Elevated cardiometabolic risk markers associated with shift work and evening chronotype

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Wanigasinghe, Dilki S. Perera, Kumari M. Rathnayake This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5826797/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background : Shift work-induced circadian disruption has been strongly linked to various cardiometabolic diseases including obesity, diabetes & cardiovascular disease. Limited studies have explored the impact of different variables such as night work durations, intensities and chronotype on cardiometabolic risk. Methods : This study aimed to determine the impact of circadian disruption on cardiometabolic risk markers in shift workers. This case-control study was conducted with 104 male workers (shift workers; n=52, mean age ±SD; 43.3±10.2 and non-shift workers; n=52, mean age ±SD; 41.2±9.8). Shift work status, durations and intensity of night shifts were determined via an interviewer administered questionnaire. Cardiometabolic risk was evaluated through anthropometric (height, weight, waist circumference and body composition), biochemical (fasting glucose and lipid profile), clinical (blood pressure) and dietary assessment (24-hr recalls from normal days and from work days). The chronotype was determined via the Munich Chronotype Questionnaire (MCTQ). Results: Shift-workers had significantly higher mean body fat percentage (31.7, 22.7% p=0.031), systolic blood pressure (138.6, 128.5 mmHg p=0.009), pulse rate (78.7, 72.3 bpm p=0.015), triglycerides (1.60, 1.30mmol/l p=0.021) and LDL-C (3.90, 3.40 mmol/l p=0.012) than non-shift workers. Evening chronotype shift workers had significantly higher visceral fat levels (12.8, 8.90 p=0.001), systolic blood pressure (137.0, 127.6 mmHg p=0.006), pulse rate (82.7, 73.3 bpm p=0.005) and LDL-C (4.00,3.40 mmol/l p=0.039) than shift workers with a morning chronotype. Conclusion: The number of working hours and the duration of current shift work were associated with cardiometabolic risk markers and the evening chronotype was significantly associated with cardiometabolic risk markers. Further research is warranted to elucidate the underlying mechanisms and inform targeted interventions for individuals engaged in shift work, considering chronotypes. Nutrition & Dietetics Cardiometabolic risk factors chronobiology chrono-nutrition chronotype circadian misalignment Introduction Circadian rhythms play crucial roles in governing the physiological & behavioral functions of the human body. Numerous observational studies have established a connection between circadian disruption & the onset of cardiometabolic diseases [ 1 , 2 ]. Circadian disruption can result from various lifestyle & environmental factors. Multiple factors, including shift work, late chronotype, late sleep timing, sleep irregularity, and late meal timing, have been identified as disruptors of circadian rhythm alignment [ 3 ]. These factors are associated with potential adverse effects on cardiometabolic health, such as increased BMI/obesity, increased blood pressure, increased dyslipidemia, inflammation, and diabetes [ 1 ]. Shift work is a risk factor for conditions like overweight, obesity, Type 2 diabetes, increased blood pressure, and metabolic syndrome. It also influences eating behavior, food choices, energy intake, and macronutrient consumption [ 2 ]. Night-shift work, in particular, not only leads to a misalignment between the body's internal circadian system and the external light-dark cycle but also induces internal desynchronization among various levels of the circadian system and disrupts the expression of clock genes in various tissues. Metabolomics studies revealed shifts in metabolite timing during night work, further misaligning with the circadian system [ 4 ]. Chronotype has been shown to play a role in the effect of shift work on health [ 5 ]. To date, multiple studies have reported that morning types may be less able to adapt to shift work than evening types [ 6 , 5 , 7 ]. The chronotype is hypothesized to influence the relationship linking shift-induced circadian disruption to cardiometabolic outcomes [ 8 , 9 ]. It was observed that individuals with an evening chronotype presented elevated levels of proteins previously associated with cardiometabolic risk [ 10 ]. Few studies have explored this connection, and the findings are inconclusive. Some studies propose that both morning-oriented and evening-oriented chronotypes could contribute to the risk of cardiometabolic outcomes. [ 11 , 12 ]. However, research into the role of chronotype in the effect of shift work on metabolic risk factors is still lacking [ 13 ]. The influence of circadian misalignment on cardiometabolic health has been extensively studied, but there exists a notable gap in understanding how these dynamics manifest among adults specially security officers, particularly