Elevated oxidative stress biomarkers in adults with segmented sleep patterns

Abstract

Study Objectives:

We investigated the association between different sleep patterns and inflammatory and oxidative stress biomarkers in adults.

Methods:

A total of 321 consented adults who fulfilled the inclusion criteria were recruited in this cross-sectional study. The inclusion criteria were mainly based on apparently healthy adults aged 18–59 years. To identify sleep patterns, participants were requested to wear the actigraph for 1 week for 24 hours a day. Fasting blood was collected from each participant at day 8. The blood serum was analyzed for inflammatory and oxidative stress biomarkers. Sleep patterns were defined as monophasic (1 episode of night sleep) biphasic (2 episodes of sleep; night and aternoon siesta), and polyphasic sleep pattern (3 or more sleep episodes).

Results:

There was no correlation between night sleep duration, total sleep in 24 hours, and napping among inflammatory and oxidative stress biomarkers: high-sensitivity C-reactive protein, malondialdehyde, total glutathione, and basal oxidizability status. Actigraphy reports showed 3 sleep patterns in this cohort, monophasic (24.3%), biphasic-napping (45.2%) and polyphasic (30.5%). Individuals with segmented sleep patterns were significantly associated with oxidative stress biomarkers. A polyphasic sleep pattern was significantly associated with higher basal oxidizability status (P = .023), whereas a biphasic sleep pattern showed higher malondialdehyde (P = .036) as compared to a monophasic sleep pattern. Total glutathione was significantly higher in monophasic sleepers (P = .046). There was no difference in serum high-sensitivity C-reactive protein among all sleep patterns.

Conclusions:

Segmented sleep in polyphasic and biphasic sleep patterns is associated with higher serum malondialdehyde and basal oxidizability status in particular. Further studies are recommended on the cardiometabolic impact of oxidative stress biomarkers in individuals with segmented sleep.

Citation:

Al Lawati I, Zadjali F, Al-Abri MA. Elevated oxidative stress biomarkers in adults with segmented sleep patterns. J Clin Sleep Med. 2024;20(6):959–966.

BRIEF SUMMARY

Current Knowledge/Study Rationale: A sedentary lifestyle and altered sleep habits are observed to be dominant among the Middle Eastern population. Based on the findings of recent studies, segmented sleep patterns associated with short night sleep duration and longer nap duration are practiced by the majority of adults. This may act as a potential risk factor for cardiometabolic disorders and could have more negative implications associated with a proinflammation state and oxidative stress that could affect physiological and psychological functions.

Study Impact: Studying inflammatory and oxidative stress biomarkers as vital tools can contribute to proper diagnosis, therapeutic progress, intervention efficiency, and sleep hygiene program construction and screen disease progression. This can play a role in the reduction of cardiometabolic risk and, hence, would promote a healthy lifestyle.

INTRODUCTION

The lifestyle of Middle Easterners has rapidly changed over the last 40 years. This has involved altered sleep habits resulting from economic development, technology, and late-night socializing.¹ A sedentary lifestyle has become dominant and less exercise is practiced.² At night, an active social life has become prominent and is prolonged to midnight, leading to later night sleep times and longer siestas the following day.¹

Based on the findings of a recent study in Oman, 3 major sleeping patterns were practiced by adults: monophasic (single episode of nocturnal sleep per 24 hours), biphasic (2 episodes of sleep per 24 hours, 1 at night and an afternoon nap), and polyphasic (≥ 3 episodes of sleep per 24 hours, 1 at night, 1 after dawn prayers, and an afternoon nap).¹ The biphasic pattern prevailed over other patterns, followed by the polyphasic pattern. Both biphasic and polyphasic sleep are segmented patterns and showed high prevalence of relatively short night sleep duration (< 7 hours), daytime sleepiness, long afternoon naps (> 60 minutes), and longer total sleep duration in 24 hours.¹

