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. 2026 Aug 22;133(8):1891–1902. doi: 10.1007/s00702-026-03214-z

High social jetlag in healthy youth: differences in tryptophan levels and regional gray matter volume

Masatoshi Yamashita 1,2,3,✉, Qiulu Shou 1,3, Masanori Fujieda 1,3,4, Hidehiko Okazawa 3,5, Yoshiyuki Hirano 3,6, Kuriko Kagitani-Shimono 3, Yoshifumi Mizuno 1,3,4,✉
PMCID: PMC13545142  PMID: 42631723

Abstract

Social jetlag (SJL), defined as a mismatch in midsleep timing between school or workdays and free days, has been associated with adverse mental health, cognitive, and brain outcomes in adults. However, its effects in youth remain insufficiently studied. We investigated the effects of SJL on these factors and on tryptophan metabolites implicated in sleep, fatigue, and social function. Based on SJL assessed using the Munich ChronoType Questionnaire, 89 healthy youths aged 6–17 years were classified into high-SJL (≥ 1 h; n = 25) or low-SJL (< 1 h; n = 64) groups. Fatigue, chronotype, Trail Making Test (TMT) performance, urinary tryptophan and 5-hydroxyindoleacetic acid (5-HIAA) levels, gray matter volume (GMV), and resting-state functional connectivity (FC) were evaluated. Compared with the low-SJL group, the high-SJL group exhibited greater fatigue (p-FDR = 0.034), a later chronotype (p-FDR = 0.00003), and higher urinary tryptophan levels (p-FDR = 0.014). Moreover, the high-SJL group showed smaller GMV in the left inferior occipital/fusiform region (p-FWE = 0.002), right inferior temporal region (p-FWE = 0.006), right angular gyrus (p-FWE = 0.012), left temporal pole (p-FWE = 0.013), and left superior parietal lobule (p-FWE = 0.024). However, TMT performance, urinary 5-HIAA levels, and FC did not differ between groups. An SJL of ≥ 1 h was associated with elevated tryptophan levels and structural vulnerability in temporal pole, ventral occipitotemporal regions, and parietal regions. These findings suggest that higher SJL may contribute to molecular and region-specific structural disadvantages relevant to socioemotional and higher-order cognitive processes in youth.

Graphical abstract

graphic file with name 702_2026_3214_Figa_HTML.webp

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Supplementary Information

The online version contains supplementary material available at https://doi.org/10.1007/s00702-026-03214-z.

Keywords: Healthy youth, Serotonin, Social jetlag, Brain structure, Tryptophan

Introduction

The biological clock is an internal timing system that generates 24-h cycles and aligns with environmental signals on a daily basis (Zou et al. 2022). Conflicts of biological time with socially constrained schedules lead to social–circadian misalignment, known as social jetlag (SJL) (Roenneberg et al. 2012). SJL has also been measured as the discrepancy in midsleep time between weekdays and weekends (Roenneberg et al. 2012). Roenneberg et al. (2013) reported that nearly two-thirds of European adults experience SJL of ≥ 1 h. In addition, previous studies have indicated associations of SJL with adverse physiological outcomes (e.g., menstrual abnormalities) and with an increased risk of psychiatric disorders (e.g., depression) in young adults and workers (Islam et al. 2020; Komada et al. 2019; Meram et al. 2025; Min et al. 2023). Notably, a recent study of 6,890 adolescents (mean age 11.95 years) revealed that greater SJL predicted worse cognitive and academic performance (Li et al. 2024). These findings suggest that SJL plays an important role in maintaining mental health and cognitive function. Given the increasing concern about misaligned social and circadian schedules across all ages, identifying the neurobiological mechanisms (e.g., neurochemical and neural bases) underlying SJL at an early stage of development is important.

Generally speaking, SJL is a form of circadian disruption, and thus, may also influence the neurochemical mechanisms regulating the sleep–wake cycle. One such mechanism involves tryptophan, an essential amino acid known for its role as a biomarker of sleep regulation and central fatigue (Yamashita 2020). For example, Yamashita and Yamamoto (2017) reported that, compared with control rats, rats with sleep–wake cycle abnormalities showed elevation of tryptophan levels and depletion of its metabolite, serotonin, in the brain. These changes were associated with central fatigue induction. Moreover, serotonin depletion disrupts the circadian timing by desynchronizing neuronal activity in sleep–wake regulatory centers (i.e., the suprachiasmatic nucleus) (Miyamoto et al. 2012). These findings indicate that tryptophan–serotonin pathways are sensitive to sleep and circadian rhythm disruption. Beyond its role as a biomarker of sleep–wake regulation and central fatigue, the tryptophan–serotonin pathway has also been implicated in socioemotional and cognitive functions, such as prosocial behavior, psychomotor activity, and executive function (Hogenelst et al. 2015; Williams et al. 2007; Yamashita and Yamamoto 2014; 2017). However, whether higher SJL is accompanied by changes in the endogenous tryptophan–serotonin metabolism in young individuals remains unclear.