those engaged in night shift work. Therefore, the aim of our present study was to determine the impact of the chrono effect on cardiometabolic risk markers in shift workers using night work parameters and chronotype as key variables. This case control study would be beneficial in designing effective interventions to prevent cardiometabolic diseases among shift workers. Methods Study design and participants For this study, participants were recruited from an export processing zone in Sri Lanka. The inclusion criterion was healthy male individuals aged between 40–60 years working full time on rotating shifts. Non-shift workers, comprising regular day-time workers, served as the reference group. Recruitment strategies involve various approaches such as posters, telephone calls, messages, and notices, that target workers from fire and security units. Shift workers were defined as those engaging in rotating shifts, alternating between day shifts (mainly between 07:30 am–4:00 pm), night shifts (mainly between 11:00 pm–7:45 am), and/or night shifts from 06:00 am–6:00 am or 4:00 pm – 6:00 am. Conversely, non-shift workers were individuals not participating in rotating and night shifts in their work life. The exclusion criteria included females, individuals using statins, antihypertensives, or treatments for diabetes, those with higher smoking and alcoholic status, individuals with psychological disorders, and participants who had lost their legs. The presentation outlined the study's objectives and extended invitations for participation without offering financial incentives. The participants had access to group nutrition education on the basis of the study's findings. Before the study commenced, participants signed an informed consent form outlining objectives, methodology, and the intent to publish results. Among the 123 initially recruited participants, 5 were excluded because of the use of antihypertensives and treatments for diabetes, 11 were excluded because they were females, 2 were excluded because they were lost to follow-up, and 1 was excluded he had the highest alcoholic status. The main analysis included a total of 104 participants (52 shift workers & 52 non-shift workers). This case-control study received approval from the Ethics Review Committee of the Faculty of Livestock, Fisheries & Nutrition, Wayamba University of Sri Lanka. Sample size Sample size was calculated to detect a difference in cardiometabolic risk markers between shift and non-shift workers. A medium effect size was assumed (Cohen's d = 0.5), based on previous studies [ 11 ]. The power calculation through G*Power indicated that, a minimum of 52 participants in each group was required for 80% power level and a significance level of α = 0.05. The study aimed to have a total sample size of 104 participants, with an allocation of 52 individuals in each group, based on an anticipated response rate of 75%. This calculation ensures adequate statistical power to detect accurate significant differences in cardiometabolic risk markers between the two groups. Data collection Shift work Information about participants' current shift work status (including work schedules and types of shifts), the frequency of (night) shifts (number of each shift type per month), and the duration of (night) shift work, as well as the total number of years of (night) shift work, was collected during the baseline and follow-up assessments. An interviewer administered questionnaire was used to collect information from the workers. The interviews were conducted in person. The questionnaire comprehensively covered all major aspects of the shift work. Anthropometrics Height was measured using a stadiometer to the nearest 0.5 cm. While participants were wearing light clothing, weight and BMI were determined by using the multi-frequency segmental body composition analyser Tanita MC-780 (Tanita, Japan) with the following settings: standard body type and − 1 kg for clothing. Waist & hip circumferences were measured using a measuring tape following a standardized protocol that provided detailed instructions for accurate measurement. All measurements were taken prior to the start of the work shift, with participants in light clothing and no shoes. If there was a difference of more than one unit (in centimeters/kilograms) between the first two measurements, a third measurement was taken. The average of the three measurements was subsequently calculated. Body mass index (BMI) was calculated as the ratio of weight (in kg) to the square of height (in m 2 ). Cardiometabolic risk markers Blood samples (3.5 mL) were collected into serum separator tubes (BD vacutainer, UK) from participants following a 12-hour overnight fast (before the commencement of a day shift for both day and night workers) and left at room temperature for 30 min until centrifugation at 1700 × g for 15 min at 20°C to obtain serum which was stored at − 20°C until