Metabolic dysfunction and circadian rhythm abnormalities in adolescents with sleep disturbance
Akemi Tomoda, MD*, Junko Kawatani, MD*, Takako Joudoi, MD**, Akinobu Hamada, PhD***, Teruhisa Miike, MD**
Department of Child Developmental Sociology*, Department of Child Development**, and Department of Pharmacy***,
Faculty of Medical and Pharmaceutical Sciences, Kumamoto University, Kumamoto, Japan
Correspondence address: Akemi Tomoda, MD, PhD
Department of Child Developmental Sociology, Faculty of Medical and Pharmaceutical Sciences, Kumamoto University, 1-1-1 Honjo, Kumamoto 860-8566, Japan.
Tel.: +81-96-373-5197; Fax: 81-96-373-5200 E-mail: [email protected]
(Accepted on February 26, 2009)
Key Words: Circadian rhythm sleep disorders; cardiographic R–R interval; autonomic dysfunction; human clock genes; glucose metabolism
No. of words in the abstract: 185
No. of words in the text (excluding abstract, acknowledgments, financial disclosures, legends and references): 3,608
No. of figures: 1 No. of tables: 2
No. of supplementary material: 0
Abstract
Background: Sleep disturbance attributable to circadian rhythm abnormalities
frequently occurs in previously healthy children and adolescents who often complain of
gastrointestinal discomfort after meals.
Methods: Glucose metabolism, autonomic function, and human clock gene expression
in whole blood cells were investigated in 18 adolescent patients with circadian rhythm
sleep disorder.
Results: Glucose tolerance was significantly lower in the patients than in normal
controls: the mean sigma blood glucose level was significantly higher (P < 0.05) and
the insulinogenic index was significantly lower (P < 0.05) in the patient group than in
controls. Messenger ribonucleic acid level of hPer2 was significantly higher at 6:00 in
the control subjects, but in only 3 of the 18 patients. Component analysis of
cardiographic R–R interval revealed that high-frequency component peaks were
suppressed significantly in the patient group compared to the controls (P < 0.001).
Conclusions: Metabolic and endocrine dysfunctions were identified in adolescents with
sleep disturbance as decreased glucose tolerance and absence of human clock gene
regulation in whole blood cells. Their brain dysfunction attributable to sleep
disturbances might cause such peripheral autonomic imbalance and carbohydrate
metabolic dysfunction.
Introduction
Increasingly, circadian rhythm sleep disorders have been reported in pediatric
and adolescent populations. Pediatric practitioners now commonly encounter sleep
disturbance in previously healthy children and adolescents (Stein et al., 2001; Giannotti
et al., 2002; Boergers et al., 2007). The characteristic clinical features are well known,
but the specific causes remain unknown. New types of circadian rhythm sleep disorders,
such as familial advanced sleep phase syndrome (ASPS) and delayed sleep phase
syndrome (DSPS), non-24-h sleep-wake syndrome (non-24), and
morningness-eveningness have been described during the last decade. Such disorders
are probably caused by various disturbances of circadian expression of the clock gene
(Ebisawa et al., 2001; Toh et al., 2001; Iwase et al., 2002; Wijnen et al., 2002; Archer et
al., 2003; Pirovano et al., 2005; Takimoto et al., 2005). Polymorphisms in clock genes
are known to induce circadian rhythm sleep disorders. For example, mutations in the
period2 (Per2) gene (S662G) or casein kinase1 δ (CK16) gene (T44A) cause familial
ASPS; furthermore, missense polymorphisms in the Per3 (V647G) and CK1e (S408N)
genes increase or decrease the risk of developing DSPS.
Children now commonly present with indefinite or definite complaints of
sleep disturbance, general fatigue, and gastrointestinal discomfort, for which exhaustive
medical and psychosocial evaluations have not revealed acceptable explanations
(Ivanenko and Johnson, 2008). Especially, fatigue and gastrointestinal discomfort have
been noted as quite severe in our patients (Tomoda et al., 1997). Our clinical experience
suggests that patients with sleep disturbances might experience changes in biological
clock function, which induce autonomic imbalance, engendering symptoms such as
general fatigue and gastrointestinal discomfort. Such symptoms interfere greatly with
normal function at school in children and adolescents (Tomoda et al., 2001). A few
previous reports have described interactions between sleep-disordered breathing and
metabolic abnormalities in overweight and obese children and adolescents (de la Eva et
al., 2002; Verhulst et al., 2007). However, no metabolic functional study has
investigated non-obese children with sleep disturbances to discover whether such
clinical symptoms are associated with abnormal metabolic function caused by desynchronization of biorhythms and autonomic imbalance. Hence, these studies are likely to overestimate the effects of metabolic dysfunction, may confound
sleep-disordered-related differences with obesity-related differences, and may mistakenly identify preexisting autonomic dysfunction that were risk factors for
developing persistent impaired glucose tolerance.
