The Possible Association Between Circadian Rhythm Chronotypes and Unhealthy Metabolic Phenotypes in Overweight/ Obese Women

Document Type: Original article

Authors

Department of Community Nutrition, School of Nutritional Sciences and Dietetics, Tehran University of Medical Sciences, Tehran, Iran

Abstract

Background: Circadian rhythm affects individual body function and health. Although it can regulate many biological processes in the body such as hormone release and eating habits, little is known about the relationship between circadian rhythm and metabolic disorders. Therefore, the purpose of the present research was to examine the association between Circadian Rhythm Chronotypes (CRC) and metabolic health status components, in patients with overweight and obesity.
Methods: A cross-sectional study was conducted at the healthcare centres of Tehran. Patients with Overweight and Obesity (OW/OB) (n 374) aged 18 to 56 years were rectruited. All participants were assessed for metabolic health status components based on Karelis criteria (HDL, LDL, TG, hs-CRP and HOMA-IR). Morningness - Eveningness Questionnaire (MEQ) was used for chronotype categorizing. Also, bioelectrical impedance analysis (BIA) was applied to evaluate body composition. Regression model in crude and adjusted model was used to assess the relation between CRC and metabolic status.
Results: The differences in CRC were not significant in terms of Karelis criteria components except hs-CRP (p=0.02). Results had shown that the trend of hs-CRP tends to decrease from eveningness chronotype to morningness chronotype and it significantly increased in crude model (p=0.007) and adjusted model (p=0.03). In addition, hs-CRP had decreased significantly in morningness chronotype in comparison with eveningness chronotype in crude model [OR (95% CI): 0.28 (0.10 to 0.79), p=0.01].
Conclusion: This study indicated an association between hs-CRP and CRC in women patients with overweight and obesity, which indicates that inflammation may have a relation to the CRC and metabolic status.

Keywords


Abstract
Background: Circadian rhythm affects individual body function and health. Although it can regulate many biological processes in the body such as hormone release and eating habits, little is known about the relationship between circadian rhythm and metabolic disorders. Therefore, the purpose of the present research was to examine the association between Circadian Rhythm Chronotypes (CRC) and metabolic health status components, in patients with overweight and obesity.
Methods: A cross-sectional study was conducted at the healthcare centres of Tehran. Patients with Overweight and Obesity (OW/OB) (n 374) aged 18 to 56 years were rectruited. All participants were assessed for metabolic health status components based on Karelis criteria (HDL, LDL, TG, hs-CRP and HOMA-IR). Morningness - Eveningness Questionnaire (MEQ) was used for chronotype categorizing. Also, bioelectrical impedance analysis (BIA) was applied to evaluate body composition. Regression model in crude and adjusted model was used to assess the relation between CRC and metabolic status.
Results: The differences in CRC were not significant in terms of Karelis criteria components except hs-CRP (p=0.02). Results had shown that the trend of hs-CRP tends to decrease from eveningness chronotype to morningness chronotype and it significantly increased in crude model (p=0.007) and adjusted model (p=0.03). In addition, hs-CRP had decreased significantly in morningness chronotype in comparison with eveningness chronotype in crude model [OR (95% CI): 0.28 (0.10 to 0.79), p=0.01].
Conclusion: This study indicated an association between hs-CRP and CRC in women patients with overweight and obesity, which indicates that inflammation may have a relation to the CRC and metabolic status.
Keywords: Circadian rhythm chronotypes, Obesity, Unhealthy metabolic status
Introduction
The prevalence of obesity has increased worldwide in recent years making it a major public health concern (1). Obesity is associated with a reduced life expectancy, largely because obese individuals are at an increased risk of type 2 diabetes, Cardiovascular Disease (CVD), and metabolic disorders (2). However, based on previous studies, not all obese subjects are at a high risk of mortality. According to this point, obesity can be discussed in terms of Metabolically Healthy Obesity (MHO) that is defined in patients with obesity (Body Mass Index-BMI≥30 kg/m2) who show no sign of conditions associated with the metabolic syndrome (Mets) (impaired glucose metabolism, hypertension and dyslipidemia with a normal CVD risk profile). Second, Metabolically Unhealthy Obesity (MUO) is defined in obese individuals (BMI≥30 kg/m2) with at least two mets risk factors (3,4).
Obesity, regardless of MUO and MHO status is associated with inflammation due to inflammatory response to pathogens that includes systemic increases in circulating inflammatory cytokines such as interleukins, tumor necrosis factor α, and High Sensitive C-Reactive Protein (hs-CRP) and other inflammatory factors (5). Previous studies demonstrate that MUO individuals have higher inflammation compared to their MHO counterparts and experience more adverse effect on body function (6-8).
It has been suggested that inflammatory response can be exacerbated by circadian rhythm misalignment (9). Circadian rhythms are physiologic and behavioral cycles with periodic recurrence of approximately 24 hours generated by the Suprachiasmatic Nucleus (SCN) located at the base of the brain in front of the pituitary gland for rhythm oscillation (10). The rhythms control a variety of biological processes, such as sleep-wake cycle, body temperature, feeding, hormone secretion, glucose homeostasis, lipid metabolism and immune system function (11-13). Different Circadian Rhythm Chronotypes (CRC) have shown the ability to change circadian oscillations of many body functions. Moreover, circadian rhythm misalignment can be induced by sleep deprivation in evening chronotypes. However, there is no study to examine the association between circadian rhythm and metabolic status in overweight and obese women. Therefore, the overall goal of this project was to define the association between circadian rhythm misalignment and metabolically unhealthy status in overweight and obese women.