analysis. These serum samples were used to determine lipids [total cholesterol (TC), HDL cholesterol (HDL-C), and triglycerides (TAG)], and glucose, with the use of an automated biochemistry analyzer (Thermo Scientific, Indiko, Finland) with reagents from Thermo Scientific (Thermo Scientific, Finland). Fasting LDL cholesterol (LDL-C) was estimated by using the Friedewald formula [ 14 ]. Blood pressure was measured in triplicate by using an OMRON M6 automatic digital blood pressure monitor. A trained research student performed these measurements, ensuring that each measurement was taken at least 5 minutes apart, and the average of the three readings was used in the data analysis. Dietary assessment Dietary intake was assessed using a single 24-hour dietary recall (representing both normal days and work days). Participants were provided with a common guidance during group sessions at their workplace on how to provide dietary information. This guidance included instructions on measuring food quantities (using glasses, cups, spoons), specifying food preparation details (such as recipes, commercial brands, and restaurant names), and recording meal starting times on research forms. Researchers estimated the amounts of food and beverages consumed based on the information provided by participants in household measurements, aided by photographs of common household utensils. Participants were questioned by researchers trained in food measurement units about their eating habits. All meal and snack intakes were reported along with their respective intake times. A trained dietitian reviewed the forms to ensure completeness and consistency. Chronotype The assessment of chronotype was conducted via the Munich Chronotype Questionnaire (MCTQ) which is a validated tool for assessing individual chronotypes, which reflect preferences in circadian rhythms (Ref). The chronotype was determined on the basis of participants’ reported mid-sleep time (MSF) on free days. In the questionnaire, the participants provided information regarding the time they spent before sleeping (sleep onset) and the time they woke up. Sleep duration (SD) was calculated as the difference between the reported sleep onset and awakening times. The MSF was computed via the following formula: 𝑀𝑆𝐹 = 𝑆𝑂 – 𝑆𝐷/ 2 A higher MSF indicates an evening-oriented chronotype, whereas a lower MSF indicates a morning-oriented chronotype. Individuals who reported using an alarm clock on free days were excluded from the exploratory analysis, as their MSF values may be influenced by external factors and may not accurately reflect their internal circadian rhythm [ 15 ]. Statistical analysis The statistical analysis aimed to explore associations, trends, and differences in the collected data, employing various tests and models to assess the study's objectives. All analyses were performed via SPSS version 26.0. Descriptive statistics were calculated to present the basic features of the study variables. The means and standard deviations were reported for continuous variables, whereas frequencies and percentages were reported for categorical variables. Independent t-tests were used to compare demographic characteristics between shift workers and non-shift workers. Multiple linear regression analyses, adjusting for potential confounding factors such as age and family history of cardiovascular disease, were conducted to investigate associations between night work parameters and various cardiometabolic risk factors. Night work parameters included current work status, years of current shift work, hours duration of current shift work, and intensity of night work. Multiple linear regression analyses were also performed to examine cardiometabolic risk factors among current non-shift workers and shift workers, stratified by chronotype (morning-oriented and evening-oriented). The significance level for all analyses was set at p < 0.05. Results Characteristics of the study population The study included a total of 104 participants, shift workers (n = 52) and non-shift workers (n = 52). Table 1 elucidates distinct differences in demographic characteristics, physiological measures, and dietary intake between shift workers and non-shift workers. Compared with non-shift workers, shift workers presented a mean age of 43.3 years (± SD 10.2), whereas non-shift workers presented a mean age of 41.2 years (± SD 9.8) (p = 0.451). In terms of physiological measures, shift workers presented a higher pulse rate with a mean of 78.7 beats per minute (± SD 15.2) than non-shift workers with a mean of 72.3 beats per minute (± SD 11.2) (p-value = 0.015). Body fat percentage revealed a significant difference, showing a higher mean percentage in shift workers (31.7% ±SD 41.6) compared to non-shift workers (22.7% ±SD 5.8) with a p-value of 0.031. Additionally, LDL-C level (p = 0.081) showed trends towards significance, suggesting potential differences between