Segmented sleep patterns associated with short night sleep duration and longer nap duration may act as a potential risk factor for cardiometabolic disorders and biological stress induction in the body.^3,4 Poor sleep quality, long naps, and a sedentary lifestyle with minimal movement combined are the lead risk factors for cardiometabolic disorders.⁵ Short night sleep duration has more negative implications associated with a proinflammation state and oxidative stress that could affect physiological and psychological functions.⁶

Previous studies found a relatively high prevalence (23%) of metabolic syndrome in the region.^7–9 This high prevalence of metabolic syndrome among adults could be also correlated with segmented sleep patterns, insufficient sleep duration, and poor-quality sleep, which may have an important implication for public health.¹⁰ Short sleep duration and poor quality act as stress factors that can modulate inflammation and the oxidative stress environment in the body, promoting the production of biomarkers in the blood.¹¹ Oxidative stress is considered an imbalance condition associated with an increase of oxidants or decrease of antioxidants due to body stress.¹² Some studies suggest that short sleep duration, sleep deprivation, and fragmentation contribute to the development of endothelial dysfunction, which may subsequently lead to cardiovascular diseases.¹³

Biomarkers of inflammation including C-reactive protein (CRP), tumor necrosis factor alpha, and oxidative stress were studied in sleep deprivation, sleep disturbances, and short sleepers.^14–19 Studying these biomarkers might contribute to the development of proper intervention schemes and sleep hygiene programs, which in turn could increase awareness about the importance of healthy sleep. This would help in the reduction of metabolic syndrome and cardiometabolic risk and, hence, would promote healthier lifestyles.^20,21 However, the correlation between segmented sleep patterns (eg, monophasic, biphasic, and polyphasic) and inflammatory and oxidative stress biomarkers has not yet been investigated. Therefore, this study aimed to investigate the association between different sleep patterns and durations and inflammatory and oxidative stress biomarkers in adults.

METHODS

Study design, population, and ethical approval

This was a cross-sectional study based on a method published previously.^1,22 Participants were recruited through a variety of methods, including press releases, emails, and brochures distributed at local schools, government establishments, and community events. Selection was based on first-come and no allocation step was made because the study did not have groups. The study was conducted in Muscat, the capital city and the largest metropolitan area in Oman, from April 2016–October 2018 to limit seasonal variation in sleep patterns. The data for this study were collected from a total of 321 consented volunteers (no incentives were given to the participants) who agreed to wear the actigraphy device for 7 consecutive days. Inclusion criteria were apparently healthy men and women aged 18–59 years with no known history of cancer, stroke, compensated cardiovascular disease, or psychiatric illness based on their medical records. Pregnant and breast-feeding women, mothers with children below 1 year of age, and shift workers were excluded from the study. The participants must not have traveled across time zones during the past month. The study was approved by the Medical Research Ethics Committee at Sultan Qaboos University (MREC # 1078).

Study instruments

The participants were requested to wear the actigraph (SOMNOwatch plus; SOMNOmedics GmbH, Randersacker, Germany) for 1 week for 24 hours. They were asked to avoid traveling across time zones during the period of the study. Fasting blood was collected from each participant directly on day 8 after actigraphy submission. The blood serum was analyzed for the selected inflammatory and oxidative stress biomarkers. The selected inflammatory biomarker for the current study was high-sensitivity C-reactive protein (hs-CRP) (cobas c501; Roche, Germany), and the oxidative stress biomarkers were malondialdehyde (MDA) (K739-100; BioVision Incorporated, Milpitas, California), total glutathione (GSH) antioxidant (K261-100; BioVision Incorporated, Milpitas, California), and basal oxidizability status (BOS) to estimate the serum/plasma antioxidative capacity. Copper reduction (Cu⁺³ to Cu⁺²) was used in this assay as an inducer of oxidation in each sample of all participants. This was done by measuring the first reading of absorbance at 245 nm of ultraviolet signals within 5 minutes using spectrophotometry.²³ The BOS of each sample at 245 nm was calculated by subtracting the absorbance value of each sample from its control (BOS = Sample absorbance reading − Control absorbance reading). The BOS readings were then compared between different sleep patterns.

