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Chronobiology International
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Later chronotype is associated with higher
hemoglobin A1c in prediabetes patients
Thunyarat Anothaisintawee, Dumrongrat Lertrattananon, Sangsulee
Thamakaison, Kristen L. Knutson, Ammarin Thakkinstian & Sirimon
Reutrakul
To cite this article: Thunyarat Anothaisintawee, Dumrongrat Lertrattananon, Sangsulee
Thamakaison, Kristen L. Knutson, Ammarin Thakkinstian & Sirimon Reutrakul (2017): Later
chronotype is associated with higher hemoglobin A1c in prediabetes patients, Chronobiology
International, DOI: 10.1080/07420528.2017.1279624
To link to this article: http://dx.doi.org/10.1080/07420528.2017.1279624
Published with license by Taylor &
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Anothaisintawee, Dumrongrat
Lertrattananon, Sangsulee Thamakaison,
Kristen L. Knutson, Ammarin Thakkinstian,
and Sirimon Reutrakul
Published online: 27 Jan 2017.
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Later chronotype is associated with higher hemoglobin A1c in prediabetes
patients
Thunyarat Anothaisintaweea,b, Dumrongrat Lertrattananona, Sangsulee Thamakaisona, Kristen L. Knutsonc*,
Ammarin Thakkinstianb, and Sirimon Reutrakuld
aDepartment of Family Medicine, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bangkok, Thailand; bSection for Clinical
Epidemiology and Biostatistics, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bangkok, Thailand; cSection of Pulmonary and
Critical Care, Department of Medicine, The University of Chicago, Chicago, Illinois, USA; dDivision of Endocrinology and Metabolism,
Department of Medicine, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bangkok, Thailand
ABSTRACT
The circadian system is known to play a role in glucose metabolism. Chronotype reflects the
interindividual variability in the phase of entrainment. Those with later chronotype typically prefer
later times in the day for different activities such as sleep or meals. Later chronotype has been
shown to be associated with metabolic syndrome, increased diabetes risk and poorer glycemic
control in type 2 diabetes patients. In addition, “social jetlag”, a form of circadian misalignment
due to a mismatch between social rhythms and the circadian clock, has been shown to be
associated with insulin resistance. Other sleep disturbances (insufficient sleep, poor sleep quality
and sleep apnea) have also been shown to affect glucose metabolism. In this study, we explored
whether there was a relationship between chronotype, social jetlag and hemoglobin A1c (HbA1c)
levels in prediabetes patients, independent of other sleep disturbances. A cross-sectional study
was conducted at the Department of Family Medicine, Ramathibodi Hospital, Bangkok, from
October 2014 to March 2016 in 1014 non-shift working adults with prediabetes. Mid-sleep time
on free day adjusted for sleep debt (MSFsc) was used as an indicator of chronotype. Social jetlag
was calculated based on the absolute difference between mid-sleep time on weekdays and
weekends. The most recent HbA1c values and lipid levels were retrieved from clinical laboratory
databases. Univariate analyses revealed that later MSFsc (p = 0.028) but not social jetlag (p = 0.48)
was significantly associated with higher HbA1c levels. Multivariate linear regression analysis was
applied to determine whether an independent association between MSFsc and HbA1c level
existed. After adjusting for age, sex, alcohol use, body mass index (BMI), social jetlag, sleep
duration, sleep quality and sleep apnea risk, later MSFsc was significantly associated with higher
HbA1c level (B = 0.019, 95% CI: 0.00001, 0.038, p = 0.049). The effect size of one hour later MSFsc
on HbA1c (standardized coefficient = 0.065) was approximately 74% of that of the effect of one
unit (kg/m2) increase in BMI (standardized coefficient = 0.087). In summary, later chronotype is
associated with higher HbA1c levels in patients with prediabetes, independent of social jetlag and
other sleep disturbances. Further research regarding the potential role of chronotype in diabetes
prevention should be explored.
