Pilz, L. K. | bioRxiv Science

bioRxiv2025

Hair test reveals plasticity of human chronotype

Circadian clocks govern daily physiological and behavioral processes and are crucial for health, yet disruptions can lead to various diseases. Chronotype, the state of circadian timing, varies between individuals and is reflected in behaviors such as sleep-wake patterns, cognitive performance, and physical activity. This interindividual variability is influenced by both genetic factors and environmental cues, but the relative contributions of each remain unclear, particularly in terms of plasticity - how much chronotype can shift in response to lifestyle and environmental factors. The gold standard for chronotype assessment, dim-light melatonin onset (DLMO), is invasive and impractical for large-scale studies, while blood-based molecular biomarker tests, which estimate internal time, show promise but are limited by practicality. Here, we introduce HairTime, a novel assay that estimates chronotype from a single hair sample collected at one point during the day. HairTime was developed and evaluated in two studies: a training study and a validation study, where it demonstrated a strong ability to predict chronotype, with DLMO as the comparison. This non-invasive method is suitable for large-scale, longitudinal studies and clinical practice. We assessed HairTime using over 4,000 samples, observing a normal distribution of chronotype across the population, with its estimation associating with age, sex, and notably, work schedules. The association with work schedules reveals the plasticity of chronotype, as workdays circadian timing earlier, highlighting that societal factors can influence and modify an individuals internal rhythm. Additionally, we explore the concept of circadian amplitude, finding that lower amplitude rhythms in hair follicle cells are linked to reduced chronotype prediction accuracy. Our results highlight that both intrinsic circadian mechanisms and external factors, such as lifestyle and work schedules, shape chronotype. HairTime offers an innovative tool for understanding circadian rhythms, facilitating personalized chronotherapy to improve health outcomes by aligning treatments with an individuals biological rhythms.

systems biology↗

bioRxiv2021

Validation of the Munich Actimetry Sleep Detection Algorithm for estimating sleep-wake patterns from activity recordings

Periods of sleep and wakefulness can be estimated from wrist-locomotor activity recordings via algorithms that identify periods of relative activity and inactivity. Here, we evaluated the performance of our Munich Actimetry Sleep Detection Algorithm (MASDA). MASDA uses a moving 24-hour-threshold and correlation procedure estimating relatively consolidated periods of sleep and wake. MASDA was validated against sleep logs and polysomnography. Sleep-log validation was performed on 2 field samples collected over 54 and 34 days (median) in 34 adolescents and 28 young adults. Polysomnographic validation was performed on a clinical sample of 23 individuals undergoing 1 night of polysomnography. Epoch-by-epoch analyses were conducted and comparisons of sleep measures via Bland-Altman plots and correlations. Compared with sleep logs, MASDA classified sleep with a median sensitivity of 80% (IQR = 75-86%) and specificity of 91% (87-92%). Mean onset and offset times were highly correlated (r = 0.86-0.91). Compared with polysomnography, MASDA reached a median sensitivity of 92% (85-100%), but low specificity of 33% (10-98%), owing to the low frequency of wake episodes in the nighttime polysomnographic recordings. MASDA overestimated sleep onset (~21 min) and underestimated wake after sleep onset (~26 min), while not performing systematically different from polysomnography in other sleep parameters. These results demonstrate the validity of MASDA to faithfully estimate sleep-wake patterns in field studies. With its good performance across day- and nighttime, it enables analyses of sleep-wake patterns in long recordings performed to assess circadian and sleep regularity and is therefore an excellent objective alternative to sleep logs in field settings.

neuroscience↗