Materials and Methods
Study population
This cross-sectional study included 374 overweight and obese adult women aged 18 to 56 years, who referred to healthcare centers in Tehran, Iran. Participants were in good general health with a BMI of 25 kg/m2 or greater. The study protocol was approved by the Ethics Commission of Tehran University of Medical Sciences (IR.TUMS.VCR.REC.1395.1480). All participants signed a written informed consent. The exclusion criteria were as follows: history of hypertension, impaired renal and liver function, regular use of medicine other than birth control pills, cardiovascular disease, diabetes mellitus, and menopause, intake of alcohol, smoking, and pregnancy or lactation period. In addition, those women with chronic disease affecting their diet, those who followed special regimen, and those with any body weight fluctuations over the last year were excluded from the study.

Measurement of body composition and physical activity
The body composition analyzer (in body 720, Korea) was used to measure weight, BMI, Total Body Water (TBW), Fat Mass (FM), Fat-Free Mass (FFM), and body fat percentage (%) of the subjects following a standardized procedure according to guidelines. This electrical impedance analyzer measures the resistance of body tissues to the flow of an electrical signal sent through both feet and hands. The proportion and amount of body fat and fat-free mass can be calculated when the current flows more easily through certain parts of the body. International Physical Activity Questionnaire (IPAC) was used to assess Physical Activity (PA). Activity levels were classified into low, moderate, and high groups, as described by the IPAQ scoring protocol (14).

Determination of metabolic health status
MHO was defined based on Karelis cardiometabolic
abnormalities, presenting individuals afflicted
with obesity (BMI≥30 kg/m2) with at least four
of the followings: HOMA ≤2.7, triglycerides
(TG)≤1.7 mmol/l, High - Density Lipoprotein -Cholesterol (HDL-C)≥1.3 mmol/l, Low-Density Lipoprotein-Cholesterol≤2.6 mmol/l, high-sensitivity C-reactive protein (hs-CRP)≤3.0 mg/l. On the other hand, metabolically unhealthy status was defined as obesity (BMI≥30 kg/m2) with at least two risk factors of metabolic components based on Karelis et al criteria (15). In order to compare the differences in circadian rhythm status, participants were assigned into two groups of MHO, and MUO.