ARTICLE HISTORY
Received 10 October 2016
Revised 16 December 2016
Accepted 4 January 2017
KEYWORDS
Chronotype; circadian
rhythm; hemoglobin A1c;
prediabetes; sleep
Introduction
In the United States, the prevalence of diabetes
was estimated to be 9.3% in 2012 (National
Center for Chronic Disease Prevention and
Health Promotion, 2014). Three times as many,
37%, were estimated to have prediabetes based
on hemoglobin A1c (HbA1c) or fasting glucose
levels, translating to 86 million people. Globally,
an estimated 318 million people had impaired
glucose tolerance in 2015 according to the
International Diabetes Federation, with a projected
alarming increase to 482 million in 2040. Without
interventions, 5.8%–18.3% of people with predia-
betes develop diabetes yearly (Knowler et al., 2002;
Ramachandran et al., 2006; Tuomilehto et al.,
2001). Although some of the diabetes risk factors,
such as family history or ethnicity, are not modifi-
able, lifestyle interventions with diet and exercise
can reduce diabetes risk by as much as 58%
(Knowler et al., 2002; Tuomilehto et al., 2001).
CONTACT Sirimon Reutrakul sreutrak10800@gmail.com CDE, Division of Endocrinology and Metabolism, Department of Medicine, Ramathibodi
Hospital, Mahidol University, 270 Rama VI Rd, Ratchathewi, Bangkok 10400, Thailand. Tel, Fax: +6622011647.
*Kristen L. Knutson is currently affiliated with the Center for Circadian and Sleep Medicine, Department of Neurology, Northwestern University, Chicago, Illinois.
CHRONOBIOLOGY INTERNATIONAL
http://dx.doi.org/10.1080/07420528.2017.1279624
Published with license by Taylor & Francis Group, LLC. © 2017 Thunyarat Anothaisintawee, Dumrongrat Lertrattananon, Sangsulee Thamakaison, Kristen L. Knutson, Ammarin
Thakkinstian, and Sirimon Reutrakul
This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License (http://creativecommons.org/licenses/by-nc-nd/4.0/),
which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited, and is not altered, transformed, or built upon in any way.
Therefore, identifying additional modifiable risk
factors may lead to a research to develop novel
lifestyle interventions to reduce incident diabetes.
The circadian system, controlled by the master
circadian clock located in the hypothalamus, plays a
major role in regulating daily rhythms of sleep/wake
cycle, central and peripheral tissue metabolism, and
hormonal secretions (Huang et al., 2011). The cen-
tral clock is synchronized to the light-dark cycle and
relays the information via various pathways to the
peripheral organs, leading to coordinated rhythms.
Circadian misalignment occurs when sleep and/or
meal timing is out ofsynchrony with the light-dark
cycle (environment) or the central circadian clock
(endogenous), or when there is a desynchrony
between the central clock and the peripheral clocks
in peripheral organs. There is evidence that the
circadian system plays a role in glucose metabolism
and that circadian misalignment can lead to glucose
intolerance. Experimental circadian misalignment
in healthy humans resulted in reduced glucose tol-
erance and increased inflammatory markers, sug-
gesting an increase in cardiometabolic risk (Buxton
et al., 2012; Leproult et al., 2014; McHill et al., 2014;
Morris et al., 2015; Scheer et al., 2009). In our
modern society, night-shift work, which requires
workers to eat and be active during their circadian
night and sleep during their circadian day, repre-
sents an extreme form of circadian misalignment.
Night-shift workers have increased risk of health
problems, including obesity (Pan et al., 2011), meta-
bolic syndrome (Karlsson et al., 2003) and cardio-
vascular disease (Boggild et al., 1999). Moreover, a
recent meta-analysis of 10 cohort studies (262 294
participants) revealed that shift work was associated
with a 40% increase in the risk of developing dia-
betes (Anothaisintawee et al., 2015). Another meta-
analysis also reported an increased diabetes risk in
shift workers, although at a lower estimate of 9%
(Gan et al., 2015). There appeared to be an increas-
ing risk in those with a longer duration of shift work
(Pan et al., 2011).
At an individual level, the circadian clock
entrains differently to the light-dark zeitgeber, ear-
lier or later depending on the characteristics of the
clock. The relationship between the internal day
and external day (e.g. between the minimum of
the core body temperature and dawn) is
called “phase of entrainment” or “chronotype”
(Roenneberg & Merrow, 2016). Chronotype is also
used to describe personality traits associated with
the preferred times of the day for activities such as
sleep or meal intake (Roenneberg &Merrow, 2016).
Evening types (or late chronotypes) typically have
later bedtime than those who are morning types (or
early chronotypes). Questionnaires have been
developed to assess morningness-eveningness pre-
ference (Horne & Ostberg, 1976; Smith et al., 1989).
