The study’s central innovation lies in its co-twin comparison design. Rather than simply comparing unrelated individuals, the researchers examined pairs of twins raised in the same household where one twin used screens considerably more than the other. Because identical twins share essentially all of their genes and both identical and fraternal twins share much of their family environment, differences in mental health outcomes between twins within the same pair cannot easily be attributed to those shared factors. When the researchers applied this within-family comparison, most of the observed links between screen use and mental health problems weakened considerably, particularly among identical twins, a pattern that points strongly toward genetic and environmental influences shared within families.

The clearest associations in the conventional, between-family analyses emerged during adolescence and for interactive screen activities such as gaming, chatting and surfing the internet. In contrast, the links for passive activities such as watching television or videos were significantly weaker or absent altogether. This distinction between active and passive engagement is one of the study’s most striking findings, and it complicates the simple picture of screen time as a uniform risk factor. Fifteen-year-olds who spent more than six hours a day on interactive screen activities showed more symptoms of depression or anxiety and were more than twice as likely to be diagnosed with such a condition later in life, compared with peers who spent no more than two hours a day using screens.

Those headline numbers, however, tell only part of the story. When the researchers compared twins within the same family, the strength of these associations dropped markedly. The correlations did not disappear entirely, but they became significantly weaker, suggesting that both genes and upbringing play an important role in shaping the apparent relationship. According to Tong Gong, research specialist at the Department of Medical Epidemiology and Biostatistics at Karolinska Institutet and the study’s corresponding author alongside senior researcher Lu Yi, the results indicate that the association may in some cases have been overestimated because it has not been possible to fully account for shared family factors in earlier research.

The implications of this methodological insight extend well beyond the specific question of screens. Much of the existing literature on digital technology and youth mental health relies on observational comparisons between unrelated individuals, which are vulnerable to confounding by any factor that differs across families. Genetic predispositions to both heavy screen use and internalising problems, parenting styles, socioeconomic conditions and household routines could all contribute to a spurious association. Twin designs, by holding these shared influences constant, provide a natural experiment that can separate within-person effects from between-family differences. The attenuation observed in this study, especially among monozygotic pairs, suggests that a meaningful share of the reported screen-mental health connection in the broader literature may reflect such confounding.

At the same time, the researchers are careful to emphasise that the findings do not mean screen time is irrelevant to mental health. Interactive screen activities during adolescence were still linked to an increased risk of mental health problems later in life, although the uncertainty around the estimates increased when shared family factors were included in the analyses. In other words, the data cannot rule out a genuine effect of heavy interactive use; they simply show that the effect, if it exists, is smaller and harder to detect than raw population-level comparisons imply. For a field that has produced widely varying estimates of harm, this nuance matters. It shifts the scientific conversation from whether screens are good or bad toward a more precise question of which activities, in which contexts, and for which individuals, may carry risk.

The study also carries important limitations that the authors acknowledge. It is based on self-reported data on screen use, which does not capture exactly what young people were doing on their screens or why they were using them. Self-reports of media habits are known to be imperfect, and the coarse categories used in the study cannot distinguish, for example, between social gaming with friends and solitary late-night scrolling. Furthermore, the data were collected before social media and smartphones became as integral a part of young people’s everyday lives as they are today, raising questions about how the findings generalise to a generation for whom mobile, algorithmically curated content is the default mode of engagement.

The research team, which included collaborators from Sweden, Norway, Denmark, Germany and Australia, plans to address these gaps in the next phase of work. The researchers intend to examine different types of screen-based activities in greater detail, studying their content, the motivation behind their use, problematic use patterns, and the activities that screen time replaces. By mapping these dimensions, they hope to gain a better understanding of how the complex relationships between digital media and well-being vary across different age groups and between girls and boys. Emma Frans, senior research specialist at the Department of Medical Epidemiology and Biostatistics at Karolinska Institutet and co-author of the study, argues that future recommendations should not focus solely on how much time children and young people spend in front of screens. To be able to give good advice, she notes, we also need to understand what they are doing on their screens, why they are doing it, and which individual and family-related factors may affect their well-being.

