The research team, led by Dr. Judith Carroll, associate professor of psychiatry at UCLA Health, set out to test whether treating a common sleep disorder could change something as fundamental as the speed at which the body ages. Insomnia is remarkably prevalent later in life: between 10 and 25 percent of adults over 65 in the United States are estimated to have clinical insomnia, a disorder characterized by persistent difficulty falling or staying asleep that impairs daytime functioning. Earlier observational studies had already linked insomnia to advanced biological aging, as measured by changes in DNA methylation, and to elevated risks of age-related diseases including cardiovascular disease, diabetes, and mortality. What remained unknown was whether the relationship was causal in a treatable direction, whether fixing the insomnia might actually slow the aging clock.

To address that question, the investigators conducted a randomized controlled trial in which nearly 100 older adults with insomnia were assigned to one of two interventions. The first was cognitive behavioral therapy for insomnia, often abbreviated CBT-I, which is considered the gold-standard, first-line treatment for the disorder. CBT-I is a structured, multi-session program delivered with a trained clinician, designed to identify and change the specific thoughts and behaviors that perpetuate sleeplessness, such as irregular sleep schedules, conditioned arousal in bed, and catastrophic beliefs about the consequences of a bad night. The second arm was sleep education therapy, an active control condition that provides general informational content about sleep and healthy sleep habits but omits the individualized behavioral techniques that define CBT-I. Comparing the two groups allowed the researchers to separate the specific effects of behavioral insomnia treatment from the benefits of simply learning more about sleep.

The biological readout of the trial came from blood. Samples were drawn before the eight-week treatment period began and again at follow-up visits over the following two years. From these samples, researchers extracted DNA and applied three widely used aging calculators, known as epigenetic clocks: DunedinPACE, GrimAge, and PC-PhenoAge. These tools rest on DNA methylation, a chemical modification in which small methyl groups are attached to specific positions in the genome, influencing whether genes are switched on or off. Methylation patterns shift in characteristic ways as people age, and statistical models trained on large datasets can translate a person’s methylation profile into an estimate of biological age or, as in this study, a pace of aging measured across time. Because the same participants were followed longitudinally, the clocks could capture changes within individuals rather than merely comparing different people.

The results were notable on two fronts. First, participants who underwent cognitive behavioral therapy had a nearly three-fold greater likelihood of achieving insomnia remission compared with those who received sleep education alone, reinforcing the established clinical superiority of CBT-I for older adults. Second, and more novel, insomnia treatment significantly slowed the pace of biological aging as measured by the DunedinPACE epigenetic clock, one of the three calculators tested. DunedinPACE is designed to quantify how fast a person is aging right now, rather than how old they appear, making it particularly sensitive to changes in the rate of biological decline over a study period. The fact that a behavioral intervention, delivered over just eight weeks, produced a detectable difference in this measure over two years of follow-up is the core of the study’s significance.

Dr. Carroll framed the implications in terms that reach well beyond sleep medicine. Insomnia in older adults, she noted, is often treated as a quality-of-life issue, but the findings suggest it may also be a modifiable risk factor for how the body ages. She drew an analogy to established preventive counseling: just as clinicians advise patients on physical activity and diet to support healthy aging, the study points to sleep as another important behavior worth targeting to improve long-term health. If replicated, that framing could elevate insomnia treatment from an optional comfort measure to a component of preventive care for aging populations, with potential downstream effects on the burden of cardiovascular disease, metabolic disease, and other conditions linked to accelerated biological aging.

The study’s authors are careful to spell out its limitations, and those caveats matter for how the findings should be interpreted. Participants were recruited from a single site in Los Angeles, California, which raises questions about generalizability. The authors state that a larger, multisite study with more diverse participants in terms of age, ethnicity, and race is needed to verify the results. They also note that more research is required to determine whether treatment response differs by sex, and whether other insomnia treatment options, such as medication or Tai Chi, could produce similar effects on the pace of aging. In other words, the trial establishes that CBT-I can slow an epigenetic measure of aging in this population, but it does not yet establish which patients benefit most, or whether alternative therapies confer the same biological advantage.