shift and non-shift workers. Dietary intake variables indicated minor distinctions between the two groups. The percentage of energy from added sugar was slightly higher in shift workers (6.76% ±SD 0.79) compared to non-shift workers (5.98% ±SD 0.75), with a significant p-value of 0.001. Several other variables included in the analysis had higher p-values, suggesting that there were no significant differences between the two groups. However, it is important to note that given the limitations of unadjusted analysis, the results based on independent t-tests require further investigation to provide conclusive findings. Adjustments for potential confounders in subsequent analyzes in Table 2 and Table 3 may provide a more nuanced understanding of the observed differences. Cardiometabolic risk factors by shift work parameters among shift workers and non-shift workers The investigation delving into the association between night work parameters and cardiometabolic risk factors in Table 2. In the comparison of current work status, where non-shift workers served as the reference group, notable disparities surfaced. Shift workers presented a significantly higher waist: hip by 0.02 cm (95% CI: -0.05, 0.01), elevated systolic blood pressure (SBP) by 10.10 mmHg (95% CI: -17.28, -2.98), an increased pulse rate by 6.46 (95% CI: -11.66, -1.26), an increased fasting triglycerides level by 0.25mmol/l (%95 CI: (-0.03, 0.54), higher LDL-C level by 0.35mmol/l (%95 CI: -0.75, 0.04) and a higher body fat percentage by 9.0% (95% CI: -20.5, 2.53). Exploring the impact of years of current shift work, significant mean differences emerged. For those engaged in 5–10 years of shift work, a notable increase in waist: hip ratio (0.05, 95% CI: -0.09, -0.01) and SBP (12.56 mmHg, 95% CI: -21.9, -3.15) was evident. Individuals with over 10 years of current shift work displayed a pronounced rise in pulse rate (8.38, 95% CI: -14.39, -2.38), fasting total cholesterol (0.40, 95% CI: -0.05, 0.86), fasting triglycerides (0.34, 95% CI: 0.01, 0.68) and visceral fat (2.89 g, 95% CI: -5.06, -0.72). In the examination of hours duration of current shift work, significant mean differences were identified. Notably, individuals working 8–12 hours manifested an increased waist: hip (0.05, 95% CI: -0.94, -0.16) and SBP (12.56 mmHg, 95% CI: -21.98, -3.15). Those working 12–24 hours demonstrated a marginal increase in SBP (1.36 mmHg, 95% CI: -9.67, 6.94) and a higher pulse rate (8.38, 95% CI: -14.39, -2.38). The visceral fat content also exhibited notable differences, with 8–12 hours (2.89 g, 95% CI: -5.06, -0.72) and 12–24 hours (0.09 g, 95% CI: -1.81, 2.01). Examining the intensity of night work, with non-shift work as the reference, significant mean differences surfaced. Those working fewer than 15 nights showcased elevated SBP (9.88 mmHg, 95% CI: 1.32, 18.44), triglycerides (0.32, 95% CI: (-0.68, 0.02), pulse rate (6.37, 95% CI: 0.14, 12.61), visceral fat content (3.71 g, 95% CI: 1.79, 5.62) and body fat percentage (15.21%, 95% CI: 1.64, 28.77). Notably, individuals working more than 15 nights manifested an increased LDL-C level (0.55, 95% CI: (0.05, 1.05). Cardiometabolic risk factors among current non-shift workers and shift workers, stratified by chronotype The investigation of cardiometabolic risk factors among current non-shift workers and shift workers, stratified by chronotype, is shown in Table 3. The comprehensive array of selected cardiometabolic risk factors included BMI (kg/m²), SBP (mmHg), DBP (mmHg), pulse rate, visceral fat (g), body fat analysis (%), fasting glucose (mmol/l), total cholesterol (mmol/l), triglycerides (mmol/l), HDL-C (mmol/l) & LDL-C (mmol/l). The analysis was divided into two distinct sections on the basis of chronotype: to examine circadian rhythm patterns. Within each section, participants were further categorized into non-shift workers and shift workers, with non-shift workers considering as the reference group. In the morning-oriented chronotype section, none of the cardiometabolic risk factors presented a p-value below the 0.05 significance threshold. Conversely, within the evening-oriented chronotype section, several notable mean differences were observed. Shift workers with an evening-oriented chronotype demonstrated a significant mean difference of 9.39 mmHg (95% CI: -19.19, 0.33) in SBP, 0.53 mmol/l (95% CI: -1.18, 0.05) in LDL-C, 9.44 (95% CI: -16.49, -1.95) in pulse rate and 3.88g (95% CI: -6.54, -1.27) in visceral fat content were noted, indicating distinct cardiometabolic profiles among shift workers with an Evening-oriented chronotype. Discussion The research results presented provide a comprehensive overview of the complex relationships among demographic data, physiological parameters, dietary intake and cardiometabolic risk factors in the context of shift work. Thorough investigation of cardiometabolic risk factors in relation to various shift work parameters