Objective sleep pattern identification

Based on previously conducted studies,^1,22,23 3 sleep patterns were identified in this cohort using the SOMNOwatch plus actigraphy device. Actigraphs were given to the consented participants, who were requested to wear the watch for 7 consecutive days for 24 hours a day. The following instructions were given to them clearly: (1) to wear the watch day and night for a full week, (2) to make sure that the watch was in direct contact with the skin, (3) to press on the marker button when in bed before sleeping at night or during the day, (4) to not remove the watch unless for bathing or washing, and (5) to return the device to the clinic after 1 week (day 8) for data download. The data were collected and timing and duration were scored in all actigraphy records to ensure precision.

For each individual, the objective sleep pattern per week was generated and visualized via the software as images or figures after scoring. The rest period was identified by signals of low amplitude and less frequency (threshold > 800 MHz) generated by the actigraphy watch. For individuals who showed variation in sleep patterns during 1 week the predominant sleep pattern was selected. The predominant sleep pattern was defined as 4 or more instances of the same sleep pattern per week accounting for more than 50%.^1,22 Based on frequency of sleep periods throughout the day, sleep patterns were classified into 3 categories: (1) a monophasic pattern, which essentially consisted of sleeping once per 24-hour period, usually at night; (2) a biphasic pattern, sleeping twice per 24-hour period, 1 episode at night and an afternoon nap; and (3) a polyphasic pattern, sleeping multiple times in a 24-hour period, usually 3 periods or more, usually 1 at night, 1 one after dawn prayers, and an afternoon nap.¹

Statistics

A 1-sample Kolmogorov–Smirnov test for normality was conducted. Nonparametric analysis was performed. Descriptive analysis was done to calculate the frequency, percentages, medians, and ranges of the continuous data. Outliers were excluded and defined as 1.5 of interquartile range (+75 and −25).

The continuous variables were night sleep duration in hours, daytime duration in 24 hours, nap duration in minutes, and the level of inflammatory and oxidative stress biomarkers, and the categorical variables were the 3 groups of sleep patterns (monophasic, biphasic, and polyphasic) and sex (men and women). Adjustment for P values by confounding factors (eg, age, sex, sleep duration, or body mass index [BMI]) was done by linear regression.

The correlation between the 3 sleep durations and both the inflammatory and oxidative stress biomarkers were analyzed using the Spearman test. A chi-squared test was used to find the association between sex and other descriptive variables including age groups, educational status, and employment status. The study parameters of biomarker levels in men and women were analyzed using the Mann–Whitney U test. The Kruskal–Wallis test, the statistic for a nonparametric 1-way analysis of variance, was used to test the differences between medians for the 3 groups of sleep patterns and the level of inflammation and oxidative stress biomarkers. The P values reported were 2-sided; P values < .05 were considered statistically significant.

RESULTS

Descriptive data of study participants

Based on the 1-sample Kolmogorov–Smirnov test for normality, the current data were not normally distributed and therefore nonparametric analysis was conducted and median and ranges were reported. Out of the 321 participants, 171 (53.3%) were men and 150 (46.7%) were women (Table 1). The median age was 31 years and the median BMI was 25.80 (range = 7.56). Young adults who fell into the age group of 18–39 years accounted for 69.5% (n = 223), of which 52% were women (n = 116) and 48% were men (n = 107). Participants from the middle age group (40–59 years) represented 30.5% (n = 98) of the study sample size (there were 3 participants who were > 59 years of age and they are incidental findings within the collected data), out of which 34.7% were women (n = 34) and 65.3% were men (n = 64). The study group included participants with an undergraduate education (n = 277; 86.3%) and 282 of the participants (87.9%) were employed. Exercise was practiced by only 44.23% of the total sample (n = 105; 61.4% men and n = 37; 24.7% women) with a median of 130 minutes per week (Table 1).