In addition, chronotype can be assessed by using
the mid-sleep times (midpoint of sleep between
sleep onset and wake time) on free days, with
further correction for sleep debt accumulated over
the work week (herein will be referred to as MSFsc)
and can be captured using the Munich ChronoType
Questionnaire (Roenneberg et al., 2007b). Those
with later chronotype usually experience a mild
form of circadian misalignment due to a greater
degree of misalignment between social rhythms
and the circadian clock, a phenomenon called
“social jetlag” (Wittmann et al., 2006). Social jetlag
results from shifting sleep timing between work-
days and free days resembling traveling across
time zones. In non-shift workers, recent evidence
suggested that evening preference or later chrono-
type as well as greater social jetlag was associated
with adverse effects on cardiometabolic function,
including higher body mass index (BMI) or over-
weight/obesity (Olds et al., 2011; Parsons et al.,
2015; Roenneberg et al., 2012) and metabolic syn-
drome (Parsons et al., 2015; Yu et al., 2015). In
some of these studies that measured chronotype
and social jetlag simultaneously, the associations
between chronotype and metabolic parameters
were not significant after adjusting for social jetlag
(Parsons et al., 2015; Roenneberg et al., 2012).
Recently, two large population-based studies of
more than 6000 participants revealed that evening
chronotype was associated with an increased risk of
having type 2 diabetes [odds ratio 1.73 (Yu et al.,
2015) and 2.5 in men and women combined
(Merikanto et al., 2013)]. However, in the Nurses’
Health Study 2 involving 64 615 women followed
for 6 years, later chronotypes were not a risk factor
for type 2 diabetes while early chronotypes had a
13% reduction in risk compared to intermediate
chronotypes (Vetter et al., 2015). Among patients
with type 2 diabetes, later chronotypes and evening
preferences have been found to be associated with
2 T. ANOTHAISINTAWEE ET AL.
poorer glycemic control (Osonoi et al., 2014;
Reutrakul et al., 2013). These data demonstrate
the contribution of circadian regulation on glucose
metabolism.
In addition to circadian regulation, other sleep
disturbances are known to influence glucose metabo-
lism, including poor sleep quality, abnormal sleep
duration and obstructive sleep apnea (OSA)
(Reutrakul & Van Cauter, 2014). To date, little is
known about the role of circadian regulation on glu-
cose metabolism in those with prediabetes or
impaired glucose metabolism. Since this is a high-
risk population for developing diabetes, targeted
interventions could be further studied if such a rela-
tionship is found. The aim of this study was to exam-
ine whether chronotype, as assessed by MSFsc, and
social jetlag were independently associated with
HbA1c levels in patients with prediabetes. We
hypothesized that later chronotype would be asso-
ciated with higher HbA1c levels, independent of
other sleep disturbances and relevant covariates.
Materials and methods
This cross-sectional study is a part of a prediabetes
(PreDM) cohort study. In brief, this cohort study was
conducted at the outpatient clinic of Department of
Family Medicine, Ramathibodi Hospital, Bangkok,
Thailand, and the participants were recruited during
October 2014 to March 2016. Adult patients with a
diagnosis of prediabetes, defined as fasting plasma
glucose (FPG) between 100 and 125 mg/dl (5.6 and
6.9 mmol/L) or HbA1c between 5.70% and 6.49%
(38.80–47.44 mmol/mol), were invited to participate
(American Diabetes Association, 2016). The partici-
pants will be followed for at least 5 years or until they
develop diabetes mellitus. Baseline information was
used for this cross-sectional study. Participants were
excluded if they were shift workers, or had HbA1c
level ≥6.5% (48.0 mmol/mol) or FPG ≥126 mg/dl
(≥7.0 mmol/L). The study’s protocol was approved
by the Ethical Clearance Committee, Faculty of
Medicine Ramathibodi Hospital. All participants
gave written informed consent.
Data collection
Participants were interviewed to obtain information
regarding age, sex, marital status, educational level
(primary school, secondary school or college), family
history of diabetes mellitus in first-degree relatives,
history of smoking (never versus current/past users)
and alcohol use (never versus current/past users).
Date of diagnosis of prediabetes, history of underlying
diseases (i.e. diagnosis of hypertension or dyslipide-
mia) and height were extracted from the patient’s
medical records by investigating physicians (TA, ST
and DL). Weight was measured on the date of inter-
view. BMI was calculated by dividing weight (kilo-
gram) by height2 (meter2).