Our guiding hypothesis is that their brain dysfunction due to sleep disturbances might cause such peripheral autonomic imbalance and carbohydrate
metabolic dysfunction. The goal of the present study was to evaluate the autonomic nerve components of the cardiographic R–R interval, sleep pattern, daily expression of the clock gene, hPer2 in whole blood cells, and metabolic function in adolescent patients with circadian rhythm sleep disorders.
Methods
Protocol
This study included 18 unmedicated patients with circadian rhythm sleep
disorders. They were 7 boys and 11 girls aged 12–17 years (mean age, 15.3 years;
standard deviation [SD], 1.7 years) who were referred to our laboratory during
2005–2007 for examination of insomnia and fatigue. All patients satisfied diagnostic
criteria for circadian rhythm sleep disorders of the Diagnostic and Statistical Manual of
Mental Disorders, 4th Edition, Text Revision (DSM-IV-TR®). The diagnosis was made
by three raters using the Structured Clinical Interview. The severity of those symptoms
was measured using self-reported ratings (performance status scores), as described
previously (Kuratsune et al., 2002; Tomoda et al., 2007). Their performance status
scores on admission were higher than 5 (mean, 5.6; SD, 0.8).
For at least one month prior to the initial assessment, prophylactic drugs (e.g.
tranquilizers) were not given. Patients who had just recently started treatment with antidepressants or hypotension drugs, or who were diagnosed as having neurological illness, migraine, obstructive sleep apnea, below average intelligence, or serious psychopathology were excluded from the study. Serious psychopathology was evaluated by referral to at least one psychiatrist if the patient presented with some indicative symptoms. No patient had a history of drug abuse. Table 1 presents physical characteristics of the present subjects. The protocol was approved by the Committee of Life Ethics, Graduate School of Medicine, Kumamoto University. All participants gave written informed consent.
Experimental procedure for human clock gene measurement
Subjects were exposed to natural and fluorescent lighting of the institution
during the awake period. Lights were turned off during the sleeping period. An
indwelling catheter was placed in the antecubital vein for a 24-h period. Blood samples
were taken at 4-h intervals beginning at 10:00 a.m. on the second day of hospitalization
and continued until 6:00 a.m. of the following day. Samples were obtained under dim
light (less than 30 Lux) without waking the patients during the sleeping period. We
previously reported that subjects 12 years of age and older show similar metabolic
characteristics to those of an adult (Iwatani et al., 1997). Therefore, we recruited 10 men
aged 20–41 years (mean age, 27.4 years; SD, 6.1 years) as normal subjects from whom
data were obtained (Reppert and Weaver, 2002; Takimoto et al., 2005): none had below-average intelligence, physical problems, psychiatric psychopathology, or
irregular sleep or meal schedules.
Blood was collected in blood RNA kit tubes (PAXgene; Qiagen K.K., Tokyo,
Japan). The tubes were incubated at room temperature for 24 h; then the total
ribonucleic acid (RNA) was isolated according to the manufacturer’s instructions. For
quality assessment of total RNA during protocol development, deoxyribonucleic acid
(DNA) digestion of the samples was performed with the RNase-Free DNase Set
(Qiagen K.K.). Synthesis of complementary DNA was conducted (ReverTra Ace-α-®;
Toyobo Co. Ltd., Osaka, Japan) for use with the reverse-transcription polymerase chain
reaction (RT-PCR) kit. Quantitative real-time RT-PCR (TaqMan®) was performed
using a sequence detection system (ABI PRISM® 7900; Applied Biosystems, Foster
City, CA) to determine the expression levels of hPer1, hPer2, hPer3, hBmal1, hClock,
and housekeeping gene hβ-actin expression relative to hβ-actin, with the standard protocol described by the manufacturer. Relative expression of the clock gene was
determined as the ratio of expression of the clock gene to that of the β-actin gene for
each sample. Values were normalized so that the peak value equaled 100%. The
TaqMan® hβ-actin control reagents and primer sets, Assays-on-DemandTM Gene Expression Product for hPer1, hPer2, hPer3, hBmal1, and hClock were purchased from
Applied Biosystems for the following: hPer1, Hs00242988_m1; hPer2,
Hs00256144_m1; hPer3, Hs00213466_m1; hBmal1, Hs00154147_m1; hClock,
Hs00231857_m1. In addition, hPer2 was selected as the daily expression of the clock
gene for determination of the circadian profile (Takimoto et al., 2005).
