Circadian Rhythm of the Human Plasma Proteome

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S. Jóhönnuson, Henriette P. Sennels, Henrik L. Jørgensen, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6695734/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 22 Aug, 2025 Read the published version in Clinical Proteomics → Version 1 posted 8 You are reading this latest preprint version Abstract Background Plasma is the most used clinical specimen, yet circadian variation in plasma proteins remains largely unexplored. We aimed to identify circadian-regulated proteins in healthy individuals and assess their potential diagnostic implications, and highlight how circadian awareness can advance future biomarker research. Methods Twenty-four healthy young individuals were studied under highly controlled conditions. Venous blood was drawn every three hours over a 24-hour period, yielding 216 samples, of which 208 high-quality plasma samples were analyzed via high-throughput mass spectrometry. The missing data were filtered and imputed, and rhythmicity was assessed using Cosinor-based modeling with Benjamini-Hochberg correction. Tissue and pathway enrichment analyses were performed using the DAVID functional annotation tool. Findings Of 523 proteins that passed quality thresholds, 138 (~ 26%) exhibited significant circadian oscillations. Tissue enrichment analysis revealed that most rhythmic proteins originated from the liver and platelets, with additional enrichment in a variety of tissue types. Pathway enrichment showed circadian regulation of hemostasis, immune signaling, integrin-mediated processes, glucose metabolism, and protein synthesis. Notably, 36 clinically utilized biomarkers, including albumin, amylase, and cystatin C exhibited circadian variation, suggesting that failing to account for temporal fluctuations may reduce diagnostic precision. Interpretation These findings demonstrate that over one-quarter of the human plasma proteome is under circadian control. Such oscillations might have direct clinical implications, as the time-of-day may alter biomarker accuracy. Incorporating circadian timing into diagnostic and research protocols, through standardized sampling or time-sensitive reference intervals, could improve patient care and inform future biomarker discoveries. Further research in larger, more diverse populations is needed to generalize these results and streamline circadian-aware practices in clinical practice. Biomarker Circadian Rhythm Mass-Spectrometry Plasma Proteins Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Blood is the most used diagnostic specimen. Despite significant advancements in biomarker discovery, most clinical diagnostics do not account for circadian fluctuations in circulating proteins. This gap could lead to misinterpretation of results and suboptimal treatment timing, affecting patient care and research outcomes. Understanding these temporal variations could improve diagnostic accuracy, optimize clock-based therapeutic strategies, and enhance biomarker research. The circadian rhythm (Latin: circa meaning ‘approximately’ and diem meaning ‘day’) governs 24 hour oscillations in biological processes. It is regulated by a central biological clock in the hypothalamic suprachiasmatic nucleus (SCN), also known as the master clock, and by peripheral clocks in tissues throughout the body. While the functions of central and peripheral clocks are well understood, their interactions remain unclear. At the cellular level, a transcriptional-translational feedback loop drives rhythmic gene expression, leading to oscillations in biological functions throughout the day 1 . These rhythms are modulated by zeitgebers (German: ‘time givers’), which are external and internal cues, signals that synchronize (entrain) the biological rhythm to the 24 hour sleep and wake cycle. Light is the primary zeitgeber, but feeding, exercise, and temperature also play crucial roles in circadian entrainment 2 . Proteins are the most frequently used biomarkers 3 . However, little is known about the circulating proteome during circadian rhythm in healthy humans. High-throughput proteomic analysis involves the study of proteins, including their expression pattern and function, and this is typically obtained by mass spectrometry-based approaches. This technology is increasingly being used, not only for biomarker research, and is increasingly implemented in clinical diagnostics 4 . To investigate the circadian regulation of plasma proteins, we analyzed plasma samples from 24 healthy individuals. Blood samples were collected nine times over a 24 hour-period while the participants remained in a standardized environment with controlled light exposure, food intake, movement, and sleep conditions. Mass spectrometry (MS)-based proteomics was applied to analyze plasma protein dynamics. By identifying circadian-regulated plasma proteins, this study aims to provide insight into potential diagnostic implications, and highlight how circadian awareness can inform optimal timing for blood-based diagnostics and therapeutic interventions. Research in context Evidence before this study Blood sampling is fundamental to diagnosis and treatment in hospital settings. However, research on circadian rhythms in the plasma proteome, and their implications for diagnostic accuracy, remains limited. We searched Google Scholar up to February 26, 2025, using the terms “Plasma Proteins and Circadian Rhythm”, “Plasma Proteins and Diurnal Variations”, “Circadian Rhythm and Plasma Proteome” and “Mass Spectrometry, Plasma Proteomics and Circadian Rhythm”. We identified fewer than five relevant studies, most of which relied on SomaScan aptamer-based assays that provide only partial protein coverage and may introduce bias in specificity and quantitation. Consequently, unbiased mass-spectrometry analyses of circadian rhythm protein variation in healthy individuals are lacking. This gap hinders our understanding of how time-of-day protein fluctuations affect patient testing, protein research and ultimately diagnostic accuracy. Added value of this study Using a high-throughput, unbiased mass spectrometry approach with multiple time point sampling in 24 healthy individuals, we found that 26% of plasma proteins exhibited significant circadian rhythms. Our study offers a comprehensive overview of how the plasma proteome varies by time-of-day, providing critical insights that can guide future research and help refine diagnostic protocols. Implications of all the available evidence These findings suggest that time-of-day fluctuations in plasma proteins may be relevant to the interpretation of clinical blood tests. However, additional research in larger and more diverse cohorts is needed to determine whether standardized sampling times or time-sensitive reference ranges would improve diagnostic accuracy and patient outcomes. Moreover, our results imply that previous proteomic investigations may have been influenced by the lack of circadian consideration, emphasizing the potential value of carefully timed sampling and circadian considerations in future diagnostics, treatment and research. Methods Study Design and Approvals The Bispebjerg Study of Diurnal Variation has been described previously 5 and is summarized here. This prospective time-series analysis involved a 24-hour hospital stay under standardized conditions. Participants spent 15 hours awake in ordinary daylight or room light (mean light intensity 219 lux) and 9 hours asleep in the dark (mean 0.04 lux), from 11:00 PM to 8:00 AM. During the day the, the participants were prohibited from napping but permitted low-intensity activities such as walking, television watching and reading 5 . Standardized isocaloric, low-fat, sugar-free meals were provided at 9:30 AM, 1:00 PM and 7:00 PM. Water intake was not restricted. Meals were identical for all participants, without individual caloric adjustments or measurement of food intake. Meal composition has been described previously 6 and is presented in Fig. 1 . The participants fasted for 11 hours before the start of the study 5 . The study was conducted in 2008 at the Department of Clinical Biochemistry, Bispebjerg Hospital, Copenhagen, Denmark. It was approved by the local independent ethics committee (protocol number H-B-2008-011) and the Danish Data Protection Agency (journal number 2008-41-1821). It was conducted according to the Helsinki Declaration, with all participants signing a written informed consent. The trial has been retrospectively registered at clinicaltrials.gov with the identifier NCT06166368. Participants Eligible participants were healthy men aged 18–45 years with a regular sleep-wake cycle and hemoglobin concentration greater than 8.0 mmol/L. Exclusion criteria included an acute or chronic illness, use of medication within the past 30 days, regular tobacco use, night-shift work, recent time zone shift (travel), or increased alcohol consumption or smoking within the last 14 days prior to the study. Strict inclusion/exclusion criteria were implemented to ensure homogeneity and minimize participant variation. Participants were recruited through advertisements at the Faculty of Health Science, University of Copenhagen. A total of twenty-four healthy Caucasian male volunteers aged 20–40 (mean age 26 years) were included 5 . Procedures Blood sampling was performed every three hours over a 24-hour period, beginning at 9:00 AM, for a total of nine collections (Fig. 1 ). Samples were drawn from the cubital vein in alternating arms at each time point using minimal tourniquet application and collected into serum clot activator tubes coated with microscopic silica particles (Greiner Bio-one, Frickenhausen, Germany). Tubes were centrifuged, plasma was isolated, and immediately stored at -80°C until analysis. During the wake period, blood was drawn following a 10-minute rest, with participants seated at a 45-degree angle in a hospital bed, legs extended and positioned horizontally. During the sleep period, blood samples were collected with minimal disturbance using low-intensity red light while participants remained in a supine position. Additional measurements included blood pressure, pulse and self-reported height and weight. The body mass index (BMI, kg/m 2 ) was calculated from height and weight. Light intensity was measured using the RS 180–7133 lux meter (RS Components, Corby, United Kingdom) 5 . Proteomic analysis Each plasma sample was aliquoted into a 96-well plate and prepared on an Agilent Bravo Liquid Handling Platform. Samples were diluted 1:10 with lysis buffer (1M Tris, 0.5M Tris(2-carboxyethyl)phosphine (TCEP) and 0.5M Chloroacetamide (CAA) in H 2 O) and incubated at 95°C for 10 minutes. After cooling to RT, a Trypsin/LysC mixture (1 µg to 100 µg protein) was added and incubated for 4 h at 37°C at 1000 rpm. The enzymatic reaction was quenched by adding 64 µl of 0.2% TFA. The samples were loaded onto Evotips according to the manufacturer’s recommendations (Evosep Biosystem, Denmark). Briefly, Evotips were prepared by washing with buffer B (100% acetonitrile (ACN), 1% formic acid (FA)), activated by soaking in isopropanol, and equilibrated with 20 µl of buffer A (1% FA) before loading the samples. Evotips were then loaded with 250 ng of peptides per sample, followed by a wash with 20 µl of buffer A. Each step was followed by a 1-minute centrifugation at 700g to facilitate liquid passage. Finally, the Evotips were stored in buffer A to prevent drying. LC–MS/MS analysis was performed using an Orbitrap Astral mass spectrometer (Thermo Scientific) coupled to an Evosep One system (Evosep Biosystem, Denmark). Peptide separation was carried out on a commercial 8 cm analytical ‘Performance’ column (EV1109, Evosep Biosystem, Denmark) using the predefined 60 samples per day method (21-minute gradient) and analyzed in data-independent acquisition mode. The mass spectrometer operated in positive mode. Full MS spectra (380–980 m/z) were acquired using the Orbitrap analyzer with a resolution of 240,000 at 200 m/z. Precursor ions were isolated with an automatic gain control (AGC) target of 500% (5e6 charges) and a maximum injection time (maxIT) of 3 ms. In parallel to the full MS scan, fragment spectra of 200 consecutive windows (3-Th width) within the 380–980 m/z precursor mass range were recorded using the Astral analyzer operating at the resolution of 80,000. Precursor ions were isolated with an AGC target of 500% (5e4 charges) and a maxIT of 5 ms, then fragmented at 25% normalized collision energy. Statistical analysis Data processing Raw mass spectrometry data were initially processed using DIA-NN (version 1.9) in a data-independent acquisition (DIA) search 7 . Further data processing was conducted using python, where a stringent filtering approach was applied to address missing data: (1) samples with a low protein count—defined as values below 1.5 times the interquartile range (IQR) from the 25th percentile of the combined distribution—were excluded, and (2) proteins missing in more than 40% of samples were removed. Log 2 transformation was applied to the data, and remaining missing values were imputed using the variational autoencoder implemented in PIMMS 8 . Data analysis Differential protein abundance according to circadian rhythm was assessed using the CosinorPy python package 9 with multiple hypothesis correction applied using the Benjamini-Hochberg method. Adjusted p-values less than 0.05 were considered statistically significant. Protein abundances and p-values were visualized using heatmaps. Agglomerative hierarchical clustering with single linkage was performed to identify clusters of proteins with similar circadian patterns. Heatmaps, combined with clustering dendrograms and sankey plots of Reactome Pathways, were used to highlight changes in significant proteins, protein clusters, and their associated biological relevance 10 . Protein abundances in heatmaps were z-scored. To externally validate our findings, we retrieved routine clinical laboratory data from the laboratory information database LABKA, encompassing timestamped measurements of albumin, activated partial thromboplastin time (aPTT), and prothrombin time (PT/INR). Data extraction was restricted to samples collected across hospitals within the Capital Region of Denmark from April 1 to April 7, 2025. Circadian rhythmicity was subsequently assessed via Cosinor analysis, as described previously for the proteomic data. Enrichment analysis Tissue and pathway enrichment analyses were conducted for identified protein clusters individually using the Database for Annotation, Visualization, and Integrated Discovery (DAVID) version, 2024 11,12 . We provided a list of significant proteins under their official gene symbols (e.g., B2M, ALB) and used Homo sapiens (9606) as the background species. Tissue enrichment was performed with the UP_TISSUE database, excluding pathological, fetal, fluid-based (e.g., serum, plasma, cerebrospinal fluid), and placental enrichments. Pathway enrichment used the REACTOME_PATHWAY database, and we further supported these findings with Gene Ontology Biological Processes (GOTERM_BP_FAT) analysis. Enrichments with Benjamini–Hochberg–adjusted p-values below 0.05 were deemed significant. Outcome The primary outcome was to assess circadian variation in plasma protein levels through blood samples collected every 3 hours over 24 hours from all participants. The secondary outcome was to determine whether circadian variations in plasma proteins have diagnostic relevance and potential implications for clinical decision-making. The study design, controlled environment, and strict inclusion/exclusion criteria minimized the influence of zeitgebers and confounders. Steps to reduce bias included standardized sample collection protocols to minimize measurement and performance bias. Role of the funding source The funder of the study had no role in study design, data collection, data analysis, data interpretation, or writing of the report. Results From the 24 healthy individuals (age 20–40 years, mean 26.0 ± 5.2 years), a total of 216 plasma samples were collected over 24 hours. Eight samples (3.7%) were excluded due to insufficient quality, defined by low protein count (below 1.5×IQR from the 25th percentile), leaving 208 high-quality samples available for final proteomic analysis. Baseline characteristics of participants are presented in Table 1 5 . Table 1 Baseline Characteristics of Study Participants (n = 24 healthy men). Data are presented as mean (standard deviation, SD) with corresponding 95% confidence interval (95% CI). BMI (body mass index) was calculated from self-reported weight and height 5 . Variable Mean (SD) Range (95% CI) Clinically Accepted Normal Ranges Age (Years) 26 (5.2) 23.8–28.2 Weight (kg) 76.6 (6.6) 73.8–79.4 Height (m) 1.83 (0.05) 1.82–1.85 Body mass index, BMI (kg/m 2 ) 22.9 (1.6) 22.2–23.6 18.5–24.9 Pulse (beats per minute) 66 (9.9) 61.8–70.2 60–100 Systolic blood pressure (mmHg) 128 (10.2) 123.7–132.3 ≤ 120 Diastolic blood pressure (mmHg) 68 (8.2) 64.5–71.5 ≤ 80 Hemoglobin concentration (mmol/L) 9.2 (0.6) 8.95–9.45 8.3–10.5 (men) Number of participants: 24 healthy men Participant characteristics and measurements. SD, standard deviation; CI, confidence interval. A total of 832 unique plasma proteins were identified, with a median of 533 proteins per sample (Fig. 2 a). After stringent quality filtering, 523 proteins (62.9%) remained for circadian rhythm analysis. Using CosinorPy Rhythmometry with z-scoring and stringent Benjamini–Hochberg correction, we identified 138 proteins (26.4%) exhibiting significant circadian rhythmicity (as shown in Supplementary Table 5) (mean adjusted p-value: 0.0093, SD: 0.015; 95% CI: 0.0068–0.012). Proteins with adjusted p-values below 0.05 following Benjamini–Hochberg correction were considered significantly rhythmic. The variation in protein levels across the day and across individuals (coefficient of variation) correlated significantly both with the residual standard error from the CosinorPy analysis and with the amplitude of the rhythmic peak (Pearson’s r = 0.79 (p-value = 8.121e-113) and r = 0.76 (p-value = 9.23e-98), respectively) (Fig. 2 b). Additionally, the proteins exhibiting circadian rhythmicity were overrepresented among proteins with high coefficient of variation (Odds Ratio = 1.755, p-value = 0.006) and high amplitude (Odds Ratio = 4.67, p-value = 3.91e-13). Hierarchical clustering analysis revealed two distinct circadian clusters, an afternoon cluster (Cluster A; 91 proteins) and an early morning cluster (Cluster B; 47 proteins) (Fig. 3 ). Tissue enrichment analysis (DAVID) annotated 129 of 138 rhythmic proteins (93.5%), and was performed separately for the two distinct clusters: afternoon and early morning. Cluster A predominantly originated from the liver (66 proteins, adjusted p = 5.8×10⁻ 17 ) and platelets (34 proteins, adjusted p = 2.3×10⁻²⁶), but additional enriched tissues included lymphoblasts (16 proteins), T-cells (10 proteins), skeletal muscle (14 proteins), keratinocytes (7 proteins), skin (21 proteins), tongue (13 proteins), fibroblasts (6 proteins), erythrocytes (3 proteins), and lungs (23 proteins), as shown in Supplementary Table 3. Cluster B was significantly enriched only in the liver (27 proteins; adjusted p = 1.4×10⁻ 5 ) (Fig. 3 and Fig. 4 a). Reactome pathway enrichment (DAVID) annotated 79 proteins (86.8%) in Cluster A, and 32 proteins (68.1%) in Cluster B, and revealed extensive circadian rhythmicity across multiple clinically and biologically critical pathways. Among the most significant enrichments were pathways associated with platelet function and coagulation, such as "Platelet degranulation", "Response to elevated platelet cytosolic Ca²⁺", “Platelet activation, signaling, and aggregation”, and the broader "Hemostasis" pathway. Platelet activation and hemostasis was the only common denominator between Cluster A and Cluster B, although it was more significantly enriched in Cluster A (adjusted p = 5.2×10⁻ 14 to 2.7×10⁻ 19 ) compared to Cluster B (adjusted p = 9.4×10⁻ 3 to 1.1×10⁻ 4 ) (Fig. 3 and Fig. 4 b). Cluster A was significantly enriched in pathways involving immune system function and response, including "Neutrophil degranulation" and the broader "Innate immune system" processes. Additionally, enrichments in pathways associated with “Rho GTPase Signal Transduction”, “Cell-Matrix, Cell-Cell Adhesion, and Cytoskeletal Remodeling”, “Axon Guidance and Neural development”, “Oncogenic RAS/RAF-MAPK Signaling and Integrin Signaling”, “Interleukin-12 and JAK/STAT Cytokine Signaling”, “Glucose Metabolism” including “Glunoneogenesis” and “Glycolysis”, and more (Supplementary table 4). Importantly, Cluster B enrichment extended beyond coagulation-related pathways to include significant circadian regulation in protein metabolism and growth signaling processes, such as "Regulation of Insulin-like Growth Factor transport and uptake by IGFBPs", "Post-translational protein phosphorylation", and “Metabolism of proteins”. Notably, pathways involving lipoprotein metabolism and transport also displayed significant rhythmicity, including “Plasma lipoprotein assembly, remodeling, and clearance”, “Chylomicron assembly”, and “HDL remodeling”. Additionally, enrichments in pathways associated with the coagulation cascade and specifically, fibrin clot formation were significant in the early morning peak cluster of proteins. The Gene Ontology Biological Processes (GOBP) enrichment analysis (DAVID) matched 89 proteins from Cluster A (97.8%) and 46 proteins from Cluster B (97.9%), and corroborated the findings described earlier, providing further biological insight into circadian regulation across diverse physiological systems. Both clusters showed particularly robust enrichment in processes related to coagulation and wound healing, although it was again stronger in Cluster A (Data not shown). Collectively, these detailed enrichment analyses using Reactome and GOBP databases emphasize the wide-ranging impact of circadian biology on fundamental clinical and biological functions. The acrophase – defined as the actual peak time in clock hours (0–24 hr), representing the time of day at which each protein reaches its maximum abundance – was used to characterize the temporal distribution of circadian-regulated plasma proteins. Cluster A proteins exhibited a median acrophase of 16.50 hr (IQR: 12.87–17.10 hr), corresponding approximately to late afternoon. Cluster B proteins peaked earlier in the day, with a median acrophase of 3.31 hr (IQR: 2.03–5.80 hr), representing early morning hours (Fig. 5 ). Circadian-regulated proteins showed a mean amplitude of 0.172 (SD: 0.0107; 95% CI: 0.151–0.194), but interestingly, Cluster A had a significantly higher amplitude with a mean of 0.215 (SD: 0.132; 95% CI: 0.19–0.24) versus 0.091 (SD: 0.05; 95% CI: 0.076–0.11) in Cluster B. No significant changes in protein abundance were observed in relation to meal intake (Supplementary Table 1). Through cross-referencing our significant rhythmic proteins against the standard clinical diagnostic assay preference list from the Danish regional health authority (Region H, Sundhedsplatformen) — which reflects clinical practice guidelines and diagnostic standards currently implemented across hospitals in the Capital Region of Denmark — we identified 36 proteins (26.1% of all significantly rhythmic proteins) currently used as biomarkers in routine clinical practice (Table 2 ). These clinically important biomarkers span multiple essential diagnostic categories, including coagulation factors (e.g., fibrinogen alpha chain [FGA], coagulation factor V [F5], and protein C [PROC]), markers of liver and kidney function (albumin [ALB], cystatin C [CST3]), inflammatory biomarkers (calprotectin subunits S100A8 and S100A9), endocrine-related proteins (insulin-like growth factor 1 [IGF1]), proteins indicative of cardiac and skeletal muscle injury (creatine kinase M-type [CKM], lactate dehydrogenase A [LDHA]), and a marker for acute pancreatitis (amylase [AMY2A]). Additionally, critical immune-related diagnostic markers such as complement proteins, human leukocyte antigen B (HLA-B), and immunoglobulins also demonstrated clear circadian rhythmicity. Table 2 Clinical Relevance of Circadian-Regulated Proteins in Human Plasma. The listed blood tests correspond to clinical routine assays according to the standardized regional clinical laboratory test preference list in Denmark (EPIC system, Region H, Denmark). Circadian-regulated proteins linked to each test are indicated alongside descriptions of their diagnostic, prognostic, or monitoring significance in clinical practice. Clinical Test Circadian-Regulated Protein(s) Clinical Significance aPTT F11; F5; F9; PROC; SERPINC1; FGA Coagulation status; Bleeding disorders; Liver function monitoring PT (INR) F5; FGA Coagulation status; Liver synthetic function assessment D-Dimer FGA Thrombosis diagnosis; Disseminated intravascular coagulation (DIC) Fibrinogen FGA Coagulation status; Acute-phase inflammatory response Albumin ALB Nutritional status; Liver function; Chronic illness monitoring Cystatin C CST3 Kidney function; Glomerular filtration rate estimation (eGFR) Calprotectin S100A8/S100A9 (complex) Systemic inflammation marker; Leukocyte activation Beta-2-Microglobulin B2M Tumor marker (Myeloma, CLL, lymphoma); Kidney function assessment Complement Activity tests ( CH50 test ): C3; C1r ( AH50 test ): C3; CFD; CFHR1 Complement system evaluation; Immunodeficiency diagnosis Complement C3 C3 Complement activation; Inflammatory/autoimmune disorders Complement C1r C1r Classical complement pathway activity Prostaglandin D-Synthase PTGDS Inflammation marker; Sleep regulation (research) Insulin-Like Growth Factor I IGF1 Growth disorders; Nutritional/endocrine assessmnet Creatine Kinase CKM Muscle injury diagnosis; Rhabdomyolysis; Myopathy Glyceraldehyde-3-phosphate dehydrogenase GAPDH Cellular damage; Hemolysis Heparin-PF4-IGG (HIT) PF4 Heparin-induced thrombocytopenia diagnosis Erythrocyte Transketolase Activity Test TK Vitamin B1 (Thiamine) deficiency diagnosis; Nutritional assessment Protein C PROC Thrombophilia evaluation; Coagulation disorders diagnosis HLA-AB HLA-B Transplant compatibility; Immunogenetic profiling Myoglobin MB Acute myocardial infarction (AMI); Rhabdomyolysis LDH LDHA Tissue injury; Hemolysis; Prognostic monitoring Total IgA IGHA2 Immunodeficiency diagnosis; Immune status monitoring Free Kappa Chains (Ig) IGKV2D-29; IGKV2-28; IGKV2-29 Plasma cell disorder diagnosis (Myeloma, MGUS); Therapeutic monitoring Free Lambda Chains (Ig) IGLV3-25; IGLV3-19; IGLV2-11; IGLV3-1 Plasma cell disorder diagnosis (Myeloma, MGUS); Therapeutic monitoring Free Kappa/Lambda Chains (Ig) Ratio IGKV2D-29; IGKV2-28; IGKV2-29; IGLV3-25; IGLV3-19; IGLV2-11; IGLV3-1 Plasma cell disorders diagnosis; Disease monitoring; Clonality analysis CD56 Flow Cytometry (whole blood) NCAM1 NK/T-cell malignancy diagnosis; Immunophenotyping Amylase AMY2A Acute pancreatitis diagnosis; Pancreatic function assessment Antithrombin SERPINC1 Thrombosis risk; Coagulation inhibitor deficiency Coagulation Factor XI F11 Hemophilia C; Bleeding disorder evaluation Coagulation Factor V F5 Factor V deficiency diagnosis; Thrombophilia evaluation Transferrin-Receptor Fragment TFRC Iron deficiency diagnosis; Iron metabolism assessment Coagulation Factor IX F9 Hemophilia B; Factor IX Deficiency; Vitamin K deficiency Abbreviations : aPTT, activated partial thromboplastin time; PT, prothrombin time; AMI, acute myocardial infarction; MGUS, monoclonal gammopathy of undetermined significance; eGFR, estimated glomerular filtration rate; HIT, heparin-induced thrombocytopenia; CLL, chronic lymphocytic leukemia; LDH, lactate dehydrogenase; Ig, immunoglobulin. To explore whether our findings extended beyond controlled conditions, we analyzed timestamped routine clinical laboratory results from the LABKA database. This dataset included all measurements of albumin, aPTT, and PT (INR) conducted in hospitals across the Capital Region of Denmark over a one-week period (April 1–7, 2025), encompassing both healthy and clinically diverse patient populations. All three analyses exhibited significant circadian rhythms following Benjamini–Hochberg correction: Albumin (adjusted p = 1.1×10⁻¹⁶; mesor = 33.7 g/L, amplitude = 4.02 g/L, peak at 13:12), aPTT (adjusted p = 6.9×10⁻⁴; mesor = 30.69 s, amplitude = 2.05 s, peak at 01:57), and PT (adjusted p = 5.7×10⁻⁹; mesor = 1.14, amplitude = 0.04, peak at 04:49) (Fig. 6 ). Discussion In this study, we demonstrate that one fourth of the human plasma proteome display circadian rhythmicity. Using a rigorous, unbiased mass spectrometry-based proteomics approach in a cohort of healthy young individuals under highly controlled conditions, we identified 138 proteins with distinct circadian patterns. In line with our final analyses, we identified two distinct circadian clusters of plasma proteins: one peaking in the afternoon (Cluster A; 91 proteins, median peak at 16:30) and another in the early morning (Cluster B; 47 proteins, median peak at 03:19). While Cluster A showed a more diverse tissue origin—including platelets, liver, skeletal muscle, immune cells, and several others—Cluster B was significantly enriched only in the liver. Functionally, both clusters shared platelet activation and hemostasis, although these processes were more strongly enriched in Cluster A. Notably, Cluster B alone encompassed insulin-like growth factor regulation, protein metabolism, and lipoprotein metabolism, suggesting these pathways peak during the early morning or night hours. By contrast, Cluster A included prominent pathways in innate immunity, Rho GTPase signaling, integrin signaling, oncogenic RAS/RAF‐MAPK signaling, and carbohydrate metabolism (gluconeogenesis and glycolysis). Intriguingly, the afternoon‐peaking proteins (Cluster A) also exhibited a higher rhythmic amplitude (~ 0.22) compared to Cluster B (~ 0.09), implying more pronounced day‐time oscillations. These rhythmic fluctuations occurred independently of food intake, indicating that meal timing did not drive the observed variations. Critically, 36 of these circadian-regulated proteins (26% of the total rhythmic proteins) are already measured in routine