Morningness-Eveningness Questionnaire (MEQ)
A validated Morningness - Eveningness Questionnaire (MEQ) was used to measure whether a participant’s circadian rhythm produces peak alertness in the morning, in the evening, or in between. Reliability and validity of the MEQ has been already approved in Iran (16). Completed extensive MEQ comprised of components like sleep habits, sleep history, family history, sleep quality, morningness - eveningness traits, depression, mania, and seasonality of symptoms.  MEQ scores ranged from 16 to 86; scores of 41 and below indicate “evening chronotype”, scores between 42-58 indicate “intermediate chronotype”, and scores of 59 and above indicate “ morningness chronotype” (17).

Statistical analysis
Descriptive analysis was used to describe the mean (standard deviation) and study population characteristics. The relationship between qualitative variables and MEQ values was assessed with the chi-square test. The relationship between quantitative variables and MEQ values was determined by one-way analysis of variance. In order to analyze the potential association between MUO and circadian rhythm, binary general linear model was used in crude and adjusted models (Counfounding factors including age, BMI and physical activity). P-value lower than 0.05 was considered statistically significant. Statistical analysis was performed using SPSS version 22.0 (SPSS, Chicago, IL, US). All statistical analyses were performed using the IBM SPSS Version 22.0 (IBM Corp. Armonk, NY, USA) software.

Results
Study population characteristics
A sample of 374 overweight and obese adult women aged 18 to 56 years was recruited in the current study. The characteristics of study participants are shown in table 1. The mean age, height, and weight of women were 35.1 years, 160.9 cm, and 80.8 kg, respectively. Moreover, other characteristics including lipid profile, body composition, and biochemical parameters of participants have been shown.
In order to examine the association of circadian rhythm with metabolic status, participants were categorized based on circadian rhythm including morningness, intermediate and eveningness chronotypes. The differences between characteristics of participants among the MEQ chronotypes are shown in table 2. The results from this study revealed that weight (p=0.03) and Fat Mass Index (FMI) (p=0.006) showed significant differences among MEQ chronotypes. After adjustment for confounding factors, the effect of age (p=0.01) was more in morningness chronotype compared to other MEQ chronotypes.

Frequency of abnormal metabolic status based on Karelis criteria
The differences between MEQ chronotypes and Karelis criteria components of participants are shown in table 3. There were no statistically significant differences in LDL, HDL, TG, and HOMA index among MEQ chronotypes. However, intermediate chronotype participants in comparison with MEQ chronotypes had higher level of hs-CRP.

Table 1. Characteristics of study population

      

Min

Max

Mean

SD

MEQ-score

7.00

71.00

36.52

8.32

Age (year)

18.00

56.00

35.12

8.69

Weight )kg)

59.50

119.50                  

80.89             

12.45

Height )cm)

147.50

179.00

160.92

10.14

BMI (kg/m²)

25.00

40.70

30.33

3.65

FFM (kg)

35.30

67.70

46.62

5.43  

FM (kg)

19.40

53.20                  

33.43                

7.60

GLU (mg/dl)

67.00

202.00

88.57

11.38

TG (mg/dl)

37.00

512.00

118.39

64.54

HDL (mg/dl)

18.00

84.00  

47.76               

10.87

LDL (mg/dl)

34.00

282.00

96.98

26.86

hs-CRP (mg/l)

0.00

22.73

4.05

4.54

HOMA-IR

1.29

16.59

3.39

1.92

SGOT (µKat/L)

7.00

70.00

18.52

8.05

SGPT (µKat/L)

4.00 

121.00

18.82

14.46 

 

Table 2. Characteristics of study population sub grouped by MEQ

Circadian chronotypes a

 

Eveningness

(n 127)

Intermediate

(n 132)     

Morningness

(n 115) 

*p-value

p-valueα

Age (year)

36.19 ±8.18

35.89 ± 8.49

37.73 ± 8.38       

 0.364

0.011

Weight) kg)