Plasma cortisol assay for the circadian profile
Blood was drawn into tubes and then centrifuged to obtain plasma, which was
stored at -80°C until assay. The cortisol level was determined using commercially
available radioimmunoassay and enzyme-linked immunosorbent assays (Reppert and Weaver, 2002; Takimoto et al., 2005). To obtain data in normal age-matched subjects, we recruited 10 healthy schoolchildren, 7 males and 3 females aged 12–19 years (mean
age, 16.5 years), none of whom had below average intelligence, a physical problems, or
psychiatric psychopathology.
Evaluation of autonomic function
Component analysis of the cardiographic R–R intervals was conducted to
evaluate autonomic nervous system function in all patients and control subjects using
the method described previously (Pomeranz et al., 1985; Tomoda et al., 2007).
Electrocardiograms were measured for 10 min. Subsequently, a computer program
(PC9801-VX; NEC Corp., Tokyo, Japan) automatically calculated the peak power
(amplitude) of each spectral component of their cardiographic R–R intervals as
described previously (Pomeranz et al., 1985; Tomoda et al., 2007).
To evaluate autonomic nervous system function, component analysis of the
cardiographic R–R intervals was performed by extracting the peak power (amplitude) of
two spectral components: the low-frequency components (LFC) (0.05–0.15 Hz) known
as a sympathetic component, and the high-frequency components (HFC) (around 0.25
Hz) known as a parasympathetic component in the supine resting position. The ratio of
LFC/HFC represents the balance between the sympathetic and parasympathetic
functions. The amplitude value for each spectral component was given in absolute units
calculated using this system.
To obtain data in normal age-matched subjects, 50 healthy schoolchildren
aged 6–20 years (mean age, 16.8 years) were recruited as subjects from the community
targeted towards school students. None had below-average intelligence, physical
problems, or psychiatric psychopathology.
Evaluation of carbohydrate metabolism
A 3-h oral glucose tolerance test was performed the morning after a subject
had fasted overnight. After the fasting blood sample was drawn, a subject was given a
solution containing a predetermined amount of glucose based on body weight (1.75 g/kg
to a maximum of 75 g). After glucose ingestion, blood samples were drawn at 30, 60,
90, 120, 150, and 180 min to measure blood glucose (BG) levels and immunoreactive
insulin (IRI) response. Serum BG level was determined using the glucose oxidase
reaction method. Serum IRI response was measured using radioimmunoassay (Eiken
Chemical Co. Ltd., Tokyo, Japan).
The BG levels, IRI response, cumulative BG (sigma BG), cumulative IRI
(sigma IRI), insulin/glucose ratio (delta IRI/delta BG), and insulinogenic index (sigma
IRI/sigma BG) were then compared to normal control data that had been reported
previously for 8 subjects aged 12–16 years without a personal or family history of
diabetes mellitus or any factor affecting glucose metabolism (Iwatani et al., 1997). The
control subjects were within ±2.0 SD of standard height, and within ±20% of ideal body
weight. All indices were calculated using the same methods as those reported previously
(Iwatani et al., 1997).
Measurement of deep body temperature
Deep body temperature was assessed as an indicator of the core body
temperature (CBT) using a body temperature monitor (Terumo Corp., Tokyo, Japan)
positioned below the Lanz's point of the anterior abdominal wall using a subdermal
probe fixed tightly in place with adhesive medical tape. Measurements were made every
30 min for three consecutive days. To obtain data in normal age-matched subjects, 9
healthy schoolchildren were recruited as subjects. They were 6 males and 3 females,
aged 10–21 years (mean age, 17.3 years), as reported elsewhere (Tomoda et al., 1997).