Table 1.

Descriptive data of the study population.

	All	Men	Women	P	Adjusted P
n (%)	321	171 (53.3%)	150 (46.7%)
Age (years)	31 (41)	34 (41)	23.5 (38)	.004	—
Age groups, n (%)
18–39 years	223 (69.5)	107 (48)	116 (52)	.023	—
40–59 years	98 (30.5)	64 (65.3)	34 (34.7)
Educational status, n (%)
Precollege school	12 (3.7)	5 (2.9)	7 (4.6)	NS	—
Undergraduate	277 (86.3)	150 (86.7)	125 (82.2)
Postgraduate	32 (10)	18 (10.4)	20 (13.2)
Employment status, n (%)
Employed	282 (87.9)	161 (94.2)	121(80.7)	.004	—
Unemployed	39 (12.1)	10 (5.8)	29 (19.3)
hs-CRP (mg/l)	1.71 (48.0)	1.75 (12.45)	1.23 (12.01)	NS	NS
MDA (nmol/µl)	0.73 (4.35)	0.77 (4.28)	0.66 (4.10)	NS	NS
Total GSH (ng/µl)	1.05 (6.41)	1.15 (3.42)	0.94 (3.12)	.005	.045
BOS (absorbance at 245 nm)	0.66 (1.54)	0.65 (1.54)	0.68 (1.38)	NS	NS

Data shown are median (range) or count (%). P values are for chi-squared test and Mann–Whitney U test. Adjusted P values for sex were obtained from linear regression after adjustment for age and body mass index. BOS = basal oxidizability status, GSH = glutathione, hs-CRP = high-sensitivity C-reactive protein, MDA = malondialdehyde, NS = not significant.

General descriptions of the 4 study biomarkers are given in Table 1. There was a significant difference between males and females and total GSH. Men had significantly higher levels of total serum GSH than women independent of age and BMI (adjusted P = .045) (Table 1). No differences were observed between sex and hs-CRP, MDA, or BOS. There was an insignificant, weak correlation between age and biomarkers (Figure 1), except in men for hs-CRP (r = .49, P < .001) and BOS (r = .20, P = .01) (Figure 1).

Figure 1. Correlation between age and both inflammatory and oxidative stress biomarkers in men and women.

Spearman correlation coefficient rho (r) and P values are displayed for males and females. BOS = basal oxidizability status measured at absorbance 245 nm, GSH = glutathione, Hs-CRP = high-sensitivity C-reactive protein, MDA = malondialdehyde, NS = not significant.

Correlation between night sleep duration, total sleep duration in 24 hours, and nap duration and oxidative stress and inflammatory biomarkers

Serum levels of oxidative stress (MDA, total GSH, and BOS) and the inflammatory biomarker (hs-CRP) were not correlated with total day sleep, night sleep, and afternoon nap duration. Correlation coefficients were below 0.069 and statistically not significant (P > .05) (data not shown). We adjusted the association for sex, age, exercise, and BMI using linear regression (Table 2) and no significant association was found even after adjustment, with a weak effect shown by the values of the beta coefficient (Table 2).

Table 2.

Association of biomarkers with sleep duration (total sleep duration, night sleep, and afternoon nap).

	hs-CRP (mg/l)		MDA (nmol/µl)		Total GSH (ng/µl)		BOS (A245nm)
	Coefficient	P	Coefficient	P	Coefficient	P	Coefficient	P
Total 24-hour sleep	0.11 (0.21)	.16	−0.02 (0.03)	.89	−0.01 (0.03)	.91	−0.10 (0.01)	.92
Night sleep (hours)	0.38 (0.25)	.64	0.01 (0.03)	.69	−0.004 (0.04)	.99	−0.24 (0.02)	.96
Afternoon nap (minutes)	−0.10 (0.01)	.54	−0.001 (0.001)	.65	0.001 (0.001)	.48	0.001 (0.001)	.31

Dependent variable: biomarkers; independent variables: different sleep duration adjusted for age, sex (men = 1, women = 2), exercise, and body mass index. BOS (A245nm) = basal oxidizability status measured at absorbance 245 nm, GSH = glutathione, hs-CRP = high-sensitivity C-reactive protein, MDA = malondialdehyde.