clinical practice, as reflected in the Danish guidelines, underscoring the clinical importance of recognizing circadian patterns in widely used biomarkers and highlights a potential role for circadian‐informed testing protocols. Our findings are consistent with prior studies but also extend the understanding of circadian plasma proteomics significantly. A recent study (2024), employing a SomaScan aptamer-based methodology, observed circadian rhythmicity of 15% of proteins 13 , fewer than our observed 26.4%. Methodological differences, specificially the broader, unbiased coverage provided by mass spectrometry, likely explain the higher proportion of rhythmic proteins identified in our study. Notably, similar to our findings, they also reported protein oscillations primarily peaking in the early morning and afternoon hours. Additionally, circadian regulation of hemostatic proteins has previously been observed in ELISA-based studies 14 and is further supported by studies using constant routine protocols demonstrating circadian variation in platelet activation 15 . Our comprehensive proteomic analysis confirms that many proteins involved with the hemostasis system have a circadian rhythm and further expands the understanding by identifying rhythmic proteins in diverse biological processes, emphasizing the broader relevance of circadian variation in clinical diagnostics and protein research. Our unbiased MS-based proteomics method significantly extends beyond previous targeted studies, providing an innovative and robust framework for biomarker discovery and research. Unlike targeted proteomic techniques such as ELISA or aptamer-based assays (e.g. SomaScan), MS-based proteomics offers direct identification and quantification of peptide sequences, dramatically reducing specificity and quantification biases. Recent technological advancements in MS-based proteomics have markedly enhanced sensitivity, robustness, and throughput, enabling the comprehensive and precise characterization of complex biological systems. Consequently, our methodological approach provides a more complete and unbiased exploration of circadian rhythmicity in plasma proteins, facilitating discoveries that may have been overlooked using targeted approaches, and setting new standards for circadian biomarker identification and clinical diagnostics 16 . These rhythmic fluctuations of biological processes support essential physiological functions such as hemostasis, energy metabolism, and immune regulation. Dysregulation of these rhythms can predispose individuals to adverse health effects 17 such as metabolic 18 , inflammatory 19 , psychiatric 1 , and cardiovascular diseases 20 , underscoring the clinical importance of accurately characterizing temporal protein dynamics. Clinically, our results suggest significant implications for biomarker discovery and diagnostic accuracy. Ignoring circadian rhythms could lead to inconsistent biomarker validation, inaccurate reference intervals, and suboptimal therapeutic monitoring. For instance, circadian variation in hemostatic proteins may influence the optimal timing of diagnostic tests or medication administration for conditions like atrial fibrillation if the risk of thrombotic events are found to be varying throughout the day. A prime illustration of circadian-aware testing is serum cortisol measurement. Because cortisol normally peaks in the early morning and reaches a nadir at night, clinicians measure morning serum cortisol to screen for adrenal insufficiency, whereas late‐night salivary cortisol helps diagnose hypercortisolism (Cushing’s syndrome). This timing ensures more accurate assessment of hypothalamic–pituitary–adrenal axis function 21 . Building upon these established principles, our findings suggest that many other clinically used biomarkers with significant circadian oscillations may also benefit from standardized sampling times or time‐adjusted reference intervals to improve diagnostic precision and therapeutic outcomes. Proteins such as amylase, found in our study to have a circadian rhythm and is commonly used as a biomarker to assess acute and chronic pancreatitis 22 , and cystatin C, which can be used to estimate the glomerular filtration rate and monitor kidney function 23 , may benefit from the use of time-sensitive reference ranges to improve diagnostic precision and patient management. The presence of similar circadian patterns in routinely collected clinical data further reinforces the broader relevance of our findings. Observing significant rhythms in albumin, aPTT, and PT (INR) across a clinically heterogeneous population suggests that circadian variation is not limited to controlled conditions, but is a robust feature of real-world clinical biomarkers. This highlights the importance of accounting for sampling time in both diagnostic interpretation and biomarker research. While the LABKA dataset was not designed as a validation study, the consistent oscillatory patterns observed lend additional translational support to our findings and highlight the real-world complexity of applying circadian biology to clinical diagnostics. Notably, the peak time of albumin differed between our controlled study and the LABKA dataset, shifting from nighttime to early afternoon. This discrepancy likely reflects behavioral and environmental influences present in clinical settings—such as feeding patterns, posture, and activity—that are tightly regulated under experimental conditions. In addition, albumin is a negative acute-phase protein 24 , and lower levels observed in night-time hospital samples may reflect a sampling bias toward acutely ill patients, whereas daytime testing includes individuals undergoing routine or elective evaluations. These factors may jointly contribute to the observed phase shift and highlight the need for further research into how health status and care context shape circadian biomarker patterns. Although integrating circadian-aware sampling into clinical diagnostics introduces practical and logistical challenges, such as more complex scheduling and adjustments to reference intervals, the potential improvements in diagnostic accuracy and treatment optimization are substantial. Thus, future guidelines should consider incorporating time-of-day recommendations for biomarkers with pronounced rhythmicity to enable more precise and individualized medical care. Strengths and limitations Our study’s strengths include a thoroughly controlled experimental environment, standardized sampling conditions, and advanced mass spectrometry methods minimizing potential biases inherent in antibody- or aptamer-based assays. Furthermore, robust statistical analysis with the CosinorPy rhythmometry, including z-scoring and correction for multiple testing using the Benjamini-Hochberg procedure, strengthened confidence in the rhythmicity estimates and reduced the risk of false discovery. However, several limitations exist. Firstly, generalizability is limited by our homogeneous study population of healthy young men with regular lifestyles, which may not reflect the circadian variability seen in clinical populations with broader demographic and behavioral diversity. Secondly, the exclusion of women is a notable limitation, particularly given that sex-specific differences in circadian regulation of liver gene expression have been demonstrated in mice 25 , and that female mice have shown greater circadian resilience under disruptive conditions 26 . While these findings may not fully translate to humans, they underscore the need for future studies to include both sexes to evaluate potential sex-based differences in plasma circadian proteomics. Additionally, out of a total of 216 plasma samples collected, eight (3.7%) were excluded due to low protein count, potentially introducing minor bias. Nevertheless, the stringent filtering and robust statistical methodology mitigate the impact of these missing data points, preserving the overall validity of our findings. Future Research Our findings open several important avenues for future research. Further studies should expand investigations into populations beyond healthy young men, including diverse age groups, females, and individuals with varying chronotypes or circadian disruptions (e.g., shift work, metabolic disorders), as well as exploring how commonly prescribed medications may alter circadian regulation. Additionally, prospective clinical trials evaluating the practical integration of circadian-aware blood sampling protocols into routine diagnostics could clarify the impact on diagnostic accuracy, therapeutic effectiveness, and patient outcomes. Finally, mechanistic studies are needed to reveal the molecular pathways underpinning circadian oscillations of plasma proteins, potentially revealing novel therapeutic targets and biomarkers. As our findings were obtained under tightly controlled experimental conditions, future studies should examine how real-world clinical rhythms—such as exposure to artificial lighting, irregular dietary habits, and shift-work—affect plasma protein rhythmicity in more variable environments. Conclusion In conclusion, our comprehensive proteomic analysis reveals extensive circadian regulation within the human plasma proteome, identifying rhythmic fluctuations in 26% of the human plasma proteome measured using the described conditions. By using an unbiased mass spectrometry approach, we provide accurate characterization and overcome methodological biases inherent in previous targeted studies, highlighting numerous oscillatory proteins with a potential clinical relevance. Recognizing these circadian variations through standardized sampling protocols or the establishment of time-sensitive reference intervals could significantly improve diagnostic accuracy, therapeutic monitoring, and the design of future biomarker studies. This conclusion is further supported by the observation of similar circadian patterns in routinely collected clinical laboratory data, reinforcing the applicability of our findings to real-world diagnostics. Future research should explore circadian variation in broader and more diverse clinical populations, mechanisms underpinning the underlying mechanisms, and evaluate how time-aware practices can be pragmatically implemented in routine care. Integrating circadian biology into both clinical diagnostics and biomarker development represents a pivotal step toward more precise, personalized, and time-informed medicine. Declarations Contributors NJWA conceived the study and initiated the investigation of circadian rhythms in the human plasma proteome. HPS and HLJ conducted the clinical experiments and HPS provided the plasma samples. CR prepared the samples for analysis, and GZP performed the mass spectrometry analysis. CYCY conducted the preliminary data analysis. ABN carried out the statistical analysis, and together with EMSJ, performed the final data analysis and interpretation. NJWA, CYCY, HPS, JH, and ABN contributed with intellectual input and critical discussion. EMSJ wrote the manuscript. All authors reviewed and approved the final version of the manuscript. Declaration of interests NJWA has received funding from and served on scientific advisory panels and/or speakers’ bureaus for Boehringer Ingelheim, MSD/MERCK, Roche, Novo Nordisk and Mercodia. The remaining authors have no conflicts of interest to declare. Data sharing Data availability The mass spectrometry proteomics data will deposited to the ProteomeXchange Consortium via the Proteomics Identifications Database (PRIDE) partner repository upon publication. Data will also be made available in the form of an interactive online app upon publication. Code availability The jupyter notebooks will be available at github upon publication together with the source data behind the analysis. Acknowledgements We thank the Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Denmark, for performing mass spectrometry analysis of the plasma samples. We are also grateful to Fie Johanne Andreasen for providing the regional clinical laboratory test preference list (EPIC system, Region H, Denmark). Some visual elements used in Figure 1 were created with BioRender.com. Funding NJWA is supported by the European Foundation for the Study of Diabetes Future Leader Award (NNF21SA0072746), Independent Research Fund Denmark (1052-00003B, 10.46540/4308-00056B, 10.46540/4285-00131B), Novo Nordisk Foundation (NNF23OC0084970, NNF19OC0055001 and NNF24OC0088402). The Novo Nordisk Foundation Center for Protein Research is supported financially by the Novo Nordisk Foundation (NNF14CC0001). Copyright © 2025 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). References Walker WH 2nd, Walton JC, DeVries AC, Nelson RJ. Circadian rhythm disruption and mental health. Transl Psychiatry. 2020;10(1):28. Ruan W, Yuan X, Eltzschig HK. Circadian rhythm as a therapeutic target. Nat Rev Drug Discov. 2021;20(4):287–307. Geyer PE, Voytik E, Treit PV, Doll S, Kleinhempel A, Niu L, Müller JB, Buchholtz ML, Bader JM, Teupser D, Holdt LM, Mann M. Plasma Proteome Profiling to detect and avoid sample-related biases in biomarker studies. EMBO Mol Med. 2019;11(11):e10427. Birhanu AG. Mass spectrometry-based proteomics as an emerging tool in clinical laboratories. Clin Proteom. 2023;20(1):32. Sennels HP, Jørgensen HL, Hansen AL, Goetze JP, Fahrenkrug J. Diurnal variation of hematology parameters in healthy young males: the Bispebjerg study of diurnal variations. Scand J Clin Lab Invest. 2011;71(7):532–41. Zilstorff DB, Richter MM, Hannibal J, Jørgensen HL, Sennels HP, Wewer Albrechtsen NJ. Secretion of glucagon, GLP-1 and GIP may be affected by circadian rhythm in healthy males. BMC Endocr Disord. 2024;24(1):38. Demichev V, Messner CB, Vernardis SI, Lilley KS, Ralser M. DIA-NN: neural networks and interference correction enable deep proteome coverage in high throughput. Nat Methods. 2020;17(1):41–4. Webel H, Niu L, Nielsen AB, Locard-Paulet M, Mann M, Jensen LJ, Rasmussen S. Imputation of label-free quantitative mass spectrometry-based proteomics data using self-supervised deep learning. Nat Commun. 2024;15(1):5405. Moškon M. CosinorPy: a python package for cosinor-based rhythmometry. BMC Bioinformatics. 2020;21(1):485. Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, Davis AP, Dolinski K, Dwight SS, Eppig JT, Harris MA, Hill DP, Issel-Tarver L, Kasarskis A, Lewis S, Matese JC, Richardson JE, Ringwald M, Rubin GM, Sherlock G. Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat Genet. 2000;25(1):25–9. Huang da W, Sherman BT, Lempicki RA. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc. 2009;4(1):44–57. Sherman BT, Hao M, Qiu J, Jiao X, Baseler MW, Lane HC, Imamichi T, Chang W. DAVID: a web server for functional enrichment analysis and functional annotation of gene lists (2021 update). Nucleic Acids Res. 2022;50(W1):W216–21. Specht A, Kolosov G, Cederberg KLJ, Bueno F, Arrona-Palacios A, Pardilla-Delgado E, Ruiz-Herrera N, Zitting KM, Kramer A, Zeitzer JM, Czeisler CA, Duffy JF, Mignot E. Circadian protein expression patterns in healthy young adults. Sleep Health. 