Recording of the sleep-wake rhythm
All patients kept daily recordings (logs) of their times of sleeping and
awaking for 4 or more weeks. These logs were used to analyze their sleep patterns
during a 24-h period. According to the International Classification of Sleep Disorders
(ICSD-R) revised by the Association of Sleep Disorders Center in North America in
1997 (American Academy of Sleep Medicine, 1997), our patients were diagnosed with
DSPS, non-24, irregular sleep, or hypersomnia. Actually, DSPS is characterized by a
difficulty in falling asleep at night and inability to be aroused easily in the morning; this
diagnosis corresponds to DSM-IV-TR®: 327.31: circadian rhythm sleep disorder.
Non-24 presents as sleep–wake cycles longer than 24 h. This corresponds also to
DSM-IV-TR®: 327.30. Irregular sleep is characterized by no recognizable circadian
patterns of sleep onset or waking time, which consists of disorganized and variable
episodes of sleep and waking behavior, and also corresponds to DSM-IV-TR®: 327.30.
Hypersomnia involves sleep times longer than 9 h but without organic abnormalities;
this corresponds to DSM-IV-TR®: 307.47.
Evaluation of depressive symptoms
The severity of depressive symptoms was measured using the self-rating
depression scale, which is similar to the children’s depression inventory used in
pediatric psychiatry (Kovacs, 1981).
Statistical methods
Daily variation of messenger RNA (mRNA) expression was analyzed using
analysis of variance. The peak of the circadian profile was compared in both groups
using Mann–Whitney’s U-test. Glucose and insulin measurements were analyzed using
the unpaired Student’s t-test and Welch’s t-test (Iwatani et al., 1997). Data are presented
as the mean ± SD. Statistical significance was defined as P < 0.05.
Results
Daily variation of hPer2 in whole blood cells
Table 2 shows the peak phase (acrophase) of hPer2 mRNA expression in
whole blood cells of the patients and control subjects. The mRNA level of hPer2 was
significantly higher at 6:00 in the control subjects (Takimoto et al., 2005). In contrast, the mRNA level of hPer2 was higher at 6:00 in only 3 patients, at 2:00 in 3, at 10:00 in
4, at 14:00 in 3, and at 18:00 in 5. The timing of the hPer2 peak expression level was
significantly later in the patients than in the control subjects (P < 0.05,
Mann–Whitney’s U-test; Fig. 1).
The most phase-advanced cases (cases 1, 2, 11) showed the hPer2 peak at 2:00,
although the most phase-delayed cases (cases 9, 10, 15–17) showed the hPer2 peak at
18:00.
Cortisol circadian rhythm
During the study period, no female subject (11 patients with sleep disturbance
and 3 controls) was in menstruation. Regarding gender differences in the area under the
curve of the cortisol circadian rhythm, no significant difference was found between
male and female patient groups (P = 0.19, Mann–Whitney’s U-test). Consequently, we
merged the male and female patients for additional analyses.
The peak level of cortisol occurred at 6:00 in 10 of the 18 patients and the 10
controls (Table 2). The phase-shifted peak level of cortisol was found in 8 patients: at
2:00 in 1, at 10:00 in 4, at 18:00 in 1, and at 22:00 in 2. The area under the curve of the
cortisol circadian rhythm in the patients (203.5 ± 50.3) was not significantly smaller
than that of the controls (202.8 ± 28.4). The cortisol peak level time was not
significantly later in the patients than in the control subjects (P > 0.05,
Mann–Whitney’s U–test).
The most phase-advanced case (case 18) showed a cortisol peak at 2:00,
although the most phase-delayed cases (cases 4, 10) showed a cortisol peak at 22:00.
Autonomic function
Table 2 summarizes the power of LFC (sympathetic component) and HFC (parasympathetic component) of each patient. Component analysis of the cardiographic R–R interval revealed that the mean HFC peak in the supine resting position was
significantly suppressed in the patient group compared to the controls (8.4±6.2 vs.
20.6±7.6, P < 0.001, Student’s t-test). The mean LFC peak was lower in the patient
group compared to the controls (8.8±2.9 vs. 13.2±4.0, P = 0.001, Student’s t-test). The
mean LFC/HFC radio in the patient group was significantly higher than in the controls
(1.55±0.96 vs. 0.69±0.20, P < 0.001, Student’s t-test).