expands our understanding of the diverse effects of irregular work schedules on cardiovascular and metabolic health. The increased waist-to-hip ratio, systolic blood pressure (SBP), pulse rate, triglycerides, LDL-C, and body fat percentage in shift workers represent potential health risks that require comprehensive attention. When analyzing the correlation between night shifts and cardiometabolic risk factors, our results are consistent with previous research indicating an association between current night work and increased cardiometabolic risk markers [ 3 ]. However, it is important to note that there is not complete uniformity across all studies. Discrepancies may be attributed to methodological differences, including possible over adjustment of confounding factors, different definitions of night shift work, and the influence of selection bias [ 16 ]. The recognized increased waist-to-hip ratio in shift workers implies severe consequences for cardiovascular health, as an increased ratio is often linked to an increased risk of cardiovascular diseases. A cross-sectional study of female hospital employees established that those engaged in rotating shift systems exhibited an increased waist circumference compared to their non-shift working counterparts [ 17 ]. Investigating the underlying mechanisms contributing to this relationship is important for the development of targeted interventions. Shift workers exhibit significantly elevated SBP and pulse rate compared with non-shift workers. The elevated pulse rate might indicate increased activity of the sympathetic nervous system, which reflects the physiological stressors due to irregular working schedules [ 18 ]. Blood pressure levels tend to rise in periods of wakefulness, especially during work shifts, in contrast to non-working hours. Considering that working hours often coincide with the night-time; a period during which blood pressure is normally supposed to fall; engagement in shift work, particularly at night, could disrupt the normal pattern of blood pressure, leading the risk of cardiovascular disease (CVD) [ 19 ] A variety of epidemiological studies has continually shown higher blood pressure levels in shift workers compared to those working regular daytime shifts [ 18 ] Our analysis of fasting lipid profiles, which includes parameters such as total cholesterol, triglycerides, and LDL-C, showed some interesting trends among the shift workers. Despite changes in the overall levels of total cholesterol not being statistically significant, the trends in the levels of LDL-C suggests potential implications in cardiovascular risk. Shift workers revealed a tendency toward higher LDL-C levels compared to non-shift workers. High levels of LDL-C have often been associated with an increased risk of cardiovascular disease [ 3 , 10 , 13 ]. Moreover, increased body fat percentage leads to questions about the metabolic consequences of circadian disruptions. Investigating these aspects can possibly lead the way toward tailored interventions that could reduce potential health risks associated with shift work. Further unraveling the paths relating shift work, body composition, and metabolic health will be important in developing targeted intervention strategies. (OR: 1.44; 95% CI 1.06–1.95) and higher body mass index (BMI) (β: 0.56 kg m − 2, 95% CI 0.10–1.03) in shift workers [ 3 ], compared with day workers. Further research on the long-term health effects of these demographic and physiological differences is warranted to appropriately develop health interventions for those with prolonged exposure to shift work. The significantly large increment in SBP among shift workers with 5–10 years of working underscores the cumulative nature of cardiovascular risk associated with long-term exposure to non-normal working hours. Similarly, one study shows that those who had a history of night work for 10 or more years also showed a trend toward larger differences in cardiometabolic risk factors [ 3 ]. Knowing the temporal dynamics of these changes and modifiable factors would help design targeted interventions. The steep increase in pulse rate among those with more than 10 years of current shift work raises questions about the adaptability of the cardiovascular system to prolonged exposure to irregular work schedules. Investigating whether this is a transient response or a sustained adaptation will provide insights into the long-term consequences of shift work on cardiovascular function. The study shows significant differences in total cholesterol levels, among individuals with more than 10 years of current shift work. Elevated triglyceride levels and visceral fat level were observed among shift workers, particularly those with more than 10 years of current shift work, and this may indicate that night work parameters could be related to lipid metabolism. Moreover, this study of visceral fat content in relation to various