In addition, depressive symptoms, previously
reported to be related to glycemic control
(Lustman et al., 2000), were assessed using the vali-
dated Thai version of the Center for Epidemiologic
Studies-Depression (CES-D) scale (Radloff, 1977;
Trangkasombat et al., 1997). The scores ranged
from 0 to 60, of which higher scores indicate more
severe depressive symptoms.
Chronotype and social jetlag assessments
Participants reported their usual bedtime, wake-up
time, sleep onset latency and actual sleep duration
on weekdays and weekends over the previous
month. From these, we calculated the mid-sleep
time separately for weekdays and weekends as the
midpoint between sleep onset and wake time. The
metric of chronotype, mid-sleep time on free days
adjusting for sleep debt (MSFsc), was derived from
mid-sleep time on weekend nights with further
adjustment for the sleep debt taking into account
the sleep duration average of weekends and week-
days as follows: MSFsc = mid-sleep time on week-
end night – 0.5*[SDF -(5*SDW + 2*SDF)/7], where
SDF is the calculatedsleep duration on weekend
nights and SDw is the calculated sleep duration on
weekday nights, as outlined in the Munich
ChronoType Questionnaire (Roenneberg et al.,
2004, 2007a]. Social jetlag was calculated based on
the absolute difference between mid-sleep time on
weekdays and weekends (Roenneberg et al., 2012).
Sleep assessments
Average self-reported sleep duration was calculated
as [(sleep duration on weekdays * 5) + (actual sleep
duration on weekend * 2)]/7. Sleep duration was
derived from the question “During the past month,
how many hours of actual sleep did you get at
CHRONOBIOLOGY INTERNATIONAL 3
night?”, which was asked separately for weekdays
and weekends.
To assess sleep quality independent of sleep dura-
tion, we utilized a modified Pittsburgh Sleep Quality
Index (PSQI) score (Knutson et al., 2006b). The
PSQI score evaluates sleep duration and quality
within the past month, with a higher score indicating
worse sleep (Buysse et al., 1989), and has been vali-
dated in a Thai population (Sitasuwan et al., 2014).
Themodified PSQI score excludes the sleep duration
component from the PSQI (Knutson et al., 2006b).
Participants reported whether they had a diag-
nosis of OSA. Those without a previous diagnosis
were interviewed using the Berlin questionnaire to
assess the risk of having OSA, which categorizes
respondents as high or low risk of having OSA
(Netzer et al., 1999). The questionnaire was pre-
viously validated in a Thai population (Suksakorn
et al., 2014). Participants who had a diagnosis of or
were at high risk for OSA were grouped together
as the presence or high risk of OSA (OSA risk).
HbA1c and lipid levels
HbA1c reflects an average of glucose levels in the
preceding three months. The most recent HbA1c
values of study’s participants were retrieved from
laboratory databases, Medical statistic Unit,
Ramathibodi Hospital. Around 43% of the HbA1c
values were obtained on the date of the interview,
21.3% were obtained before and 35.5% after the date
of the interview (at an average of 3 and 4 months),
respectively. The time lag between the date of per-
forming HbA1c and the date of interview did not
exceed 180 days. The HbA1c assay at Ramathibodi
Hospital has been NGSP (National Glycohemoglobin
Standardization Program) certified.
Lipid levels, within one year of the interview date,
were obtained frommedical records. These included
total cholesterol, high-density lipoprotein (HDL),
low-density lipoprotein (LDL) and triglycerides
levels. A one -year interval was chosen as the
American Heart Association recommended moni-
toring lipid levels every 3–12 months in those on
treatment as clinically indicated (Stone et al., 2014)
Statistical analysis
Normal distribution of study data was evaluated
by exploring skewness and kurtosis of the data.
The data were presented as means and standard
deviations (SDs), if the data were normally dis-
tributed; otherwise, they were presented as med-
ians and interquartile ranges (IQRs). Univariate
linear regression analysis was applied to assess the
association among demographic (i.e. age, sex,
educational level, history of smoking and alcohol
use, BMI, depressive symptoms and lipid levels),
sleep parameters, (i.e. sleep quality, sleep dura-
tion and OSA risk), MSFsc and social jetlag, and
HbA1c level. Multivariate linear regression ana-
lysis was applied to determine the independent
association between MSFsc and HbA1c levels.