For readers wondering what DNA methylation has to do with sleep, the connection is part of a broader scientific effort to quantify aging at the molecular level. Epigenetic clocks like GrimAge and PC-PhenoAge were developed by training algorithms on methylation data from thousands of people, teaching them to predict chronological age or age-related health outcomes from chemical signatures scattered across the genome. DunedinPACE, developed using data from the long-running Dunedin birth cohort, estimates the pace of aging by tracking how quickly these signatures change. Studies have repeatedly shown that lifestyle factors, stress, and disease can shift these measures, and that faster pace scores predict earlier onset of chronic illness and mortality. The UCLA trial adds an important experimental element to this literature: rather than observing that insomniacs age faster, it tested whether treating the insomnia changes the trajectory, and found that it does, at least on one of the three clocks examined.

It is also worth emphasizing what the study does not claim. The researchers measured epigenetic aging over a two-year window; they did not report that treated participants lived longer or avoided disease, outcomes that would require much longer follow-up. The effect was observed on DunedinPACE but not highlighted for GrimAge or PC-PhenoAge, a pattern that underscores how sensitive these conclusions can be to the choice of clock. And as a secondary analysis from a randomized controlled trial, the aging findings, while compelling, are best viewed as hypothesis-generating until confirmed in larger and more diverse cohorts. The authors declared no competing interests, and the trial’s randomized design, longitudinal blood sampling, and use of validated epigenetic instruments give the result a methodological strength that much of the observational sleep-aging literature lacks.

Even with those qualifications, the message for the estimated millions of older adults living with untreated insomnia is concrete and actionable. An effective, widely available, medication-free treatment already exists, and the new evidence suggests its benefits may extend beyond better nights into the biology of aging itself. As the population over 65 grows, interventions that are inexpensive, scalable, and free of pharmacological side effects carry obvious public health appeal. The UCLA findings will now need to be tested at scale, but they add sleep to a short list of everyday behaviors, alongside diet and exercise, for which rigorous trial data suggest a direct link to the pace at which our bodies grow old.

Subject of Research: Cognitive behavioral therapy for insomnia and epigenetic measures of biological aging in older adults

Article Title: Cognitive behavioral therapy may reduce biological aging among older adults with insomnia

Article References: Cognitive behavioral therapy may reduce biological aging among older adults with insomnia. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: insomnia, cognitive behavioral therapy, biological aging, epigenetic clocks, DNA methylation, older adults, DunedinPACE, sleep health, randomized controlled trial, The Lancet Healthy Longevity, UCLA Health, healthy longevity

The research team, led by Yunxiang Wan of the Max Planck Institute for Demographic Research in Rostock, together with Marwân-al-Qays Bousmah and Marília R. Nepomuceno, drew on the Chinese Longitudinal Healthy Longevity Survey, one of the largest ongoing studies of its kind in the world. Covering the period from 2002 to 2018, the survey follows thousands of older Chinese adults across repeated interviews, recording not only whether they survive from one wave to the next but how they assess their own lives. The researchers used this rich dataset to estimate what demographers call happy life expectancy: the expected number of years a person at a given age will live, and of those, how many will be spent in a state of happiness.

Technically, the study employs a cohort-based multistate life table method, an approach that deserves unpacking because it represents a methodological advance over most previous work. Traditional healthy life expectancy calculations typically rely on period measures, blending mortality rates and happiness prevalence from a single point in time as if today’s older population would experience tomorrow’s conditions. The multistate framework, by contrast, treats life as a set of transitions: individuals can move between a happy state and an unhappy state, and eventually to death, with probabilities estimated from the observed data. By structuring the analysis around birth cohorts, the researchers could compare people born roughly ten years apart as they moved through the same age ranges, isolating genuine generational change from the noise of period fluctuations.