shift work parameters helps to understand the potential risk factors of metabolic disorders. The differences in visceral fat and SBP between those working 8–12 h and 12–24 h of current shift work are notable, indicating the influence of work duration on metabolic outcomes. Longitudinal studies, following changes in SBP and visceral fat over time, coupled with serial dietary assessment and physical activity measurements, will add much-needed detail to the understanding of metabolic implications of irregular work schedules. The higher levels of LDL-C in people who work at night more than 15 nights per month suggest a potential dose-response relationship between the frequency of night work and adverse lipid profiles. Few studies have explored the length and frequency of night shifts and how these are associated with cardiometabolic risk factors, and this heterogeneity makes direct comparisons difficult [ 3 ]. Also, it remains unclear why perpetual night work frequency is associated with higher levels of SBP, pulse rate, visceral fat, percentage body fat, and TAG levels [ 20 , 11 , 21 ]. Notably, a recent study suggested that there is no clear association between a history of night work or the frequency of night shifts and cardiometabolic risk factors [ 11 ]. On the other hand, it has been observed by a longitudinal study that more years of night shift work were associated with an increased risk of CVD [ 13 ]. Shift work, characterized by circadian rhythm disturbances, may lead to variations in diet, insulin sensitivity, and hormonal control, which may elevate triglycerides [ 2 ]. The relationship of increased triglyceride, total cholesterol, and LDL-C levels in certain subgroups of shift workers points toward a complex interplay between the nature of night work and lipid metabolism. This complex interplay of these associations is probably influenced by a variety of lifestyle factors, disturbances in circadian rhythms, and physiological stressors. Further research is needed to identify the underlying mechanisms that give rise to the observed lipid-related differences and to develop targeted interventions that address the specific problems faced by shift workers. Stratifying the analysis by chronotype adds a layer of complexity to our understanding of the interplay between circadian rhythms and shift work. Morning-oriented types did not differ significantly in cardiometabolic risk factors whether they worked non-shift or shift work. In the evening-oriented chronotype category, however, some significant differences in mean values emerged, especially in the shift worker group. Notable mean differences in SBP, LDL-C, pulse rate, and visceral fat content further underlined the distinct cardiometabolic profiles associated with evening-oriented shift workers. Recent studies also suggest that for shift workers, an evening chronotype is related to a higher risk of obesity [ 11 ]. Moreover, some findings from studies not in the setting of shift work point out that having an evening chronotype may be associated with a higher likelihood of cardiometabolic diseases [ 22 , 23 ]. Notably, a large difference in BMI between day and shift workers was particularly pronounced among those with evening chronotypes (β: 0.97 kg m-2, 95% CI 0.21–1.73) while no such difference was found among those with morning chronotypes (β: 0.04 kg m-2, 95% CI -0.85 to 0.93) [ 3 ]. Further research is needed to understand the mechanisms by which chronotype, shift work, and cardiometabolic outcomes are linked, in order to develop effective targeted interventions. Chronotype-based intervention, for example, personalized sleep hygiene recommendations or optimized work schedules, could be used to try to minimize the health risks associated with shift work. Observed chronotype differences between those assigned to the evening-oriented chronotype group could imply a need for tailored interventions and health management strategies among shift workers, especially for individuals presenting different chronotypes. Furthermore, the call for more research indicates the dynamic nature of occupational health research and the constant search for better preventive measures. It is also imperative to find out whether cardiometabolic profiles in evening-oriented chronotype shift workers are reversible or open to intervention. That being said, longitudinal studies on the effectiveness of chronotype-tailored interventions in preventing adverse health effects of shift work would be very informative and helpful in providing valuable evidence for occupational health practice. When interpreting the results of this study, several limitations must be considered. Sample size may limit the statistical power and generalizability of findings. The intermediate chronotype group was excluded due to an insufficient number of participants in this category. Expanding the sample size could have strengthened the observed associations