Variables that had p-values less than 0.10 from
the univariate linear regression model were con-
sidered in the multivariate linear regression ana-
lysis. In addition, age, sleep duration, sleep
quality, OSA risk and social jetlag were included
as they were previously reported to be associated
with glycemic control (Knutson et al., 2006b;
Parsons et al., 2015; Reutrakul & Van Cauter,
2014). Lastly, interactions between MSFsc and
different variables, including sex, BMI, OSA
risk, sleep duration and sleep quality, were
assessed.
p-values less than 0.05 were considered signifi-
cant. All analyses were performed using STATA
version 14.
Results
A total of 1014 participants were included in the
study. Their baseline demographic, circadian and
sleep characteristics are presented in Table 1. The
mean age of participants was 62.4 (SD = 8.7)
years. Two-thirds of the participants were female
(66.5%) and had been diagnosed with hyperten-
sion (68.4%). Mean BMI was 26.0 (4.0) kg/m2 and
most of the study’s participants had a diagnosis of
dyslipidemia as an underlying disease. Median
HbA1c value (IQR) was 5.83% (4.31%–6.49%)
[40.2 mmol/mol, (23.6–47.4)] and median CES-
D score was 7 (0–46). On average, participants
had slightly later bedtimes and wake times on
weekends than on weekdays with a mean MSFsc
of 1:57 (1:11) a.m. Mean self-reported sleep dura-
tion was 5.80 (1.45) hours and OSA risk was
found in approximately 30% of the participants.
None of the participants was using metformin.
4 T. ANOTHAISINTAWEE ET AL.
Association between HbA1c and demographics,
sleep and circadian parameters
To explore the correlations between HbA1c levels
and demographics, sleep and circadian parameters,
univariate linear regression analyses were per-
formed (Table 2). Age, educational level, smoking
and depressive symptoms were not significantly
associated with HbA1c levels. Being female and
having higher BMI were associated with higher
HbA1c levels while those consuming alcohol had
lower HbA1c when compared with nonusers.
Triglycerides, but not other lipid levels, were asso-
ciated with higher HbA1c. Later chronotype
(MSFsc) was found to be significantly associated
with higher HbA1c [β-coefficient = 0.20 (95% CI:
0.002, 0.038), p = 0.028], while social jetlag was not
significantly related with HbA1c. For sleep
parameters, participants with a high risk of OSA
had significantly higher HbA1c levels than those
with a low risk of OSA. Sleep duration and sleep
quality were not found to be associated with
HbA1c levels.
Multiple regression analysis was performed to
determine whether chronotype was independently
associated with HbA1c (Table 3, n = 960). After
adjusting for age, sex, alcohol use, BMI, triglycer-
ides, sleep duration, sleep quality (modified PSQI),
OSA risk and social jetlag, later MSFsc remained
significantly associated with higher HbA1c level (B
= 0.019, 95% CI: 0.00001, 0.038, p = 0.049). In
addition, female sex (p = 0.001), higher BMI (p =
0.009), higher triglycerides levels (p = 0.007) and
being at high risk for OSA (p = 0.004) were inde-
pendently associated with a higher HbA1c level.
There were no significant interactions found
between MSFsc and different variables, including
sex, BMI (<25 versus ≥25 kg/m2), OSA risk (pre-
sent or absent), sleep duration (<7, 7–8, >8 h) and
sleep quality.
Discussion
In this large cohort, we demonstrated for the first
time that later chronotype is independently asso-
ciated with higher HbA1c levels in patients with
prediabetes, after adjusting for demographics and
sleep disturbances, including sleep duration, sleep
quality and the risk for OSA, as well as social
jetlag. For example, two individuals with predia-
betes whose MSFsc differs by two hours (all other
covariates the same) would be expected to have
different HbA1c levels, specifically 5.70%
(38.8 mmol/mol) versus 5.74% (39.2 mmol/mol).
Although this effect size is relatively modest, it is
approximately 74% of the effect of 1 unit of BMI
on HbA1c levels in this cohort (as indicated by
standardized coefficients, Table 3), and BMI is one
of the strongest risk factors for diabetes. It is
possible that the relatively small effect size, com-
pared to the previous report in patients with type 2
diabetes (Reutrakul et al., 2013), is related to the
narrower range of HbA1c values in this popula-
tion. Although later MSFsc is associated with more
social jetlag in this study (data not shown), social
jetlag itself was not related to glycemic status.
These data further support the role of circadian
Table 1. Descriptive demographic, sleep and circadianpara-
meters (n = 1014).
