Brain network analysis using morphological intrinsic divergence: differences in small-world properties of morphological similarity networks among healthy young adults with different chronotypes.

PurposeGlycemic response to the same meal depends on daytime and alignment of consumption with the inner clock, which has not been examined by individual chronotype yet. This study examined whether the 2-h postprandial and 24-h glycemic response to a meal with high glycemic index (GI) differ when consumed early or late in the day among students with early or late chronotype.MethodsFrom a screening of 327 students aged 18–25 years, those with early (n = 22) or late (n = 23) chronotype participated in a 7-day randomized controlled cross-over intervention study. After a 3-day observational phase, standardized meals were provided on run-in/washout (days 4 and 6) and intervention (days 5 and 7), on which participants received a high GI meal (GI = 72) in the morning (7 a.m.) or in the evening (8 p.m.). All other meals had a medium GI. Continuous glucose monitoring was used to measure 2-h postprandial and 24-h glycemic responses and their variability.ResultsAmong students with early chronotype 2-h postprandial glucose responses to the high GI meal were higher in the evening than in the morning (iAUC: 234 (± 92) vs. 195 (± 91) (mmol/L) × min, p = 0.042). Likewise, mean and lowest 2-h postprandial glucose values were higher when the high GI meal was consumed in the evening (p < 0.001; p = 0.017). 24-h glycemic responses were similar irrespective of meal time. Participants with late chronotype consuming a high GI meal in the morning or evening showed similar 2-h postprandial (iAUC: 211 (± 110) vs. 207 (± 95) (mmol/L) × min, p = 0.9) and 24-h glycemic responses at both daytimes.ConclusionsDiurnal differences in response to a high GI meal are confined to those young adults with early chronotype, whilst those with a late chronotype seem vulnerable to both very early and late high GI meals. Registered at clinicaltrials.gov (NCT04298645; 22/01/2020).

Individuals differ in their preferred timing of sleep and activity, which is referred to as a chronotype. The timing shows a wide distribution; extremely early chronotypes may wake up when the extremely late chronotypes fall asleep. The chronotype is supposed to be determined by the central circadian clock located in the suprachiasmatic nuclei (SCN) of the hypothalamus because the phasing of the pineal melatonin rhythm, which is driven by the SCN, correlates with the sleep timing preference. In addition to the SCN, circadian oscillators are also present in most if not all bodily cells. These peripheral clocks are synchronized by the central SCN clock and by other tissue-specific entraining cues. At the molecular level, the circadian oscillations are based on a complex, self-sustaining mechanism that drives the rhythmical expression of clock genes and their proteins. The aim of the present field study was to elucidate whether the changes in the internal timing of early and late chronotypes, as expressed by changes in the phases of their mid-sleep and melatonin secretion, can also be detected at the molecular clockwork level in subjects examined under real-life conditions. Ninety-five adult volunteers were chronotyped using an adapted Munich chronotype questionnaire to assess their mid-sleep phase, and 6 subjects with early chronotypes and 6 with late chronotypes were chosen for the study. For the assessment of the circadian phase, the subjects provided samples of saliva for the melatonin assay and samples of oral mucosa for the determination of clock gene Per1, Per2, and Rev-erbα mRNA levels every 4 h during a 24-h period. The significant correlation between the phase of the melatonin profile and timing of mid-sleep confirmed the classification of the subjects according to their chronotype. The circadian phases of the Per1, Per2, and Rev-erbα expression profiles in the oral mucosa were advanced in the early chronotypes compared with those in the late chronotypes (p < .001) and correlated significantly with the mid-sleep phase of the individual subjects. Moreover, the circadian phases of the Per1 expression profiles of individual subjects correlated significantly with the phases of their melatonin profiles (p < .05), whereas the correlation for the Per2 and Rev-erbα phases was nonsignificant, although the trend was the same. Our results demonstrate that the individual chronotype in humans living in real-life conditions affects not only the phasing of the daily melatonin rhythm in saliva but also the phasing of Per1, Per2, and Rev-erbα clock gene expression profiles in buccal mucosa cells. This report represents the first demonstration that the human peripheral circadian clock may sense the individual's chronotype under field study conditions. The data contribute to our understanding of the mechanisms underlying human chronotypes in real life. (Author correspondence: sumova@biomed.cas.cz)

