Metabolic syndrome (MetS) represents a constellation of interrelated cardiometabolic risk factors, including central obesity, dyslipidemia, hypertension, and hyperglycemia, that substantially increase the risk of cardiovascular disease, type 2 diabetes mellitus, and mortality [1]. The global prevalence of metabolic syndrome has risen dramatically over recent decades, with estimates suggesting that approximately one-quarter of the world's adult population is affected [2]. This escalating burden has significant implications for public health systems and individual quality of life.

The modern workplace has undergone substantial transformations, with sedentary office work becoming increasingly predominant across diverse industries. Office employees typically engage in prolonged sitting, minimal physical activity during work hours, and exposure to various psychosocial stressors [3]. These occupational characteristics create conditions potentially conducive to metabolic dysfunction and may contribute to the elevated cardiometabolic risk observed in white-collar workers.

Sleep duration has emerged as a significant modifiable determinant of metabolic health. Epidemiological investigations have consistently demonstrated associations between both short and long sleep duration and adverse cardiometabolic outcomes, including obesity, diabetes, and metabolic syndrome [4]. Contemporary work demands, including extended hours, shift requirements, and technology-related intrusions into personal time, may compromise sleep quantity and quality among office workers [5]. The biological mechanisms linking sleep deprivation to metabolic dysfunction involve alterations in appetite-regulating hormones, glucose metabolism, inflammation, and autonomic function.

Occupational stress represents another potentially modifiable risk factor for metabolic syndrome. The job demand-control model, proposed by Karasek, posits that high psychological demands combined with low decision latitude produces "job strain," a particularly harmful form of work stress [6]. Chronic exposure to occupational stress activates the hypothalamic-pituitary-adrenal axis and sympathetic nervous system, promoting visceral adiposity, insulin resistance, and dyslipidemia through neuroendocrine pathways [7].

While sleep duration and occupational stress have been independently associated with metabolic syndrome, their potential interactive effects remain incompletely understood. Stress and sleep are biologically interconnected, with occupational stress potentially disrupting sleep patterns while sleep deprivation may impair stress coping capacity [8]. Sedentary office employees may be particularly susceptible to these interacting exposures due to the nature of their work environment.

Despite growing recognition of workplace factors in cardiometabolic health, significant research gaps persist. Most investigations have examined sleep duration or occupational stress in isolation, and studies specifically focusing on sedentary office workers are limited. Furthermore, potential synergistic effects between inadequate sleep and work stress on metabolic outcomes have received insufficient attention.

Therefore, this study aimed to investigate the associations between sleep duration, occupational stress, and metabolic syndrome among sedentary office employees, and to examine potential interactive effects of these exposures on metabolic syndrome risk.

Study Design and Setting

This cross-sectional study was conducted at tertiary care centre.

Sample Size Calculation

Sample size was determined using G*Power software version 3.1.9.7. Based on anticipated odds ratio of 2.0 for the association between short sleep duration and metabolic syndrome from published literature, with α=0.05, power=0.85, and expected metabolic syndrome prevalence of 25%, a minimum of 380 participants was required. Accounting for incomplete data (10%), target enrollment was established at 420 participants.

Participant Selection

Employees were recruited through workplace health screening programs and informational sessions. Inclusion criteria comprised: (1) age 25-55 years; (2) full-time employment (≥35 hours/week) in sedentary office position for minimum 2 years; (3) primarily desk-based work (≥6 hours/day sitting); and (4) willingness to undergo clinical and laboratory assessments.

Exclusion criteria included: (1) diagnosed sleep disorders (obstructive sleep apnea, insomnia requiring medication, narcolepsy); (2) shift work or rotating schedules; (3) current use of medications affecting metabolic parameters (antidiabetics, lipid-lowering agents, antihypertensives) unless these were outcomes of interest; (4) pregnancy or lactation; (5) known endocrine disorders (thyroid disease, Cushing syndrome); (6) chronic inflammatory conditions; (7) psychiatric disorders requiring medication; and (8) recent major illness or hospitalization within 3 months.

Sleep Duration Assessment

Sleep duration was assessed using a standardized sleep questionnaire incorporating items from the Pittsburgh Sleep Quality Index. Participants reported typical weekday and weekend sleep durations, sleep onset latency, wake after sleep onset, and sleep quality over the preceding month. Weighted average sleep duration was calculated: [(weekday duration × 5) + (weekend duration × 2)] / 7. Participants were categorized as short sleepers (<6 hours/night), normal sleepers (6-8 hours/night), or long sleepers (>8 hours/night).

Occupational Stress Assessment

Occupational stress was evaluated using the Job Content Questionnaire (JCQ), a validated instrument based on the demand-control model. The questionnaire comprises subscales for psychological job demands (5 items), decision latitude (9 items), and social support (8 items). Job strain was calculated as the ratio of demands to decision latitude, with high job strain defined as ratio >1.0. Additionally, participants were categorized into four job strain quadrants: low strain (low demands/high control), passive (low demands/low control), active (high demands/high control), and high strain (high demands/low control).