Demographic data
Age (years) 62.4 (8.7)
Female 674 (66.5)
Educational level 361 (35.7)
Primary school or less 274 (27.1)
Secondary school 377 (37.3)
College or higher
Smoking status 966 (95.3)
Never
Current/past
48 (4.7)
Alcohol use
Never 822 (81.1)
Current/past 192 (18.9)
Family history of diabetes 435 (42.9)
Hypertension 691 (68.4)
Dyslipidemia 922 (91.1)
Total Cholesterol (mg/dL)
High-density lipoprotein
(mg/dL)
206.1 (36.0)
54.5 (13.9)
126.2 (31.6)
Low-density lipoprotein (mg/dL) 139.9 (71.3)
Triglycerides (mg/dL)
BMI (kg/m2) 26.0 (4.0)
HbA1c (%) 5.83 (4.31-6.49)
HbA1c (mmol/mol) 40.2 (23.6-47.4)
CES-D score 7 (0-46)
Chronotype and social jetlag
Chronotype (MSFsc, h:min) 01:57 (1:11)
Bedtime weekday (h:min) 21:58 (1:15)
Bedtime weekend (h:min) 22:03 (1:18)
Wake time weekday (h:min) 5:15 (1:13)
Wake time weekend (h:min) 5:31 (1:21)
Social jetlag (min) 0 (0–192)
Sleep parameters
Sleep duration (h) 5.80 (1.45)
Modified PSQI score 4 (0–15)
OSA risk 303 (29.9)
Data are presented as mean (SD), median (inter-quartile range) or n (%).
Time is presented in 24-hour clock time
CHRONOBIOLOGY INTERNATIONAL 5
regulation on glucose metabolism and raise the
possibility of sleep timing adjustment as a diabetes
prevention strategy.
Studies experimentally manipulating levels of cir-
cadian misalignment in healthy volunteers have eluci-
dated possible mechanisms linking circadian
misalignment to abnormal glucose metabolism.
Increased glucose levels (both fasting and postpran-
dial, between 6% and 17%), without adequate pan-
creatic β-cell insulin response, were found after 6–21
days of circadian misalignment (Buxton et al., 2012;
Leproult et al., 2014; McHill et al., 2014; Morris et al.,
2015; Scheer et al., 2009). This was accompanied by a
worsening of cardiometabolic parameters including
increased mean arterial blood pressure (Scheer et al.,
2009), decreased energy expenditure (McHill et al.,
2014), elevated inflammatory markers (Leproult
et al., 2014) and free fatty acids (Morris et al., 2015),
and alterations in appetite-regulating hormones
(Scheer et al., 2009). These changes were found to be
independent of changes in sleep duration (Leproult
et al., 2014). In a population-based study, evening
chronotype, which is typically associated with a mild
form of circadian misalignment, has been associated
with metabolic syndrome (Yu et al., 2015), type 2
diabetes (Merikanto et al., 2013; Yu et al., 2015) and
lower HDL cholesterol (Wong et al., 2015).
In our study, social jetlag, one indicator of cir-
cadian misalignment, was not related with HbA1c
level. Whether this was related to the fact that the
Table 2. Univariate linear regression analysis between HbA1c and demographic, sleep and circadian
parameters.
Unstandardized
coefficient (B) 95% CI for B
Standardized
coefficient p-value
Demographic data
Age (years) −0.001 −0.004, 0.001 −0.031 0.326
Female 0.09 0.04, 0.13 0.120 <0.001
Educational levela
Secondary school 0.01 −0.04, 0.06 0.014 0.705
College or higher −0.001 −0.051, 0.048 −0.002 0.964
Current/past smoking −0.03 −0.13, 0.07 −0.020 0.508
Alcohol use −0.06 −0.12, −0.01 −0.070 0.023
Cholesterol (mg/dL) 0.0002 −0.0004, 0.0008 0.018 0.580
HDL (mg/dL) −0.001 −0.003, 0.0004 −0.049 0.131
LDL (mg/dL) 0.0002 −0.003, 0.0004 0.017 0.586
Triglycerides (mg/dL) 0.0004 0.0001, 0.0007 0.087 0.007
BMI 0.011 0.005, 0.016 0.123 <0.001
CES-D score 0.0001 −0.004, 0.004 0.0021 0.947
Chronotype and social Jetlag
Chronotype (MSFsc) 0.020 0.002, 0.038 0.069 0.028
Social jet lag (min) 0.02 −0.03, 0.06 0.020 0.484
Sleep parameters
Sleep duration −0.01 −0.02, 0.01 −0.025 0.420
Modified PSQI −0.0004 −0.008, 0.007 −0.004 0.911
OSA risk 0.08 0.03, 0.12 0.103 0.001
a Reference = primary school or less.