Carbohydrate metabolism
Table 2 summarizes the result of glucose tolerance test (sigma BG level, sigma IRI, the insulin/glucose ratio, and the insulinogenic index) of each patient. The mean BG level was not significantly higher in the patient group than in the controls at
any time interval following oral glucose ingestion, except at 30 and 120 min (both P <
0.05). The mean plasma insulin concentration in the patient group was not significantly
different from the controls at any time interval following oral glucose ingestion, except
at 120 and 150 min (P <0.001 and P <0.05, respectively). However, individual patient
insulin levels varied widely compared with the corresponding BG levels. The insulin
level did not correlate with the BG level in some patients. The mean sigma BG level in
the patient group was significantly higher than that of controls (910.3 ± 189.9 vs. 865.1
± 60.5 mg/dl, P = 0.027). However, the mean sigma IRI was not significantly different
(patients vs. controls = 431.6 ± 194.8 vs. 892.8 ± 440.5 µU/ml, P = 0.103). The
insulin/glucose ratio, the initial insulin response 30 min after glucose ingestion, was not
significantly different (patients vs. controls = 0.95 ± 0.63 vs. 2.43 ± 1.03, P = 0.315).
However, a significant difference was found in the insulinogenic index (patients vs.
controls = 0.48 ± 0.20 vs. 1.04 ± 0.50, P = 0.044).
CBT circadian rhythms
As presented in Table 2, CBT showed rhythmic changes in all 18 patients. The
nadir (lowest CBT time in 24 h) in the control subjects was recorded at 3.41 ± 0.57 a.m.,
but occurred earlier, at 2.00 ± 0.02 a.m., in 4 patients, and occurred later, at 8.41 ± 0.16
p.m., in 14 patients. The relative advance or delay in occurrence was determined in
comparison to the time defined for the control subjects. The CBT nadir level did not
occur significantly later in the patients than in the control subjects (P > 0.05,
Mann–Whitney’s U–test).
The most phase-advanced case (case 11) showed the CBT nadir at 1:30. The
most phase-delayed case (case 4) showed the CBT nadir at 19:00.
Sleep disturbance in patients
Based on self-recorded sleep-wake logs, all 18 patients were diagnosed as
having one of the four types of sleep disturbance: 10 had DSPS, 5 had non-24, 1 had
irregular sleep, and 2 had hypersomnia (Table 2). However, patients in these four
categories showed no significant differences in the duration of sleep disturbance, age of
symptom onset, current age, hPer2 acrophase, cortisol acrophase, or CBT nadir.
Self-rating depressive scale score
The mean self-rating depressive scale score was 52 ± 8.5, with 89% of
patients showing a predisposition for depression (40 points or more) (Table 1).
Discussion
The findings obtained in this study suggest that physiological homeostasis
might be seriously impaired by sleep deprivation and emotional distress, as reflected
clearly by depressive symptoms in these patients. Easy fatigability and disturbed
learning and memorization are among the primary characteristics of sleep disturbance
and chronic fatigue in adolescents (Miike et al., 2004). Fatigue and gastrointestinal
discomfort were quite severe in our patients. Another feature of this illness is the
individuality of symptom patterns and the unpredictability of symptom severity.
It is particularly interesting that diurnal hypersecretion of glucocorticoids and
altered regulation of the hypothalamo–pituitary–adrenocortical axis are known in
patients with poorly controlled or uncontrolled diabetes (Roy et al., 1998; Archer et al.,
2003; Chiodini et al., 2006). We found no cortisol hypersecretion in the present patients,
suggesting the absence of diabetic status. However, those patients with sleep
disturbance had glucoregulatory dysfunction. Results of a previous study show that
emotionally stressful events result in hyperglycemia in diabetic patients (Lustman et al.,
1981). On the other hand, sleep deficit has a harmful impact on carbohydrate
metabolism and endocrine function, even in healthy subjects (Spiegel et al., 1999).
Abnormalities of the biological stress response (hypothalamic–pituitary–adrenal axis
and autonomic nervous system) were also identified in a previous animal study, the
results of which suggested that cortisol can act directly on the central nervous system
(Sandoval et al., 2003). Multiple factors including autonomic nervous system
dysfunction, derangement of neuropeptides in the hypothalamus, and hormonal
imbalance might also affect the glucoregulatory metabolism.
Circadian rhythms exist in most mammals, including humans. They are
controlled by a circadian clock. In humans, the sleep–wake cycle and hormonal
secretions such as the cortisol and autonomic cycles (e.g., body temperature, blood
pressure, peak flow) are regulated by a circadian clock, which is synchronized with the
24-h day by environmental time cues, especially light (Hastings, 1997). The sleep–wake
cycle is the circadian rhythm that is most readily perceived in life. The biological clock
(circadian clock) in human beings is formed and regulated through interrelationships of
various clock genes such as Per1, Per2, Per3, Bmal1, Clock, Cry1, Cry2, Bmal,
Rev-ervA, CK1 δ/ε, and glycogen synthase kinase 3-β (GSK3ß) (Gietzen and Virshup, 1999; Jones et al., 1999; Ebisawa et al., 2001; Toh et al., 2001; Takano et al., 2004;
Vanselow et al., 2006). All of these polymorphisms apparently affect the
phosphorylation of clock proteins.