For parents, educators and policymakers, the study offers a measured rather than alarmist message. Simple time-based screen limits, the findings suggest, may be a blunt instrument if the true drivers of risk lie in the nature of the activity, the motivation behind it, or pre-existing vulnerabilities that screens merely reflect. A teenager who games intensively may be doing so for reasons that also elevate their risk of depression, and reducing screen hours alone may not address the underlying issue. At the same time, the persistence of some association even within twin pairs leaves open the possibility that heavy interactive use contributes directly to poorer mental health, particularly during the sensitive adolescent period.

The study, titled Recreational Screen Use and Internalising Problems from Preadolescence to Young Adulthood in a Longitudinal Twin Cohort with Co-Twin Comparison, was primarily funded by the Marianne and Marcus Wallenberg Foundation and the Swedish Brain Foundation. The authors note that one co-author, Henrik Larsson, has received research grants, honoraria and consultancy fees from pharmaceutical companies outside the scope of this study, while the other researchers declare no conflicts of interest. As debates over youth mental health and digital technology continue to intensify worldwide, this large-scale twin analysis adds a crucial layer of methodological rigour, reminding researchers and the public alike that correlation between screen time and psychological distress may say as much about families and genes as it does about screens themselves.

Subject of Research: The relationship between recreational screen use and internalising mental health problems in young people examined through a longitudinal twin cohort study

Article Title: Twin study sheds new light on screen time and mental health

Article References: Twin study sheds new light on screen time and mental health. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: screen time, mental health, twin study, adolescence, depression, anxiety, genetics, Karolinska Institutet, Nature Human Behaviour, gaming, longitudinal study, youth well-being

The research team, led by Tong Gong and Yi Lu of Karolinska Institutet, drew on the Child and Adolescent Twin Study in Sweden, a national cohort that has tracked participants at ages 9, 15, 18 and 24. Because the cohort was linked to national patient registers and prescription drug records, the researchers could examine not only self-reported symptoms of depression and anxiety but also clinically diagnosed disorders and antidepressant dispensing over follow-up periods extending more than a decade. This combination of symptom scales and hard clinical endpoints is rare in screen time research, which has often relied on small samples and coarse self-reports.

The headline results were striking at the population level. At ages 15 and 18, screen time showed a clear dose-response pattern with internalizing symptoms: the more hours per day young people spent on screens, the higher their depression and anxiety scores. Comparing heavy users, defined as those spending more than six hours per day, with restrictive users at two hours or less, the standardized differences in symptom scores ranged from 0.20 to 0.38 in the full sample. The associations were markedly stronger in girls than in boys for interactive activities such as gaming, chatting and browsing, with heavy use corresponding to symptom elevations of 0.37 to 0.51 standard deviations in girls, compared with 0.19 to 0.22 in boys.

The clinical picture was even more dramatic. Heavy interactive screen use at age 15 was associated with a roughly two- to threefold increased risk of being diagnosed with a depressive or anxiety disorder by age 28, with weekday heavy use showing an adjusted hazard ratio of 3.37. Even daily gaming at age 9, reported by parents, was linked to a 38 percent higher risk of clinical outcomes through age 22. Passive activities such as watching television, videos and DVDs showed far weaker and mostly non-significant associations, suggesting that what young people do on screens may matter as much as how long they spend there.

But the study’s most consequential findings emerged when the researchers turned to the twin structure of their data. Monozygotic twins share essentially all of their inherited genes, while dizygotic twins share on average half, and both types share their rearing environment. By comparing one twin who used screens heavily with their co-twin who did not, the co-twin control design effectively removes confounding from shared genetics and family background, factors that ordinary observational studies cannot measure. If the screen-mental health link were purely causal, it should persist within identical twin pairs.

It did not, at least not robustly. Within discordant dizygotic pairs, effect estimates attenuated modestly. Within discordant monozygotic pairs, the attenuation was far more pronounced: at age 15, estimates shrank by 32 to 95 percent relative to the full sample and were no longer statistically significant after correction for multiple testing. At age 18, the corresponding estimates were attenuated by 16 to 45 percent. In the longitudinal analyses, the strong cross-age associations between heavy interactive use at 15 and symptoms at 18, and between use at 18 and symptoms at 24, were reduced by roughly 30 to 60 percent within twin pairs and fell below the significance threshold. The pattern of progressive but incomplete attenuation is consistent with a mixture of genetic confounding, shared environmental confounding and possibly a small residual direct effect.