Table 3. Multiple regression analysis with HbA1c as an outcome (n = 960).
Unstandardized
coefficient (B) 95% CI for B
Standardized
coefficient
95% CI for
standardized
coefficient p-value
Age (years) −0.0005 −0.003, 0.002 −0.012 −0.079, 0.054 0.721
Female 0.082 0.034, 0.130 0.114 0.048, 0.180 0.001
Alcohol use −0.065 −0.124, −0.007 −0.075 −0.142, −0.008 0.029
BMI (kg/m2) 0.007 0.002, 0.013 0.087 0.021, 0.152 0.009
Triglyceride (mg/dl) 0.0004 0.0001, 0.0007 0.086 0.023, 0.149 0.007
Chronotype
(MSFsc)
0.019 0.00001, 0.038 0.065 0.0004, 0.130 0.049
Social jetlag (min) 0.018 −0.028, 0.064 0.026 −0.039, 0.090 0.435
Sleep duration (h) −0.008 −0.026, 0.010 −0.033 −0.112, 0.045 0.405
Modified PSQI −0.007 −0.017, 0.002 −0.060 −0.139, 0.020 0.141
OSA risk 0.071 0.023, 0.119 0.096 0.032, 0.160 0.004
6 T. ANOTHAISINTAWEE ET AL.
majority of our participants (68%) had no social
jetlag was uncertain. However, this suggests that
the association between later chronotype and glu-
cose metabolism is independent of social jetlag in
our sample. The effect of circadian phase on glu-
cose metabolism may be different from that of
circadian misalignment. In an experimental study
designed to distinguish the effects of the endogen-
ous circadian system and circadian misalignment,
the circadian system and circadian misalignment
had independent influences on glucose metabo-
lism (Morris et al., 2015). Postprandial glucose
levels were 17% higher in the biological evening
than in the morning. The early-phase postprandial
insulin response was 27% lower in the evening,
indicative of insufficient β-cell response influenced
by circadian phase, while circadian misalignment
increased the postprandial glucose levels by 6%
despite a 14% higher late-phase postprandial insu-
lin response, suggesting reduced insulin sensitivity
(Morris et al., 2015).
As later chronotypes typically sleep and eat later
during the 24 h day, these behaviors may be
potential mediators linking chronotype to cardio-
metabolic risk factors (Reutrakul & Knutson,
2015). Shorter sleep duration was found to be
associated with later chronotype (Fabbian et al.,
2016), possibly partly due to earlier wake time
than desired, especially during workdays, to con-
form to normal social schedule. In addition, sleep
quality is usually poorer in those with later chron-
otype (Fabbian et al., 2016). Both short sleep dura-
tion and poor sleep quality have been linked to
insulin resistance, glucose intolerance and
increased diabetes risk (Reutrakul & Van Cauter,
2014). Although later MSFsc was associated with
poorer sleep quality in this study (result not
shown), sleep duration and quality were not pre-
dictors of HbA1c, suggesting other potential
mechanisms in this sample. Meal timing can also
play a role as exposure to food at an inappropriate
time of the day could lead to misalignment
between the central and peripheral clocks, result-
ing in abnormal metabolism and weight gain
(Garaulet & Gomez-Abellan, 2014). An experi-
ment in healthy volunteers revealed that isocaloric
diet consumption at dinner was found to be asso-
ciated with an 8% higher postprandial glucose and
14% lower insulin response compared with
breakfast time (Morris et al., 2015). Night eating
was also reported to be associated with poorer
glycemic control in the type 2 diabetes population
(Hood et al., 2014). Moreover, a recent rando-
mized crossover study in type 2 diabetes patients
found that consuming two larger meals earlier in
the day (breakfast and lunch) compared with six
small meals for 12 weeks resulted in a greater
reduction in body weight and FPG, along with
higher insulin sensitivity (Kahleova et al., 2014).