Association of different sleep patterns with inflammatory and oxidative stress biomarkers

We then investigated the relation between the 3 sleep patterns with inflammatory biomarker hs-CRP and biomarkers of oxidative stress: total GSH levels, lipid peroxide MDA levels, and serum BOS. Serum levels of hs-CRP were not different between the 3 sleep patterns (P > .05) (Figure 2A). The level of MDA was significantly higher in individuals with a biphasic sleep pattern, and it was significantly different between monophasic and biphasic sleep patterns (P = .036) but not with polyphasic sleep (Figure 2B). Total GSH level showed a significant difference between monophasic and both biphasic and polyphasic sleep patterns. Individuals with monophasic sleep showed significantly higher levels of total GSH (P = .046) (Figure 2C). The BOS was significantly different across the sleep patterns. Individuals with a polyphasic sleep pattern had significantly higher BOS, followed by those with monophasic and biphasic sleep patterns (P = .023) (Figure 2D). We then performed linear regression to adjust for confounders of sex, age, exercise, and BMI (Table 3). We found sleep patterns remained a significant factor associated with BOS independent of BMI, sex, and age despite showing a minor effect with a beta coefficient of 0.08 (standard error, 0.02) (Table 3).

Figure 2. Association between sleep patterns and both inflammatory and oxidative stress biomarkers.

Dot plot showing median (middle bar) of biomarkers in individuals with monophasic (n = 78), biphasic (n = 145), and polyphasic sleep patterns (n = 98). *Post hoc P value < .05 of Kruskal–Wallis test. (A) hs-CRP is not different between the 3 sleep patterns. (B) Malondialdehyde is significantly higher in the biphasic sleep pattern (P = .036). (C) Total glutathione level is significant in the monophasic sleep pattern (P = .046). (D) BOS is significantly higher in the polyphasic sleep pattern (P = .023). BOS = basal oxidizability status, hs-CRP = high-sensitivity C-reactive protein.

Table 3.

Linear regression between sleep patterns and biomarkers of oxidative stress and inflammation.

	B Coefficient (SE)	P
hs-CRP (mg/l)	−0.45 (0.25)	.17
MDA (nmol/µl)	−0.02 (0.4)	.98
Total GSH (ng/µl)	−0.03 (0.05)	.42
BOS (A245nm)	0.08 (0.02)	<.0001 (polyphasic pattern)

Dependent variable: biomarkers; independent variables: different sleep patterns (1 monophasic [1 nocturnal sleep episode], 2 biphasic (2 episodes [1 nocturnal episode and afternoon nap], 3 polyphasic [3 or more episodes that include nocturnal sleep, after dawn sleep, and afternoon nap]), age, sex (men = 1, women = 2), exercise, and body mass index. B coefficients and P values are for sleep patterns only. BOS (A245nm) = basal oxidizability status measured at absorbance 245 nm, GSH = glutathione, hs-CRP = high-sensitivity C-reactive protein, MDA = malondialdehyde, SE = standard error.