Because the survey data do not follow each cohort from birth to death, the authors estimated what they term partial-cohort happy life expectancies, covering specific age ranges rather than full lifespans. This is a careful and honest solution to a common data limitation in longitudinal ageing studies. The core comparison pits paired cohorts against each other: for each age range examined, the experience of the earlier-born cohort is set against that of the later-born cohort a decade younger. If the later cohort spends a larger fraction of its expected years happy, that is evidence of real generational improvement rather than a statistical artifact of comparing different ages at different calendar times.

The headline finding is striking. Across all the age ranges examined, later-born cohorts of older Chinese adults showed statistically significant increases in the proportion of life spent happy. Crucially, the mechanism behind this improvement matters. The gains came primarily from an expansion in the absolute number of happy years, while total life expectancy itself remained largely stable across cohorts. In other words, these generations were not living dramatically longer, but they were living happier within roughly the same span of years. This combination, more happy years without more total years, is precisely what researchers call compression of unhappiness: the unhappy portion of life is being squeezed into a smaller and smaller window, typically pushed toward the very end of life.

The concept of compression is central to modern longevity science. For decades, researchers have debated whether added years of life come bundled with added years of illness and dependency, a scenario known as expansion of morbidity, or whether the burden of poor health is compressed into a shorter terminal period. Evidence has been mixed across countries and across measures of health. By shifting the outcome from physical health to subjective wellbeing, this study adds a new dimension to that debate. A society can, in principle, compress disability while leaving misery intact, or extend physical health while emotional suffering lingers. The Chinese evidence suggests that, at the population level, the emotional dimension has been improving in the favorable direction, with unhappiness increasingly concentrated at the end of life rather than spread across the older years.

But the study’s second major finding tempers any celebratory reading. The cohort improvements were unevenly distributed across the population in ways that mirror, and in some cases amplify, existing social inequalities. Women experienced larger gains than men in both absolute and relative terms. This is notable because women in many societies report lower levels of subjective wellbeing than men despite living longer, a paradox sometimes attributed to widowhood, disability, and economic insecurity in old age. In China, the analysis found that women’s gains were large enough to reverse an initial disadvantage in happy life expectancy, meaning that earlier cohorts of women could expect fewer happy years than men, while later cohorts overtook them. For a country where older women have historically faced structural disadvantages, this reversal is a significant demographic shift.

The socioeconomic gradient is equally consequential. Urban residents and those with formal education saw greater improvements in happy longevity than their rural and less-educated counterparts. This pattern suggests that the benefits of China’s rapid economic development over the study period, which encompassed extraordinary growth in incomes, pensions, healthcare coverage, and infrastructure, flowed disproportionately to groups already better positioned to take advantage of them. Rural older adults, who often lack adequate pension coverage and depend on family support or continued labor, and those who never entered the formal education system, appear to have been left behind in the happiness transition. The result is a widening gap in happy life expectancy between social groups, a trend that echoes findings on healthy life expectancy in many countries but is especially sharp given the pace of change in China.

These findings arrive at a critical policy moment. China’s population is ageing faster than almost any in history, driven by decades of low fertility and rising longevity, and the country’s old-age support systems are under mounting strain. If the additional years older Chinese people are living were years of misery, the fiscal and social costs of ageing would be compounded by an epidemic of late-life suffering. The compression of unhappiness documented here offers a more optimistic baseline: the ageing of the population is not, on current trends, synonymous with a growing mass of unhappy elders. Yet the widening disparities complicate that picture. A national average that conceals a growing divide between urban educated women thriving in their later years and rural uneducated men struggling through them is a fragile foundation for policy planning. Pension adequacy, rural healthcare access, and educational expansion all emerge, implicitly, as levers of happy longevity, not merely of economic policy.

The study also carries a broader methodological message for the international research community. Measuring happy life expectancy at all remains rare, and doing so with a cohort perspective is rarer still. Most national statistics systems track life expectancy with great precision but treat subjective wellbeing as a soft afterthought, if they track it at all. This analysis demonstrates that wellbeing can be integrated rigorously into the demographic toolkit, yielding estimates that are comparable across groups and over time. As other societies with rapidly ageing populations, from Japan to Italy to the United States, confront the same question of whether longer lives are better lives, the Chinese experience documented by Wan and colleagues offers both a template for measurement and a cautionary tale: happiness in old age can improve generation after generation, but without deliberate attention to equity, the happy years may belong mostly to those who need them least.

