“Previous studies have found that sleep is largely linked to aging and the pathological burden of the brain. Our study goes further and shows that too little and too much sleep are associated with faster aging in nearly every organ, supporting the idea that sleep is important in maintaining organ health within a coordinated brain-body network, including metabolic balance and a healthy immune system,” says study leader Junhao Wen, assistant professor of radiology at Columbia University Vagelos College of Physicians and Surgeons.

The research was published in Nature.

Biological Clocks Reveal How Organs Age

Scientists are increasingly using aging clocks to estimate whether a person is aging biologically faster or slower than their chronological age. These tools rely on machine learning and biological information (e.g., proteins from a minimally invasive blood test) to calculate patterns associated with aging.

Many aging clocks provide a single measure for the entire body. However, different organs can age at different speeds. One familiar example is the decline in ovarian function that contributes to the biological clock associated with female fertility.

Wen and his colleagues have been developing aging clocks that focus on individual organs. The goal is to provide more detailed and potentially more personalized information about a person’s health.

“Everyone is excited by these aging clocks and their ability to predict disease and mortality risk,” Wen says. “But to me, the more exciting question is, can we link aging clocks to a lifestyle factor that can be modified in time to slow aging?”

Finding a Sleep Sweet Spot

Sleep offered researchers an ideal way to explore that question because mounting evidence suggests that sleep plays an important role in health. Wen also had a personal interest in the issue.

“I’m also a light sleeper and was getting worried about the effects on myself,” says Wen.

To create the aging clocks, Wen used information from about half a million participants in the UK Biobank. Machine learning was applied to identify biological signatures associated with aging in different organs.

The researchers built clocks using several types of information, including structural measurements from medical imaging, proteins associated with specific organs, and molecules detected in the blood.

“In the liver, for example, we have an aging clock built with protein data, an aging clock of metabolic data, and an aging clock of imaging data,” Wen says. “This allows us to see whether sleep is distinctively associated with aging clocks derived from multiple omics and molecular layers.”

The team then compared sleep duration (as reported by each Biobank participant) with biological age estimates from 23 aging clocks covering 17 organ systems.

Too Little and Too Much Sleep Linked to Faster Aging

A clear U-shaped pattern appeared across the body. People reporting short sleep (fewer than 6 hours) and long sleep (greater than 8 hours) tended to show faster biological aging.

The lowest levels of aging were seen among people who reported sleeping between 6.4 and 7.8 hours each day.

Importantly, the findings do not show that sleep duration by itself causes organs to age faster or slower. Instead, they suggest that sleeping either too little or too much could be a sign of poorer health throughout the body.

Sleep Duration Tied to Diseases Across the Body

The results also point to a broad connection between sleep, the brain, and the rest of the body.

Short sleep was significantly associated with depressive episodes and anxiety disorders, consistent with earlier research connecting insufficient sleep with mental health problems.

It was also associated with obesity, type 2 diabetes, hypertension, ischemic heart disease, and heart arrhythmias.

Both short and long sleep were linked to chronic obstructive pulmonary disease and asthma. They were also associated with several digestive disorders, including gastritis and gastroesophageal reflux disease.

Wen says, “The broad brain-body pattern is important because it tells us that sleep duration is a deeply embedded part of our entire physiology, with far-reaching implications across the body.”

Sleep, Aging, and Late Life Depression

The organ-specific aging clocks may also help scientists understand how sleep is connected to individual diseases. Wen and his colleagues explored this possibility by examining late life depression.

The researchers could not establish whether differences in sleep duration caused late life depression or whether depression itself changed how long people slept.

To investigate further, the team used “mediation analysis” to examine whether biological aging might help explain the relationship between short or long sleep and late life depression.

The results suggested that short sleep may be more directly connected with the burden of late-life depression. Long sleep, in contrast, may influence depression through pathways reflected in aging clocks for the brain and adipose tissue.

“This has a strong implication for future sleep management and future therapeutics,” Wen says. “Our study suggests there may be different biological pathways between long and short sleepers that lead to the same outcome, late-life depression, and we shouldn’t treat them the same way.”

To better understand the DNA sequences that control this process, researchers in the laboratory of University of California San Diego Professor James T. Kadonaga focused on an important DNA element known as the “initiator.” The initiator marks the location where the information encoded in a gene begins to be converted, or expressed, into a functional product.

AI Decodes the Initiator Sequence

In the new study, led by graduate student researcher Torrey Rhyne-Carrigg, the team used high-throughput DNA sequencing to measure gene expression activity across approximately 500,000 different versions of the initiator.

The researchers then used those results to train a machine learning system, a form of artificial intelligence, to identify the characteristic DNA pattern associated with the initiator. Once the model had decoded that signature, the team searched human genes for the sequence and found that roughly 60% contain the initiator.

“These AI models were found to provide, for the first time, strong predictions of the presence or absence of the initiator in human genes, and were thus able to decode the DNA base sequence pattern of the initiator,” said Kadonaga, a professor in the UC San Diego Department of Molecular Biology, School of Biological Sciences.