SJL has also been found to affect brain activity. A previous study reported that young adults with SJL of ≥ 1 h show alterations in spontaneous low-frequency neural activity in several brain regions compared with those without SJL during resting-state functional magnetic resonance imaging (fMRI) (Nechifor et al. 2020). In adolescence (ages approximately 11–13 years), Yang et al. (2023), using the Adolescent Brain Cognitive Development Study data, found that a greater SJL was associated with alterations in resting-state functional connectivity (FC) between widespread cortical networks and subcortical regions. Given the broad, population-oriented design of the Adolescent Brain Cognitive Development Study, the cohort necessarily included diagnostically heterogeneous participants (e.g., those with neurodevelopmental and psychiatric disorders), which may dilute associations that are SJL-specific. However, the structural brain underpinnings of SJL in healthy young individuals have not been examined directly, and the relationship between regionally specific gray matter volume (GMV) differences and resting-state FC alterations remains unclear. Accordingly, if structural loci differ according to SJL status, exploratory FC analyses using these loci as seed regions may help determine whether SJL-related structural differences are accompanied by alterations in functional coupling.

Collectively, evidence to date has indicated that SJL can alter mental health, cognitive function, and brain activity. However, most previous studies on this topic have focused on adults and working individuals, whereas the neurobiological mechanisms in healthy young individuals have received scant attention. Given that childhood and adolescence are sensitive periods for circadian consolidation and brain network reorganization (Hagmann et al. 2010; Roenneberg et al. 2012; Uhlhaas et al. 2009), clarifying whether SJL is accompanied by molecular, structural, and functional differences during this developmental period is important.

This cross-sectional study addressed two primary research questions in healthy young individuals (aged 6–17 years). First, do individuals with high SJL (≥ 1 h) differ from those with low SJL (< 1 h) in urinary tryptophan and 5-hydroxyindoleacetic acid (5-HIAA), an index of serotonergic turnover? Second, do individuals with high SJL show alterations in brain structure compared with those with low SJL? We also explored whether regions showing structural differences exhibit region-specific resting-state FC differences when used as seed regions. To address these questions, we compared urinary tryptophan and 5-HIAA levels and brain structure between individuals with high and low SJL, and additionally conducted exploratory seed-based resting-state FC analyses. In addition, we compared behavioral measures, including fatigue, chronotype, and Trail Making Test (TMT) performance. We included the TMT as a brief and widely used exploratory measure (Bowie and Harvey 2006; Reitan and Wolfson 1985) because executive function, including cognitive flexibility, is important for everyday functioning (Gündüz et al. 2021). The TMT has also been applied to school-age children and adolescents (Gündüz et al. 2021) and was feasible within our multimodal protocol with a relatively low participant burden. We hypothesized that young individuals with high SJL would show higher tryptophan and lower 5-HIAA levels, consistent with findings from a rat model of sleep–wake cycle anomalies. We further surmised that high SJL would be associated with reduced GMV in one or more regions and explored whether these regions showed altered seed-based resting-state FC. Given growing concerns about sleep-related social and psychiatric outcomes, clarifying the impact of SJL in young individuals may inform developmental models of brain and mental health and support the development of child-focused sleep and circadian guidelines.​

Methods

Participants

The Research Ethics Committee of the University of Fukui approved the study protocol (Assurance No. FU-20220061) and the procedures of the study complied with the principles of the Declaration of Helsinki. All parents provided written informed consent, and all children provided assent. The study involved 92 healthy young individuals, aged 6–17 years. Such individuals who did not receive special-support education were recruited from the local community. None of the participants had any history of neurological, cardiovascular, or psychiatric illness and none had contraindications for MRI. Data from 3 individuals were excluded from the analysis because of claustrophobia that impeded the MRI scan. Thus, data from 89 healthy young individuals were used in the final analysis.

Psychological questionnaires

The Munich ChronoType Questionnaire (Roenneberg et al. 2003) was used to assess the participants’ SJL and chronotype. This standardized self-rating scale assesses an individual’s habitual sleep–wake timing on work/school days and on free days. The variables consisted of (1) sleep start (bedtime and sleep onset latency), (2) sleep end (wake-up time), (3) alarm clock usage, and (4) sleep duration (total amount of time between sleep start and sleep end). Additionally, the midpoint of sleep on free days was calculated as the midpoint between sleep onset and wake-up time. However, most individuals accumulate sleep debt during the school day and extend their sleep time on free days. Therefore, the corrected midpoint of sleep on free days, known as the chronotype index, was calculated as the midpoint of sleep on free days minus a correction for sleep debt equal to half the difference between sleep duration on free days and average sleep duration over the week. In addition, SJL was defined as the absolute difference between the midpoint of sleep on school days and the midpoint of sleep on free days. Larger values indicated greater misalignment between biological and social sleep timing. For group comparisons, we defined high-SJL (≥ 1 h) and low-SJL (< 1 h) groups, as previously reported (Hunt et al. 2025; Roenneberg et al. 2012).