This study cannot clarify the relation between the circadian profile of the
clock genes and the symptoms of circadian rhythm sleep disorders. Currently, the
markers of circadian rhythms are considered to be the profiles of plasma melatonin,
cortisol, and core body temperature (Tomoda et al., 1997). However, even if these
markers show normal rhythmic patterns, certain patients suffer from circadian rhythm
sleep disorders and indeterminate symptoms, suggesting that these markers may not be
reliable for the diagnosis of circadian rhythm sleep disorders.
However, autonomic and metabolic dysfunction causing sleep disturbance may
be related to the hPer2 phase shift because of chronobiological abnormality. Results of
a previous study indicate that such disturbances might be related closely to the
desynchronization of biorhythms, particularly the circadian rhythm of body temperature
and the sleep–wake rhythm (Tomoda et al., 2000; Tomoda et al., 2001). Previous and
present results suggest that sleep deprivation may originate from a dysfunctional
network of brain areas related to the circadian rhythm and peripheral nervous system
involved in the autonomic nervous system including cardiac function and
gastrointestinal digestion. However, dysregulation of the circadian rhythm is neither the
only nor the dominant factor in the pathogenesis of such conditions. Immunological,
autonomic, and neuroendocrine abnormalities might be mutually dependent and
reinforcing factors. More studies must be done to elucidate this mechanism and to
reveal the relation between clock gene expression in the suprachiasmatic nucleus and
the peripheral blood cells. Furthermore, additional study of a larger series of cases will
elucidate the usefulness of this technique.
We reported previously that these patients’ clinical psychosomatic symptoms
might be related closely to decreased cerebral blood flow, especially in the frontal lobe
and higher-order level cognitive dysfunction in patients with sleep disturbance (Tomoda
et al., 1995; Tomoda et al., 2007). We believe that results of this study demonstrate that their brain dysfunction attributable to sleep disturbances might cause such peripheral autonomic imbalance and carbohydrate metabolic dysfunction. However, much larger samples would be needed to qualify the association between brain dysfunction and peripheral dysfunctions accurately.
Results of this study also suggest that the monitoring of human clock genes in whole blood cells, which might be functionally important for the molecular control of the circadian pacemaker as well as in suprachiasmatic nucleus, might be useful to evaluate internal synchronization. Our findings related to hPer2 gene expression in this study do not necessarily imply that they are of diagnostic importance. They are
descriptive data describing physical and functional conditions. Medications that normalize the rhythmicity are of possible therapeutic importance if the shifted peak of the circadian hPer2 gene level is a cause of the sleep disturbance.
Acknowledgments
This study was performed with the support of Special Coordination Funds for
Promoting Science and Technology from the Japanese Ministry of Education, Culture,
Sports, Science, and Technology. We thank Yuki Korenaga and Tomoko Yamaguchi for their assistance in subjects’ recruitment.
Financial Disclosures
There are no conflicts of interest including any financial, personal, or other
relationships with persons or organizations for any author related to the work described
in this article.
Figure Legends
Figure 1. Histogram of the differences in peak expression level of hPer2. The timing of
the hPer2 peak expression level is a strong indicator of abnormality. *: F(1,26) = 0.61, P = 0.028, Mann-Whitney’s U–test.
References
Archer, S. N., Robilliard, D. L., Skene, D. J., Smits, M., Williams, A., Arendt, J. and von Schantz, M., 2003. A length polymorphism in the circadian clock gene Per3 is linked to delayed sleep phase syndrome and extreme diurnal preference. Sleep.
26, 413-415.
Boergers, J., Hart, C., Owens, J. A., Streisand, R. and Spirito, A., 2007. Child sleep disorders: associations with parental sleep duration and daytime sleepiness. J Fam Psychol. 21, 88-94.
Chiodini, I., Di Lembo, S., Morelli, V., Epaminonda, P., Coletti, F., Masserini, B., Scillitani, A., Arosio, M. and Adda, G., 2006. Hypothalamic-pituitary-adrenal activity in type 2 diabetes mellitus: role of autonomic imbalance. Metabolism.