Subject of Research: Trends in happy life expectancy and socioeconomic inequality among older adults in China, 2002–2018

Article Title: Are older adults in China living longer happy years? A cohort-based multistate analysis, 2002–2018

Article References: Wan, Y., Bousmah, M.-A.-Q., & Nepomuceno, M. R. (2026). Are older adults in China living longer happy years? A cohort-based multistate analysis, 2002–2018. Genus. https://doi.org/10.1186/s41118-026-00298-z

Image Credits: AI Generated

DOI: 10.1186/s41118-026-00298-z

Keywords: happy life expectancy, population ageing, China, cohort analysis, multistate life table, compression of morbidity, subjective wellbeing, socioeconomic inequality, older adults, gender differences, longitudinal survey, demography

The international research team, drawing on data from Taiwan and Japan, has developed and validated the first population-based prediction equations for estimating appendicular skeletal muscle mass, or ASM, specifically calibrated for Asian bodies. The work is notable not only for its scale, encompassing 13,582 adults across three major cohorts, but also for its practical payoff: the best-performing equation estimates a person’s arm and leg muscle mass using nothing more than age, sex, height and weight. No scanner, no blood draw, no specialized equipment. For the vast majority of community clinics in Asia, where dual-energy X-ray absorptiometry (DXA) machines and bioelectrical impedance devices remain scarce, that simplicity could transform sarcopenia screening from an occasional hospital referral into a routine part of every health check-up.

The diagnostic challenge the study addresses is rooted in how sarcopenia is defined. Both the European Working Group on Sarcopenia in Older People and the Asian Working Group for Sarcopenia place muscle mass at the core of diagnosis. The gold-standard tools, however, are cumbersome. Magnetic resonance imaging and computed tomography distinguish intramuscular fat from contractile tissue with unmatched precision but are far too expensive and inaccessible for population screening. DXA, which measures lean soft tissue in the limbs, is the routine clinical standard but is concentrated in hospitals. Bioelectrical impedance analysis is portable yet suffers from accuracy problems that vary by device, method and ethnicity. Prediction equations derived from the United States National Health and Nutrition Examination Survey (NHANES) had shown excellent performance in Western populations, with R-squared values of 0.89 to 0.91, but the researchers suspected they would falter in Asia.

That suspicion was grounded in well-documented biology. Body composition and anthropometric profiles differ substantially between Asian and Western populations, and even the trajectory of age-related muscle loss differs. Western studies report significant muscle decline after age 50 in both sexes, whereas research from Taiwan, Japan, Korea and Hong Kong shows that Asian women tend to maintain their muscle mass until roughly age 80. Extrapolating Western equations to Asian cohorts, the team reasoned, could introduce systematic errors precisely where diagnostic accuracy matters most.

To test that hypothesis and fill the gap, the investigators designed a rigorous three-phase validation framework. Phase 1 externally validated four existing NHANES-derived equations across three cohorts: the I-Lan Longitudinal Aging Study (ILAS) from Taiwan with 2,780 participants, the Nutrition and Health Survey in Taiwan (NAHSIT) with 6,853 participants, and Japan’s National Institute for Longevity Sciences-Longitudinal Study of Aging (NILS-LSA) with 3,949 participants. In every case, muscle mass had been measured by DXA, defined as the sum of lean soft tissue in all four limbs, and low muscle mass was classified using the AWGS 2019 thresholds of less than 7.0 kilograms per square meter for men and less than 5.4 for women.