Impact of exercise timing on perceived appetite and food reward in early and late chronotypes: An exploratory study in a male Saudi sample

People characterized as late chronotype have elevated type 2 diabetes and cardiovascular disease risk compared to early chronotype. It is unclear how chronotype is associated with insulin sensitivity, metabolic flexibility, or plasma TCA cycle intermediates concentration, amino acids (AA), and/or beta-oxidation. This study examined these metabolic associations with chronotype. The Morningness-Eveningness Questionnaire (MEQ) was used to classify adults with metabolic syndrome (ATP III criteria) as either early (n = 15 [13F], MEQ = 64.7 ± 1.4) or late (n = 19 [16F], MEQ = 45.5 ± 1.3) chronotype. Fasting bloods determined hepatic (HOMA-IR) and adipose insulin resistance (Adipose-IR) while a 120-minute euglycemic clamp (40 mU/m2/min, 5 mmoL/L) was performed to test peripheral insulin sensitivity (glucose infusion rate). Carbohydrate (CHOOX) and fat oxidation (FOX), as well as nonoxidative glucose disposal (NOGD), were also estimated (indirect calorimetry). Plasma tricarboxylic acid cycle (TCA) intermediates, AA, and acyl-carnitines were measured along with VO2max and body composition (DXA). There were no statistical differences in age, BMI, fat-free mass, VO2max, or ATP III criteria between groups. Early chronotype, however, had higher peripheral insulin sensitivity (P = 0.009) and lower HOMA-IR (P = 0.02) and Adipose-IR (P = 0.05) compared with late chronotype. Further, early chronotype had higher NOGD (P = 0.008) and greater insulin-stimulated CHOOX (P = 0.02). While fasting lactate (P = 0.01), TCA intermediates (isocitrate, α-ketoglutarate, succinate, fumarate, malate; all P ≤ 0.04) and some AA (proline, isoleucine; P = 0.003-0.05) were lower in early chronotype, other AA (threonine, histidine, arginine; all P ≤ 0.05) and most acyl-carnitines were higher (P ≤ 0.05) compared with late chronotype. Greater insulin sensitivity and metabolic flexibility relates to plasma TCA concentration in early chronotype.

Late chronotype (LC) correlates with reduced metabolic insulin sensitivity and cardiovascular disease. It is unclear if insulin action on aortic waveforms and inflammation is altered in LC versus early chronotype (EC). Adults with metabolic syndrome (n = 39, MetS) were classified as either EC (Morning‐Eveningness Questionnaire [MEQ] = 63.5 ± 1.2) or LC (MEQ = 45.5 ± 1.3). A 120 min euglycemic clamp (40 mU/m2/min, 90 mg/dL) with indirect calorimetry was used to determine metabolic insulin sensitivity (glucose infusion rate [GIR]) and nonoxidative glucose disposal (NOGD). Aortic waveforms via applanation tonometry and inflammation by blood biochemistries were assessed at 0 and 120 min of the clamp. LC had higher fat‐free mass and lower VO2max, GIR, and NOGD (between groups, all p ≤ 0.05) than EC. Despite no difference in 0 min waveforms, both groups had insulin‐stimulated elevations in pulse pressure amplification with reduced AIx75 and augmentation pressure (AP; time effect, p ≤ 0.05). However, EC had decreased forward pressure (Pf; interaction effect, p = 0.007) with insulin versus rises in LC. Although LC had higher tumor necrosis factor‐α (TNF‐α; group effect, p ≤ 0.01) than EC, both LC and EC had insulin‐stimulated increases in TNF‐α and decreases in hs‐CRP (time effect, both p ≤ 0.01). Higher MEQ scores related to greater insulin‐stimulated reductions in AP (r = −0.42, p = 0.016) and Pf (r = −0.41, p = 0.02). VO2max correlated with insulin‐mediated reductions in AIx75 (r = −0.56, p < 0.01) and AP (r = −0.49, p < 0.01). NOGD related to decreased AP (r = −0.44, p = 0.03) and Pf (r = −0.43, p = 0.04) during insulin infusion. LC was depicted by blunted forward pressure waveform responses to insulin and higher TNF‐α in MetS. More work is needed to assess endothelial function across chronotypes.

