CP-A cells to multiply and evolve into fat cells.

“We discovered that the body’s fat-making process is driven by LIFR. While young mice don’t require this signal to make fat, older mice do,” explained Wang. “Our research indicates that LIFR plays a crucial role in triggering CP-As to create new fat cells and expand belly fat in older mice.”

Using single-cell RNA sequencing on samples from people of various ages, Wang and her colleagues next studied APCs from human tissue in the lab. Again, the team also identified similar CP-A cells that had an increased number in middle-aged people’s tissue. Their discovery also illustrates that CP-As in humans have high capacity in creating new fat cells.

“Our findings highlight the importance of controlling new fat-cell formation to address age-related obesity,” said Wang. “Understanding the role of CP-As in metabolic disorders and how these cells emerge during aging could lead to new medical solutions for reducing belly fat and improving health and longevity.”

Future research will focus on tracking CP-A cells in animal models, observing CP-A cells in humans and developing new strategies that eliminate or block the cells to prevent age-related fat gain.

The study’s first authors are City of Hope’s Guan Wang, Ph.D., and UCLA’s Gaoyan Li, Ph.D.