The Phase 1 results confirmed both the promise and the limits of the imported equations. Performance varied across cohorts, with R-squared values ranging from a modest 0.52 in ILAS for the weakest equation to a strong 0.89 in NAHSIT for the best. Equation 4 from the NHANES set consistently performed best, achieving the lowest root mean square error in ILAS (1.88) and NAHSIT (1.52), while Equation 6 excelled in the Japanese cohort with an RMSE of 1.45. Diagnostic discrimination was respectable, with areas under the receiver operating characteristic curve reaching 0.91 in men and 0.89 in women in the Taiwanese national survey. But the variability across populations signaled that a one-size-fits-all approach borrowed from Western data was not good enough.

Phase 2 therefore built new Asian-specific equations from scratch using the NAHSIT cohort, the largest and most demographically diverse of the three, which included adults of all ages with 21.2 percent under 40. The team log-transformed candidate variables, including age, sex, height, weight and routine blood and urine markers, and used univariate, multivariate and stepwise regression to select predictors. Judged by R-squared, the Akaike information criterion and the Bayesian information criterion, the winning model retained only log age, sex, log height and log weight. The final Equation 4, fitted to the full dataset, takes the form ASM = 0.01926437 × age^(−0.065719) × 0.832924731^(Female) × Weight^(0.698532) × Height^(0.841383), a compact multiplicative formula that any spreadsheet can evaluate.

Its performance was exceptional. Within NAHSIT, the equation achieved an R-squared of 0.90, an RMSE of 1.45 and a median prediction error of just 0.06 kilograms, with no systematic or proportional bias on Bland-Altman analysis. For detecting low muscle mass under AWGS 2019 criteria, it reached an area under the curve of 0.90 in both sexes, with sensitivity of 0.85 and specificity of 0.80 in men and sensitivity of 0.87 and specificity of 0.80 in women. Correlations between estimated and measured skeletal muscle index stood at 0.82 for both sexes. Then came the decisive test: Phase 3 applied the Taiwanese-derived equations to the independent Japanese NILS-LSA cohort, and the models held up. Equations 4 and 5 again achieved AUCs of 0.90 in men and 0.85 in women, with correlations between estimated and actual muscle index ranging from 0.75 to 0.84 and minimal bias, demonstrating genuine cross-population generalizability.

The clinical implications are immediate. Because Equation 4 requires only age, sex, height and weight, general practitioners, nurses and community health workers can screen for low muscle mass using data already collected at every routine check-up, without serum creatinine measurements or imaging. Early identification opens the door to timely referral, resistance exercise programs and nutritional support, the interventions proven to slow functional decline. The stakes are high: although 97.2 percent of Asian healthcare professionals are aware of sarcopenia, only 42.9 percent actually diagnose it in practice, largely because DXA and BIA access is limited. The study’s authors caution that their participants were community-dwelling adults, that the Japanese validation cohort excluded people under 40, and that different DXA systems were used across cohorts, though each model was developed and validated within a single cohort to preserve measurement consistency. Future work should extend validation to other Asian ethnicities and test whether the equations predict hard outcomes over time. For now, the message is clear: four numbers on a clipboard may be enough to catch a silent disease before it steals a person’s strength.

Subject of Research: Development and three-phase validation of population-based prediction equations for appendicular skeletal muscle mass in Asian adults

Article Title: Population‐Based Prediction Equations for Appendicular Skeletal Muscle Mass: A Three‐Phase Validation Study in 13 582 Asian Adults

Article References: Lai, H.-Y., Zhang, S., Chen, I.-T., Otsuka, R., Lee, W.-J., Liang, C.-K., Yen, K.-H., Arai, H., Hsiao, F.-Y., & Chen, L.-K. (2026). Population‐Based Prediction Equations for Appendicular Skeletal Muscle Mass: A Three‐Phase Validation Study in 13 582 Asian Adults. Journal of Cachexia, Sarcopenia and Muscle, 17(5), Article e70360. https://doi.org/10.1002/jcsm.70360

Image Credits: AI Generated

DOI: 10.1002/jcsm.70360

Keywords: sarcopenia, appendicular skeletal muscle mass, prediction equations, DXA, Asian Working Group for Sarcopenia, muscle mass screening, aging, Taiwan, Japan, NHANES, bioelectrical impedance, healthy longevity