2024;10(1S):S41–51. Budkowska M, Lebiecka A, Marcinowska Z, Woźniak J, Jastrzębska M, Dołęgowska B. The circadian rhythm of selected parameters of the hemostasis system in healthy people. Thromb Res. 2019;182:79–88. Scheer FA, Michelson AD, Frelinger AL 3rd, Evoniuk H, Kelly EE, McCarthy M, Doamekpor LA, Barnard MR, Shea SA. The human endogenous circadian system causes greatest platelet activation during the biological morning independent of behaviors. PLoS ONE. 2011;6(9):e24549. Guo T, Steen JA, Mann M. Mass-spectrometry-based proteomics: from single cells to clinical applications. Nature. 2025;638(8052):901–11. Foster RG. Sleep, circadian rhythms and health. Interface Focus. 2020;10(3):20190098. Schrader LA, Ronnekleiv-Kelly SM, Hogenesch JB, Bradfield CA, Malecki KM. Circadian disruption, clock genes, and metabolic health. J Clin Invest. 2024;134(14):e170998. Feuth T. Interactions between sleep, inflammation, immunity and infections: A narrative review. Immun Inflamm Dis. 2024;12(10):e70046. Chaput JP, Biswas RK, Ahmadi M, Cistulli PA, Rajaratnam SMW, Bian W, St-Onge MP, Stamatakis E. Sleep regularity and major adverse cardiovascular events: a device-based prospective study in 72 269 UK adults. J Epidemiol Community Health. 2025;79(4):257–64. Nieman LK. Recent Updates on the Diagnosis and Management of Cushing's Syndrome. Endocrinol Metab (Seoul). 2018;33(2):139–46. Szatmary P, Grammatikopoulos T, Cai W, Huang W, Mukherjee R, Halloran C, Beyer G, Sutton R. Acute Pancreatitis: Diagnosis and Treatment. Drugs. 2022;82(12):1251–76. Chen DC, Potok OA, Rifkin D, Estrella MM. Advantages, Limitations, and Clinical Considerations in Using Cystatin C to Estimate GFR. Kidney360. 2022;3(10):1807–14. Jain S, Gautam V, Naseem S. Acute-phase proteins: As diagnostic tool. J Pharm Bioallied Sci. 2011;3(1):118–27. Astafev AA, Mezhnina V, Poe A, Jiang P, Kondratov RV. Sexual dimorphism of circadian liver transcriptome. iScience. 2024;27(4):109483. Anderson ST, Meng H, Brooks TG, Tang SY, Lordan R, Sengupta A, Nayak S, Mřela A, Sarantopoulou D, Lahens NF, Weljie A, Grant GR, Bushman FD, FitzGerald GA. Sexual dimorphism in the response to chronic circadian misalignment on a high-fat diet. Sci Transl Med. 2023;15(696):eabo2022. Additional Declarations Competing interest reported. NJWA has received funding from and served on scientific advisory panels and/or speakers’ bureaus for Boehringer Ingelheim, MSD/MERCK, Roche, Novo Nordisk and Mercodia. The remaining authors have no conflicts of interest to declare. Supplementary Files AppendixA.pdf Cite Share Download PDF Status: Published Journal Publication published 22 Aug, 2025 Read the published version in Clinical Proteomics → Version 1 posted Editorial decision: Revision requested 23 Jun, 2025 Reviews received at journal 27 May, 2025 Reviewers agreed at journal 27 May, 2025 Reviewers agreed at journal 23 May, 2025 Reviewers invited by journal 21 May, 2025 Editor assigned by journal 20 May, 2025 Submission checks completed at journal 20 May, 2025 First submitted to journal 19 May, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6695734","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":459948223,"identity":"c7c6b034-6c74-4f4f-8314-05ea16d8a2e6","order_by":0,"name":"Elvar M. S. 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Jørgensen","email":"","orcid":"","institution":"University of Copenhagen","correspondingAuthor":false,"prefix":"","firstName":"Henrik","middleName":"L.","lastName":"Jørgensen","suffix":""},{"id":459948230,"identity":"dc6fdc78-4bc1-4b45-b77d-62a3da95296c","order_by":3,"name":"Jens Hannibal","email":"","orcid":"","institution":"Copenhagen University Hospital - Bispebjerg","correspondingAuthor":false,"prefix":"","firstName":"Jens","middleName":"","lastName":"Hannibal","suffix":""},{"id":459948231,"identity":"fee09141-3f7e-4433-93d1-14617be815b9","order_by":4,"name":"Ching-Yan Chloé Yeung","email":"","orcid":"","institution":"Copenhagen University Hospital – Bispebjerg and Frederiksberg","correspondingAuthor":false,"prefix":"","firstName":"Ching-Yan","middleName":"Chloé","lastName":"Yeung","suffix":""},{"id":459948236,"identity":"69b3753b-7b94-47df-bc59-1fd6a7f7e453","order_by":5,"name":"Christine Rasmussen","email":"","orcid":"","institution":"Copenhagen University Hospital - Bispebjerg","correspondingAuthor":false,"prefix":"","firstName":"Christine","middleName":"","lastName":"Rasmussen","suffix":""},{"id":459948237,"identity":"9e854993-4c04-4cb0-a298-a9907e969b94","order_by":6,"name":"Gabriela Zofia Prus","email":"","orcid":"","institution":"University of Copenhagen","correspondingAuthor":false,"prefix":"","firstName":"Gabriela","middleName":"Zofia","lastName":"Prus","suffix":""},{"id":459948238,"identity":"a6dd937a-f337-4d94-89a1-9ce866c0d76b","order_by":7,"name":"Nicolai J. Wewer Albrechtsen","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABMUlEQVRIie3PMUvDQBQH8BcC6XI16wtK8xUuBKJOfpUUwSmLW8EYEgrtYpwrBPwKfoTIwXUJdU3BIV2cMqSLuFi8a3TQpIqbSP7cPe4e94N7AF26/MWoYheAAD0ir6ksSiEKqVfaTlxJ1Jqg7NFvCWwJfCIa1v12cthTV4XrH4E+7vOq8h8Dcxrzi9Ho8kDfj1MoswY5Hms2dTkCsr3Tmxl/QpotzpZZNidGsnCVJG8QyoiGrvwJI7baDxlS9JxlNOGE5h5VSbWDbBBMSV4FMW9L5zza/ECGE5QHW1UEgZw4ShT676TtY2KW4TUSS8yiXHFm3GWebYQ8JcbMo/dJc3z6wFZF9RwMBvOYwYvPdHOaWevQD0509Kyi5A3yEfK1wbY13QlaEvzmcZcuXbr877wBXftqfS8YXQcAAAAASUVORK5CYII=","orcid":"","institution":"Copenhagen University Hospital - Bispebjerg","correspondingAuthor":true,"prefix":"","firstName":"Nicolai","middleName":"J. Wewer","lastName":"Albrechtsen","suffix":""},{"id":459948239,"identity":"50451b26-8b40-43d2-84d6-7761277f6a0f","order_by":8,"name":"Annelaura Bach Nielsen","email":"","orcid":"","institution":"Copenhagen University Hospital - Bispebjerg","correspondingAuthor":false,"prefix":"","firstName":"Annelaura","middleName":"Bach","lastName":"Nielsen","suffix":""}],"badges":[],"createdAt":"2025-05-19 06:38:31","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6695734/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6695734/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12014-025-09551-7","type":"published","date":"2025-08-22T16:29:26+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":83513894,"identity":"806f5d40-0269-401f-af28-06481cdfe01a","added_by":"auto","created_at":"2025-05-27 17:45:31","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":209639,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eOverview of the Experimental Workflow.\u003c/strong\u003e \u003cstrong\u003ea) \u003c/strong\u003eStudy design detailing blood sampling every 3 hours over 24 hours under standardized conditions, including controlled meal composition, caloric content, and precise sleep-wake intervals for participants. Participants consumed standardized mixed meals: breakfast (846 kcal, carbohydrate 47.8 energy percent (E%), protein 16.3 E%, fat 35.1 E%), lunch (636 kcal, carbohydrate 58.8 E%, protein 19.9 E%, fat 21.1 E%), and dinner (841 kcal, carbohydrate 45.3 E%, protein 19.6 E%, fat 35.1 E%). \u003cstrong\u003eb)\u003c/strong\u003e Workflow schematic illustrating plasma isolation, enzymatic digestions, peptide separation via liquid chromatography, mass spectrometry analysis (LC-MS/MS), and statistical analysis for identification of proteins exhibiting significant circadian rhythmicity.\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-6695734/v1/b627e7561da401414fb9e543.png"},{"id":83514476,"identity":"8135fdd3-4366-4bd6-8ccf-fea03cd188f4","added_by":"auto","created_at":"2025-05-27 17:53:31","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":103948,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eKey Metrics of Proteomic Data Quality.\u003c/strong\u003e \u003cstrong\u003ea) \u003c/strong\u003eDistribution of identified plasma proteins per sample. The boxplot indicates median proteomic coverage (533 proteins per sample) and individual data points reflecting variability among 216 samples collected from 24 healthy male participants. Eight of the 216 samples were excluded due to a low protein count (protein count\u0026lt;457). \u003cstrong\u003eb)\u003c/strong\u003e Bubble plots depicting the relationship between the coefficient of variation (%) and residual standard error or amplitude from the CosinorPy analysis for plasma proteins exhibiting significant circadian rhythmicity. Each dot represents a quantified protein (n=523), with significantly rhythmic proteins (n=138) highlighted. Significance is showed as point sizes (-log10 adjusted p-values (Benjamini-Hochberg correction; significance threshold indicated at adjusted p\u0026lt;0.05)). Coefficient of variation represents the variation across samples and timepoints, residual standard errors represent the distance from the fitted rhythmic regression to the actual protein measurements. The amplitude represents the magnitude of circadian oscillation of the fitted rhythmic regression.\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-6695734/v1/930d1790c4c98b4a54866f61.png"},{"id":83513897,"identity":"4d0cba2d-4bc7-4992-80ca-1d6bcd5807f4","added_by":"auto","created_at":"2025-05-27 17:45:31","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":726952,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eProtein Abundance Heatmap with Tissue and Pathway Enrichments.\u003c/strong\u003e Hierarchical clustering of circadian‐regulated plasma proteins (z‐scored abundance) sampled every three hours over 24 hours revealed two distinct clusters based on peak time (acrophase). Cluster A (91 proteins) peaks mainly in the afternoon, with a median acrophase at 16:30 (interquartile range [IQR] 12:50–17:10), whereas Cluster B (47 proteins) peaked earlier, with a median acrophase at 03:20 (IQR 02:00–05:50). Tissue enrichment is shown on the right, highlighting liver and platelet origins, among others, while Reactome pathway enrichment show 19 functional clusters of protein groups related to processes such as hemostasis, immune regulation, and metabolic pathways. Note: Acrophase values are absolute 24-h clock times (0-24 h), not times relative to sampling start.\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-6695734/v1/ab3fdc601fee677aafa276c5.png"},{"id":83513898,"identity":"a3f6d7b0-8c64-4c25-93ff-280e626c1f61","added_by":"auto","created_at":"2025-05-27 17:45:31","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":249280,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eProtein Count in Tissue and Pathway Enrichments. a)\u003c/strong\u003e Bar plot illustrating the distribution of circadian proteins across enriched tissue types, underscoring the predominance of liver‐ and platelet‐associated proteins. \u003cstrong\u003eb)\u003c/strong\u003e Bar plot quantifying the number of circadian proteins represented in each of the 19 pathway clusters.\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-6695734/v1/b90839a6dadb52f399cc9295.png"},{"id":83513895,"identity":"7f98b7ed-4b87-4039-831c-1a17ea1e0651","added_by":"auto","created_at":"2025-05-27 17:45:31","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":154032,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTemporal Dynamics of Circadian Plasma Protein Clusters.\u003c/strong\u003e \u003cstrong\u003ea)\u003c/strong\u003e Boxplot representation illustrating the median protein abundance (amplitude, normalized z-score) with interquartile ranges over 24 hours, separated into two distinct clusters. Cluster A exhibits peak abundance during the afternoon hours, whereas Cluster B peaks in the early morning. \u003cstrong\u003eb)\u003c/strong\u003e Line graph displaying individual protein trajectories across 24 hours, grouped into the same two clusters. This visualization emphasizes the consistent but opposite circadian fluctuations in protein levels, reinforcing distinct temporal dynamics for Cluster A and Cluster B. Selected representative proteins within each cluster are indicated. \u003cstrong\u003ec)\u003c/strong\u003e Polar histogram illustrating the distribution of peak times (acrophases) for circadian plasma proteins grouped into two clusters. Cluster A (Afternoon, orange) shows a concentration of peak expression in the late afternoon, while Cluster B (Early morning, purple) peaks earlier in the day. The radial axis indicates the number of proteins peaking at each time point, and the circular layout represents the 24-hour cycle. Note: Acrophase values reflect actual peak clock time in hours (0–24 hr), not relative to a reference point in the circadian cycle.\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-6695734/v1/89a410158fbba215131c6484.png"},{"id":83513901,"identity":"1560da2f-e6f8-4c1f-9661-0aa931405828","added_by":"auto","created_at":"2025-05-27 17:45:31","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":230714,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCircadian rhythms in routine clinical laboratory measurements\u003c/strong\u003e. Timestamped laboratory data from the LABKA database, illustrating circadian rhythmicity for (a) Albumin, (b) activated partial thromboplastin time (aPTT), and (c) prothrombin time (PT/INR) over a one-week observational period (April 1–7, 2025). Individual measurements (blue dots) and fitted cosinor curves (red lines) confirm significant circadian oscillations (Benjamini–Hochberg adjusted p\u0026lt;0.001 for all parameters). Albumin peaks in the early afternoon (13:12), whereas aPTT and PT peak in the early morning hours (01:57 and 04:49, respectively).\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-6695734/v1/2cd3658bac25a361dfd893a3.png"},{"id":89847806,"identity":"f5e63821-f7b3-45d6-9ef9-bd75074634b2","added_by":"auto","created_at":"2025-08-25 16:44:16","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2749314,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6695734/v1/461be18c-3780-48d5-940e-c8b165829a9b.pdf"},{"id":83514477,"identity":"fa117b28-f06c-43e8-b76b-e3592cfba02b","added_by":"auto","created_at":"2025-05-27 17:53:31","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":474774,"visible":true,"origin":"","legend":"","description":"","filename":"AppendixA.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6695734/v1/b98c16ddf4f1eefa60d5925c.pdf"}],"financialInterests":"Competing interest reported. NJWA has received funding from and served on scientific advisory panels and/or speakers’ bureaus for Boehringer Ingelheim, MSD/MERCK, Roche, Novo Nordisk and Mercodia. The remaining authors have no conflicts of interest to declare.","formattedTitle":"Circadian Rhythm of the Human Plasma Proteome","fulltext":[{"header":"Introduction","content":"\u003cp\u003eBlood is the most used diagnostic specimen. Despite significant advancements in biomarker discovery, most clinical diagnostics do not account for circadian fluctuations in circulating proteins. This gap could lead to misinterpretation of results and suboptimal treatment timing, affecting patient care and research outcomes. Understanding these temporal variations could improve diagnostic accuracy, optimize clock-based therapeutic strategies, and enhance biomarker research.