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.

Exposomics explores how the complex interplay of environmental factors — from pollutants in our water and food to social and psychological stressors — shapes our biology. By studying these combined exposures, researchers can uncover how they collectively influence health, from metabolism and heart function to brain health and disease risk.

The Perspectives article is led by the Banbury Exposomics Consortium — an interdisciplinary group of scientists who gathered at Cold Spring Harbor’s Banbury Center in 2023 to define the core principles of this rapidly evolving field. Gary Miller, PhD, a foremost expert in exposomics and faculty member at Columbia University Mailman School of Public Health, was the lead organizer of the Consortium.

Miller, Vice Dean of Research and Innovation and Professor of Environmental Health Sciences at Columbia Mailman School, co-leads the NIH-funded national coordinating center for exposomics, NEXUS. He also leads IndiPHARM, an ARPA-H-funded initiative using exposomics to predict drug interactions and enhance medication effectiveness.

Exposomics in Action

The young field is already proving its transformative potential. Researchers analyzing molecular evidence identified a specific industrial solvent as the culprit behind kidney disease clusters among factory workers. In another study, scientists merged satellite pollution mapping with residential location information to reveal how airborne particulates prematurely age the brain. Scientists analyzing thousands of circulating molecules pinpointed TMAO, a gut microbiome metabolite produced when eating red meat and dairy, as a previously overlooked major contributor to heart attack risk.

These discoveries are made possible by cutting-edge technologies and tools such as wearable sensors that track chemical exposures in real-time, satellite imagery that maps pollution down to city blocks, and ultra-sensitive mass spectrometers that detect compounds present at just one part per trillion.

A Wider Lens on Our Health

While genetics provides our biological blueprint, it explains only a fraction of chronic disease risk. The exposome captures everything that happens to us, from industrial chemicals to social stressors. Unlike traditional studies examining single exposures in isolation, exposomics integrates advanced tools to understand how environmental, social, and psychological factors collectively interact with our biology.

This approach synergizes powerfully with other “omics” sciences. When combined with genomics, proteomics, and metabolomics, exposomics creates the first complete picture of health determinants. The authors envision a future where all major disease studies incorporate exposome analysis as standard practice.

Systematically analyzing these complex interactions can improve drug development, uncover hidden drivers of disease, and address health disparities. The approach bridges precision medicine and population health.

The Way Forward

Miller and colleagues outline critical priorities for advancing exposomics. These include the development of more sensitive technologies, such as wearable or minimally invasive tools that measure an individual’s exposome; the creation of a human exposome reference to enable analysis and contextualization at the population scale; and the implementation of standardized protocols to enable AI-driven analysis of complex datasets. The field must also address ethical considerations around data privacy and the need for greater focus on the social determinants of health, the authors write.

Newly launched U.S. and European exposomics hubs now provide the infrastructure for worldwide collaboration, standardizing methods, harmonizing data, and training researchers in the cross-disciplinary skills needed to advance this field. These centers form the critical backbone for the future progress of exposomics.

“We’re now building the first systematic framework to measure how all exposures — from chemical to social — interact with biology across the lifespan. Our goal is to create actionable strategies for healthier lives,” says Miller.

Hand gestures such as gripping, pinching and grasping are driven by movements of muscles in our forearm. These movements generate small electrical signals that can be read by sensors on the skin, a technique called electromyography.

“Using sensors and machine learning, we can recognize gestures based on muscle activity,” said Jonathon Schofield, professor of mechanical and aerospace engineering at UC Davis and senior author on the paper.

EMG-based controls perform well in a lab setting and with limbs at rest. But there is a well-known problem of “position and load.” If you move your arm to a different position — say, shoulder height, or over your head — or grasp objects of different weights, the measurements change.

“In the real world, every time you move a limb and grasp something the measurement is going to change,” said graduate student Peyton Young, first author on the paper. “The neutral position (where the limb is held passively next to the body) is very different to moving around.”

Combining EMG and FMG

To address this, Young and Schofield experimented with a different type of measurement, alone and in combination with EMG. Force myography (FMG) measures how muscles in the arm bulge as they contract.

Young constructed a cuff that goes round the forearm and includes both EMG and FMG sensors. He used this device with a series of able-bodied volunteers in the lab who performed a series of arm gestures with while participants held different loads with different hand grasps. Data from the sensors was fed to a machine learning algorithm to classify the different movements into pinch, pick, fist and so on. The algorithm was trained on either EMG or FMG signals alone, or on a combination.

For each experiment, the algorithm was trained on some of the data and scored on its ability to accurately classify the rest.

