Circadian clocks govern daily physiological and behavioral processes and are crucial for health; disruptions can lead to various diseases. The circadian phase of entrainment—the phase of the internal circadian clock in relation to external environmental cycles—is influenced by both genetic and environmental factors, varies between individuals, and is reflected in daily behaviors such as sleep–wake patterns, cognitive performance, and physical activity. While circadian phase may also fluctuate within individuals, the dynamics and extent of such variation in daily life remain largely unexplored. The gold standard for circadian phase assessment, dim-light melatonin onset (DLMO), is impractical for large-scale studies, and blood-based molecular biomarkers, while promising, are limited in feasibility. To address these challenges, we developed HairTime, a noninvasive assay that estimates circadian phase from a single daytime hair sample. Developed and evaluated in two steps—a training and a validation study—HairTime demonstrated strong predictive power compared to DLMO. Suitable for large-scale studies, it was assessed using over 4,000 samples. Circadian phase estimations showed a normal distribution and were associated with age, sex, and notably, work schedules, with earlier timing on workdays, suggesting that societal factors can modulate internal rhythms. Together, these findings establish HairTime as a promising tool for assessing circadian phase in research and lay the foundation for future applications in personalized chronotherapy.

Circadian clocks orchestrate metabolic processes in the whole body and their response to food. Therefore, not only what and how much, but also when we eat has a significant impact on metabolism. In this context, daytime of carbohydrate and fat intake was recently shown to alter the metabolic state and potentially affect the disease risk. However, molecular mechanisms of this regulation in humans are poorly understood. In this crossover trial, we investigated the effects of two isocaloric 4-week dietary patterns – high carbohydrate in the morning and high fat in the afternoon (HC/HF) and the reverse (HF/HC) - on the subcutaneous adipose tissue (SAT) in overweight non-diabetic men. The SAT transcriptome was assessed using microarrays. Additionally, gene expression in peripheral blood monocytes (PBMCs) was analysed by qPCRs. Analysis of SAT samples collected across the day identified 1386 genes exhibiting diurnal oscillations. In SAT, both oscillatory and non-oscillatory genes related to lipid and glucose metabolism were modulated by the timing of macronutrient intake. Notably, expression of inflammatory response genes in SAT was elevated after HC/HF compared to HF/HC, suggesting that the HC/HF diet might promote an early proinflammatory state in SAT due to higher fat intake in the afternoon. Diet-induced remodelling of the SAT transcriptome was partly reflected in PBMCs. These findings demonstrate that diurnal macronutrient distribution significantly reshapes the SAT transcriptome, underscoring the relevance of eating timing-based (chrononutritional) strategies for prevention of metabolic dysfunction and systemic inflammation associated with obesity and type 2 diabetes.

Higher serum levels of GPNMB are linked to type 2 diabetes mellitus (T2DM) and metabolic dysfunction-associated steatotic liver disease (MASLD). Disruption of circadian rhythms also influences the development and progression of MASLD. In this study, we investigated how GPNMB modulates hepatic glycogen metabolism and its potential interaction with the hepatic circadian clock. Male DBA/2 J mice, either wild-type (GP⁺) or carrying an inactivating Gpnmb mutation (GP⁻), were fed a high-fat diet (48.4% fat) supplemented with 30% fructose in drinking water for 12 weeks. Despite similar weight gain, GP⁻ mice displayed greater global fat mass accumulation and elevated serum triglyceride and cholesterol levels. Surprisingly, GP⁻ mice showed improved glucose tolerance, whereas GP⁺ mice developed impaired glycemic control. Indirect calorimetry under thermoneutral conditions (30°C) revealed loss of diurnal rhythmicity in energy expenditure (EE) in GP⁻ mice, which was independent of food intake. Despite its preserved rhythms, hepatic clock gene expression in GP⁻ mice showed increased MESOR (e.g., Per1, Per2, and Nr1d1) and increased amplitude (e.g., Nr1d1), indicating higher expression levels throughout the day. GPNMB deficiency further impaired hepatic glycogen storage dynamics, which was attributed to reduced AKT phosphorylation (indicative of defective insulin signaling), reduced FOXO1 phosphorylation, and increased PEPCK-M. Translating our findings to human MASLD patients, GPNMB expression obtained from liver biopsies showed a clear increase across MASLD progression. Importantly, patients with metabolic dysfunction-associated steatohepatitis (MASH) and diabetes who received anti-diabetic treatment showed a reduction in hepatic GPNMB expression. Collectively, our findings suggest that GPNMB plays a role in metabolic adaptation to obesogenic diets, as a Gpnmb loss-of-function model reveals an association with impaired hepatic insulin signaling and glycogen metabolism despite improved systemic glucose tolerance in mice, whereas hepatic GPNMB upregulation correlates with MASLD progression in humans.