\u003c/p\u003e \u003cp\u003eThe circadian rhythm (Latin: \u003cem\u003ecirca\u003c/em\u003e meaning \u0026lsquo;approximately\u0026rsquo; and \u003cem\u003ediem\u003c/em\u003e meaning \u0026lsquo;day\u0026rsquo;) governs 24 hour oscillations in biological processes. It is regulated by a central biological clock in the hypothalamic suprachiasmatic nucleus (SCN), also known as the master clock, and by peripheral clocks in tissues throughout the body. While the functions of central and peripheral clocks are well understood, their interactions remain unclear. At the cellular level, a transcriptional-translational feedback loop drives rhythmic gene expression, leading to oscillations in biological functions throughout the day \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. These rhythms are modulated by \u003cem\u003ezeitgebers\u003c/em\u003e (German: \u0026lsquo;time givers\u0026rsquo;), which are external and internal cues, signals that synchronize (entrain) the biological rhythm to the 24 hour sleep and wake cycle. Light is the primary zeitgeber, but feeding, exercise, and temperature also play crucial roles in circadian entrainment \u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eProteins are the most frequently used biomarkers \u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. However, little is known about the circulating proteome during circadian rhythm in healthy humans. High-throughput proteomic analysis involves the study of proteins, including their expression pattern and function, and this is typically obtained by mass spectrometry-based approaches. This technology is increasingly being used, not only for biomarker research, and is increasingly implemented in clinical diagnostics \u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eTo investigate the circadian regulation of plasma proteins, we analyzed plasma samples from 24 healthy individuals. Blood samples were collected nine times over a 24 hour-period while the participants remained in a standardized environment with controlled light exposure, food intake, movement, and sleep conditions. Mass spectrometry (MS)-based proteomics was applied to analyze plasma protein dynamics. By identifying circadian-regulated plasma proteins, this study aims to provide insight into potential diagnostic implications, and highlight how circadian awareness can inform optimal timing for blood-based diagnostics and therapeutic interventions.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eResearch in context\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEvidence before this study\u003c/b\u003e\u003c/p\u003e \u003cp\u003eBlood sampling is fundamental to diagnosis and treatment in hospital settings. However, research on circadian rhythms in the plasma proteome, and their implications for diagnostic accuracy, remains limited. We searched Google Scholar up to February 26, 2025, using the terms \u0026ldquo;Plasma Proteins and Circadian Rhythm\u0026rdquo;, \u0026ldquo;Plasma Proteins and Diurnal Variations\u0026rdquo;, \u0026ldquo;Circadian Rhythm and Plasma Proteome\u0026rdquo; and \u0026ldquo;Mass Spectrometry, Plasma Proteomics and Circadian Rhythm\u0026rdquo;. We identified fewer than five relevant studies, most of which relied on SomaScan aptamer-based assays that provide only partial protein coverage and may introduce bias in specificity and quantitation. Consequently, unbiased mass-spectrometry analyses of circadian rhythm protein variation in healthy individuals are lacking. This gap hinders our understanding of how time-of-day protein fluctuations affect patient testing, protein research and ultimately diagnostic accuracy.\u003c/p\u003e \u003cp\u003e\u003cb\u003eAdded value of this study\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUsing a high-throughput, unbiased mass spectrometry approach with multiple time point sampling in 24 healthy individuals, we found that 26% of plasma proteins exhibited significant circadian rhythms. Our study offers a comprehensive overview of how the plasma proteome varies by time-of-day, providing critical insights that can guide future research and help refine diagnostic protocols.\u003c/p\u003e \u003cp\u003e\u003cb\u003eImplications of all the available evidence\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThese findings suggest that time-of-day fluctuations in plasma proteins may be relevant to the interpretation of clinical blood tests. However, additional research in larger and more diverse cohorts is needed to determine whether standardized sampling times or time-sensitive reference ranges would improve diagnostic accuracy and patient outcomes. Moreover, our results imply that previous proteomic investigations may have been influenced by the lack of circadian consideration, emphasizing the potential value of carefully timed sampling and circadian considerations in future diagnostics, treatment and research.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Design and Approvals\u003c/h2\u003e \u003cp\u003eThe Bispebjerg Study of Diurnal Variation has been described previously \u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e and is summarized here. This prospective time-series analysis involved a 24-hour hospital stay under standardized conditions. Participants spent 15 hours awake in ordinary daylight or room light (mean light intensity 219 lux) and 9 hours asleep in the dark (mean 0.04 lux), from 11:00 PM to 8:00 AM. During the day the, the participants were prohibited from napping but permitted low-intensity activities such as walking, television watching and reading \u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eStandardized isocaloric, low-fat, sugar-free meals were provided at 9:30 AM, 1:00 PM and 7:00 PM. Water intake was not restricted. Meals were identical for all participants, without individual caloric adjustments or measurement of food intake. Meal composition has been described previously \u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e and is presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The participants fasted for 11 hours before the start of the study \u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe study was conducted in 2008 at the Department of Clinical Biochemistry, Bispebjerg Hospital, Copenhagen, Denmark. It was approved by the local independent ethics committee (protocol number H-B-2008-011) and the Danish Data Protection Agency (journal number 2008-41-1821). It was conducted according to the Helsinki Declaration, with all participants signing a written informed consent. The trial has been retrospectively registered at clinicaltrials.gov with the identifier NCT06166368.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eParticipants\u003c/h3\u003e\n\u003cp\u003eEligible participants were healthy men aged 18\u0026ndash;45 years with a regular sleep-wake cycle and hemoglobin concentration greater than 8.0 mmol/L. Exclusion criteria included an acute or chronic illness, use of medication within the past 30 days, regular tobacco use, night-shift work, recent time zone shift (travel), or increased alcohol consumption or smoking within the last 14 days prior to the study. Strict inclusion/exclusion criteria were implemented to ensure homogeneity and minimize participant variation.\u003c/p\u003e \u003cp\u003e Participants were recruited through advertisements at the Faculty of Health Science, University of Copenhagen. A total of twenty-four healthy Caucasian male volunteers aged 20\u0026ndash;40 (mean age 26 years) were included \u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n\u003ch3\u003eProcedures\u003c/h3\u003e\n\u003cp\u003eBlood sampling was performed every three hours over a 24-hour period, beginning at 9:00 AM, for a total of nine collections (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Samples were drawn from the cubital vein in alternating arms at each time point using minimal tourniquet application and collected into serum clot activator tubes coated with microscopic silica particles (Greiner Bio-one, Frickenhausen, Germany). Tubes were centrifuged, plasma was isolated, and immediately stored at -80\u0026deg;C until analysis.\u003c/p\u003e \u003cp\u003eDuring the wake period, blood was drawn following a 10-minute rest, with participants seated at a 45-degree angle in a hospital bed, legs extended and positioned horizontally. During the sleep period, blood samples were collected with minimal disturbance using low-intensity red light while participants remained in a supine position.\u003c/p\u003e \u003cp\u003eAdditional measurements included blood pressure, pulse and self-reported height and weight. The body mass index (BMI, kg/m\u003csup\u003e2\u003c/sup\u003e) was calculated from height and weight. Light intensity was measured using the RS 180\u0026ndash;7133 lux meter (RS Components, Corby, United Kingdom) \u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n\u003ch3\u003eProteomic analysis\u003c/h3\u003e\n\u003cp\u003eEach plasma sample was aliquoted into a 96-well plate and prepared on an Agilent Bravo Liquid Handling Platform. Samples were diluted 1:10 with lysis buffer (1M Tris, 0.5M Tris(2-carboxyethyl)phosphine (TCEP) and 0.5M Chloroacetamide (CAA) in H\u003csub\u003e2\u003c/sub\u003eO) and incubated at 95\u0026deg;C for 10 minutes. After cooling to RT, a Trypsin/LysC mixture (1 \u0026micro;g to 100 \u0026micro;g protein) was added and incubated for 4 h at 37\u0026deg;C at 1000 rpm. The enzymatic reaction was quenched by adding 64 \u0026micro;l of 0.2% TFA. The samples were loaded onto Evotips according to the manufacturer\u0026rsquo;s recommendations (Evosep Biosystem, Denmark). Briefly, Evotips were prepared by washing with buffer B (100% acetonitrile (ACN), 1% formic acid (FA)), activated by soaking in isopropanol, and equilibrated with 20 \u0026micro;l of buffer A (1% FA) before loading the samples. Evotips were then loaded with 250 ng of peptides per sample, followed by a wash with 20 \u0026micro;l of buffer A. Each step was followed by a 1-minute centrifugation at 700g to facilitate liquid passage. Finally, the Evotips were stored in buffer A to prevent drying.\u003c/p\u003e \u003cp\u003eLC\u0026ndash;MS/MS analysis was performed using an Orbitrap Astral mass spectrometer (Thermo Scientific) coupled to an Evosep One system (Evosep Biosystem, Denmark). Peptide separation was carried out on a commercial 8 cm analytical \u0026lsquo;Performance\u0026rsquo; column (EV1109, Evosep Biosystem, Denmark) using the predefined 60 samples per day method (21-minute gradient) and analyzed in data-independent acquisition mode. The mass spectrometer operated in positive mode. Full MS spectra (380\u0026ndash;980 m/z) were acquired using the Orbitrap analyzer with a resolution of 240,000 at 200 m/z. Precursor ions were isolated with an automatic gain control (AGC) target of 500% (5e6 charges) and a maximum injection time (maxIT) of 3 ms. In parallel to the full MS scan, fragment spectra of 200 consecutive windows (3-Th width) within the 380\u0026ndash;980 m/z precursor mass range were recorded using the Astral analyzer operating at the resolution of 80,000. Precursor ions were isolated with an AGC target of 500% (5e4 charges) and a maxIT of 5 ms, then fragmented at 25% normalized collision energy.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003eData processing\u003c/h2\u003e \u003cp\u003eRaw mass spectrometry data were initially processed using DIA-NN (version 1.9) in a data-independent acquisition (DIA) search \u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. Further data processing was conducted using python, where a stringent filtering approach was applied to address missing data: (1) samples with a low protein count\u0026mdash;defined as values below 1.5 times the interquartile range (IQR) from the 25th percentile of the combined distribution\u0026mdash;were excluded, and (2) proteins missing in more than 40% of samples were removed. Log\u003csub\u003e2\u003c/sub\u003e transformation was applied to the data, and remaining missing values were imputed using the variational autoencoder implemented in PIMMS \u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eData analysis\u003c/h2\u003e \u003cp\u003eDifferential protein abundance according to circadian rhythm was assessed using the CosinorPy python package \u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e with multiple hypothesis correction applied using the Benjamini-Hochberg method. Adjusted p-values less than 0.05 were considered statistically significant. Protein abundances and p-values were visualized using heatmaps. Agglomerative hierarchical clustering with single linkage was performed to identify clusters of proteins with similar circadian patterns. Heatmaps, combined with clustering dendrograms and sankey plots of Reactome Pathways, were used to highlight changes in significant proteins, protein clusters, and their associated biological relevance \u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. Protein abundances in heatmaps were z-scored.\u003c/p\u003e \u003cp\u003eTo externally validate our findings, we retrieved routine clinical laboratory data from the laboratory information database LABKA, encompassing timestamped measurements of albumin, activated partial thromboplastin time (aPTT), and prothrombin time (PT/INR). Data extraction was restricted to samples collected across hospitals within the Capital Region of Denmark from April 1 to April 7, 2025. Circadian rhythmicity was subsequently assessed via Cosinor analysis, as described previously for the proteomic data.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eEnrichment analysis\u003c/h3\u003e\n\u003cp\u003eTissue and pathway enrichment analyses were conducted for identified protein clusters individually using the Database for Annotation, Visualization, and Integrated Discovery (DAVID) version, 2024 \u003csup\u003e11,12\u003c/sup\u003e. We provided a list of significant proteins under their official gene symbols (e.g., B2M, ALB) and used Homo sapiens (9606) as the background species. Tissue enrichment was performed with the UP_TISSUE database, excluding pathological, fetal, fluid-based (e.g., serum, plasma, cerebrospinal fluid), and placental enrichments. Pathway enrichment used the REACTOME_PATHWAY database, and we further supported these findings with Gene Ontology Biological Processes (GOTERM_BP_FAT) analysis. Enrichments with Benjamini\u0026ndash;Hochberg\u0026ndash;adjusted p-values below 0.05 were deemed significant.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eOutcome\u003c/h2\u003e \u003cp\u003eThe primary outcome was to assess circadian variation in plasma protein levels through blood samples collected every 3 hours over 24 hours from all participants. The secondary outcome was to determine whether circadian variations in plasma proteins have diagnostic relevance and potential implications for clinical decision-making.\u003c/p\u003e \u003cp\u003eThe study design, controlled environment, and strict inclusion/exclusion criteria minimized the influence of zeitgebers and confounders. Steps to reduce bias included standardized sample collection protocols to minimize measurement and performance bias.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eRole of the funding source\u003c/h2\u003e \u003cp\u003eThe funder of the study had no role in study design, data collection, data analysis, data interpretation, or writing of the report.