Short Sleep Duration and Its Association with Obesity and Other Metabolic Risk Factors in Kuwaiti Urban Adults

Abstract

Background: Efficient sleep duration and its quality are increasingly recognized as important contributors for maintaining normal body weight. However, lifestyle and social structure within the Arab-gulf region differ compared to those in the western world. This study was specifically conducted in Kuwait's population to investigate the link between sleep quality (SQ) and obesity in the absence of sleep apnea (SA) onset.

Methods: SQ was measured by the Pittsburgh Sleep Quality Index (PQSI) in 984 participants, then verified in 60 individuals including 20 lean (Body mass index/BMI: 18.5-24.9 kg/m²), 20 overweight (BMI: 25-29.9 kg/m²) and 20 obese (BMI: ≥30 kg/m²) through actigraph worn over the right-hip for 7 consecutive days to characterize their sleep-wake cycle, rest-activity, and physical activity. Blood samples were collected for metabolic markers.

Results: 59.6% of participants reported a PSQI score higher than 5, with 57.6% of the participants reporting less than 6 hours of sleep per day. The data show that both SQ and sleep duration are considered inadequate in comparison to the international SQ standards. We found a significant association between SQ and obesity independent of age and sex. Actigraph data further supported the independent association of sleep duration on BMI within the population (p < 0.001). Additionally, total sleep time (TST) was found to significantly correlate with several other metabolic factors including diastolic blood pressure, elevated resting heart rate (RHR), triglycerides, total cholesterol, homeostatic model assessment for insulin resistance (HOMA-IR), C-peptide, and C-Reactive Protein (CRP) secretion. Further multiple-regression analysis showed a significant independent association between blood pressure (p < 0.03), HOMA-IR (p < 0.04), and C-peptide (p < 0.3) and sleep duration.

Conclusion: These findings suggest that sleep deprivation and disturbance could be indirect factors involved in the development of not only obesity in Kuwait but also other metabolic syndromes such as type 2 diabetes.

Keywords: Kuwait; PSQI; obesity; sleep.

Figure 1

Effect of personal risk factors on sleep quality. A total of 984 individuals completed the self-reported questionnaires to help investigate the influence of personal risk factors on sleep quality, as assessed by the PSQI score across: (A) BMI groups defined as: lean (BMI < 25), overweight (BMI = 25–29.9) and obese (BMI ≥ 30); (B) Age groups; (C) Sex/gender; and (D) Job type (desk job/ non-desk job). All data are expressed as the mean ± SD. Statistical analysis was performed using one-way ANOVA (Tukey’s multiple comparisons test). Pearson’s correlation analysis was conducted between the global PSQI score and (E) BMI (kg/m²) or (F) Age (years). Each dot represents an individual value. **P < 0.01 was considered highly significant, and ****P< 0.0001 were considered extremely significant, ns was indicated as non-significant.

Figure 2

Effect of obesity on sleep duration. Participants were divided into three groups as: lean (BMI < 25), overweight (BMI = 25–29.9) and obese (BMI ≥ 30). (A) Self-reported sleep duration was compared between all three groups. (B) Pearson’s correlation analysis was conducted between self-reported sleep duration and the BMI of each individual. All data are expressed as the mean ± SD. Statistical analysis was performed using one-way ANOVA (Tukey’s multiple comparisons test). **P < 0.01 was considered highly significant, and ****P < 0.0001 were considered extremely significant.

Figure 3

Objectively measured sleep analysis and its influence on BMI. A total of 60 participants had their sleep pattern monitored for 7 consecutive days through the use of Actigraph worn on the right hip. Participants were divided into three groups according to their BMI levels as before and their sleep efficiency and its components were compared in three BMI groups (lean, overweight, and obese). (A) total efficiency of sleep, (B) total time in bed, (C) total sleep time, (D) wake after sleep onset, (E) average number of awakening and (F) Average awakening duration. All data are expressed as the mean ± SD. Statistical analysis was performed using one-way ANOVA (Tukey’s multiple comparisons test). *P < 0.05 was considered statistically significant, **P < 0.01 was considered highly significant, and ***/****P < 0.001/P < 0.0001 were considered extremely significant.

Figure 4

Correlation between sleep components and BMI. A total of 60 participants had their sleep pattern monitored for 7 consecutive days using Actigraphy. Participants were divided into three groups according to their BMI levels as lean (BMI ˂ 25 kg/m²), overweight (BMI = 25–29.9 kg/m²), and those with obesity (BMI ≥ 30 kg/m²), 20 each. Pearson’s correlation analysis was conducted between (A) efficiency of sleep, (B) total sleep time, (C) wake after sleep onset, and (D) number of awakening per night and the BMI of each individual. All data are expressed as the mean ± SD. Statistical analysis was performed using one-way ANOVA (Tukey’s multiple comparisons test).

Figure 5

Correlation between sleep components and metabolic syndrome risk factors. A total of 60 participants had their sleep pattern monitored for 7 consecutive days using Actigraphy. Pearson’s correlation analysis is presented as heatmap showing the correlations of total sleep time (TST) and wake after sleep onset (WASO) with the known risk factors for metabolic syndrome. Darker shading indicates a greater degree of correlation, and correlations with p-values > 0.05 are displayed in white.