Variations in circadian rhythm-related genes influence the individual chronotype. Here, we hypothesize that the peak of clock gene expression at 7 a.m. differs between young adults with a late chronotype and young adults with an early chronotype. Participants of the Chronotype and Nutrition nutritional trial (ChroNu study) were selected for their chronotype assessed by the Munich Chronotype questionnaire (MCTQ) and actigraphy. Total RNA was isolated from CD14+ monocytes of participants at 7 a.m. on the run-in day. Expression levels of seven clock genes (PER1, PER2, PER3, NR1D1, NR1D2, CRY1 and CRISPLD2) of individuals with early (n = 11) or late chronotypes (n = 19) were analysed by reverse transcription quantitative polymerase chain reaction. Difference in expression levels was tested by Mann Whitney-U test. The relative expression levels of the selected genes were not significantly different between individuals with early and late chronotypes (all p > 0.07). Contrary to expectation, clock gene expression levels at 7 a.m. was similar in individuals with early and late chronotypes. Further studies on larger sample sizes with multiple sampling time points should elucidate whether gene expression is altered at other day times underscoring the biological difference between individuals with early or late chronotypes.Supplementary InformationThe online version contains supplementary material available at 10.1038/s41598-025-12423-7.

ABSTRACTEveningness preference (late chronotype) was previously associated with different personality dimensions and thinking styles that were linked to creativity, suggesting that evening-type individuals tend to be more creative than the morning-types. Nevertheless, empirical data on the association between chronotype and creative performance is scarce and inconclusive. Moreover, cognitive processes related to creative thinking are influenced by other factors such as sleep and the time of testing. Therefore, our aim was to examine convergent and divergent thinking abilities in late and early chronotypes, taking into consideration the influence of asynchrony (optimal versus nonoptimal testing times) and sleep quality. We analyzed the data of 36 evening-type and 36 morning-type young, healthy adults who completed the Compound Remote Associates (CRAs) as a convergent and the Just suppose subtest of the Torrance Tests of Creative Thinking as a divergent thinking task within a time interval that did (n = 32) or did not (n = 40) overlap with their individually defined peak times. Chronotype was not directly associated with creative performance, but in case of the convergent thinking task an interaction between chronotype and asynchrony emerged. Late chronotypes who completed the test at subjectively nonoptimal times showed better performance than late chronotypes tested during their “peak” and early chronotypes tested at their peak or off-peak times. Although insomniac symptoms predicted lower scores in the convergent thinking task, the interaction between chronotype and asynchrony was independent of the effects of sleep quality or the general testing time. Divergent thinking was not predicted by chronotype, asynchrony or their interaction. Our findings indicate that asynchrony might have a beneficial influence on convergent thinking, especially in late chronotypes.

A person's chronotype reflects individual variability in diurnal rhythms for preferred timing of sleep and daily activities such as exercise and food intake. The aim of this review is to provide an overview of the evidence around the influence of chronotype on eating behaviour and appetite control, as well as our perspectives and suggestions for future research. Increasing evidence demonstrates that late chronotype is associated with adverse health outcomes. A late chronotype may exacerbate the influence of greater evening energy intake on overweight/obesity risk and curtail weight management efforts. Furthermore, late chronotypes tend to have worse diet quality, with greater intake of fast foods, caffeine and alcohol and lower intake of fruits and vegetables. Late chronotype is also associated with eating behaviour traits that increase the susceptibility to overconsumption such as disinhibition, food cravings and binge eating. Whether an individual's chronotype influences appetite in response to food intake and exercise is an area of recent interest that has largely been overlooked. Preliminary evidence suggests additive rather than interactive effects of chronotype and meal timing on appetite and food reward, but that hunger may decrease to a greater extent in response to morning exercise in early chronotypes and in response to evening exercise in late chronotypes. More studies examining the interplay between an individual's chronotype, food intake/exercise timing and sleep are required as this could be of importance to inform personalised dietary and exercise prescriptions to promote better appetite control and weight management outcomes.