Anthropometric and Clinical Measurements

Height was measured without shoes using a wall-mounted stadiometer (nearest 0.1 cm). Weight was measured in light clothing using a calibrated digital scale (nearest 0.1 kg). Body mass index (BMI) was calculated as weight/height² (kg/m²). Waist circumference was measured at the midpoint between the lowest rib and iliac crest at end-expiration using a non-stretchable tape (nearest 0.1 cm).

Blood pressure was measured using an automated oscillometric device (Omron HBP-1300, Japan) after 10 minutes seated rest. Three readings were obtained at 2-minute intervals, and the average of the second and third readings was recorded.

Laboratory Assessments

Venous blood samples (10 mL) were collected following overnight fasting (minimum 10 hours). Samples were processed and analyzed at a certified clinical laboratory. Fasting plasma glucose was measured using the hexokinase method. Lipid profile including total cholesterol, triglycerides, high-density lipoprotein cholesterol (HDL-C), and low-density lipoprotein cholesterol (LDL-C) was determined using enzymatic colorimetric methods.

Metabolic Syndrome Definition

Metabolic syndrome was defined according to the harmonized Joint Interim Statement criteria, requiring presence of at least three of five components: (1) elevated waist circumference (≥90 cm males, ≥80 cm females for Asian populations); (2) elevated triglycerides (≥150 mg/dL or drug treatment); (3) reduced HDL-C (<40 mg/dL males, <50 mg/dL females or drug treatment); (4) elevated blood pressure (systolic ≥130 mmHg or diastolic ≥85 mmHg or drug treatment); and (5) elevated fasting glucose (≥100 mg/dL or drug treatment).

Covariates

Demographic variables (age, sex, education, marital status, income), lifestyle factors (physical activity, smoking status, alcohol consumption), and family history of metabolic diseases were recorded using structured questionnaires. Physical activity was assessed using the International Physical Activity Questionnaire short form.

Statistical Analysis

Statistical analyses were performed using IBM SPSS version 28.0. Continuous variables were expressed as mean ± standard deviation, and categorical variables as frequencies and percentages. Comparisons between groups utilized independent samples t-tests, one-way ANOVA, or chi-square tests as appropriate.

• Binary logistic regression was performed to identify factors associated with metabolic syndrome. Unadjusted odds ratios were calculated initially, followed by multivariate models adjusting for age, sex, BMI, physical activity, smoking, alcohol consumption, and family history. Interaction between sleep duration and occupational stress was tested by including a product term in regression models. Stratified analyses examined associations within subgroups. Statistical significance was defined as p<0.05 (two-tailed).

Participant Characteristics

A total of 456 employees were screened, with 428 meeting eligibility criteria and completing all assessments. Demographic and clinical characteristics are presented in Table 1.

Table 1. Demographic and Clinical Characteristics of Study Participants

Data presented as mean ± SD or n (%). MetS: metabolic syndrome; BP: blood pressure; HDL-C: high-density lipoprotein cholesterol.

Mean age was 38.6 ± 9.2 years, with 54.2% males. Metabolic syndrome prevalence was 28.7% (n=123). Participants with MetS were significantly older, more likely male, had higher BMI, and lower physical activity levels. Short sleep duration (<6 hours) was present in 22.0%, and high job strain in 38.3%.

Metabolic Syndrome Prevalence by Sleep Duration and Stress Categories

Metabolic syndrome prevalence stratified by sleep duration and occupational stress categories is presented in Table 2.

Table 2. Metabolic Syndrome Prevalence by Sleep Duration and Occupational Stress Categories

Data presented as n (%) or mean ± SD. MetS: metabolic syndrome.

Short sleepers demonstrated significantly higher MetS prevalence (42.6%) compared to normal sleepers (22.4%; p<0.001). Long sleepers also showed elevated prevalence (39.6%), suggesting a U-shaped relationship. High job strain was associated with MetS prevalence of 38.4% compared to 18.6% in low strain category (p<0.001).

Combined exposure to short sleep and high stress yielded the highest MetS prevalence (52.4%), substantially exceeding that observed with either exposure alone. The mean number of MetS components demonstrated similar patterns across categories.

Multivariate Regression Analysis

Logistic regression analysis for metabolic syndrome is presented in Table 3.

Table 3. Logistic Regression Analysis for Metabolic Syndrome

Adjusted model includes age, sex, BMI, physical activity, smoking, alcohol, and family history. OR: odds ratio; CI: confidence interval.