83.98±12.68a

79.49 ± 11.27

77.68 ± 14.62a               

 0.101 

0.582

Height )cm)

161.15±6.60        

160.81±5.63          

159.37± 20.91

 0.711

0.917

BMI (kg/m²)

32.30±4.14

30.65±3.63             

30.44±3.83

 0.086

0.411

FFMI (kg)

18.41±1.55           

17.74 ±1.37           

17.90 ±1.43

 0.095

0.756

FMI )kg)

13.89± 3.10a

12.92 ±2.83b

12.53 ±3.08a.b

 0.147

0.597

GLU (mg/dl)

90.80±9.82

87.55±9.73

85.74±7.40

 0.150

0.952

SGOT (units/L)

18.60± 6.67

18.15± 7.20

17.56± 8.25

 0.823

0.464

SGPT (units/L)

20.13 ±11.14

20.37 ±13.43

19.16± 14.10

 0.964

0.524

             

 

BMI body mass index, FFMI free fat mass index, FMI fat mass index, GLU glucose, Serum Glutamic Oxaloacetic Transaminase (SGOT), Serum Glutamic Pyruvic Transaminase (SGPT)

a categorized based on Morningness -Eveningness Questionnaire, *p value resulted from ANOVA analysis

α P value resulted from general linear model after adjustmentfor age, BMI, physical activity

Table 3. Frequency of abnormal metabolic status based on Karelis criteria among different circadian chronotypes

 Circadian chronotypes

Eveningness

Intermediate

Morningness

p-value

TG(mg/dl)                (>1.7, n= 67)

7(10.5%)

38(56.7%)

22(32.8%)

0.821

HDL(mg/dl)             (<1.3, n= 206)

26(12.6%)

119(57.7%)

61(29.7%)

0.972

LDL(mg/dl)             (>2.6, n= 129)

21(16.3%)

71(55.0%)

37(28.7%)

0.335

hs-CRP (mg/l)         (>3, n= 125)

21(16.8%)

79(63.2%)

25(20.0%)

0.044

HOMA-IR               (>2.7, n= 190)

28(14.7%)

108(56.9%)

54(28.4%)

0.547

 

HDL high density lipoprotein, hs-CRP high–sensitivity C- reactive protein, HOMA homeostatic model assessment, LDL low density lipoprotein, TG triglyceride

∞ p-value resulted from chi-squared analysis

Table 4. Association of metabolic unhealthy status and its components with circadian rhythms chronotypes in crude and adjustment regression model among participants

Total population

                         Crude model                                                       Adjustment model

                         OR (95% CI)                p value    p trend          OR (95% CI)              p value     p trend

MUO

Eveningness ¥                          

Intermediate     1.37 (0.46 to 4.09)         0.562       0.507             0.94 (0.29 to 3.04)      0.912       0.881          

Morningness     1.22 (0.60 to 2.48)         0.577                             1.24 (0.57 to 2.65)      0.574            

TG

Eveningness ¥                           

Intermediate    1.36 (0.42 to 4.41)           0.601       0.588            1.89 (0.54 to 6.54)       0.312       0.572        

Morningness     1.47 (0.42 to 5.10)         0.547                            1.71 (0.46 to 6.37)       0.415            

HDL

Eveningness ¥                                                                                                                                        

Intermediate     0.97(0.36 to2.59)           0.954        0.817             1.01(0.36 to 2.78)         0.981         0.770

Morningness     0.89(0.31 to0.57)           0.841                              0.89(0.30 to 2.63)        0.844           

LDL

Eveningness ¥                          

Intermediate     0.55(0.22 to 1.38)          0.218        0.191             0.81(0.30 to 2.15)        0.677         0.260    

Morningness     0.48(0.18 to 1.30)          0.150                              0.58(0.20 to 1.67)         0.314

hs-CRP

Eveningness ¥                          

Intermediate      0.67(0.27 to 1.66)        0.391          0.007            0.97(0.35 to 2.65)         0.962         0.031