The Japanese version of the Chalder Fatigue Scale (Tanaka et al. 2008) was used to assess individuals’ fatigue. This scale consists of 11 items that assess symptoms of mental and physical fatigue. Participants were asked to respond to questions about tiredness, rest, feeling sleepy, motivation, energy, muscular strength, weakness, concentration, and speech by using a 4-point scale: “less than usual,” “much more than usual,” “better than usual,” and “much worse than usual.”

Cognitive measurement

The Japanese version of parts A and B of the TMT (Reitan and Wolfson 1985) were applied to evaluate executive function. In the TMT-A, participants were asked to draw lines, sequentially connecting 25 numbers in ascending order. In the TMT-B, participants were asked to draw lines alternately between numbers and letters (1, あ, 2, い, etc.). The time to complete each task was measured. Furthermore, we assessed TMT-Δ, calculated as the time to complete part B minus that to complete part A, which is considered an index of cognitive flexibility and set-shifting (Bowie and Harvey 2006).

Determination of tryptophan metabolite

On the day before testing, the participants were instructed to refrain from intense physical activity and from consuming caffeine-containing beverages (e.g., coffee, energy drinks, and some juices), alcohol, medications, and certain foods that can be difficult to digest (e.g., high-fat fish, red beef, and blue cheese) for 24 h (Yamashita and Yamamoto 2021). On the test day, fresh urine samples were collected after a 30-min rest. Urine samples were diluted with 6.7 mM hydrochloric acid and 2.5% perchloric acid to separate albumin, according to a previously reported method (Yamashita and Yamamoto 2021). The obtained supernatants were stored at -80 °C until analysis using high-performance liquid chromatography (HPLC). Tryptophan and 5-HIAA content were measured using HPLC with an electrochemical detector (Nanospace SI-2 3005, Osaka Soda Corporation, Osaka, Japan) and a chromate recorder (C-R8A; Shimadzu Corporation, Kyoto, Japan). Standards for these compounds were obtained from Sigma-Aldrich Inc. (St Louis, MO, USA). The mobile phase consisted of 15% methanol in a solution (pH 4.13) containing 30 mM citric acid, 10 mM disodium hydrogen phosphate, 0.5 mM sodium octyl sulfate, 50 mM sodium chloride, and 0.05 mM ethylenediaminetetraacetic acid, as previously reported (Yamashita and Yamamoto 2014; 2017; 2021). The solution was passed through a 5-μM C18 column (150 mm × 4.6 mm; TSK gel, ODS-80TM, Tosoh, Tokyo, Japan) at a flow rate of 0.7 mL/min, using a single pump (Nanospace SI-2 3101; Osaka soda Corporation, Osaka, Japan) at a column oven (Nanospace SI-I 2004; Osaka soda Corporation, Osaka, Japan) temperature of 25 °C. Tryptophan was detected electrochemically at 800 mV, with a retention time of approximately 16 min. 5-HIAA was detected electrochemically at 700 mV, with a retention time of approximately 12 min.

Statistical analysis of demographic, psychological, cognitive, and tryptophan metabolite data

Statistical analyses of demographic, psychological, cognitive, and tryptophan metabolite data were conducted using R (version 4.3.0; The R Foundation for Statistical Computing, Vienna, Austria). As the high-SJL and low-SJL groups were unequal in size, all data were initially assessed for normality and homogeneity of variance, and statistical procedures robust to unequal sample sizes were applied. These included Student’s t-tests, Welch’s t-tests, Chi-square tests, and Fisher’s exact test for descriptive comparisons, as well as linear mixed-effects models for the main behavioral and biochemical analyses.

The group effect on chronotype, fatigue, TMT-A and B performance, and urinary tryptophan metabolite levels were analyzed using a linear mixed-effects model (R packages “lmerTest,” “MuMIn,” and “jtools”). The categorical variable of group (high-SJL and low-SJL) was modeled as fixed effects. To account for non-independence of observations within-families (e.g., among siblings), family ID was modeled as a random intercept. Covariates included demographic variables (e.g., age and years of education) that showed significant differences between the high- and low-SJL groups, in addition to weekly sleep duration as previously reported (Tamura et al. 2022; Yamashita et al. 2024). For all analyses, the statistical threshold was set at p < 0.05, false discovery rate (FDR)-corrected by using the Benjamini–Hochberg method.