The clinical outcomes told a similar story. The threefold risk elevation associated with heavy weekday interactive use at age 15 dropped by 57 to 59 percent within discordant twin pairs, leaving a point estimate of a 65 percent increased risk that no longer reached significance after multiple-testing correction. Daily gaming at age 9, which showed a significant hazard ratio of 1.38 in the full sample, showed no comparable association within discordant pairs. The authors interpret this cautiously: the analyses had limited statistical power because informative discordant pairs are few, so small direct effects cannot be ruled out entirely, but the evidence for a clear causal effect of screen time on internalizing problems is weak.

The study also probed directionality, a persistent puzzle in this field. Screen use at age 9 did not predict symptoms at 15, but heavy interactive use at 15 and 18 predicted symptoms at 18 and 24 respectively. Intriguingly, the reverse pathway also appeared: adolescents with higher depressive and anxiety symptoms at 15 showed a small but measurable increase in interactive screen use at 18, with an odds ratio of 1.04 per standard deviation of symptoms. Although modest, this reverse association suggests that vulnerable young people may gravitate toward heavier digital engagement, potentially encountering more negative social comparison and mood impacts, a dynamic documented in recent studies of adolescents with mental health conditions.

The findings carry important implications for policy. Public health agencies in several countries have issued guidelines limiting recreational screen time for children and adolescents, but these rest largely on non-causal observational evidence. The new results align with earlier genetically informed work showing that screen behaviors themselves are moderately heritable, with twin-based estimates of 20 to 49 percent, and that genetic confounding accounts for a substantial share of the apparent association between media use and mental health. They also echo the influential analyses by Orben and Przybylski, who found that per-hour effects of screen use on adolescent well-being are small; the co-twin estimates here correspond to roughly 0.03 to 0.05 standard deviations of additional symptoms per extra hour of interactive use at age 18.

Yet the authors caution against dismissing the issue entirely. Within age-18 identical twin pairs, weekend heavy interactive use was still associated with 0.26 standard deviations of additional symptoms compared with restrictive use, an effect size comparable to established risk factors such as bullying victimization and childhood maltreatment, which typically fall in the 0.2 to 0.3 range. The study also has limitations: data collection began in 2004, before smartphones and social media became ubiquitous; the crude categories of interactive and passive use do not capture the complexity of modern digital life; and self-reported screen time is known to be underreported. Sensitivity analyses adjusting for physical activity and bullying victimization left the main findings essentially unchanged. The researchers conclude that heavy recreational screen use remains a legitimate public health concern, particularly during adolescence, but that shared genetic and familial factors partly explain the link to depression and anxiety, and that future research should focus on why and how different types of digital engagement affect vulnerable young people, rather than on screen time alone.

Subject of Research: Longitudinal twin study of recreational screen use and internalizing problems from preadolescence to young adulthood

Article Title: Recreational screen use and internalizing problems from preadolescence to young adulthood in a longitudinal twin cohort with co-twin comparison

Article References: Gong, T., Frans, E., Ohlis, A., Yao, S., Zhang, R., Nilsson, A., Molero, Y., Mosing, M. A., Brikell, I., Larsson, H., Lichtenstein, P., Thorell, L. B., Magnusson, P. K., Kuja-Halkola, R., & Lu, Y. (2026). Recreational screen use and internalizing problems from preadolescence to young adulthood in a longitudinal twin cohort with co-twin comparison. Nature Human Behaviour. https://doi.org/10.1038/s41562-026-02607-0

Image Credits: AI Generated

DOI: 10.1038/s41562-026-02607-0

Keywords: screen time, mental health, depression, anxiety, twin study, co-twin control design, adolescence, genetic confounding, internalizing problems, gaming, Nature Human Behaviour, epidemiology

The study, led by Marianne Nygaard of the University of Southern Denmark together with colleagues at the University of Bristol and other institutions, drew on the Danish GEMINAKAR cohort, which was established between 1997 and 2000 through the population-based Danish Twin Registry. Participants, aged 18 to 67 at baseline, underwent detailed examinations including fasting blood sampling, and were followed up more than a decade later, in 2010 to 2012. From this resource, the team assembled 132 complete monozygotic twin pairs with food frequency questionnaire data, genome-wide DNA methylation measurements, and covariate information at both time points, yielding a dataset of 264 individuals tracked across roughly twelve years of adult life.