Morningness      0.28(0.10 to 0.79)        0.017                               0.38(0.12 to 0.96)         0.047

HOMA-IR

Eveningness ¥                          

Intermediate      0.69(0.26 to 1.84)        0.461          0.460            0.84(0.30 to 2.38)         0.758         0.287

Morningness      0.56(0.20 to 1.52)        0.283                               0.69(0.23 to 2.06)        0.518

 

HDL high density lipoprotein, hs-CRP high–sensitivity C- reactive protein, HOMA homeostatic model assessment, LDL low density lipoprotein, MUO metabolically unhealthy obese, TG triglyceride

based on binary regression model in crude and adjustment models

¥Eveningness chronotype considered as reference group, OR=odds ratio, CI=confidence interval

 

N=374,  BMI body  mass index, MEQ  morningness- Eveningness Questionnaire, FFMI free fat mass index, FMI  fat mass index, SGOT serum glutamic oxaloacetic transaminase, SGPT serum glutamic pyruvic transaminase, HDL high density lipoprotein ،LDL low density lipoprotein, TG triglyceride, hs-CRP high–sensitivity c-reactive protein, GLU glucose, HOMA homeostatic model assessment

 

Association of MUO components with CRC in crude and adjusted regression model
The association between circadian rhythm and karelis components among participants, in crude and adjusted models of binary regression model is presented in table 4. It has been shown that eveningness chronotype in comparison to other chronotypes had no abnormal metabolic status based on Karelis components. However, in crude model, participants in morningness chronotype had lower odds of high hs-CRP compared to eveningness chronotype (p=0.01). Moreover, hs-CRP trend in crude (p=0.007) and adjusted (p=0.03) models was ascending from morningness chronotype to eveningness chronotype.

Discussion
In the present study, the association between CRC and MUO characteristics in women was studied. Significant association between hs-CRP as a component of metabolic health status and CRC in participants was observed. However, no significant association was found in other karelis components (LDL, HDL, TG, and HOMA). Our findings showed hs-CRP caused significant difference among chronotype groups, so higher hs-CRP level was related to eveningness chronotype compared to morningness chronotype.
Based on previous studies, evening chronotype was associated with a higher prevalence of diabetes, metabolic syndrome, cardiovascular disease, and sleep disorders (18, 19). Similarly, Baron et al demonstrated that evening chronotype groups were 2.5 times more likely to have weak general health compared with morning chronotype groups (20). Eveningness chronotype groups such as shift workers have shorter sleep durations during working days compared with the other chronotype groups which induces circadian rhythm misalignment, a critical factor in disrupting energy metabolism, lipid and glucose metabolism leading to obesity with both physical and psychiatric consequences (21).
Our results showed no association between MUO lipid profiles and circadian rhythm chronotypes. Despite our findings, Wefers et al showed circadian misalignment can lead to high Free Fatty Acids (FFA) levels and low TG levels. It may happen due to circadian regulation or high clearance of very LDL (VLDL)-TG by the skeletal muscle (22). Esquirol et al observed that shift workers are affected by circadian misalignment, and they were associated with higher FFA and TG, and lower HDL compared with day workers (23). Similarly, De Bacquer et al in a prospective study observed that shift workers compared with day workers have higher triglycerides and lower HDL (24). But in contrast to HDL and TG fluctuations, Ghiasvand et al’s as well as current study found no difference in the level of HDL and TG after adjustment for age and food type between different chronotype groups (25).