55, 1135-1140.
de la Eva, R. C., Baur, L. A., Donaghue, K. C. and Waters, K. A., 2002. Metabolic correlates with obstructive sleep apnea in obese subjects. J Pediatr. 140, 654-659.
Ebisawa, T., Uchiyama, M., Kajimura, N., Mishima, K., Kamei, Y., Katoh, M., Watanabe, T., Sekimoto, M., Shibui, K., Kim, K., Kudo, Y., Ozeki, Y., Sugishita, M., Toyoshima, R., Inoue, Y., Yamada, N., Nagase, T., Ozaki, N., Ohara, O., Ishida, N., Okawa, M., Takahashi, K. and Yamauchi, T., 2001.
Association of structural polymorphisms in the human period3 gene with delayed sleep phase syndrome. EMBO Rep. 2, 342-346.
Giannotti, F., Cortesi, F., Sebastiani, T. and Ottaviano, S., 2002. Circadian preference,
sleep and daytime behaviour in adolescence. J Sleep Res. 11, 191-199.
Gietzen, K. F. and Virshup, D. M., 1999. Identification of inhibitory
autophosphorylation sites in casein kinase I epsilon. J Biol Chem. 274, 32063-32070.
Hastings, M. H., 1997. Central clocking. Trends Neurosci. 20, 459-464.
Ivanenko, A. and Johnson, K., 2008. Sleep disturbances in children with psychiatric disorders. Semin Pediatr Neurol. 15, 70-78.
Iwase, T., Kajimura, N., Uchiyama, M., Ebisawa, T., Yoshimura, K., Kamei, Y., Shibui, K., Kim, K., Kudo, Y., Katoh, M., Watanabe, T., Nakajima, T., Ozeki, Y., Sugishita, M., Hori, T., Ikeda, M., Toyoshima, R., Inoue, Y., Yamada, N., Mishima, K., Nomura, M., Ozaki, N., Okawa, M., Takahashi, K. and Yamauchi, T., 2002. Mutation screening of the human Clock gene in circadian rhythm sleep disorders. Psychiatry Res. 109, 121-128.
Iwatani, N., Miike, T., Kai, Y., Kodama, M., Mabe, H., Tomoda, A., Fukuda, K. and Jyodoi, T., 1997. Glucoregulatory disorders in school refusal students. Clin Endocrinol (Oxf). 47, 273-278.
Jones, C. R., Campbell, S. S., Zone, S. E., Cooper, F., DeSano, A., Murphy, P. J., Jones, B., Czajkowski, L. and Ptacek, L. J., 1999. Familial advanced sleep-phase syndrome: A short-period circadian rhythm variant in humans. Nat Med. 5, 1062-1065.
Kovacs, M., 1981. Rating scales to assess depression in school-aged children. Acta Paedopsychiatr. 46, 305-315.
Kuratsune, H., Yamaguti, K., Lindh, G., Evengard, B., Hagberg, G., Matsumura, K., Iwase, M., Onoe, H., Takahashi, M., Machii, T., Kanakura, Y., Kitani, T., Langstrom, B. and Watanabe, Y., 2002. Brain regions involved in fatigue sensation: reduced acetylcarnitine uptake into the brain. Neuroimage. 17, 1256-1265.
Lustman, P., Carney, R. and Amado, H., 1981. Acute stress and metabolism in diabetes.
Diabetes Care. 4, 658-659.
Miike, T., Tomoda, A., Jhodoi, T., Iwatani, N. and Mabe, H., 2004. Learning and memorization impairment in childhood chronic fatigue syndrome manifesting as school phobia in Japan. Brain Dev. 26, 442-447.
Pirovano, A., Lorenzi, C., Serretti, A., Ploia, C., Landoni, S., Catalano, M. and Smeraldi,
E., 2005. Two new rare variants in the circadian "clock" gene may influence sleep pattern. Genet Med. 7, 455-457.
Pomeranz, B., Macaulay, R. J., Caudill, M. A., Kutz, I., Adam, D., Gordon, D., Kilborn, K. M., Barger, A. C., Shannon, D. C., Cohen, R. J. and et al., 1985. Assessment of autonomic function in humans by heart rate spectral analysis. Am J Physiol.
248, H151-153.
Reppert, S. M. and Weaver, D. R., 2002. Coordination of circadian timing in mammals.