Retrospective studies suggest that early time-of-day (ToD) infusions of immunochemotherapy may improve efficacy. However, prospective randomized controlled trials are needed to validate it. In this randomized phase 3 LungTIME-C01 trial, 210 patients with treatment naive stage IIIC–IV non-small cell lung cancer (NSCLC) lacking driver mutations were randomly assigned in a 1:1 ratio to either an early or late ToD group, defined by the administration of the first four cycles of an anti-PD-1 agent before or after 15:00 h. The primary endpoint was progression-free survival (PFS), while secondary endpoints included overall survival (OS) and objective response rate (ORR). After a median follow-up of 28.7 months, the median PFS was 11.3 months (95% confidence interval (CI) = 9.2–13.4) in the early ToD group and 5.7 months (95% CI = 5.2–6.2) in the late ToD group, corresponding to a hazard ratio (HR) for earlier disease progression of 0.40 (95% CI = 0.29–0.55; P< 0.001). The median OS was 28.0 months (95% CI = not estimable (NE)–NE) in the early ToD group and 16.8 months (95% CI = 13.7–19.9) in the late ToD group, corresponding to an HR of an earlier death of 0.42 (95% CI = 0.29–0.60; P< 0.001). Treatment-related adverse events were consistent with the established safety profile, with no new safety signals observed. No significant differences in immune-related adverse events were observed between the two groups. Over the first four cycles, morning circulating CD8⁺ T cells increased in the early ToD group, whereas they declined in the late ToD group (P< 0.001). Furthermore, the ratio of activated (CD38⁺ HLA-DR⁺) versus exhausted (TIM-3⁺PD-1⁺) CD8⁺ T cells was higher in the early ToD group (P< 0.001) compared with the late ToD group (P< 0.001). In summary, our study indicates that early ToD immunochemotherapy substantially improves PFS and OS and is associated with enhanced antitumor CD8⁺ T cell characteristics compared with late ToD treatment.

Data integration, data sharing, and standardized analyses are important enablers for data-driven medical research. Circadian medicine is an emerging field with a particularly high need for coordinated and systematic collaboration between researchers from different disciplines. Datasets in circadian medicine are multimodal, ranging from molecular circadian profiles and clinical parameters to physiological measurements and data obtained from (wearable) sensors or reported by patients. Uniquely, data spanning both the time dimension and the spatial dimension (across tissues) are needed to obtain a holistic view of the circadian system. The study of human rhythms in the context of circadian medicine has to confront the heterogeneity of clock properties within and across subjects and our inability to repeatedly obtain relevant biosamples from one subject. This requires informatics solutions for integrating and visualizing relevant data types at various temporal resolutions ranging from milliseconds and seconds to minutes and several hours. Associated challenges range from a lack of standards that can be used to represent all required data in a common interoperable form, to challenges related to data storage, to the need to perform transformations for integrated visualizations, and to privacy issues. The downstream analysis of circadian rhythms requires specialized approaches for the identification, characterization, and discrimination of rhythms. We conclude that circadian medicine research provides an ideal environment for developing innovative methods to address challenges related to the collection, integration, visualization, and analysis of multimodal multidimensional biomedical data.

Keywords: chronomedicine; data integration; data science; data visualization; time-series data.

Delayed sleep-wake phase disorder involves chronic difficulty going to bed and waking up at conventional times and often co-occurs with depression. This study compared sleep and circadian rhythms between patients with delayed sleep-wake phase disorder with depression (DSWPD-D) and without (DSWPD-ND) comorbid depression. Clinical records of 162 patients with delayed sleep-wake phase disorder (70 DSWPD-D, 92 DSWPD-ND) were analysed, including a subset of 76 patients with circadian phase determined by the dim light melatonin onset. Variables assessed included sleep behaviour on work and free days, weekly sleep duration, social jet lag, chronotype, and phase relationships between dim light melatonin onset and sleep/wake times. Mean (SD) or median [Q1-Q3] values were compared using t-tests or Mann-Whitney. Patients with DSWPD-D showed longer sleep on workdays (DSWPD-D = 7.63 hr [1.70] versus DSWPD-ND = 6.20 hr [1.59]; p < 0.001), but not on free days. DSWPD-D also showed later sleep onset (DSWPD-D = 03:30 14;hours [02:49 hours-04:23 hours], DSWPD-ND = 02:53 hours [02:00 hours-03:41 hours]; p = 0.02) and wake times (DSWPD-D = 11:30 hours [09:30 hours-13:00 hours], DSWPD-ND = 08:45 hours [07:20 hours-11:00 hours]; p < 0.01) on workdays. Furthermore, DSWPD-D showed less social jet lag (DSWPD-D = 0.38 [0.00-1.75] versus DSWPD-ND = 2.17 [1.25-3.03]; p < 0.01), and reported higher anxiety symptoms (DSWPD-D = 71.4% versus DSWPD-ND = 45.8%; p = 0.03) and medication use (DSWPD-D = 75.0% versus DSWPD-ND = 43.8%; p = 0.01). DSWPD-D also showed wider dim light melatonin onset phase relationships with dim light melatonin onset-mid-sleep (DSWPD-D = -5.77 [1.32] versus DSWPD-ND = -4.86 [1.53]; p = 0.01) and dim light melatonin onset-waketime (DSWPD-D = -9.46 [1.82]; DSWPD-ND = -8.13 [2.08]; p = 0.01). Multivariable Poisson regression, adjusted for age and sex, showed more medication use, less social jet lag, and longer weekly sleep duration as significantly associated with DSWPD-D. These findings suggest potential biopsychosocial protective factors linked to depression in delayed sleep-wake phase disorder. Further research is required to confirm these phenotypic differences and their relevance to delayed sleep-wake phase disorder aetiology and treatment.

Keywords: depression; dim light melatonin onset; sleep duration; social jet lag; workdays.