Some studies as well as our study have found no difference in TC and LDL levels between circadian chronotype groups (26-28). In spite of our results about LDL and TC, Merikanto et al examined the association between different chronotypes and cardiovascular disease factors in a large population in Finland and found that eveningness chronotype group had lower serum levels of cholesterol and LDL compared with other chronotype groups (21).
HOMA-IR index as an insulin resistance index was evaluated in our study, but showed no association with chronotypes. Despite our findings, Gonnissen et al showed circadian misalignment reduced Rapid Eye Movement (REM) sleep, specially sleep during the second part of the night. REM sleep was inversely associated with HOMA-IR index and can reduce insulin sensitivity (29).
In the current study, it was found that hs-CRP had a significant effect on chronotype groups, as higher hs-CRP level was related to eveningness chronotype compared to morning chronotypes. Several studies have shown high inflammatory markers in eveningness chronotype (30,31). Similar to our findings, Morris et al recently found that participants who experienced misalignment circadian rhythm had higher hs-CRP levels (32).
Mechanisms of increased hs-CRP level can be discussed in two ways; first, direct effect of circadian rhythm misalignment can be assessed on hs-CRP and, second, indirect effect of circadian rhythm misalignment on obesity and consequently on hs-CRP can be investigated. It was shown that circadian misalignment can have adverse effects by increasing the level of hs-CRP in participants who experienced sleep deprivation (33-35). This is in accordance with Leproult et al findings; moreover, Wright et al showed that this type of circadian misalignment not only increased the hs-CRP levels but also increased other inflammatory markers (36,37). The mechanism of increased hs-CRP is not well understood, but animal studies showed that inflammatory responses may be related to clock gene expression (38,39). Another potential factor is the role of melatonin. It has been suggested that melatonin has effects on the immune system via controlling cytokines action. Sleep deprivation may change the regulation of inflammatory responses in the absence of melatonin (40,41).
One more possible mechanism for the mentioned association can be explained by contribution of obesity in increasing inflammatory factors. Insufficient sleep may be a potential important contributing mechanism in increased accrual of adipocytes. Leptin, an adipocyte hormone signaling satiety to the brain, is reduced in acute and sustained partial sleep deprivation. Ghrelin, a hormone which signals hunger to the brain, increases during partial sleep deprivation. The combination of slow metabolism produced by sleep deprivation and increased appetite may theoretically lead to weight gain and increasing prevalence of obesity. It has been demonstrated that obesity is associated with low-grade inflammatory process characterized by the increase in circulating levels of pro-inflammatory cytokines such as IL-6, TNF-alpha and CRP in obese subjects (42,43).