DISCUSSION

The current cross-sectional study aimed to assess the level of inflammatory and oxidative stress biomarkers in individuals with different sleep patterns among the local population in Oman using actigraphy. Based on previous studies, segmented sleep patterns including biphasic and polyphasic patterns were predominant among the local and other communities.^1,22,23 The latter segmented sleep patterns were associated with short night sleep duration, long afternoon napping,^1,22,23 and increased levels of glycated hemoglobin and obesity.²⁴

The study indicated that male sex and age have significant correlation with hs-CRP and BOS. However, it also indicated that segmented sleep patterns were significantly associated with higher levels of serum oxidative stress biomarkers. A polyphasic sleep pattern was significantly associated with BOS and a biphasic sleep pattern showed significantly higher serum MDA. Levels of serum total GSH antioxidant were significant in those with a monophasic sleep pattern but lower in those with a segmented sleep pattern. No differences were observed in serum levels of the inflammatory biomarker hs-CRP between various sleep patterns.

Our findings support the hypothesis that segmented sleep patterns act as a stress factor that can modulate the oxidative stress environment in the body, promoting the production of biomarkers in the blood,¹¹ and that short night sleep and subsequent segmented sleep promote oxidation and slows down sleep’s antioxidative function, which collectively results in an increase in serum oxidizability.^25–27 Sleep deprivation activates the autonomic nervous system and raises the levels of catecholamines, leading to the production of inflammatory mediators and adhesion molecules,^28,29 which may result in greater endothelial shear stress.^30–32

Sleep patterns and reactive oxygen species

Our study results showed a significant association between segmented sleep and reactive oxygen species in participants with a polyphasic sleep pattern in particular but not in those with a monophasic sleep pattern. This could be attributed to discontinuation of sleep in segmented polyphasic sleep, which may result in greater endothelial shear stress.³¹ Several studies are in line with our results; for example, a population-based study consisting of 2,570 men aged 42–60 years from eastern Finland found that poor sleep quality was associated with elevated serum levels of sulfur–copper as an oxidative stress inducer, which may contribute to sleep regulation through prooxidative processes.³³ Similar results were found by another study that suggested differential up-regulation of oxidative stress and pathways of inflammation in acute vs chronic sleep curtailment.³⁴ Accumulation of reactive oxygen species has several harmful implications, including lipid peroxidation, protein oxidation, nucleic acid damage, enzyme inhibition, and cell toxicity and death.²⁶

Sleep patterns and MDA

Additionally, the current study revealed a significant increase of MDA levels and reduced total GSH among adults with a segmented biphasic sleep pattern. This might be related to the long afternoon naps and short night sleep duration practiced by the local people. Various studies found relatively similar results. For instance, a recent study in China found that sleep deprivation for 1 night among 20 healthy male physicians resulted in an increase in MDA levels and a decrease in GSH levels using biochemical assays before and after sleep deprivation.³⁵ Another study aimed to analyze the effect of sleep deprivation on lipid peroxidation. It specified that free radical–induced damage is caused in the blood as a result of rapid eye movement sleep deprivation, which can be shown by the estimation of MDA.³⁶ Moreover, a study that aimed to explore the biochemical, hematological, and histological effects of sleep deprivation found that MDA levels were elevated and GSH levels (an antioxidant) were reduced in blood plasma upon sleep deprivation. This suggests that changes in sleep behavior affect oxidative stress in the body.³⁷ Increased MDA levels correlate to higher lipid peroxidation induced by stress, including sleep disturbance.^36,37 Alterations in sleep patterns have been reported to affect the body’s repair and recovery functions, leading to release of MDA, which can lead to disease susceptibility or behavior.^35–37 The previous studies are in agreement with our findings in terms of the significant association between segmented sleep patterns and the elevated MDA but not with reduced total GSH levels in the blood.

Sleep patterns and GSH

In relation to the total GSH, an antioxidant that is an important biomarker to evaluate oxidative stress status,³⁸ a study reported that GSH was not found to be associated with the sleep midpoint that serves as an indicator of melatonin onset in young adults (1:54 am to 7:05 am).³⁹ This finding partially parallels our results related to a lower level of total GSH among those with segmented sleep patterns. This could be attributed to the normal range of sleep duration in this study, which could compensate the effect of segmented sleep in a biphasic sleep pattern. This suggests that the segmented sleep patterns might induce internal stress that stimulates the body to be prone to lipid oxidation with lesser antioxidant release of total GSH.³⁹ However, the monophasic sleep pattern in the current study was associated with higher GSH level in the blood. This might indicate that sleeping once per day at night would cause less internal stress.