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eFrom the 24 healthy individuals (age 20\u0026ndash;40 years, mean 26.0\u0026thinsp;\u0026plusmn;\u0026thinsp;5.2 years), a total of 216 plasma samples were collected over 24 hours. Eight samples (3.7%) were excluded due to insufficient quality, defined by low protein count (below 1.5\u0026times;IQR from the 25th percentile), leaving 208 high-quality samples available for final proteomic analysis. Baseline characteristics of participants are presented in Table\u0026nbsp;1 \u003csup\u003e5\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003eBaseline Characteristics of Study Participants (n\u0026thinsp;=\u0026thinsp;24 healthy men).\u003c/b\u003e Data are presented as mean (standard deviation, SD) with corresponding 95% confidence interval (95% CI). BMI (body mass index) was calculated from self-reported weight and height \u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean (SD)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRange (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eClinically Accepted Normal Ranges\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (Years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26 (5.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23.8\u0026ndash;28.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeight (kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e76.6 (6.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e73.8\u0026ndash;79.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeight (m)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.83 (0.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.82\u0026ndash;1.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBody mass index, BMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22.9 (1.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22.2\u0026ndash;23.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18.5\u0026ndash;24.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePulse (beats per minute)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e66 (9.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e61.8\u0026ndash;70.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e60\u0026ndash;100\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSystolic blood pressure (mmHg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e128 (10.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e123.7\u0026ndash;132.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;120\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiastolic blood pressure (mmHg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e68 (8.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e64.5\u0026ndash;71.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;80\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHemoglobin concentration (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.2 (0.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.95\u0026ndash;9.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.3\u0026ndash;10.5 (men)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eNumber of participants: 24 healthy men\u003c/p\u003e \u003cp\u003eParticipant characteristics and measurements. SD, standard deviation; CI, confidence interval.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eA total of 832 unique plasma proteins were identified, with a median of 533 proteins per sample (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea). After stringent quality filtering, 523 proteins (62.9%) remained for circadian rhythm analysis. Using CosinorPy Rhythmometry with z-scoring and stringent Benjamini\u0026ndash;Hochberg correction, we identified 138 proteins (26.4%) exhibiting significant circadian rhythmicity (as shown in Supplementary Table\u0026nbsp;5) (mean adjusted p-value: 0.0093, SD: 0.015; 95% CI: 0.0068\u0026ndash;0.012). Proteins with adjusted p-values below 0.05 following Benjamini\u0026ndash;Hochberg correction were considered significantly rhythmic. The variation in protein levels across the day and across individuals (coefficient of variation) correlated significantly both with the residual standard error from the CosinorPy analysis and with the amplitude of the rhythmic peak (Pearson\u0026rsquo;s r\u0026thinsp;=\u0026thinsp;0.79 (p-value\u0026thinsp;=\u0026thinsp;8.121e-113) and r\u0026thinsp;=\u0026thinsp;0.76 (p-value\u0026thinsp;=\u0026thinsp;9.23e-98), respectively) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb). Additionally, the proteins exhibiting circadian rhythmicity were overrepresented among proteins with high coefficient of variation (Odds Ratio\u0026thinsp;=\u0026thinsp;1.755, p-value\u0026thinsp;=\u0026thinsp;0.006) and high amplitude (Odds Ratio\u0026thinsp;=\u0026thinsp;4.67, p-value\u0026thinsp;=\u0026thinsp;3.91e-13).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eHierarchical clustering analysis revealed two distinct circadian clusters, an afternoon cluster (Cluster A; 91 proteins) and an early morning cluster (Cluster B; 47 proteins) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTissue enrichment analysis (DAVID) annotated 129 of 138 rhythmic proteins (93.5%), and was performed separately for the two distinct clusters: afternoon and early morning. Cluster A predominantly originated from the liver (66 proteins, adjusted p\u0026thinsp;=\u0026thinsp;5.8\u0026times;10⁻\u003csup\u003e17\u003c/sup\u003e) and platelets (34 proteins, adjusted p\u0026thinsp;=\u0026thinsp;2.3\u0026times;10⁻\u0026sup2;⁶), but additional enriched tissues included lymphoblasts (16 proteins), T-cells (10 proteins), skeletal muscle (14 proteins), keratinocytes (7 proteins), skin (21 proteins), tongue (13 proteins), fibroblasts (6 proteins), erythrocytes (3 proteins), and lungs (23 proteins), as shown in Supplementary Table\u0026nbsp;3. Cluster B was significantly enriched only in the liver (27 proteins; adjusted p\u0026thinsp;=\u0026thinsp;1.4\u0026times;10⁻\u003csup\u003e5\u003c/sup\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea).\u003c/p\u003e \u003cp\u003eReactome pathway enrichment (DAVID) annotated 79 proteins (86.8%) in Cluster A, and 32 proteins (68.1%) in Cluster B, and revealed extensive circadian rhythmicity across multiple clinically and biologically critical pathways. Among the most significant enrichments were pathways associated with platelet function and coagulation, such as \"Platelet degranulation\", \"Response to elevated platelet cytosolic Ca\u0026sup2;⁺\", \u0026ldquo;Platelet activation, signaling, and aggregation\u0026rdquo;, and the broader \"Hemostasis\" pathway. Platelet activation and hemostasis was the only common denominator between Cluster A and Cluster B, although it was more significantly enriched in Cluster A (adjusted p\u0026thinsp;=\u0026thinsp;5.2\u0026times;10⁻\u003csup\u003e14\u003c/sup\u003e to 2.7\u0026times;10⁻\u003csup\u003e19\u003c/sup\u003e) compared to Cluster B (adjusted p\u0026thinsp;=\u0026thinsp;9.4\u0026times;10⁻\u003csup\u003e3\u003c/sup\u003e to 1.1\u0026times;10⁻\u003csup\u003e4\u003c/sup\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb).\u003c/p\u003e \u003cp\u003eCluster A was significantly enriched in pathways involving immune system function and response, including \"Neutrophil degranulation\" and the broader \"Innate immune system\" processes. Additionally, enrichments in pathways associated with \u0026ldquo;Rho GTPase Signal Transduction\u0026rdquo;, \u0026ldquo;Cell-Matrix, Cell-Cell Adhesion, and Cytoskeletal Remodeling\u0026rdquo;, \u0026ldquo;Axon Guidance and Neural development\u0026rdquo;, \u0026ldquo;Oncogenic RAS/RAF-MAPK Signaling and Integrin Signaling\u0026rdquo;, \u0026ldquo;Interleukin-12 and JAK/STAT Cytokine Signaling\u0026rdquo;, \u0026ldquo;Glucose Metabolism\u0026rdquo; including \u0026ldquo;Glunoneogenesis\u0026rdquo; and \u0026ldquo;Glycolysis\u0026rdquo;, and more (Supplementary table 4).\u003c/p\u003e \u003cp\u003eImportantly, Cluster B enrichment extended beyond coagulation-related pathways to include significant circadian regulation in protein metabolism and growth signaling processes, such as \"Regulation of Insulin-like Growth Factor transport and uptake by IGFBPs\", \"Post-translational protein phosphorylation\", and \u0026ldquo;Metabolism of proteins\u0026rdquo;. Notably, pathways involving lipoprotein metabolism and transport also displayed significant rhythmicity, including \u0026ldquo;Plasma lipoprotein assembly, remodeling, and clearance\u0026rdquo;, \u0026ldquo;Chylomicron assembly\u0026rdquo;, and \u0026ldquo;HDL remodeling\u0026rdquo;. Additionally, enrichments in pathways associated with the coagulation cascade and specifically, fibrin clot formation were significant in the early morning peak cluster of proteins.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe Gene Ontology Biological Processes (GOBP) enrichment analysis (DAVID) matched 89 proteins from Cluster A (97.8%) and 46 proteins from Cluster B (97.9%), and corroborated the findings described earlier, providing further biological insight into circadian regulation across diverse physiological systems. Both clusters showed particularly robust enrichment in processes related to coagulation and wound healing, although it was again stronger in Cluster A (Data not shown).\u003c/p\u003e \u003cp\u003eCollectively, these detailed enrichment analyses using Reactome and GOBP databases emphasize the wide-ranging impact of circadian biology on fundamental clinical and biological functions.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe acrophase \u0026ndash; defined as the actual peak time in clock hours (0\u0026ndash;24 hr), representing the time of day at which each protein reaches its maximum abundance \u0026ndash; was used to characterize the temporal distribution of circadian-regulated plasma proteins. Cluster A proteins exhibited a median acrophase of 16.50 hr (IQR: 12.87\u0026ndash;17.10 hr), corresponding approximately to late afternoon. Cluster B proteins peaked earlier in the day, with a median acrophase of 3.31 hr (IQR: 2.03\u0026ndash;5.80 hr), representing early morning hours (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eCircadian-regulated proteins showed a mean amplitude of 0.172 (SD: 0.0107; 95% CI: 0.151\u0026ndash;0.194), but interestingly, Cluster A had a significantly higher amplitude with a mean of 0.215 (SD: 0.132; 95% CI: 0.19\u0026ndash;0.24) versus 0.091 (SD: 0.05; 95% CI: 0.076\u0026ndash;0.11) in Cluster B. No significant changes in protein abundance were observed in relation to meal intake (Supplementary Table\u0026nbsp;1).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThrough cross-referencing our significant rhythmic proteins against the standard clinical diagnostic assay preference list from the Danish regional health authority (Region H, Sundhedsplatformen) \u0026mdash; which reflects clinical practice guidelines and diagnostic standards currently implemented across hospitals in the Capital Region of Denmark \u0026mdash; we identified 36 proteins (26.1% of all significantly rhythmic proteins) currently used as biomarkers in routine clinical practice (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). These clinically important biomarkers span multiple essential diagnostic categories, including coagulation factors (e.g., fibrinogen alpha chain [FGA], coagulation factor V [F5], and protein C [PROC]), markers of liver and kidney function (albumin [ALB], cystatin C [CST3]), inflammatory biomarkers (calprotectin subunits S100A8 and S100A9), endocrine-related proteins (insulin-like growth factor 1 [IGF1]), proteins indicative of cardiac and skeletal muscle injury (creatine kinase M-type [CKM], lactate dehydrogenase A [LDHA]), and a marker for acute pancreatitis (amylase [AMY2A]). Additionally, critical immune-related diagnostic markers such as complement proteins, human leukocyte antigen B (HLA-B), and immunoglobulins also demonstrated clear circadian rhythmicity.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003eClinical Relevance of Circadian-Regulated Proteins in Human Plasma.\u003c/b\u003e The listed blood tests correspond to clinical routine assays according to the standardized regional clinical laboratory test preference list in Denmark (EPIC system, Region H, Denmark). Circadian-regulated proteins linked to each test are indicated alongside descriptions of their diagnostic, prognostic, or monitoring significance in clinical practice.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClinical Test\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCircadian-Regulated Protein(s)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eClinical Significance\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eaPTT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF11; F5; F9; PROC; SERPINC1; FGA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCoagulation status; Bleeding disorders; Liver function monitoring\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePT (INR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF5; FGA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCoagulation status; Liver synthetic function assessment\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD-Dimer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFGA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eThrombosis diagnosis; Disseminated intravascular coagulation (DIC)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFibrinogen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFGA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCoagulation status; Acute-phase inflammatory response\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlbumin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eALB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNutritional status; Liver function; Chronic illness monitoring\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCystatin C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCST3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eKidney function; Glomerular filtration rate estimation (eGFR)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCalprotectin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eS100A8/S100A9 (complex)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSystemic inflammation marker; Leukocyte activation\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBeta-2-Microglobulin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB2M\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTumor marker (Myeloma, CLL, lymphoma); Kidney function assessment\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComplement Activity tests\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(\u003cb\u003eCH50 test\u003c/b\u003e): C3; C1r\u003c/p\u003e \u003cp\u003e(\u003cb\u003eAH50 test\u003c/b\u003e): C3; CFD; CFHR1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eComplement system evaluation; Immunodeficiency diagnosis\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComplement C3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eC3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eComplement