Image acquisition

Brain scanning was performed using a 3T GE signa PET/MR scanner (General Electric Healthcare, Chicago, IL, USA). Participants’ heads were immobilized using a head-coil scanner (eight channels). High-resolution structural images were acquired using an axial T1-weighted magnetization-prepared rapid gradient-echo pulse sequence (repetition time = 8.5 ms; echo time = 3.2 ms; field-of-view = 256 × 256; matrix size = 256 × 256; voxel size = 1 × 1 × 1 mm; 176 slices). Moreover, a gradient echo-planar imaging sequence was used to obtain resting-state functional blood oxygen level-dependent (BOLD) images under the following conditions: repetition time, 2300 ms; echo time, 30 ms; flip angle, 81; number of slices, 40; slice thickness, 3.5 mm; acquisition matrix, 64 × 64; and voxel size, 3 × 3 × 3.5. The scan lasted approximately 8 min. All participants were instructed to focus continuously on the crosshairs projected onto the screen during resting-state fMRI.

Image preprocessing and statistical analysis of structural data

To pre-process T1-weighted images and calculate the GMV across the whole brain, voxel-based morphometry was performed using Statistical Parametric Mapping 12 software (SPM; Wellcome Department of Cognitive Neurology, London, UK), implemented in MATLAB R2021a. Structural T1-weighted images were first segmented to separate different types of tissues: gray matter, white matter, cerebrospinal fluid, soft tissue, and the skull. The gray matter and white matter images were then spatially normalized using diffeomorphic anatomical registration with exponentiated Lie algebra. To preserve the absolute GMV, modulation was performed on normalized gray matter images by multiplying the Jacobian determinants derived from spatial normalization. Finally, the modulated gray matter images were smoothed with an 8-mm full-width at half-maximum (FWHM) Gaussian kernel.

Nonparametric two-sample t-tests were used to compare the high-SJL and low-SJL groups, using the threshold-free cluster enhancement option for SPM with 5,000 permutations and applying the Freedman-Lane method (Freedman and Lane 1983; Winkler et al. 2014). Covariates included the same variables (e.g., age, years of education, and weekly sleep duration) used for comparing the psychological and tryptophan metabolite data between the groups, in addition to the total intracranial volume and handedness. The search space was explicitly restricted by using an additional mask defined as the intersection of a binarized gray matter probability mask (threshold: TPM > 0.2) and the SPM model-estimated analysis mask (0/1 valid-voxel mask), to confine the inference to gray matter voxels valid across all participants and to provide an appropriate multiplicity space for family-wise error (FWE) control. Statistical inference was based on permutation-derived FWE-corrected p-values within this gray matter mask, and voxels were considered significant at p-FWE < 0.05.

Exploratory seed-based resting-state FC analyses were additionally performed using regions showing significant GMV differences as seed. Furthermore, in exploratory mediation analyses, we evaluated whether urinary tryptophan metabolites (e.g., the neurochemical measure showing a group difference) mediated associations between SJL and brain measures selected from the primary group comparisons. For details of these analyses, see Online Resource 1.

Results

Distribution of social jetlag and participant characteristics

The distribution of SJL across participants is shown in Fig. 1. Using the prespecified cutoff of 1 h, 25 of 89 healthy young individuals (28.1%) were classified as high-SJL (≥ 1 h) and 64 (71.9%) as low-SJL (< 1 h). Participant characteristics are summarized in Table 1. Age and years of education, but not sex ratio, household income, body mass index, or parental educational levels, differed statistically significantly between groups. For supplementary analyses of sleep quality, depressive symptoms, anxiety symptoms, and sleep-related and physical complaints, see Online Resource 2.

Fig. 1.

Fig. 1

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Distribution of social jetlag

Table 1.

Demographics of the high-SJL group and low-SJL group

Low-SJL
(n = 64)		 High-SJL
(n = 25)		 p value
Sex (n)
Male 31 (48.43) 11 (44.00) 0.888
Female 33 (51.56) 14 (56.00)
Age (years) 10.25 (2.79) 12.56 (2.66) 0.0006
Education (years) 5.84 (2.76) 8.00 (2.61) 0.001
Household income (JPY, n)
 < 3 000 000 5 (7.81) 1 (4.00) 0.163
3 000 000–5 000 000 4 (6.25) 5 (20.00)
5 000 000–7 000 000 14 (21.85) 9 (36.00)
 ≥ 7 000 000 41 (64.06) 10 (40.00)
Weekly sleep duration (h) 8.24 (1.11) 7.96 (1.04) 0.276
BMI (kg/m2) 17.72 (2.63) 18.87 (3.16) 0.084
Father’s educational level (n)
Junior high school 3 (4.68) 1 (4.00) 0.429
High school 6 (9.37) 4 (16.00)
Junior college/vocational school 9 (14.06) 5 (20.00)
University 34 (53.12) 10 (40.00)
Graduate school 11 (17.18) 1 (4.00)
Mother’s educational level (n)
Junior high school 0 (0.00) 0 (0.00) 0.312
High school 8 (12.50) 2 (8.00)
Junior college/vocational school 27 (42.18) 16 (64.00)
University 26 (40.62) 6 (24.00)
Graduate school 3 (4.68) 1 (4.00)
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Parameters are indicated as the mean (SD) or n (%). P-values for age, years of education, household income, weekly sleep duration, and BMI (weight/height2) are from t-tests for the comparison of high-SJL group with low-SJL group. P-value for sex ratio is from chi-square tests for the comparison of high-SJL group with low-SJL group. P-value for parental educational levels is from Fisher’s exact test for the comparison of high-SJL group with low-SJL group