The technical machinery of the study reflects the current state of the art in epigenetic aging research. DNA was extracted from whole blood, bisulfite converted, and profiled on Illumina Infinium MethylationEPIC BeadChips, with stringent quality control and fixed effects functional normalization to minimize batch artifacts. From these methylation data, the researchers computed principal component-based versions of four widely used epigenetic clocks, PCHannum, PCHorvath1, PCPhenoAge, and PCGrimAge, along with DunedinPACE, a measure trained on longitudinal data that estimates the pace of aging rather than a static biological age. Age acceleration measures were derived as the residuals of each clock regressed on chronological age, and all outcomes were standardized to allow comparison across clocks.

Dietary intake was assessed with validated semiquantitative food frequency questionnaires covering 192 foods and recipes, originally developed for the Danish Diet, Cancer and Health study. Adherence to national dietary guidance was quantified with the Danish Dietary Index, a score from zero to six built on the official Danish food-based dietary guidelines and the Nordic Nutrient Recommendations. The index rewarded higher intakes of fruits and vegetables, fish, whole grains, and fiber, and penalized high intakes of meat, saturated fat, and added sugar. Each component was scored as the ratio of actual to recommended intake, with a score of one indicating full compliance for that item.

The design’s decisive feature was the co-twin control approach. Monozygotic twins share practically all of their segregating DNA and, in most cases, a highly similar childhood environment. When twins within a pair differ in their diet scores, those differences cannot be explained by their genes or by the family they grew up in. Comparing individuals across the whole sample captures both the effect of diet and everything else that makes healthy eaters different, including inherited tendencies. Comparing twins within pairs strips away the familial confounding. Because both dietary habits and epigenetic aging are themselves partly heritable, the authors argued that genetic confounding was a plausible and testable threat to the observational literature.

The initial results looked reassuringly familiar. In cross-sectional, individual-level analyses at baseline, every beta coefficient was negative, meaning that people who adhered more closely to the dietary guidelines showed decelerated epigenetic aging. The signal was strongest for the second-generation clocks: each one-unit increase in the Danish Dietary Index was associated with roughly 0.55 to 0.67 years lower PCGrimAge and a 3.1 percent lower pace of aging on DunedinPACE, corresponding to standardized effect sizes of about 0.16 to 0.23 standard deviations. The same directional tendency appeared in the follow-up data, echoing previous cross-sectional studies that linked higher diet quality to younger epigenetic ages, particularly for clocks trained to predict morbidity and mortality rather than chronological age.

Then the picture changed. When the same association was tested within twin pairs, the strong signals for PCGrimAge and DunedinPACE attenuated dramatically, leaving no robust association between dietary adherence and epigenetic age acceleration. The implication is uncomfortable: the individuals who eat well may simply be people whose genes and upbringing favor both better diets and slower biological aging, rather than people whose diets are actively slowing their clocks. Because the cohort contained only monozygotic pairs, the study could not disentangle genetic confounding from shared environmental confounding, but either way, the familial factors appear sufficient to explain much of the apparent benefit.

The longitudinal analyses added a further twist. When the researchers modeled the change in diet score from baseline to follow-up against the change in epigenetic age measures, the estimates were primarily positive, unexpectedly suggesting that improvements in dietary adherence were associated with increased, not decreased, age acceleration, although the confidence intervals were wide and the authors caution against overinterpreting this pattern. Subgroup analyses that might have clarified the result, for example comparing people who stayed at low diet scores with those who improved from low to high, were impossible because the relevant subgroups contained too few participants, around seventeen individuals, to support meaningful inference.

The authors are careful about what their findings do and do not show. The study remains observational, so reverse causality and confounding by non-shared factors cannot be excluded. Blood cell composition was deliberately not adjusted for, since doing so could remove part of the biological signal of interest, meaning that variation in cell mixture may contribute to the results. Dietary data were self-reported and retrospectively collected, which may attenuate estimates, particularly within twin pairs where the differences between co-twins are small and measurement error from both twins compounds. The sample, drawn from a cohort selected to be free of diabetes and cardiovascular disease at baseline, is healthier than the general population and relatively small, limiting statistical power and generalizability. Nearly half of eligible monozygotic pairs were excluded for missing data, although participants and non-participants were broadly comparable.