“We train the classifier on data from the gestures, then score it on its ability to predict them,” Young said.

They found that position and loading did indeed affect the accuracy of classification of gestures. Overall, a combination of EMG and FMG gave over 97 percent classification accuracy, compared to 92 percent for FMG alone and 83 percent for EMG alone.

Young is now working on a combined FMG/EMG sensor and the team is working towards an experimental prosthetic limb that uses the technology.

The approach could have a wide range of applications for prosthetics and robotics as well as for virtual reality tools, Schofield said. The team benefits enormously from being able to collaborate with clinical prosthetics experts, surgeons and biologists across UC Davis, he said.

“We wouldn’t be able to do it without exposure to actual patients and clinicians,” Schofield said.

Additional authors on the paper are Kihun Hong, Eden Winslow, Giancarlo Sagastume, Marcus Battraw and Richard Whittle, all at UC Davis. Battraw is now on the faculty at California State University, Chico.

Cell migration influences many processes in the body, including how immune cells travel to an infection site, how the brain develops and how wounds are repaired. It is also exploited by disease cells, such as in metastatic cancer. While cell movement is known to be directed by the interaction between two protein families, GPCRs and chemokines, the vast similarities between members of each family have presented a challenge in understanding how the correct pairs form and control the movement of relevant cells. The researchers developed data science approaches to identify the exact parts of each protein governing their molecular interactions.

“We found that cells have an elegant system that uses structure and disorder together to control cell migration,” said senior co-corresponding author M. Madan Babu, PhD, FRS, St. Jude Senior Vice President of Data Science and Center of Excellence for Data-Driven Discovery director, Department of Structural Biology. “With that understanding, we can now rationally introduce small changes in a chemokine’s structure to ultimately alter cell migration in desired ways.”

Small, disordered regions provide order to chemokines-GPCR pairs

The scientists uncovered how chemokines and their receptors bind select members of the GPCR family by data mining protein sequences and structural information. They compared all human chemokine-binding GPCRs and all chemokines, then compared similar chemokines and GPCRs from other species. They also looked at each protein individually at a population level, finding places that stayed the same across groups and those that differed.

“Through our data analysis, we discovered that the information for how chemokines and GPCRs select for each other is stored in small, discrete packages of highly unstructured, disordered regions,” said first and co-corresponding author Andrew Kleist, MD, PhD, St. Jude Center of Excellence for Data-Driven Discovery, Department of Structural Biology. “The mix of those small packages from both the chemokine and receptor results in the unique interaction, similar to website data encryption keys, which governs cell migration.” Kleist started the work as a graduate student in the laboratory of co-corresponding author Brian Volkman, PhD, Professor of Biochemistry at the Medical College of Wisconsin.

Websites keep sales secure with public and private digital keys. The seller and buyer each possess a public key and a private key, both of which are prime numbers. When the private and public keys are multiplied together, the resulting unique number ensures that only the two parties taking part in the transaction can exchange information while protecting that information from bad actors. The scientists found the disordered regions in these proteins acted like private keys, while the structured regions acted like public keys. The interactions of a chemokine’s disordered region with a GPCR’s structured region, within the greater context of the highly structured portions of each protein, provide cells with a unique chemical identifier for that chemokine-GPCR pair, just like verifying a pair of public and private keys. That unique identifier contains the information for cells to respond appropriately to a particular chemokine-GPCR binding, migrating towards more of that chemokine.

“Once we understood how these proteins interacted, we demonstrated we could rationally mutate them to have different properties,” Babu said. The researchers changed the regions determining the selectivity of a chosen chemokine to alter its receptor binding preferences. Co-author Lindsay Talbot, MD, St. Jude Department of Surgery, showed that the scientists could change how T cells, a type of white blood cell, move, turning down a signal that normally stops their movement.

Making forward movements with chemokines and GPCRs

“Now that we’ve shown a proof of concept, our approach will guide exploration into new medicines and improvements for existing cellular therapies,” Kleist said. “For example, it may be possible to create molecules that better lead immune cells to cancers or help recruit more blood stem cells for bone marrow transplants. In theory, any therapy using cell movement could benefit from applying these principles.”

To enable scientists and clinicians to test this, the collaborators published their data science framework online. The resource is the first step in pushing cell movement manipulation from concept into reality for patients.

“When people think about the body, we think every cell stays in place, but that’s a simplistic view,” Babu said. “Depending on the tissue, cells are moving all the time, and our new understanding of those systems opens novel avenues for therapeutic development.”

The framework to assist the rational design of chemokines and receptors is freely available at: https://github.com/andrewbkleist/chemokine_gpcr_encoding.