Predicting the Effects of DNA Mutations

The findings could help researchers anticipate how mutations affecting the initiator may alter gene activity and contribute to a range of disorders. The study’s data and AI models may also support the design of synthetic promoters, sequences that can switch genes on or off, with functions tailored for specific purposes.

More broadly, the research shows how laboratory experiments and artificial intelligence can be combined to uncover information encoded in human DNA.

“More globally, this work is a step forward in the combined use of laboratory experiments and AI to decipher the information that is embedded in the sequence of the DNA bases in humans,” said Kadonaga. “Ultimately, within the six billion bases of DNA in each of our cells, there is a gene expression code that specifies when, where and to what extent each of our genes should be turned on or off. If we had an AI model for the entire gene expression code, we would be able to predict the activity of each of the different variants of genes in different people. The new AI model for the initiator is a small but important part of this gene expression code, and I am optimistic that we will expand our AI models of the human gene expression code in the not-too-distant future.”

But these medications can also cause problems. Some patients experience nausea and other gastrointestinal side effects. Because GLP-1 drugs reduce appetite and food intake, they may also contribute to nutritional deficiencies and loss of muscle, potentially increasing the risk of frailty and other long-term health issues.

Researchers at UC Berkeley are now investigating a very different strategy for treating obesity and diabetes. Instead of reducing the amount of energy a person consumes, their approach is designed to increase the amount of energy the body uses by raising metabolic activity.

A Different Way to Target Weight Loss

In a study published August 21 in Science Advances, the team reports that a molecular compound called 5-tetradecyloxy-2-furoic acid (TOFA) can interfere with the production of lipids such as cholesterol and triglycerides. At the same time, it activates genes that encourage cells to use fat for fuel and produce more energy.

In experiments with mice, TOFA improved insulin sensitivity and glucose control, reduced triglyceride levels, and improved signs of fatty liver disease. Obese mice treated with the compound lost fat while showing no significant reduction in lean muscle mass.

“Body weight responds to two levers: taking in fewer calories, or spending more energy,” said Anders Näär, a professor of metabolic biology and nutrition at UC Berkeley and senior author of the study. “GLP-1s work almost entirely on the first, so we went after the second.”

Reviving a Compound First Discovered Decades Ago

TOFA was initially discovered in the 1970s and belongs to a group of compounds known as ACC inhibitors. These compounds reduce the body’s production of lipids.

Several ACC inhibitors have advanced into mid-stage clinical trials, but none has been approved to treat metabolic disease. One important obstacle is that many of these compounds can increase triglyceride levels, which may raise cardiovascular risk.

The UC Berkeley team found that TOFA behaves differently. In addition to acting as an ACC inhibitor, it activates PPARα and PPARδ, cellular receptors that switch on genes involved in taking up fat and burning it for energy.

In mice, this effect increased energy use by as much as 18% without causing the animals to become more physically active or increasing their body temperature. The researchers also found that TOFA did not produce the rise in triglycerides seen with some other ACC inhibitors, possibly because of its combined effects on lipid production and energy metabolism.

“TOFA appears to engage a coordinated metabolic response,” said study first author Justin Y. Lee, a postdoctoral student at UCSF who conducted the research as a Ph.D. student at Berkeley. “It is not simply blocking lipid synthesis. It is also activating energy expenditure pathways that may help the body handle excess lipid and glucose more effectively.”

One Compound Outperformed a Two-Drug Approach

The researchers also tested whether they could reproduce TOFA’s effects with two separate compounds. They gave mice one compound designed to suppress lipid production and another intended to increase energy expenditure.

That combination did not improve overall metabolic health as effectively as TOFA by itself, suggesting that TOFA’s particular combination of actions may be important to its effects.

The team then examined whether TOFA could be paired with existing GLP-1 medications. These included semaglutide, sold under the brand names Ozempic or Wegovy, and tirzepatide, sold as Mounjaro and Zepbound.

In mice, combining TOFA with these GLP-1 drugs produced larger improvements in body weight, glucose control, insulin levels, and triglycerides than either treatment produced on its own.

“In our combination experiments, TOFA worked additively or synergistically with the GLP-1 appetite-suppressing drugs, so we view it as complementary rather than as a replacement,” Näär said.

Human Testing Is Still Needed

Despite the promising results, the researchers emphasize that TOFA has so far been studied only in animals. Its safety and effectiveness in humans remain unknown and will need to be evaluated in future studies.

With support from Berkeley’s life sciences entrepreneurship ecosystem, including Nucleate and Berkeley SkyDeck, the researchers have created a company called ReRx Therapeutics to help move the research toward potential use in patients.

The research was funded through discretionary funds from UC Berkeley, with additional assistance from the UCSF Liver Center and the University of Michigan Animal Phenotyping Core.

Additional authors include Chi Zhu, Melissa A. Boldridge, Rachelle L. Stark, Lei Xu, Federico Gonzalez, Xin Tang, Kaitlyn T. Dang and Kook Son of Berkeley; Gracia Bonilla, Kashish Chetal and Ruslan I. Sadreyev of Massachusetts General Hospital; Kosuke Watari and Michael Karin of the University of California, San Diego; Christina Papa and Bilal N. Sheikh of the Helmholtz Center Munich; Prabha Ibrahim of ReRx Therapeutics.