Conclusion
To our knowledge, this is the first study that assessed the relationship between metabolic unhealthy obesity and circadian rhythm. The strength of the study was its adjustment for some major confounders. Since our study was a cross-sectional one, certainly further randomized clinical trials and observational prospective studies are needed to confirm these findings. In conclusion, our findings showed a significant association between hs-CRP and CRC in OW/OB women, which shows that inflammation may have a role in a CRC and metabolic status.

Acknowledgement
The authors thank the directors of Tehran University of Medical Sciences, for allowing us to conduct this cross-sectional study.

Conflict of Interest
None declared.

1. Kelly T, Yang W, Chen CS, Reynolds K, He J. Global burden of obesity in 2005 and projections to 2030. Int J Obesity 2008;32:1431-7.
2. Mokdad AH, Ford ES, Bowman BA, Dietz WH, Vinicor F, Bales VS, et al. Prevalence of obesity, diabetes, and obesity-related health risk factors, 2001. JAMA 2003;289:76-9.
3. Jung CH, Lee WJ, Song KH. Metabolically healthy obesity: a friend or foe? Korean J Intern Med 2017;32(4):611-21.
4. Denis GV, Obin MS. ‘Metabolically healthy obesity’: origins and implications. Mol Aspects Med 2013;34:59-70.
5. Toth LA. Sleep, sleep deprivation and infectious disease: studies in animals. Advances in neuroimmunology 1995;5(1):79-92.
6. Phillips CM, Perry IJ. Does inflammation determine metabolic health status in obese and nonobese adults? J Clin Endocrinol Metab 2013;98(10):E1610-9.
7. Karelis AD, Faraj M, Bastard JP, et al. The metabolically healthy but obese individual presents a favorable inflammation profile. J Clin Endocrinol Metab 2005;90(7):4145-50.
8. Kloting N, Fasshauer M, Dietrich A, Kovacs P, Schön MR, Kern M, et al. Insulin-sensitive obesity. Am J Physiol Endocrinol Metab 2010;299(3):E506-15.
9. Yoshida K, Hashimoto T, Sakai Y, Hashiramoto A. Involvement of the circadian rhythm and inflammatory cytokines in the pathogenesis of rheumatoid arthritis. J Immunol Res 2014;2014:6.
10. Sokolove GP. Localization of the optic lobe arcadian pacemaker with microlesions. Brain Res 1975;87:13-21.
11. Zee PC, Attarian H, Videnovic A. Circadian rhythm abnormalities. Continuum 2013;19:132-47.
12. Hand LE, Hopwood TW, Dickson SH, Walker AL, Loudon AS, Ray DW, et al. The circadian clock regulates inflammatory arthritis. The FASEB J 2016;30(11):3759-70.
13. Asher G, Sassone-Corsi P. Time for food: The intimate interplay between nutrition, metabolism, and the circadian clock. Cell 2015;161(1):84-92.
14. Vasheghani-Farahani A, Tahmasbi M, Asheri H, Ashraf H, Nedjat S, Kordi R. The Persian, last 7-day, long form of the International Physical Activity Questionnaire: translation and validation study. Asian J Sports Med 2011;2(2):106-16.
15. Karelis AD, Rabasa-Lhoret R. Inclusion of C-reactive protein in the identification of metabolically healthy but obese (MHO) individuals. Diabetes Metab 2008;34(2):183-4.
16. Rahafar A, Randler C, Díaz-Morales J, Kasaeian A, Heidari Z. Cross-cultural validity of Morningness-Eveningness Stability Scale improved (MESSi) in Iran, Spain and Germany. Chronobiol Int 2017;34(2):1-7.
17. Shahid A, Wilkinson K, Marcu S, Shapiro CM. Morningness-Eveningness Questionnaire. In: Shahid A, Wilkinson K, Marcu S, Shapiro CM, eds. STOP, THAT and One Hundred Other Sleep Scales. New York, NY: Springer New York; 2012.p.231-4.
18. Merikanto I, Lahti T, Puolijoki H, Vanhala M, Peltonen M, Laatikainen T, et al. Associations of chronotype and sleep with cardiovascular diseases and type 2 diabetes. Chronobiol Int 2013;30(4):470-7.
19. Yu JH, Yun C-H, Ahn JH, Suh S, Cho HJ, Lee SK, et al.  Evening chronotype is associated with metabolic disorders and body composition in middle-aged adults. The Journal of Clinical Endocrinology & Metabolism 2015;100:1494-502.
20. Baron KG, Reid KJ. Circadian Misalignment and Health. Int Rev Psychiatry 2014;26(2):139-54.