activation; Inflammatory/autoimmune disorders\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComplement C1r\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eC1r\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eClassical complement pathway activity\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProstaglandin D-Synthase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePTGDS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eInflammation marker; Sleep regulation (research)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInsulin-Like Growth Factor I\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIGF1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGrowth disorders; Nutritional/endocrine assessmnet\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCreatine Kinase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCKM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMuscle injury diagnosis; Rhabdomyolysis; Myopathy\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlyceraldehyde-3-phosphate dehydrogenase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGAPDH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCellular damage; Hemolysis\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeparin-PF4-IGG (HIT)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePF4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHeparin-induced thrombocytopenia diagnosis\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eErythrocyte Transketolase Activity Test\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVitamin B1 (Thiamine) deficiency diagnosis; Nutritional assessment\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProtein C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePROC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eThrombophilia evaluation; Coagulation disorders diagnosis\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHLA-AB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHLA-B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTransplant compatibility; Immunogenetic profiling\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMyoglobin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAcute myocardial infarction (AMI); Rhabdomyolysis\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLDHA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTissue injury; Hemolysis; Prognostic monitoring\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal IgA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIGHA2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eImmunodeficiency diagnosis; Immune status monitoring\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFree Kappa Chains (Ig)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIGKV2D-29; IGKV2-28; IGKV2-29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePlasma cell disorder diagnosis (Myeloma, MGUS); Therapeutic monitoring\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFree Lambda Chains (Ig)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIGLV3-25; IGLV3-19; IGLV2-11; IGLV3-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePlasma cell disorder diagnosis (Myeloma, MGUS); Therapeutic monitoring\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFree Kappa/Lambda Chains (Ig) Ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIGKV2D-29; IGKV2-28; IGKV2-29; IGLV3-25; IGLV3-19; IGLV2-11; IGLV3-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePlasma cell disorders diagnosis; Disease monitoring; Clonality analysis\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCD56 Flow Cytometry\u003c/p\u003e \u003cp\u003e(whole blood)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNCAM1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNK/T-cell malignancy diagnosis; Immunophenotyping\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAmylase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAMY2A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAcute pancreatitis diagnosis; Pancreatic function assessment\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAntithrombin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSERPINC1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eThrombosis risk; Coagulation inhibitor deficiency\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCoagulation Factor XI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHemophilia C; Bleeding disorder evaluation\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCoagulation Factor V\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFactor V deficiency diagnosis; Thrombophilia evaluation\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTransferrin-Receptor Fragment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTFRC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIron deficiency diagnosis; Iron metabolism assessment\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCoagulation Factor IX\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHemophilia B; Factor IX Deficiency; Vitamin K deficiency\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAbbreviations\u003c/b\u003e: aPTT, activated partial thromboplastin time; PT, prothrombin time; AMI, acute myocardial infarction; MGUS, monoclonal gammopathy of undetermined significance; eGFR, estimated glomerular filtration rate; HIT, heparin-induced thrombocytopenia; CLL, chronic lymphocytic leukemia; LDH, lactate dehydrogenase; Ig, immunoglobulin.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTo explore whether our findings extended beyond controlled conditions, we analyzed timestamped routine clinical laboratory results from the LABKA database. This dataset included all measurements of albumin, aPTT, and PT (INR) conducted in hospitals across the Capital Region of Denmark over a one-week period (April 1\u0026ndash;7, 2025), encompassing both healthy and clinically diverse patient populations. All three analyses exhibited significant circadian rhythms following Benjamini\u0026ndash;Hochberg correction: Albumin (adjusted p\u0026thinsp;=\u0026thinsp;1.1\u0026times;10⁻\u0026sup1;⁶; mesor\u0026thinsp;=\u0026thinsp;33.7 g/L, amplitude\u0026thinsp;=\u0026thinsp;4.02 g/L, peak at 13:12), aPTT (adjusted p\u0026thinsp;=\u0026thinsp;6.9\u0026times;10⁻⁴; mesor\u0026thinsp;=\u0026thinsp;30.69 s, amplitude\u0026thinsp;=\u0026thinsp;2.05 s, peak at 01:57), and PT (adjusted p\u0026thinsp;=\u0026thinsp;5.7\u0026times;10⁻⁹; mesor\u0026thinsp;=\u0026thinsp;1.14, amplitude\u0026thinsp;=\u0026thinsp;0.04, peak at 04:49) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we demonstrate that one fourth of the human plasma proteome display circadian rhythmicity. Using a rigorous, unbiased mass spectrometry-based proteomics approach in a cohort of healthy young individuals under highly controlled conditions, we identified 138 proteins with distinct circadian patterns.\u003c/p\u003e \u003cp\u003eIn line with our final analyses, we identified two distinct circadian clusters of plasma proteins: one peaking in the afternoon (Cluster A; 91 proteins, median peak at 16:30) and another in the early morning (Cluster B; 47 proteins, median peak at 03:19). While Cluster A showed a more diverse tissue origin\u0026mdash;including platelets, liver, skeletal muscle, immune cells, and several others\u0026mdash;Cluster B was significantly enriched only in the liver. Functionally, both clusters shared platelet activation and hemostasis, although these processes were more strongly enriched in Cluster A. Notably, Cluster B alone encompassed insulin-like growth factor regulation, protein metabolism, and lipoprotein metabolism, suggesting these pathways peak during the early morning or night hours. By contrast, Cluster A included prominent pathways in innate immunity, Rho GTPase signaling, integrin signaling, oncogenic RAS/RAF‐MAPK signaling, and carbohydrate metabolism (gluconeogenesis and glycolysis). Intriguingly, the afternoon‐peaking proteins (Cluster A) also exhibited a higher rhythmic amplitude (~\u0026thinsp;0.22) compared to Cluster B (~\u0026thinsp;0.09), implying more pronounced day‐time oscillations.\u003c/p\u003e \u003cp\u003eThese rhythmic fluctuations occurred independently of food intake, indicating that meal timing did not drive the observed variations. Critically, 36 of these circadian-regulated proteins (26% of the total rhythmic proteins) are already measured in routine clinical practice, as reflected in the Danish guidelines, underscoring the clinical importance of recognizing circadian patterns in widely used biomarkers and highlights a potential role for circadian‐informed testing protocols.\u003c/p\u003e \u003cp\u003eOur findings are consistent with prior studies but also extend the understanding of circadian plasma proteomics significantly. A recent study (2024), employing a SomaScan aptamer-based methodology, observed circadian rhythmicity of 15% of proteins \u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e, fewer than our observed 26.4%. Methodological differences, specificially the broader, unbiased coverage provided by mass spectrometry, likely explain the higher proportion of rhythmic proteins identified in our study. Notably, similar to our findings, they also reported protein oscillations primarily peaking in the early morning and afternoon hours. Additionally, circadian regulation of hemostatic proteins has previously been observed in ELISA-based studies \u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e and is further supported by studies using constant routine protocols demonstrating circadian variation in platelet activation \u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. Our comprehensive proteomic analysis confirms that many proteins involved with the hemostasis system have a circadian rhythm and further expands the understanding by identifying rhythmic proteins in diverse biological processes, emphasizing the broader relevance of circadian variation in clinical diagnostics and protein research.\u003c/p\u003e \u003cp\u003eOur unbiased MS-based proteomics method significantly extends beyond previous targeted studies, providing an innovative and robust framework for biomarker discovery and research. Unlike targeted proteomic techniques such as ELISA or aptamer-based assays (e.g. SomaScan), MS-based proteomics offers direct identification and quantification of peptide sequences, dramatically reducing specificity and quantification biases. Recent technological advancements in MS-based proteomics have markedly enhanced sensitivity, robustness, and throughput, enabling the comprehensive and precise characterization of complex biological systems. Consequently, our methodological approach provides a more complete and unbiased exploration of circadian rhythmicity in plasma proteins, facilitating discoveries that may have been overlooked using targeted approaches, and setting new standards for circadian biomarker identification and clinical diagnostics \u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThese rhythmic fluctuations of biological processes support essential physiological functions such as hemostasis, energy metabolism, and immune regulation. Dysregulation of these rhythms can predispose individuals to adverse health effects \u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e such as metabolic \u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e, inflammatory \u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e, psychiatric \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e, and cardiovascular diseases \u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e, underscoring the clinical importance of accurately characterizing temporal protein dynamics.\u003c/p\u003e \u003cp\u003eClinically, our results suggest significant implications for biomarker discovery and diagnostic accuracy. Ignoring circadian rhythms could lead to inconsistent biomarker validation, inaccurate reference intervals, and suboptimal therapeutic monitoring. For instance, circadian variation in hemostatic proteins may influence the optimal timing of diagnostic tests or medication administration for conditions like atrial fibrillation if the risk of thrombotic events are found to be varying throughout the day. A prime illustration of circadian-aware testing is serum cortisol measurement. Because cortisol normally peaks in the early morning and reaches a nadir at night, clinicians measure morning serum cortisol to screen for adrenal insufficiency, whereas late‐night salivary cortisol helps diagnose hypercortisolism (Cushing\u0026rsquo;s syndrome). This timing ensures more accurate assessment of hypothalamic\u0026ndash;pituitary\u0026ndash;adrenal axis function \u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. Building upon these established principles, our findings suggest that many other clinically used biomarkers with significant circadian oscillations may also benefit from standardized sampling times or time‐adjusted reference intervals to improve diagnostic precision and therapeutic outcomes. Proteins such as amylase, found in our study to have a circadian rhythm and is commonly used as a biomarker to assess acute and chronic pancreatitis \u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e, and cystatin C, which can be used to estimate the glomerular filtration rate and monitor kidney function \u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e, may benefit from the use of time-sensitive reference ranges to improve diagnostic precision and patient management.\u003c/p\u003e \u003cp\u003eThe presence of similar circadian patterns in routinely collected clinical data further reinforces the broader relevance of our findings. Observing significant rhythms in albumin, aPTT, and PT (INR) across a clinically heterogeneous population suggests that circadian variation is not limited to controlled conditions, but is a robust feature of real-world clinical biomarkers. This highlights the importance of accounting for sampling time in both diagnostic interpretation and biomarker research. While the LABKA dataset was not designed as a validation study, the consistent oscillatory patterns observed lend additional translational support to our findings and highlight the real-world complexity of applying circadian biology to clinical diagnostics.\u003c/p\u003e \u003cp\u003eNotably, the peak time of albumin differed between our controlled study and the LABKA dataset, shifting from nighttime to early afternoon. This discrepancy likely reflects behavioral and environmental influences present in clinical settings\u0026mdash;such as feeding patterns, posture, and activity\u0026mdash;that are tightly regulated under experimental conditions. In addition, albumin is a negative acute-phase protein \u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e, and lower levels observed in night-time hospital samples may reflect a sampling bias toward acutely ill patients, whereas daytime testing includes individuals undergoing routine or elective evaluations. These factors may jointly contribute to the observed phase shift and highlight the need for further research into how health status and care context shape circadian biomarker patterns.