BMI body mass index, JPY Japanese yen, SJL social jetlag

Psychological and cognitive measures

The psychological and cognitive data are shown in Figs. 2 and 3. For chronotype, the main effect of group in the linear mixed-effects model indicated that the high-SJL group had significantly later midsleep on free days than did the low-SJL group (Fig. 2a: β = 0.86, 95% CI [0.49, 1.24], R2 = 0.42, t = 4.58, df = 76.42, p-FDR = 0.00003; Cohen’s d = 1.50, 95% CI [0.84, 2.15]). For fatigue, the main effect of group in the linear mixed-effects model revealed that fatigue scores were significantly higher in the high-SJL than in the low-SJL group (Fig. 2b: β = 0.43, 95% CI [0.03, 0.82], R2 = 0.39, t = 2.15, df = 83.18, p-FDR = 0.034; Cohen’s d = 0.66, 95% CI [0.05, 1.27]). For TMT performance, the main effect of group was not significant (TMT-A in Fig. 3a: β = 0.03, 95% CI [-0.37, 0.45], R2 = 0.36, t = 0.18, df = 83.69, p-FDR = 0.851; Cohen’s d = 0.05, 95% CI [-0.51, 0.61]; TMT-B in Fig. 3b: β = 0.31, 95% CI [-0.07, 0.69], R2 = 0.43, t = 1.58, df = 80.00, p-FDR = 0.174; Cohen’s d = 0.42, 95% CI [-0.10, 0.95]; TMT-Δ in Fig. 3c: β = 0.35, 95% CI [-0.06, 0.78], R2 = 0.31, t = 1.63, df = 80.78, p-FDR = 0.174; Cohen’s d = 0.44, 95% CI [-0.09, 0.97]).

Fig. 2.

Fig. 2

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Chronotype and fatigue results. a The high-SJL group showed a later midpoint of sleep than that of the low-SJL group. b The high-SJL group exhibited higher fatigue scores than did the low-SJL group. White circles indicate group means, and error bars indicate standard errors. * p-FDR < 0.05, *** p-FDR < 0.001. SJL social jetlag

Fig. 3.

Fig. 3

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TMT results. a, b, and c No significant group differences were observed in TMT performances. White circles indicate group means, and error bars indicate standard errors. SJL social jetlag, TMT trail making test

Tryptophan metabolite levels

The tryptophan metabolite data are shown in Fig. 4. For tryptophan, the main effect of group in the linear mixed-effects model showed that the high-SJL group had significantly higher levels of tryptophan than did the low-SJL group (Fig. 4a: β = 0.60, 95% CI [0.18, 1.03], R2 = 0.06, t = 2.81, df = 47.01, p-FDR = 0.014; Cohen’s d = 1.14, 95% CI [0.32, 1.95]). For 5-HIAA, the main effect of group was not significant (Fig. 4b: β = 0.34, 95% CI [-0.16, 0.84], R2 = 0.05, t = 1.32, df = 83.00, p-FDR = 0.187; Cohen’s d = 0.34, 95% CI [-0.17, 0.85]).

Fig. 4.

Fig. 4

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Tryptophan metabolite results. a Tryptophan levels were higher in the high-SJL than in the low-SJL group. b No significant group difference was observed in urinary 5-HIAA levels. White circles indicate group means, and error bars indicate standard errors. * p-FDR < 0.05. 5-HIAA 5-hydroxyindoleacetic acid, SJL social jetlag

Brain measures

Brain measures are shown in Fig. 5. Whole-brain voxel-based gray matter analyses revealed significantly smaller GMVs in the left inferior occipital/fusiform region, right inferior temporal region, right angular gyrus, left temporal pole, and left superior parietal lobule of the high-SJL group than those in the low-SJL group (Fig. 5 and Online Resource 3). We then used ROIs in these brain regions with GMV reductions as the seeds for the FC analyses of resting-state fMRI data. No significant between-group differences in seed-based FC were observed for any of the five GMV-derived seed regions.

Fig. 5.