21. Merikanto I, Lahti T, Puolijoki H, Vanhala M, Peltonen M, Laatikainen T, et al. Associations of chronotype and sleep with cardiovascular diseases and type 2 diabetes. Chronobiol Int 2013;30(4):470-7.
22. Wefers J, van Moorsel D, Hansen J,  Connell NJ, Havekes B,  Hoeks J, et al. Circadian misalignment induces fatty acid metabolism gene profiles and compromises insulin sensitivity in human skeletal muscle. Proc Natl Acad Sci USA 2018;115(30):7789-94.
23. Esquirol Y, Bongard V, Mabile L, Jonnier B, Soulat JM, Perret B. Shift work and metabolic syndrome: respective impacts of job strain, physical activity, and dietary rhythms. Chronobiol International. 2009;26:544-59.
24. De Bacquer D, Van Risseghem M, Clays E, Kittel F, De Backer G, Braeckman L. Rotating shift work and the metabolic syndrome: a prospective study. Int J Epidemiol 2009;38:848-54.
25. Ghiasvand M, Heshmat R, Golpira R, Haghpanah V, Soleimani A, Shoushtarizadeh P, et al. Shift working and risk of lipid disorders: a cross-sectional study.  Lipids Health Dis 2006;5:9.
26. Knutsson A. Relationships between serum triglycerides and gamma-glutamyltransferase among shift and day workers. J Intern Med 1989;226(5):337-9.
27. Di Lorenzo L, De Pergola G, Zocchetti C, L’Abbate N, Basso A, Pannacciulli N, et al. Effect of shift work on body mass index: results of a study performed in 319 glucose-tolerant men working in a Southern Italian industry. Int J Obesity 2003;27:1353-8.
28. Knutson A, Andersson H, Berglund U. Serum lipoproteins in day and shift workers: a prospective study. Br J Ind Med 1990;47(2):132-4.
29. Gonnissen HKJ, Mazuy C, Rutters F, Martens EAP, Adam TC,  Westerterp-Plantenga MS. Sleep architecture when sleeping at an unusual circadian time and associations with insulin sensitivity. PLoS One 2013;8(8):e72877.
30. Sookoian S, Gemma C, Fernandez Gianotti T, Burgueno A, Alvarez A, Gonzalez CD, et al. Effects of rotating shift work on biomarkers of metabolic syndrome and inflammation.  J Intern Med 2007;261:285-92.
31.Puttonen S, Viitasalo K, Harma M. Effect of shiftwork on systemic markers of inflammation. Chronobiol Int 2011;28:528-35.
32. Morris CJ, Purvis TE, Mistretta J, Hu K, Scheer FAJL. Circadian misalignment increases C-reactive protein and blood pressure in chronic shift workers. J Biol Rhythms 2017;32(2):154-64.
33. Kato M, Phillips BG, Sigurdsson G, Narkiewicz K, Pesek CA, Somers VK. Effects of sleep deprivation on neural circulatory control. Hypertension 2000;35:1173-5.
34. Born J, Lange T, Hansen K, Molle M, Fehm HL. Effects of sleep and circadian rhythm on human circulating immune cells. J Immunol 1997;158(9):4454-64.
35. Morris CJ, Purvis TE, Hu K, Scheer FA. Circadian misalignment increases cardiovascular disease risk factors in humans. Proc Natl Acad Sci USA 2016;113(10):E1402-11.
36. Leproult R, Holmback U, Van Cauter E. Circadian misalignment augments markers of insulin resistance and inflammation, independently of sleep loss. Diabetes 2014;63(6):1860-9.
37. Wright KP Jr., Drake AL, Frey DJ, Fleshner M, Desouza CA, Gronfier C, et al. Influence of sleep deprivation and circadian misalignment on cortisol, inflammatory markers, and cytokine balance. Brain, Behav Immun 2015;47:24-34.
38. Castanon-Cervantes O, Wu M, Ehlen JC, Paul K, Gamble KL, Johnson RL, et al. Dysregulation of inflammatory responses by chronic circadian disruption.  J Immunol 2010;185(10):5796-805.
39. Keller M, Mazuch J, Abraham U, Eom GD, Herzog ED,  Volk HD, et al. A circadian clock in macrophages controls inflammatory immune responses. Proc Natl Acad Sci USA 2009;106(50):21407-12.
40. Guerrero JM, Reiter RJ. Melatonin-immune system relationships. Curr Top Med Chem 2002;2(2):167-79.
41. Carrillo-Vico A, Guerrero JM, Lardone PJ, Reiter RJ. A review of the multiple actions of melatonin on the immune system. Endocrine 2005;27:189-200.
42. Pi-Sunyer FX. The obesity epidemic: pathophysiology and consequences of obesity. Obes Res 2002;10 Suppl 2:97-104.
43. Lau DC, Dhillon B, Yan H, Szmitko PE, Verma S. Adipokines: molecular links between obesity and atheroslcerosis. Am J Physiol Heart Circ Physiol 2005;288(5):H2031-41.