Lastly, exposure to artificial light at night could
lead to circadian misalignment and altered meta-
bolism. Mice exposed to dim light at night had
increased weight gain and reduced glucose toler-
ance despite an equivalent caloric intake to mice
kept in a standard light/dark cycle (Fonken et al.,
2010). Nocturnal short-wavelength light, emitted
from some electronic devices, was shown to sup-
press metabolism the following morning in healthy
volunteers (Kayaba et al., 2014). Melatonin, a neu-
rohormone secreted by the pineal gland that plays
a role in circadian physiology, issuppressed by
light. Melatonin receptors are found in the pan-
creatic β-cell and may modulate insulin secretion
(Peschke et al., 2015). Indeed, low nocturnal mel-
atonin secretion was associated with an increased
risk of incident diabetes in a large population-
based study (McMullan et al., 2013). Collectively,
these changes can help explain the association
between chronotype and impaired glucose meta-
bolism. Our study is limited by the lack of infor-
mation on light exposure and meal timing.
Our finding of the independent association
between OSA risk and higher HbA1c is in agree-
ment with previous studies as OSA is known to be
associated with insulin resistance and contributes
to poorer glycemic control, independent of obesity
(Pamidi & Tasali, 2012). The effect size of chron-
otype on HbA1c is approximately two-thirds of
that of OSA risk in the current study. Both chron-
otype and OSA risk independently contributed to
higher HbA1c in our study.
The strength of this study is the inclusion of a
relatively large number of participants and the use of
comprehensive standardized questionnaires. To our
knowledge, this is the first study exploring the rela-
tionship among chronotype, social jetlag and glyce-
mia in the prediabetes population. However, it has
some limitations. Sleep characteristics were not
CHRONOBIOLOGY INTERNATIONAL 7
objectively measured. In some participants, HbA1c
and sleep assessments did not occur on the same
date. However, it has been shown that the results of
PSQI remained relatively stable over 1 year (Knutson
et al., 2006a). HbA1c itself reflects an average glucose
level over three months, and is unlikely to change
significantly over a few months in our participants
who were not taking any diabetes medications. In
addition, information on light exposure at night,
meal timing, caloric intake and physical activity
were not available. Ideally, including nondiabetic
participants would allow us to better understand
the relationship between chronotype and glycemia,
and explore HbA1c at a broader level. Lastly, the
participants were recruited from one center in
Thailand, which might limit its generalizability to
other regions of Thailand or to other countries.
In summary, later chronotype is independently
associated with higher HbA1c levels in patients
with prediabetes, supporting the role of circadian
regulation in glucose metabolism. Future diabetes
prevention research should test the effect of
adjusting sleep timing, potentially along with
adjusting other related behaviors, including meal
timing and artificial light exposure, particularly in
a high-risk group with prediabetes.
Acknowledgments
We would like to thank all the participants in the study.
Declaration of interest
T.A., D.L., S.T., K.L.K. and A.T. have nothing to disclose. S.R.
received lecture fees from Sanofi Aventis, Medtronic and
Novo Nordisk, equipment support from ResMed, Thailand,
and research grant from Merck Sharp & Dohme Corp, U.S.A.
The study was supported in part by a research grant from
Investigator-Initiated Studies Program of Merck Sharp &
Dohme Corp, U.S.A. (MSIP# 0000-349) and from Thailand
Research Organization Network (grant No. 58-051). The
opinions expressed in this paper are those of the authors
and do not necessarily represent those of Merck Sharp &
Dohme Corp.
Author Contributions
T.A. conceptualized the study, researched the data, wrote the
manuscript, contributed to discussion, reviewed/edited
manuscript and is the guarantor of this work and, as such,
had full access to the data in the study and takes
responsibility for the integrity of the data and the accuracy
of the data analyses. D.L. and S.T. researched data and
reviewed/edited the manuscript. K.L.K. contributed to dis-
cussion, reviewed/edited the manuscript. A.T. analyzed the
data, wrote the manuscript, contributed to discussion and
reviewed/edited the manuscript. S.R. conceptualized the
study, researched the data, wrote the manuscript, contributed
to discussion and reviewed/edited the manuscript.
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10 T. ANOTHAISINTAWEE ET AL.
	Abstract
	Introduction
	Materials and methods
	Data collection
	Chronotype and social jetlag assessments
	Sleep assessments
	HbA1c and lipid levels
	Statistical analysis
	Results
	Association between HbA1c and demographics, sleep and circadian parameters
	Discussion
	Acknowledgments
	Declaration of interest
	Author Contributions
	References