PURPOSE: Late chronotype (LC) is linked to insulin resistance & cardiovascular disease. However, it is unclear if insulin reduces aortic waveforms & inflammation in LC versus early chronotype (EC). METHODS: Using the Morning-Eveningness Questionnaire (MEQ), adults with metabolic syndrome (MetS; 54.9 ± 1.1y; VO2MAX 22.2 ± 0.7 ml/kg/min, 3.5 ± 0.1 ATP-III score) were classified as either LC (n = 19 (16F); MEQ = 45.5 ± 1.3) or EC (n = 20 (16F); MEQ = 63.5 ± 1.2). A 120 min euglycemic clamp (40 mU/m2/min, 90 mg/dl) with indirect calorimetry was used to determine metabolic insulin sensitivity (glucose infusion rate (GIR)) & non-oxidized glucose disposal (NOGD; GIR-total carbohydrate oxidation). Aortic waveforms via applanation tonometry were taken at 0 & 120-min of the clamp & included augmentation index (AIx75), augmentation pressure (AP), pulse pressure amplification (PPA), mean arterial pressure (MAP), as well as forward (Pf) & backward (Pb) pulse wave. Inflammation (ICAM, VCAM, hs-CRP, TNF-α, & MMP-7) was also assessed before and after the clamp. RESULTS: While age, fat mass, & ATP III score were similar between groups, LC had higher FFM (P = 0.04) and lower VO2MAX (P = 0.05), GIR (P < 0.01) and NOGD (P < 0.01) than EC. No difference in 0 min waveforms were noted. Nonetheless, LC had increases in Pf versus EC (∆:1.9 ± 1.1 vs. -2.3 ± 1.0 mmHg; P = 0.007) during insulin infusion, though reflection magnitude decreased similarly (∆: -6.0 ± 2.2 vs. -5.9 ± 2.3%; P = 0.02). Further, both LC and EC had elevated PPA (∆: 0.04 ± 0.02 vs. 0.05 ± 0.02 mmHg; P = 0.02) & reduced AP (∆: -2.0 ± 1.0 vs. -2.8 ± 1.1 mmHg; P = 0.02) in response to insulin, while only EC had lower AIx75 (P = 0.02). LC had increased TNF-α (P = 0.04) and decreased MMP-7 (P = 0.04) in response to insulin, and EC decreased in hs-CRP (P = 0.009), ICAM (P = 0.009), and VCAM (P = 0.09). VO2MAX correlated with insulin-mediated reductions in AIx75 (r = -0.56, P < 0.01) and AP (r = -0.49, P < 0.01), while NOGD related to decreased AP during insulin (r = -0.44, P = 0.03) and Pf (r = -0.43, P = 0.04). Insulin-mediated reductions in VCAM also correlated with lower MAP during the clamp (r = 0.41, P = 0.03). CONCLUSIONS: LC was depicted by altered aortic waveform and inflammatory responses to insulin in MetS. More work is needed to assess peripheral endothelial function in chronotype. Funding: NIH RO1-HL130296

