Emerging research on circadian misalignment and cardiometabolic health of adolescents
Bibliographic record
Abstract
The recent original study by Morales-Ghinaglia et al. [1] reports a novel finding regarding the association between circadian misalignment and cardiometabolic health in adolescents. Among adolescents with circadian misalignment (represented as actigraphic assessment of sleep midpoint, variability of sleep midpoint, and social jetlag, a misalignment of sleep timing between school nights and weekends), there was a stronger positive association between visceral adiposity and metabolic syndrome than among adolescents without circadian misalignment. In other words, circadian misalignment exacerbated the association between visceral adiposity and metabolic syndrome in this adolescent sample. These results provide a clear message to adolescents, caregivers, clinicians, and policymakers: beyond the well-known benefits of sufficient sleep duration for a broad range of health outcomes, sleep timing and regularity may also influence adolescent health and well-being. Furthermore, adolescents with obesity or metabolic syndrome should be screened for circadian misalignment as a potential modifiable target for interventions that seek to improve cardiometabolic health. The public health research implications of the current findings strengthen and advance three emerging trends in the field of sleep health: (1) the integration of several sleep health measures into cardiovascular disease (CVD) research, (2) a heightened focus on sleep regularity as a distinct component of sleep health, and (3) recognition from policymakers about the structural determinants of irregular sleep timing (e.g. early school start times) and the potential harms they carry for adolescent sleep and physical health. In 2022, the American Heart Association (AHA) updated and expanded their “Life’s Simple 7” paradigm of cardiovascular disease risk factors to the “Essential 8” [2] to include sleep duration. While the literature on the links between sleep health and cardiovascular risk spans many decades, the incorporation of sleep into the AHA recommendations marks a wider scientific acceptance of the importance of sleep health coinciding with related public health messages, primarily in adults [2]. Given the population most affected by CVD, often the samples studied are older adults, such as the Multi-Ethnic Study of Atherosclerosis (mean age = 69 years), which found that the inclusion of sleep health measures in a CVD risk score improved a predictive association with incident CVD risk [3]. The Morales-Ghinaglia study [1] bolsters this finding that sleep health is associated with cardiovascular health, but in a much younger population (mean age = 16 years). In addition, the findings demonstrate that sleep and circadian health are not only directly related to cardiovascular outcomes, but also interact with other factors such as visceral adiposity. Continued research on the multiple dimensions of sleep health that contribute to cardiovascular and metabolic health across the lifespan is essential to future sleep health research to allow for early interventions. In 2023, the National Sleep Foundation published an expert consensus panel report emphasizing the value of sleep regularity for well-being [4]. Using a systematic literature review informed by 63 scientific studies, the expert panelists reached affirmative consensus on the statement that daily regularity in sleep timing is important for health and performance. They also agreed that when sleep duration is insufficient during weekdays (or school/work days), obtaining catchup sleep on weekends (or free days) is important for health. The Morales-Ghinaglia et al. study [1] extends this contribution regarding the potential effects of sleep regularity on physical health to include adolescents. As with the AHA decision to include sleep in the Essential 8, the studies reviewed by the consensus panel on sleep regularity and health were primarily conducted among adults. More research on sleep regularity is necessary among adolescent populations given the high rates of circadian phase delays combined with early school start times that exacerbate sleep irregularity, such as social jetlag [5]. Indeed, one study in a large national sample of diverse adolescents demonstrated an average level of social jetlag of 2.8 hours in adolescents aged 15 [6]. Furthermore, recent findings from the same sample show that social jetlag is associated with more anxious symptoms, depressive symptoms in females, and less healthy eating behaviors [6–8]. In addition, in this same sample of adolescents, actigraphic sleep variability was associated with less frequent breakfast consumption [9], which may increase the risk for poor metabolic health [10]. A third implication of the present findings is that structural changes, such as later school start times, are necessary for addressing poor sleep health and its negative health impacts on adolescents. Based on over 30 years of research, numerous medical organizations support a school start time of 08:30 am or later in secondary school for optimal health and functioning of adolescents [5]. In recent years, California and Florida have both passed legislation mandating a school start time of 08:30 am or later, and many other states within the United States are considering related policies. When schools have later school start times, not only do students get longer sleep duration during the week, they also have reduced social jetlag. The findings by Morales-Ghinaglia et al. [1] suggest that early school start times, which lead to increased sleep irregularity and social jetlag through a mismatch between school- and weekend-night sleep timing, may predispose certain adolescents to higher risk of metabolic syndrome than those who attend schools with later start times more aligned with their endogenous circadian rhythms. The study [1] of this relatively large sample of adolescents in the Pennsylvania State Cohort has many of the hallmarks of a well-conducted observational study. Specifically, the study measured sleep using wrist actigraphy and examined five established metabolic syndrome components (i.e. waist circumference, mean arterial pressure, homeostasis model assessment of insulin resistance, triglycerides, and high-density lipoprotein). Despite the many strengths of this paper, there are some limitations that provide an opportunity to discuss areas of future research. For example, the study was unable to account for sociodemographic, family, and contextual factors that may confound associations between circadian misalignment and cardiometabolic health. For example, what is happening in the homes of adolescents with later bedtimes, more irregular sleep, and poorer cardiometabolic health? Do household and contextual factors such as financial security, physical safety, pervasive discrimination, housing instability, family conflict, and heightened vigilance play a role in interrupting sleep routines, and are these stressors also contributing to both visceral adiposity and metabolic outcomes? In addition, while this observational study is well designed, it did not investigate the physiological mechanisms that may have contributed to the observed associations or the longitudinal associations of visceral adiposity and circadian misalignment with metabolic syndrome. Future research should examine the predictors and underlying pathways that contribute to metabolic syndrome. With an enhanced understanding of both the contextual and physiological mechanisms, researchers can better identify intervention targets. Overall, this high-impact study highlights the need for adolescent sleep health research to embrace circadian factors, such as later sleep midpoint, sleep irregularity, and social jetlag, that may contribute to poor health and well-being. From a policy and family-systems perspective, a thorough consideration of the social and contextual factors that affect sleep timing, as well as the biological mechanisms involved may help with the development of policies, recommendations, interventions, and behaviors that could help improve sleep health and overall adolescent well-being. Financial Disclosures: Both authors are partially supported by the Eunice Kennedy Shriver National Institute of Child Health and Human Development (R01HD073352) and the Della Pietra Family Foundation. LH has received consulting fees from Idorsia Pharmaceuticals and honoraria/travel support for lectures and consulting supported by the University of Miami, Auburn University, Baylor University, Harvard University, New York University, Columbia University/Princeton University, and the National Sleep Foundation. GMM has received honoraria from the National Sleep Foundation. Nonfinancial Disclosures: none.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".