Fig. 5

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Regional gray matter volume and resting-state functional connectivity results. The high-SJL group showed smaller GMVs in the left inferior occipital/fusiform region, left temporal pole, left superior parietal lobule, right angular gyrus, and right inferior temporal region than those in the low-SJL group. AnG angular gyrus, GMV gray matter volume, IO/Fur inferior occipital/fusiform region, ITR inferior temporal region, SPL superior parietal lobule, TP temporal pole

Mediation analyses testing whether tryptophan mediated associations between SJL and GMV in regions showing significant group differences are reported in Online Resource 4.

Discussion

The present study primarily investigated differences in urinary tryptophan and 5-HIAA levels and regional GMV between youth with high SJL and those with low SJL, and additionally included exploratory seed-based resting-state FC analyses. We also examined behavioral outcomes as secondary measures. The main findings revealed that, compared with the low-SJL group, the high-SJL group (i) showed higher urinary tryptophan levels and (ii) had smaller GMV in the temporal pole, ventral occipitotemporal regions, and parietal regions. In contrast, urinary 5-HIAA levels and seed-based resting-state FC did not differ between the groups. Behaviorally, the high-SJL group also demonstrated later midsleep on free days and greater fatigue than did the low-SJL group, whereas TMT performance did not differ between the groups. Exploratory mediation analyses did not identify significant indirect effects of tryptophan on associations between SJL and regional GMV (see Online Resource 4 for interpretation). These findings may suggest region-specific structural alterations associated with higher SJL along with an increase in tryptophan levels in youth. For supplementary analyses examining continuous associations between SJL and behavioral, cognitive, biochemical, and neuroanatomical measures, see Online Resource 5.

The behavioral comparisons revealed that the high-SJL group showed a later chronotype (later midsleep on free days) than did the low-SJL group. Consistent with the findings of (Roenneberg et al. 2019), later chronotypes tend to accrue more weekday sleep debt and compensate by delaying and extending weekend sleep, which amplifies their SJL. Moreover, we observed greater fatigue in the high-SJL group, in line with a previous study reporting an association between SJL and fatigue in Japanese adolescents, even after adjusting for sleep duration (Tamura et al. 2022). Considering that later weekend wake times can delay the circadian phase and increase SJL (Taylor et al. 2008), young individuals with greater SJL may experience more persistent fatigue, potentially via accumulated weekday sleep debt that is associated with such a phase delay.

Furthermore, consistent with the later chronotype and greater fatigue, the high-SJL group showed elevated urinary tryptophan levels compared with the low-SJL group. Tryptophan, along with serotonin, has been implicated in sleep–circadian regulation and central fatigue (Miyamoto et al. 2012; Yamashita 2020). A previous study reported that, compared with young adults who received an oral placebo, those who received oral tryptophan had higher plasma levels of free tryptophan and increased sleepiness and lethargy (Morgan et al. 2007), suggesting that greater tryptophan availability may influence arousal regulation and sleep–wake timing. Consistently, in rats with sleep–wake cycle anomalies, tryptophan elevation and serotonin depletion in the brain were associated with induction of central fatigue-like states, accompanied by reduced social interaction and inattention (Yamashita and Yamamoto 2014; 2017). Accordingly, the present findings provide the novel evidence connecting such tryptophan-focused mechanistic research on sleep and circadian disruption with naturally occurring social misalignment during development.

In addition, a previous study showed that individuals who took tryptophan capsules displayed more quarrelsome and less agreeable behavior at home than did those who took placebo capsules (Hogenelst et al. 2015), suggesting that greater tryptophan availability may contribute to altered interpersonal behavior with close others. Moreover, Williams et al. (2007) reported that experimental manipulation of tryptophan availability altered neural activity in socioemotional processing-related regions, such as the temporal pole, superior temporal sulcus, and amygdala, during facial emotion processing tasks. Against this background, the present findings suggest the possibility that in youth, SJL of ≥ 1 h may be accompanied by altered tryptophan metabolism, which affects socioemotional functioning in addition to sleep and circadian regulation.

Despite a clear elevation in urinary tryptophan in the high-SJL group, urinary 5-HIAA levels were comparable between the groups, suggesting precursor elevation without detectable changes in downstream serotonergic turnover. This discrepancy might be explained by the involvement of another metabolic pathway: approximately 95% of tryptophan is metabolized via the kynurenine pathway (Yamashita 2020). Among the various metabolites synthesized in this pathway, kynurenic acid, an antagonist of N-methyl-D-aspartate (NMDA) and α7-nicotinic acetylcholine (nACh) receptors, was elevated in the hypothalamus of rats with sleep–wake cycle anomalies (Yamashita and Yamamoto 2014; 2017). NMDA and nACh receptors, which modulate glutamate and acetylcholine release at synapses, respectively, have been implicated in sleep–wake regulation (Brown et al. 2012; Dash et al. 2009). Furthermore, in rats given intraperitoneal kynurenine, an increase in kynurenic acid levels in the brain was associated with a reduced duration of rapid eye movement sleep and increased wake time as measured using polysomnography (Pocivavsek et al. 2017). This suggests a crucial role for brain kynurenic acid alterations in sleep–wake architecture. We propose that young individuals with a greater SJL may show a tendency toward increased routing of tryptophan to the kynurenine pathway, which might influence glutamatergic and cholinergic signaling, which, in turn, may help explain neural alterations associated with social–circadian misalignment.