Late chronotype (LC) is related to obesity and altered food intake throughout the day. But whether appetite perception and gut hormones differ among chronotypes is unclear. Thus, we examined if early chronotype (EC) have different appetite responses in relation to food intake than LC. Adults with obesity were categorized using the Morningness-Eveningness Questionnaire (MEQ) as either EC (n = 21, 18F, MEQ = 63.9 ± 1.0, 53.7 ± 1.2 yr, 36.2 ± 1.1 kg/m2) and LC (n = 28, 24F, MEQ = 47.2 ± 1.5, 55.7 ± 1.4 yr, 37.1 ± 1.0 kg/m2). Visual analog scales were used during a 120 min 75 g oral glucose tolerance test (OGTT) at 30 min intervals to assess appetite perception, as well as glucose, insulin, GLP-1 (glucagon-like polypeptide-1), GIP (glucose-dependent insulinotrophic peptide), PYY (protein tyrosine tyrosine), and acylated ghrelin. Dietary intake (food logs), resting metabolic rate (RMR; indirect calorimetry), aerobic fitness (maximal oxygen consumption (VO2max)), and body composition dual-energy X-ray absorptiometry (DXA) were also assessed. Age, body composition, RMR, and fasting appetite were similar between groups. However, EC had higher satisfaction and fullness as well as reduced desires for sweet, salty, savory, and fatty foods during the OGTT (P < 0.05). Only GIP tAUC0–120 min was elevated in EC versus LC (p = 0.01). Daily dietary intake was similar between groups, but EC ate fewer carbohydrates (p = 0.05) and more protein (p = 0.01) at lunch. Further, EC had lower caloric (p = 0.03), protein (p = 0.03) and fat (p = 0.04) intake during afternoon snacking compared to LC. Dietary fat was lower, and carbohydrates was higher, in EC than LC (p = 0.05) at dinner. Low glucose and high insulin as well as GLP-1 tAUC60–120 min related to desires for sweet foods (p < 0.05). Taken together, EC had more favorable appetite and lower caloric intake later in the day compared with LC.

Patients with Parkinson's disease (PD) exhibit structural and functional alterations in both primary and high-order cognitive networks, but the interactions within aberrant functional networks and relevant structural foundation remains unexplored. In this study, the functional networks (FN) and the morphometric similarity networks (MSN) were constructed respectively based on the time-series data and gray matter volume from the MRI data of PD patients and controls. The efficiency, average controllability and k-shell values of the FN and MSN were calculated to evaluate their ability of information transmission and identify structural and functional abnormalities in PD. The abnormal regions were categorized into five types: regions with MSN abnormalities, regions with FN abnormalities, regions with both MSN and FN abnormalities, regions with abnormalities only in MSN but not in FN and regions with abnormalities only in FN but not in MSN. Further, the dynamic causal model (DCM) was used to evaluate the causal relationship of information flow between the identified regions. In the network property analysis of the FN, PD patients showed decreased global efficiency and connectivity in the visual network (VIS) and increased global efficiency in higher-order cognitive networks, including the ventral attention network (VAN), default mode network (DMN), and the limbic network (LIM) but no difference in MSN. In the DCM analysis of the regions, PD patients exhibited increased excitatory transition from the visual areas to the superior frontal gyrus, whereas had disturbed information flow from the visual areas to the insula and the orbitofrontal cortex. These findings suggest changes in structural and functional brain of PD patients, and advance our understanding of PD pathogenesis from different neural dimensions.

Incorporating with machine learning technology, neuroimaging markers which extracted from structural Magnetic Resonance Images (sMRI), can help distinguish Alzheimer's Disease (AD) patients from Healthy Controls (HC). In the present study, we aim to investigate differences in atrophic regions between HC and AD and apply machine learning methods to classify these two groups. T1-weighted sMRI scans of 158 patients with AD and 145 age-matched HC were acquired from the ADNI database. Five kinds of parameters (i.e. cortical thickness, surface area, gray matter volume, curvature and sulcal depth) were obtained through the preprocessing steps. The recursive feature elimination (RFE) method for support vector machine (SVM) and leave-one-out cross validation (LOOCV) were applied to determine the optimal feature dimensions. Each kind of parameter was trained by SVM algorithm to acquire a classifier, which was used to classify HC and AD ultimately. Moreover, the ROC curves were depicted for testing the classifiers' performance and the SVM classifiers of two-dimensional spaces took the top two important features as classification features for separating HC and AD to the maximum extent. The results showed that the decreased cortical thickness and gray matter volume dramatically exhibited the trend of atrophy. The key differences between AD and HC existed in the cortical thickness and gray matter volume of the entorhinal cortex and medial orbitofrontal cortex. In terms of classification results, an optimal accuracy of 90.76% was obtained via multi-parameter combination (i.e. cortical thickness, gray matter volume and surface area). Meanwhile, the receiver operating characteristic (ROC) curves and area under the curve (AUC) were also verified multi-parameter combination could reach a better classification performance (AUC=0.94) after the SVM-RFE method. The results could be well prove that multi-parameter combination could provide more useful classified features from multivariate anatomical structure than single parameter. In addition, as cortical thickness and multi-parameter combination contained more important classified information with fewer feature dimensions after feature selection, it could be optimum to separate HC from AD to take the top two important features of them to construct SVM classifiers in two-dimensional space. The proposed work is a promising approach suggesting an important role for machine-learning based diagnostic image analysis for clinical practice.