\u003c/p\u003e \u003cp\u003eAlthough integrating circadian-aware sampling into clinical diagnostics introduces practical and logistical challenges, such as more complex scheduling and adjustments to reference intervals, the potential improvements in diagnostic accuracy and treatment optimization are substantial. Thus, future guidelines should consider incorporating time-of-day recommendations for biomarkers with pronounced rhythmicity to enable more precise and individualized medical care.\u003c/p\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eStrengths and limitations\u003c/h2\u003e \u003cp\u003eOur study\u0026rsquo;s strengths include a thoroughly controlled experimental environment, standardized sampling conditions, and advanced mass spectrometry methods minimizing potential biases inherent in antibody- or aptamer-based assays. Furthermore, robust statistical analysis with the CosinorPy rhythmometry, including z-scoring and correction for multiple testing using the Benjamini-Hochberg procedure, strengthened confidence in the rhythmicity estimates and reduced the risk of false discovery. However, several limitations exist. Firstly, generalizability is limited by our homogeneous study population of healthy young men with regular lifestyles, which may not reflect the circadian variability seen in clinical populations with broader demographic and behavioral diversity. Secondly, the exclusion of women is a notable limitation, particularly given that sex-specific differences in circadian regulation of liver gene expression have been demonstrated in mice \u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e, and that female mice have shown greater circadian resilience under disruptive conditions \u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. While these findings may not fully translate to humans, they underscore the need for future studies to include both sexes to evaluate potential sex-based differences in plasma circadian proteomics. Additionally, out of a total of 216 plasma samples collected, eight (3.7%) were excluded due to low protein count, potentially introducing minor bias. Nevertheless, the stringent filtering and robust statistical methodology mitigate the impact of these missing data points, preserving the overall validity of our findings.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eFuture Research\u003c/h2\u003e \u003cp\u003eOur findings open several important avenues for future research. Further studies should expand investigations into populations beyond healthy young men, including diverse age groups, females, and individuals with varying chronotypes or circadian disruptions (e.g., shift work, metabolic disorders), as well as exploring how commonly prescribed medications may alter circadian regulation. Additionally, prospective clinical trials evaluating the practical integration of circadian-aware blood sampling protocols into routine diagnostics could clarify the impact on diagnostic accuracy, therapeutic effectiveness, and patient outcomes. Finally, mechanistic studies are needed to reveal the molecular pathways underpinning circadian oscillations of plasma proteins, potentially revealing novel therapeutic targets and biomarkers. As our findings were obtained under tightly controlled experimental conditions, future studies should examine how real-world clinical rhythms\u0026mdash;such as exposure to artificial lighting, irregular dietary habits, and shift-work\u0026mdash;affect plasma protein rhythmicity in more variable environments.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, our comprehensive proteomic analysis reveals extensive circadian regulation within the human plasma proteome, identifying rhythmic fluctuations in 26% of the human plasma proteome measured using the described conditions. By using an unbiased mass spectrometry approach, we provide accurate characterization and overcome methodological biases inherent in previous targeted studies, highlighting numerous oscillatory proteins with a potential clinical relevance. Recognizing these circadian variations through standardized sampling protocols or the establishment of time-sensitive reference intervals could significantly improve diagnostic accuracy, therapeutic monitoring, and the design of future biomarker studies.\u003c/p\u003e \u003cp\u003eThis conclusion is further supported by the observation of similar circadian patterns in routinely collected clinical laboratory data, reinforcing the applicability of our findings to real-world diagnostics. Future research should explore circadian variation in broader and more diverse clinical populations, mechanisms underpinning the underlying mechanisms, and evaluate how time-aware practices can be pragmatically implemented in routine care. Integrating circadian biology into both clinical diagnostics and biomarker development represents a pivotal step toward more precise, personalized, and time-informed medicine.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eContributors\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNJWA conceived the study and initiated the investigation of circadian rhythms in the human plasma proteome. HPS and HLJ conducted the clinical experiments and HPS provided the plasma samples. CR prepared the samples for analysis, and GZP performed the mass spectrometry analysis. CYCY conducted the preliminary data analysis. ABN carried out the statistical analysis, and together with EMSJ, performed the final data analysis and interpretation. NJWA, CYCY, HPS, JH, and ABN contributed with intellectual input and critical discussion. EMSJ wrote the manuscript. All authors reviewed and approved the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNJWA has received funding from and served on scientific advisory panels and/or speakers\u0026rsquo; bureaus for Boehringer Ingelheim, MSD/MERCK, Roche, Novo Nordisk and Mercodia. The remaining authors have no conflicts of interest to declare.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData sharing\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe mass spectrometry proteomics data will deposited to the ProteomeXchange Consortium via the Proteomics Identifications Database (PRIDE) partner repository upon publication. Data will also be made available in the form of an interactive online app upon publication.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eCode availability\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe jupyter notebooks will be available at github upon publication together with the source data behind the analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank the Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Denmark, for performing mass spectrometry analysis of the plasma samples. We are also grateful to Fie Johanne Andreasen for providing the regional clinical laboratory test preference list (EPIC system, Region H, Denmark). Some visual elements used in Figure 1 were created with BioRender.com.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNJWA is supported by the European Foundation for the Study of Diabetes Future Leader Award (NNF21SA0072746), Independent Research Fund Denmark (1052-00003B, 10.46540/4308-00056B, 10.46540/4285-00131B), Novo Nordisk Foundation (NNF23OC0084970, NNF19OC0055001 and NNF24OC0088402). The Novo Nordisk Foundation Center for Protein Research is supported financially by the Novo Nordisk Foundation (NNF14CC0001).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCopyright\u003c/strong\u003e \u0026copy; 2025 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eWalker WH 2nd, Walton JC, DeVries AC, Nelson RJ. Circadian rhythm disruption and mental health. Transl Psychiatry. 2020;10(1):28.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRuan W, Yuan X, Eltzschig HK. Circadian rhythm as a therapeutic target. Nat Rev Drug Discov. 2021;20(4):287\u0026ndash;307.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGeyer PE, Voytik E, Treit PV, Doll S, Kleinhempel A, Niu L, M\u0026uuml;ller JB, Buchholtz ML, Bader JM, Teupser D, Holdt LM, Mann M. Plasma Proteome Profiling to detect and avoid sample-related biases in biomarker studies. EMBO Mol Med. 2019;11(11):e10427.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBirhanu AG. Mass spectrometry-based proteomics as an emerging tool in clinical laboratories. Clin Proteom. 2023;20(1):32.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSennels HP, J\u0026oslash;rgensen HL, Hansen AL, Goetze JP, Fahrenkrug J. Diurnal variation of hematology parameters in healthy young males: the Bispebjerg study of diurnal variations. Scand J Clin Lab Invest. 2011;71(7):532\u0026ndash;41.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZilstorff DB, Richter MM, Hannibal J, J\u0026oslash;rgensen HL, Sennels HP, Wewer Albrechtsen NJ. Secretion of glucagon, GLP-1 and GIP may be affected by circadian rhythm in healthy males. BMC Endocr Disord. 2024;24(1):38.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDemichev V, Messner CB, Vernardis SI, Lilley KS, Ralser M. DIA-NN: neural networks and interference correction enable deep proteome coverage in high throughput. Nat Methods. 2020;17(1):41\u0026ndash;4.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWebel H, Niu L, Nielsen AB, Locard-Paulet M, Mann M, Jensen LJ, Rasmussen S. Imputation of label-free quantitative mass spectrometry-based proteomics data using self-supervised deep learning. 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Kidney360. 2022;3(10):1807\u0026ndash;14.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJain S, Gautam V, Naseem S. Acute-phase proteins: As diagnostic tool. J Pharm Bioallied Sci. 2011;3(1):118\u0026ndash;27.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAstafev AA, Mezhnina V, Poe A, Jiang P, Kondratov RV. Sexual dimorphism of circadian liver transcriptome. iScience. 2024;27(4):109483.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAnderson ST, Meng H, Brooks TG, Tang SY, Lordan R, Sengupta A, Nayak S, Mřela A, Sarantopoulou D, Lahens NF, Weljie A, Grant GR, Bushman FD, FitzGerald GA. Sexual dimorphism in the response to chronic circadian misalignment on a high-fat diet. Sci Transl Med. 2023;15(696):eabo2022.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"clinical-proteomics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"clip","sideBox":"Learn more about [Clinical Proteomics](http://clinicalproteomicsjournal.biomedcentral.com/)","snPcode":"12014","submissionUrl":"https://submission.nature.com/new-submission/12014/3","title":"Clinical Proteomics","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Biomarker, Circadian Rhythm, Mass-Spectrometry, Plasma, Proteins","lastPublishedDoi":"10.21203/rs.3.rs-6695734/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6695734/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003ePlasma is the most used clinical specimen, yet circadian variation in plasma proteins remains largely unexplored. We aimed to identify circadian-regulated proteins in healthy individuals and assess their potential diagnostic implications, and highlight how circadian awareness can advance future biomarker research.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eTwenty-four healthy young individuals were studied under highly controlled conditions. Venous blood was drawn every three hours over a 24-hour period, yielding 216 samples, of which 208 high-quality plasma samples were analyzed via high-throughput mass spectrometry. The missing data were filtered and imputed, and rhythmicity was assessed using Cosinor-based modeling with Benjamini-Hochberg correction. Tissue and pathway enrichment analyses were performed using the DAVID functional annotation tool.\u003c/p\u003e\u003ch2\u003eFindings\u003c/h2\u003e \u003cp\u003eOf 523 proteins that passed quality thresholds, 138 (~\u0026thinsp;26%) exhibited significant circadian oscillations. Tissue enrichment analysis revealed that most rhythmic proteins originated from the liver and platelets, with additional enrichment in a variety of tissue types. Pathway enrichment showed circadian regulation of hemostasis, immune signaling, integrin-mediated processes, glucose metabolism, and protein synthesis. Notably, 36 clinically utilized biomarkers, including albumin, amylase, and cystatin C exhibited circadian variation, suggesting that failing to account for temporal fluctuations may reduce diagnostic precision.\u003c/p\u003e\u003ch2\u003eInterpretation\u003c/h2\u003e \u003cp\u003eThese findings demonstrate that over one-quarter of the human plasma proteome is under circadian control. Such oscillations might have direct clinical implications, as the time-of-day may alter biomarker accuracy. Incorporating circadian timing into diagnostic and research protocols, through standardized sampling or time-sensitive reference intervals, could improve patient care and inform future biomarker discoveries. Further research in larger, more diverse populations is needed to generalize these results and streamline circadian-aware practices in clinical practice.\u003c/p\u003e","manuscriptTitle":"Circadian Rhythm of the Human Plasma Proteome","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-27 17:45:27","doi":"10.21203/rs.3.rs-6695734/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-06-23T08:17:19+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-27T19:54:04+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"53877118378042944260438003553361773533","date":"2025-05-27T08:03:35+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"288335713206882131383292506740190320421","date":"2025-05-23T14:02:56+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-05-21T13:57:59+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-05-20T08:11:15+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-05-20T08:09:54+00:00","index":"","fulltext":""},{"type":"submitted","content":"Clinical Proteomics","date":"2025-05-19T06:36:39+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"clinical-proteomics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"clip","sideBox":"Learn more about [Clinical Proteomics](http://clinicalproteomicsjournal.biomedcentral.com/)","snPcode":"12014","submissionUrl":"https://submission.nature.com/new-submission/12014/3","title":"Clinical Proteomics","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c17da92d-d0c4-4cf7-9714-c6799ae9fddf","owner":[],"postedDate":"May 27th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-08-25T16:40:04+00:00","versionOfRecord":{"articleIdentity":"rs-6695734","link":"https://doi.org/10.1186/s12014-025-09551-7","journal":{"identity":"clinical-proteomics","isVorOnly":false,"title":"Clinical Proteomics"},"publishedOn":"2025-08-22 16:29:26","publishedOnDateReadable":"August 22nd, 2025"},"versionCreatedAt":"2025-05-27 17:45:27","video":"","vorDoi":"10.1186/s12014-025-09551-7","vorDoiUrl":"https://doi.org/10.1186/s12014-025-09551-7","workflowStages":[]},"version":"v1","identity":"rs-6695734","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6695734","identity":"rs-6695734","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}