Beyond these molecular findings suggesting involvement of both sleep–circadian regulation and socioemotional functioning, several brain regions showed smaller GMV in the high-SJL than in the low-SJL group. Among these regions, the temporal pole is noteworthy because of its central role in socioemotional processing. The involvement of this region is also consistent with previous findings of a smaller temporal pole GMV in adults with sleep abnormalities (e.g., poor sleep quality, sleep–wake cycle disruption, and idiopathic rapid eye movement sleep behavior disorder) (Amorim et al. 2018; Sun et al. 2020; Wang et al. 2025). Our findings suggest that SJL of ≥ 1 h is associated with temporal pole-related structural differences in youth.

The temporal pole is a paralimbic hub for socioemotional processing, including person-specific knowledge, affective meaning, and mental state attribution (Herlin et al. 2021; Olson et al. 2007; Tsukiura et al. 2002; Völlm et al. 2006). Benetti et al. (2010) reported an association between attachment-related anxiety and reduced temporal pole GMV in healthy adults. This finding suggests that structural reduction in this region may lead to diminished emotional and social engagement. Moreover, childhood and adolescence are marked by heightened emotional instability (Bailen et al. 2018), suggesting that this instability may be related to abnormalities in the temporal pole. Accordingly, reduced temporal pole GMV in young individuals with higher SJL may reflect greater vulnerability in terms of socioemotional processing during this stage.

Outside the temporal pole, the high-SJL group also showed smaller GMV in the left inferior occipital/fusiform region, left superior parietal lobule, right angular gyrus, and right inferior temporal region than did the low-SJL group. Ventral occipitotemporal regions are broadly involved in visual processing (e.g., perception of faces and color), and fusiform and inferior temporal areas have also been implicated in visual mental imagery (Ganis et al. 2004; Spagna et al. 2024). The superior parietal lobule is implicated in voluntary attention and top-down processes (e.g., retrieval search and monitoring), whereas the angular gyrus is involved in recollection-related orienting and the mnemonic content recovery (Cabeza et al. 2008; Vilberg and Rugg 2008). These findings may point to localized structural vulnerability within ventral occipitotemporal and parietal regions subserving higher-order cognitive processes.

Finally, although greater SJL was associated with elevated tryptophan levels and regional GMV differences, it did not affect TMT performance. The failure to detect a significant effect of SJL on the TMT may be explained by the neural profile in our participants with higher SJL. This neural profile did not primarily involve core frontoparietal executive networks supporting cognitive flexibility and processing speed (Oswald et al. 2022; Shirdel et al. 2025). Thus, these structural findings may have had only a limited effect on TMT performance. Another possible reason is the developmental trajectory of TMT performance in school-age children. Vakil et al. (2008) reported that TMT performance improves markedly with age, suggesting that age-related developmental change has a strong influence on individual differences in TMT performance. In our cross-sectional sample, which had a wide age range, this developmental gradient may have attenuated observable SJL-related differences in TMT performance, even though age and educational level were statistically controlled.

Practical implications, limitations, and future directions

From a practical perspective, these findings suggest that a high SJL in healthy young individuals may be more than just a difference in daily sleep habits, given its association with molecular and regional GMV differences. One possible implication is that consideration of school schedules, such as later start times, may be meaningful from both neurobiological and sleep health perspectives. Additionally, practical strategies to reduce excessive shifts in weekend wake time may benefit from self-monitoring and feedback approaches (e.g., sleep diaries or digital sleep-tracking tools that help visualize sleep timing across school days and free days).

Our study had several limitations. First, the cross-sectional design precluded causal inference: the differences observed in healthy young individuals with higher SJL cannot be attributed to prolonged SJL exposure. Second, urinary tryptophan and 5-HIAA were not corrected using creatinine normalization or specific gravity. Although diet, caffeine intake (e.g., energy drinks, coffee, and some juices), and urine collection conditions were standardized, hydration status may have influenced the urinary metabolite levels. Therefore, the present findings should be interpreted with caution in light of potential effects of urinary dilution. Third, cognitive assessment was limited to the TMT. Fourth, some potentially relevant lifestyle and sleep-related factors, such as diurnal activity levels, daytime naps, and long-term patterns of substance or medication use, were not assessed.