Sleep has strong links to the symptomology of fibromyalgia syndrome (FMS), a diffuse musculoskeletal pain disorder. Information about the involvement of the circadian clock is, however, sparse. In this study, 1548 individuals with FMS completed an online survey containing questions on demographics, stimulant consumption, sleep quality, well-being and subjective pain, chronotype (assessed by the Munich ChronoType Questionnaire, MCTQ), and FMS impact. Chronotype (expressed as the mid-sleep-point on free days, corrected for sleep deficit on workdays, MSF(sc)) significantly correlated with stress-ratings, so-called "memory failures in everyday life," fatigue, FMS impact, and depression but not with anxiety. When chronotypes were categorized into 3 groups (early, intermediate, late), significant group differences were found for sum scores of perceived stress, memory failures in everyday life, fatigue, FMS impact, and depression but not anxiety, with late chronotypes being more affected than early chronotypes. Sleepiness ratings were highest in early chronotypes. Challenges of sleep quality and subjective pain were significantly increased in both early and late chronotypes. The results show that according to their reports, late chronotypes are more affected by fibromyalgia.

Introduction Chronotype is defined as an individual’s propensity to sleep at a specific time in a 24-hour cycle with late chronotype associated with poorer health outcomes including cancer. The role of chronotype on lifestyle behaviors remains relatively undefined in ovarian cancer. The Lifestyle Intervention for oVarian cancer Enhanced Survival study is testing whether 1205 women randomized to a diet and physical activity intervention for 24-months will have longer progression-free survival versus attention control. Here we determine the frequency and predictors of late versus early and mid chronotypes in disease-free ovarian cancer survivors. Methods 894 ovarian cancer survivors with baseline measures were included in analyses. Chronotypes were determined using self-reported time to bed (early- &amp;lt; 9 pm; mid- ≥ 9 pm - ≤12 am; late- &amp;gt;12 am) captured through the Pittsburgh Sleep Quality Index. Demographic, diet and physical activity data were captured with validated questionnaires and BMI measured in clinic. Descriptive statistics and logistic regression, adjusted for smoking status and race, were performed. Results 12.4% of women were late chronotype with significant differences between chronotypes observed for race, smoking history, sleep duration, and physical activity (p &amp;lt; 0.05). Late chronotype reported fewer hours of sleep per night (6.54 ± 1.51hrs) compared to mid (7.10± 1.31hrs) and early (7.74 ± 1.30hrs) chronotype. Blacks had higher odds of being late chronotype, OR 4.28 (95% CI 2.16-8.46). Late chronotype were more likely to report a history of smoking and lower recreational activity and had a higher mean BMI of 29.1± 6.0 kg/m2 compared to mid and early chronotype 27.8± 6.2 kg/m2 and 27.4± 5.4kg/m2, respectively. No significant differences were observed for sleep or diet quality, age, education or employment status. Conclusion Results of this analysis are consistent with other community-based population studies with regard to chronotype and race. Ovarian cancer is aggressive and late chronotype are more likely to have other risk factors that elevate risk of recurrence (obesity, tobacco use and inactivity. Six-month data are being analyzed by treatment arm and will provide important insights as to the role of sleep phase and lifestyle behaviors in this vulnerable population. Support NCT00719303; NCI R01CA186700-01A1

IntroductionA person’s chronotype is their entrained preference for sleep time within the 24 hours clock. It is described by the well-known concept of the ‘lark’ (early riser) and ‘owl’ (late...