Sleep patterns and hs-CRP

Our investigation showed no difference in hs-CRP levels among those with different sleep patterns, perhaps due to the normal average of sleep duration among this cohort. The median for night sleep duration for all sleep patterns per week was 7 hours; however, it was 7.9 hours for total sleep duration in 24 hours and 53 minutes for the afternoon nap. The previous medians were within the normal cutoff points selected for this study, which might have contributed to the insignificant association between sleep patterns and the selected biomarkers. Furthermore, hs-CRP is a variable biomarker and might have contributed to the nonsignificant difference among the identified sleep patterns.³⁰ In addition, the majority of the study participants were healthy young adults who were at lower risk of developing sleep and cardiometabolic disorders. This means that people might adapt to any sleep pattern as long as they have a consolidated night sleep duration within the normal range of 7–9 hours.

In this context, some studies support our findings. For instance, a study involving 907 adults in the Wisconsin Sleep Cohort study found no association between CRP and a total sleep duration of 6.3 hours based on polysomnography.⁴⁰ Similar results were found in a population of 991 Japanese workers based on the Pittsburgh Sleep Quality Index, which indicated that unfavorable sleep is associated with activation of low-grade systemic inflammation.⁴¹

In contrast, our results contradict other studies that reported a significant association between short or long sleep duration and inflammatory and oxidative stress biomarkers.^14–16,28 This contradiction could be due to age differences compared to the current study; those studies included both sexes with wider age ranges (18–59 years). Moreover, the previous studies used questionnaire-based surveys, which are subject to recall bias,²² unlike our study, which measured the sleep patterns objectively using actigraphy, which is considered a strength of the study.

CONCLUSIONS

In conclusion, this study indicates a significant association between segmented sleep patterns, polyphasic and biphasic in particular, with the oxidative stress biomarkers BOS and MDA. However, BOS was significantly higher in the polyphasic sleep pattern characterized by multiple sleep episodes and associated with short night sleep duration and long afternoon naps compared to the biphasic and monophasic patterns.

Strengths of this study include the assessment of sleep parameters using actigraphy. Assessing sleep duration and timing with actigraphy provided a more objective estimate than self-reports, which have modest (57%) agreement with actual sleep patterns.²² Furthermore, actigraphy allowed the measurement of sleep patterns over an extended period of time outside a sleep laboratory for 1 week in the natural environment. Additionally, using validated inflammation and oxidative stress biomarkers is another strength of this study.

Limitations

Although extreme efforts were made to maintain maximum accuracy and precision, this study was subject to some limitations. The cross-sectional nature of this study design indicates association only and does not allow cause–effect conclusions, which hampers its generalizability. Other confounding factors were also not fully explored in this study and include, food or nutritional habits, smoking, objective exercise measurement, and work or family stressors. The majority of participants included in the study were young adults 18–39 years of age. This inequality in the age group distribution could have affected the findings and limited the study to a certain age group that would not reflect the definite characteristics of those middle-aged or older. Despite the actigraphy objectivity and accuracy in sleep–wake cycle assessment, there are still difficulties in defining total sleep duration in 24 hours and alignment with circadian rhythm.

DISCLOSURE STATEMENT

All authors read and approved the final manuscript. This study was funded by grant number RC/MED/PHYS/15/01 from the Research Council of Oman. The authors report no conflicts of interest.

ABBREVIATIONS

BMI

body mass index

BOS

basal oxidizability status

CRP

C-reactive protein

GSH

total glutathione

Hs-CRP

high-sensitivity C-reactive protein

MDA

malondialdehyde