Future studies with longitudinal designs are required to determine whether healthy young individuals with high SJL show developmental trajectories in tryptophan metabolites and brain structure that differ from those of individuals with low SJL. As the GMV of several brain regions has been shown to decrease with age (Arleo et al. 2024), it will be important to examine whether high SJL is associated with accelerated age-related reductions in brain volume. Investigation of metabolic mechanisms that may regulate elevated tryptophan levels in individuals with high SJL is also warranted. One possible mechanism may involve competition with other large neutral amino acids, such as branched-chain amino acids, which share the system L transporter at the blood–brain barrier with tryptophan and may therefore influence its availability in the brain (Yamashita 2020).

Conclusion

This study revealed evidence of neurobiological alterations in healthy young individuals with greater SJL. Young individuals with higher SJL exhibited increased urinary tryptophan levels and smaller GMV in several brain regions, including the left temporal pole, than did those with lower SJL, suggesting tryptophan elevation and temporal pole-related structural differences relevant to socioemotional processing. In contrast, urinary 5-HIAA levels did not differ between the groups, indicating that they had no detectable increase in downstream serotonergic turnover. This pattern suggests that increased tryptophan levels may be preferentially metabolized into the kynurenine pathway under conditions of greater SJL. In addition, no significant group differences were observed in seed-based resting-state FC. Taken together, SJL of ≥ 1 h is related to structural differences involving the temporal pole, along with tryptophan elevation. These findings provide new insights into how SJL may be associated with neural vulnerability in socioemotional processing during childhood and adolescence.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary Material 1 (25.5KB, docx)
Supplementary Material 2 (26.8KB, docx)
Supplementary Material 3 (18.6KB, docx)
Supplementary Material 4 (168.2KB, docx)
Supplementary Material 5 (418.2KB, docx)
Supplementary Material 5 (418.2KB, docx)

Acknowledgements

This study was conducted at the University of Fukui under the supervision of Yoshifumi Mizuno. Masatoshi Yamashita was affiliated with the University of Fukui during the research period. All supplementary analyses and revision-related work were later conducted using research resources and institutional support at Hamamatsu University School of Medicine. We are grateful to Tomoe Morita and Manami Onogi for supporting us with the experiments. This study was conducted using the PET/MRI scanner and related facilities of the Biomedical Imaging Research Center, University of Fukui. This work was supported by a Grant-in-Aid for Scientific Research (KAKENHI) from the Japan Society for the Promotion of Science (grant number 23K12814 to Masatoshi Yamashita), a grant from Taiju Life Social Welfare Foundation (award years 2023 and 2025 to Masatoshi Yamashita), Life Science Innovation Center (grant number LSI24101 to Masatoshi Yamashita), the Kawano Masanori Memorial Public Interest Incorporated Foundation for Promotion of Pediatrics (award year 2022 to Yoshifumi Mizuno), the Mother and Child Health Foundation (award year 2024 to Yoshifumi Mizuno), and Research Grants from the University of Fukui (academic year 2024 to Yoshifumi Mizuno). These funding sources were not involved in the study design or implementation; the collection, analysis, or interpretation of data; the preparation, review, or approval of the manuscript; or the decision to submit the manuscript for publication.

Author contributions

Masatoshi Yamashita: Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Validation, Visualization, Writing—original draft, Writing—review & editing. Qiulu Shou: Investigation, Writing—review & editing. Masanori Fujieda: Investigation, Writing—review & editing. Hidehiko Okazawa: Resources, Writing—review & editing. Yoshiyuki Hirano: Resources, Writing—review & editing. Kuriko Kagitani-Shimono: Resources, Writing—review & editing. Yoshifumi Mizuno: Funding acquisition, Resources, Supervision, Writing—review & editing.

Data availability

The data will be made available via the Child Developmental MRI (CDM) project database, which is currently under construction. Additionally, data will be provided upon signing a data sharing agreement and after receiving a research proposal along with evidence of approval from the requester’s institutional review board.

Declarations

Conflict of interest

The authors have no relevant financial or non-financial interests to disclose.

Ethical approval

The Research Ethics Committee of the University of Fukui approved the study protocol (Assurance No. FU-20220061). The procedures complied with the principles of the Declaration of Helsinki.

Consent to participate

All parents provided written informed consent, and all children provided assent.

Footnotes

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Contributor Information

Masatoshi Yamashita, Email: ymasa09@hama-med.ac.jp.

Yoshifumi Mizuno, Email: mizunoy@u-fukui.ac.jp.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary Material 1 (25.5KB, docx)
Supplementary Material 2 (26.8KB, docx)
Supplementary Material 3 (18.6KB, docx)
Supplementary Material 4 (168.2KB, docx)
Supplementary Material 5 (418.2KB, docx)
Supplementary Material 5 (418.2KB, docx)

Data Availability Statement

The data will be made available via the Child Developmental MRI (CDM) project database, which is currently under construction. Additionally, data will be provided upon signing a data sharing agreement and after receiving a research proposal along with evidence of approval from the requester’s institutional review board.

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