In multivariate analysis, short sleep duration (OR=2.48, 95% CI: 1.56-3.94, p<0.001) and high job strain (OR=2.12, 95% CI: 1.38-3.26, p<0.001) were independently associated with metabolic syndrome after adjusting for demographic and lifestyle factors. Long sleep duration also demonstrated increased risk (OR=1.94, p=0.026). Significant interaction was observed between short sleep and high job strain (OR for interaction=1.86, p=0.018), indicating synergistic effects exceeding the sum of individual contributions.

Additional independent predictors included age, male sex, BMI, physical inactivity, and family history of metabolic diseases.

This cross-sectional study demonstrates significant independent associations between short sleep duration, high occupational stress, and metabolic syndrome among sedentary office employees. Furthermore, we observed synergistic interaction between inadequate sleep and work stress, with combined exposure conferring substantially elevated metabolic syndrome risk. These findings highlight the importance of addressing both sleep and stress factors in workplace health promotion programs.

The metabolic syndrome prevalence of 28.7% in our sedentary office worker cohort is concerning and consistent with studies documenting elevated cardiometabolic risk in white-collar populations. Sedentary work patterns, characterized by prolonged sitting and minimal physical activity, have been independently associated with metabolic dysfunction [9]. Our observation that physical inactivity independently predicted metabolic syndrome supports the importance of addressing sedentary behavior in occupational health interventions.

The association between short sleep duration and metabolic syndrome aligns with a substantial body of epidemiological evidence. Meta-analyses have consistently demonstrated that both short and long sleep duration are associated with increased metabolic syndrome risk, with optimal duration typically falling within the 7-8 hour range [10]. Our findings confirm this U-shaped relationship, with both short (<6 hours) and long (>8 hours) sleep associated with elevated risk.

Biological mechanisms linking sleep deprivation to metabolic dysfunction are increasingly well characterized. Experimental sleep restriction studies demonstrate alterations in glucose tolerance, insulin sensitivity, and appetite-regulating hormones including leptin and ghrelin [11]. Additionally, sleep deprivation activates inflammatory pathways and the hypothalamic-pituitary-adrenal axis, promoting visceral adiposity and cardiovascular risk [12]. These mechanisms likely operate chronically in habitually short sleepers.

The strong association between high job strain and metabolic syndrome supports the relevance of the demand-control model for cardiometabolic health. Chandola and colleagues demonstrated that chronic work stress was associated with metabolic syndrome in the Whitehall II cohort, with effects partially mediated through health behaviors and neuroendocrine pathways [13]. Our observation that high strain (high demands combined with low control) conferred the greatest risk aligns with this theoretical framework.

The synergistic interaction between short sleep and high job strain represents a novel contribution of this study. The combined exposure yielded metabolic syndrome prevalence exceeding 50%, substantially greater than either factor alone. This interaction may reflect bidirectional relationships between stress and sleep, with occupational stress disrupting sleep quality and duration while sleep deprivation impairs stress coping capacity and amplifies physiological stress responses [14]. From a public health perspective, this interaction suggests that interventions addressing both domains simultaneously may yield greater benefits than targeting either in isolation.

The high prevalence of both short sleep (22.0%) and high job strain (38.3%) in our cohort underscores the magnitude of these exposures in contemporary office work environments. Technology-enabled constant connectivity, competitive work cultures, and organizational pressures may contribute to both inadequate sleep and elevated stress among office workers [15]. Workplace policies addressing work-life boundaries and reasonable demands may benefit metabolic health.

The clinical implications of our findings are substantial. Occupational health programs should incorporate screening for sleep duration and occupational stress as components of cardiometabolic risk assessment. Employees demonstrating inadequate sleep and high stress represent a particularly high-risk subgroup warranting targeted intervention. Evidence-based approaches including sleep hygiene education, stress management training, and organizational modifications may reduce metabolic syndrome risk [16].

Several limitations warrant acknowledgment. The cross-sectional design precludes causal inference regarding temporal relationships between exposures and metabolic syndrome. Self-reported sleep duration may be subject to recall bias; objective assessment using actigraphy would strengthen future investigations. Additionally, participants with diagnosed sleep disorders requiring medication were excluded, potentially limiting generalizability. Finally, the urban corporate setting may not reflect conditions in other work environments.

This study demonstrates that both short sleep duration and high occupational stress are independently associated with metabolic syndrome among sedentary office employees, with significant synergistic interaction when both exposures are present. The prevalence of inadequate sleep and work stress in this population, combined with their potent effects on metabolic health, represents a substantial public health concern. These findings emphasize the importance of comprehensive workplace health promotion programs that address sleep hygiene and stress management alongside traditional targets such as physical activity and nutrition. Employers and occupational health professionals should recognize the interconnected nature of sleep, stress, and metabolic health, implementing policies and interventions that support adequate sleep opportunity and reduce excessive job demands. Future longitudinal research should examine whether improvements in sleep duration and reductions in occupational stress translate to decreased metabolic syndrome incidence in working populations.

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