Even so, the study’s strengths are substantial, and its conclusion is a sobering one for the epigenetic anti-aging boom. No strong independent association between dietary adherence and epigenetic aging survived the twin comparison, and the authors conclude that apparent benefits of healthy eating on epigenetic aging may partly reflect genetic and shared environmental factors rather than diet itself. This does not mean diet is irrelevant to aging; randomized trials of dietary interventions have reported slowing of Horvath clock and GrimAge measures, and the broader evidence that high dietary quality reduces cardiovascular disease, cancer, and all-cause mortality remains intact. What the twin data challenge is the specific causal story written into methylation-based age estimates. As epigenetic clocks move from research labs toward commercial wellness products, the Danish twins offer a reminder that correlation, even molecular correlation measured across hundreds of thousands of CpG sites, is not automatically causation, and that the family you come from may shape both your plate and your methylation patterns in ways no questionnaire can fully untangle.

Subject of Research: The association between adherence to Danish dietary guidelines and epigenetic aging measures in monozygotic twins

Article Title: Dietary pattern and epigenetic aging: a longitudinal twin study

Article References: Nygaard, M., Elliott, H. R., Smith, G. D., Relton, C., Kyvik, K. O., & Dalgård, C. (2026). Dietary pattern and epigenetic aging: a longitudinal twin study. GeroScience. https://doi.org/10.1007/s11357-026-02550-y

Image Credits: AI Generated

DOI: 10.1007/s11357-026-02550-y

Keywords: epigenetic aging, DNA methylation, epigenetic clocks, twin study, dietary patterns, Danish Dietary Index, DunedinPACE, GrimAge, co-twin control design, confounding, GeroScience, longitudinal study

The research, led by a team at Peking University working with the Chinese National Twin Registry, took a two-pronged approach. On one side, the investigators analyzed twin data from 2,548 individuals, comparing identical twins, who share essentially all of their DNA, with fraternal twins, who share roughly half. On the other side, they mined GWAS summary statistics from more than 92,615 participants in Asian cohorts. By estimating heritability through both classical twin modeling and modern SNP-based methods, they could directly compare the two traditions and quantify exactly where the numbers diverge.

The traits under scrutiny were ten of the most clinically important cardiometabolic measures: body mass index (BMI), waist-to-hip ratio (WHR), hemoglobin A1c (HbA1c), fasting blood glucose (FBG), systolic and diastolic blood pressure (SBP and DBP), total cholesterol (TC), triglycerides (TG), low-density lipoprotein cholesterol (LDL-C), and high-density lipoprotein cholesterol (HDL-C). Together, these markers capture the biological landscape of obesity, glucose metabolism, blood pressure regulation, and blood lipid profiles—the very factors that drive the world’s leading causes of death.

The headline result is striking. Twin-based analyses yielded moderate to high heritability estimates, ranging from 0.34 to 0.72 across the ten traits, meaning that between a third and nearly three-quarters of the variation in these measures could be attributed to genetic differences. GWAS-based estimates, by contrast, came in far lower, between 0.10 and 0.22. This confirms, in an Asian population, the same pattern long observed in European cohorts: twin studies see more heritability than GWAS can account for, leaving a substantial residue that must be explained by other mechanisms.

One leading candidate for the missing heritability has been non-additive genetic effects—particularly dominance effects, where the combined action of two alleles at a locus produces an effect that simple additive models cannot capture. To test this, the team built structural equation models that decompose trait variance into additive genetic, dominance genetic, shared environmental, and unique environmental components. The verdict was unambiguous: dominance genetic variance was statistically significant only for BMI, with an estimated dominance component of 0.42, and no evidence of non-additive effects appeared anywhere in the GWAS summary statistics. For most cardiometabolic traits, the dominant role of simple additive inheritance stands, and the missing heritability cannot be pinned on gene-gene interactions of this kind.