Nature. 418, 935-941.
Roy, M. S., Roy, A. and Brown, S., 1998. Increased urinary-free cortisol outputs in diabetic patients. J Diabetes Complications. 12, 24-27.
Sandoval, D. A., Ping, L., Neill, A. R., Morrey, S. and Davis, S. N., 2003. Cortisol acts through central mechanisms to blunt counterregulatory responses to
hypoglycemia in conscious rats. Diabetes. 52, 2198-2204.
Spiegel, K., Leproult, R. and Van Cauter, E., 1999. Impact of sleep debt on metabolic and endocrine function. Lancet. 354, 1435-1439.
Stein, M. A., Mendelsohn, J., Obermeyer, W. H., Amromin, J. and Benca, R., 2001.
Sleep and behavior problems in school-aged children. Pediatrics. 107, E60.
Takano, A., Uchiyama, M., Kajimura, N., Mishima, K., Inoue, Y., Kamei, Y., Kitajima, T., Shibui, K., Katoh, M., Watanabe, T., Hashimotodani, Y., Nakajima, T., Ozeki, Y., Hori, T., Yamada, N., Toyoshima, R., Ozaki, N., Okawa, M., Nagai, K., Takahashi, K., Isojima, Y., Yamauchi, T. and Ebisawa, T., 2004. A missense variation in human casein kinase I epsilon gene that induces functional alteration and shows an inverse association with circadian rhythm sleep disorders.
Neuropsychopharmacology. 29, 1901-1909.
Takimoto, M., Hamada, A., Tomoda, A., Ohdo, S., Ohmura, T., Sakato, H., Kawatani, J., Jodoi, T., Nakagawa, H., Terazono, H., Koyanagi, S., Higuchi, S., Kimura, M., Tukikawa, H., Irie, S., Saito, H. and Miike, T., 2005. Daily expression of clock genes in whole blood cells in healthy subjects and a patient with circadian rhythm sleep disorder. Am J Physiol Regul Integr Comp Physiol. 289,
R1273-1279.
Toh, K. L., Jones, C. R., He, Y., Eide, E. J., Hinz, W. A., Virshup, D. M., Ptacek, L. J.
and Fu, Y. H., 2001. An hPer2 phosphorylation site mutation in familial advanced sleep phase syndrome. Science. 291, 1040-1043.
Tomoda, A., Jhodoi, T. and Miike, T., 2001. Chronic fatigue and abnormal biological rhythms in school children. J Chronic Fatigue Syndrome. 60, 607-612.
Tomoda, A., Miike, T., Honda, T., Fukuda, K., Kai, Y., Nabeshima, M. and Takahashi, M., 1995. Single-photon emission computed tomography for cerebral blood flow in school phobias. Curr Ther Res. 56, 1088-1093.
Tomoda, A., Miike, T., Yamada, E., Honda, H., Moroi, T., Ogawa, M., Ohtani, Y. and Morishita, S., 2000. Chronic fatigue syndrome in childhood. Brain Dev. 22, 60-64.
Tomoda, A., Miike, T., Yonamine, K., Adachi, K. and Shiraishi, S., 1997. Disturbed circadian core body temperature rhythm and sleep disturbance in school refusal children and adolescents. Biol Psychiatry. 41, 810-813.
Tomoda, A., Mizuno, K., Murayama, N., Joudoi, T., Igasaki, T., Miyazaki, M. and Miike, T., 2007. Event-Related Potentials in Japanese Childhood Chronic Fatigue Syndrome (CCFS). J Pediatr Neurol. 5, 199-208.
Vanselow, K., Vanselow, J. T., Westermark, P. O., Reischl, S., Maier, B., Korte, T., Herrmann, A., Herzel, H., Schlosser, A. and Kramer, A., 2006. Differential effects of PER2 phosphorylation: molecular basis for the human familial advanced sleep phase syndrome (FASPS). Genes Dev. 20, 2660-2672.
Verhulst, S. L., Schrauwen, N., Haentjens, D., Rooman, R. P., Van Gaal, L., De Backer, W. A. and Desager, K. N., 2007. Sleep-disordered breathing and the metabolic syndrome in overweight and obese children and adolescents. J Pediatr. 150, 608-612.
Wijnen, H., Boothroyd, C., Young, M. W. and Claridge-Chang, A., 2002. Molecular genetics of timing in intrinsic circadian rhythm sleep disorders. Ann Med. 34, 386-393.