That leaves gene-environment interactions as a prime suspect, and here the study delivered some of its most intriguing findings. Using variance components models that allow genetic and environmental influences to vary across levels of environmental exposures, the researchers identified twelve small but statistically significant interaction effects. These involved age, smoking, alcohol consumption, education, and mental health status—factors that modulate how strongly genetic variants express themselves in measurable traits. In other words, the same set of genes may push blood pressure or blood sugar to different degrees depending on whether a person smokes, drinks, has reached a certain age, or carries psychological stress. The effect sizes were modest, but the pattern is consistent with the idea that genes and environment do not operate in separate silos; they collaborate, and that collaboration quietly shapes the heritability we observe.

The study also mapped the genetic correlations between pairs of cardiometabolic traits, asking whether the same genes influence more than one measure. The answers ranged from low to high, spanning 0.09 to 0.90, and—crucially—the patterns estimated from twin data broadly matched those derived from GWAS. This convergence matters. It suggests that the discrepancy between twin and GWAS heritability estimates does not reflect fundamentally different genetic pictures, but rather differences in what each method can detect. Twin models capture all inherited variation, including rare variants and effects in genomic regions poorly tagged by genotyping arrays, while GWAS-based estimates reflect only the additive effects of common SNPs that are well captured in the data.

There is also a broader significance to the population in which this work was conducted. The vast majority of large-scale genetic studies have been performed in populations of European ancestry, and researchers have warned repeatedly that findings from these studies do not always translate to other groups. Differences in allele frequencies, linkage disequilibrium patterns, and environmental contexts can all alter how genetic risk manifests. By grounding both the twin analyses and the GWAS heritability estimates in Asian populations, this study helps close a critical gap in the global picture of cardiometabolic genetics, providing evidence that the fundamental genetic architecture—high twin heritability, modest SNP heritability, minimal dominance, and pervasive gene-environment interplay—holds across ancestries.

Methodologically, the work is a showcase of integration. The twin component relied on classical structural equation modeling of monozygotic and dizygotic pairs, with zygosity confirmed through questionnaire-based assessment validated by methylation array data. The GWAS component drew on publicly available summary statistics from Asian cohorts, applying SNP-based heritability methods that estimate the variance explained by common variants through their correlations with the trait. By running both approaches on the same ten traits and comparing their outputs side by side, the team created a natural experiment in which each method could serve as a check and complement to the other.

For clinicians and public health researchers, the implications are tangible. The confirmation that BMI carries a substantial dominance component suggests that some obesity risk is inherited in ways that current additive polygenic models may underrepresent, potentially affecting the accuracy of genetic risk scores. The dozen identified gene-environment interactions point to modifiable levers—smoking cessation, alcohol moderation, mental health support, and attention to age-related changes—that could blunt genetic susceptibility. And the finding that genetic correlations between cardiometabolic traits are broadly consistent across methods strengthens confidence that pleiotropy, the phenomenon of one gene influencing multiple traits, is real and quantifiable. As the era of precision medicine accelerates, studies like this one provide the calibrated foundation on which genetic prediction, prevention, and treatment strategies must ultimately rest. The mystery of missing heritability is not solved in a single paper, but by systematically ruling out dominance effects, spotlighting gene-environment interactions, and demonstrating cross-method consistency in an understudied population, this work takes a substantial and welcome step toward that goal.

Subject of Research: Estimation of heritability and gene-environment interactions for cardiometabolic traits by integrating twin and GWAS data in Asian populations

Article Title: Revealing genetic foundations underlying cardiometabolic traits: integrating twin and GWAS data in Asian populations

Article References: Hong, X., Li, M., Cao, W., Lv, J., Yu, C., Huang, T., Sun, D., Liao, C., Pang, Y., Hu, R., Gao, R., Yu, M., Zhou, J., Wu, X., Liu, Y., Yin, S., Gao, W., & Li, L. (2026). Revealing genetic foundations underlying cardiometabolic traits: integrating twin and GWAS data in Asian populations. International Journal of Obesity. https://doi.org/10.1038/s41366-026-02209-w

Image Credits: AI Generated

DOI: 10.1038/s41366-026-02209-w

Keywords: heritability, twin study, GWAS, cardiometabolic traits, missing heritability, gene-environment interaction, BMI, blood pressure, lipids, genetic correlation, Chinese National Twin Registry, Asian populations