with shift work, chronotype, and cardiometabolic risk markers. Efforts were made to control collinearity by performing the variance inflation factor analysis, but some degree of residual multicollinearity may not be completely avoided. Some confounding factors such as stress and quality of sleep may have impacted findings. Additionally, since this is a case-control study, some risk factors may have changed by the time of data collection due to participants' awareness of their condition. This may have influenced the true relationship of certain current factors. Abbreviations MCTQ Munich chronotype questionnaire BMI Body mass index MSF Mid sleep Time CVD Cardiovascular disease LDL-C Low density lipoprotein cholesterol HDL-C High density lipoprotein cholesterol TAG Triglycerides TC Total cholesterol SBP Systolic blood pressure DBP Diastolic blood pressure Declarations Ethics approval and consent to participate: Ethical approval was obtained from the Ethics Review Committee of the Faculty of Livestock, Fisheries and Nutrition, Wayamba University of Sri Lanka, Makandura, NWP, Sri Lanka 60170 (202311H16). Consent for publication: Not Applicable Disclaimer : There are no conflicts of interest. Availability of data and materials : The data presented in this study are available upon reasonable request from the first author. Competing interests : The authors declare that they have no competing interests. Authors’ contributions : ASW designed the study, conducted the research, analyzed the data, conducted the statistical analysis, prepared Tables, and drafted the main manuscript under the guidance of KMR. DSP assisted in the acquisition and analysis of data. KMR guided in designing the research, data interpretation, and statistical analyses. All authors critically reviewed the manuscript and approved the final submitted version of the manuscript. Acknowledgements : This research received no specific grant from any funding agency in the public, commercial or not-for-profit sectors. We express appreciation to the participants for their enthusiastic cooperation with this study and the nutrition research team of the Department of Applied Nutrition, Wayamba University of Sri Lanka, for their valuable support. Authors’ information (optional ): Authors and affiliations : Department of Applied Nutrition, Faculty of Livestock, Fisheries & Nutrition, Wayamba University of Sri Lanka, Makandura, Gonawila NWP, Sri Lanka Amanda S. Wanigasinghe, Dilki S. Perera and Kumari M. Rathnayake Corresponding author: Correspondence to Kumari M. Rathnayake. Conclusion The detailed analysis of cardiometabolic risk factors exhibits increased waist-to-hip ratio, systolic blood pressure, pulse rate, and percentage of total body fat in shift workers. Distinct trends in the lipid profile, such as elevated levels of triglycerides, total cholesterol, and LDL-C, were prominent in shift workers with more than 10 years of shift work and those who worked more than 8 hours per shift. The findings indicate that the number of working hours and the length of current shift work were associated with cardiometabolic risk indicators. Stratification by chronotype indicated that evening chronotype shift workers showed significantly more elevated levels of cardiometabolic risk markers, including systolic blood pressure, LDL-C levels, pulse rate, and visceral fat content, compared to morning chronotype shift workers. This highlights the greater susceptibility of evening chronotypes to adverse cardiometabolic risk markers. The findings presented in this paper give a base for future research studies in the development of better preventive measures in promoting health and well-being among shift workers. References Baidoo VA, Knutson KL (2023) Associations between circadian disruption and cardiometabolic disease risk: A review. Obesity. 10.1002/oby.23666 Hemmer A, Marescha J, Dibner C, Dorribo V, Perrig S, Genton L, Collet TH (2021) The Effects of Shift Work on Cardio-Metabolic Diseases and Eating Patterns. 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J Biol Rhythm. 10.1177/0748730402239679 Boegea HL, Bhattia MZ, St-Onge MP (2021) Circadian rhythms and meal timing: impact on energy balance. Curr Opin Biotechnol. 10.1016/j.copbio.2020.08.009 Ritonja J, Aronson KJ, Day AG, Korsiak J, Tranmer J (2018) Investigating Cortisol Production and Pattern as Mediators in the Relationship Between Shift Work and Cardiometabolic Risk. Can J Cardiol. 10.1016/j.cjca.2018.02.006 Marqueze EC, Ulhôa MA, Moreno CRDC (2013) Effects of irregular-shift work and physical activity on cardiovascular risk factors in truck drivers. Rev Saude Publica. 10.1590/s0034-8910.2013047004510 Bloomfield D, Park A (2015) Night time blood pressure dip. World J Cardiol. 10.4330/wjc.v7.i7.37 Proper KI, Langenberg DVD, Rodenburg W, Vermeulen RCH, Beek AJVD, Steeg HV, Kerkhof LWMV (2016) The Relationship Between Shift Work and Metabolic Risk Factors: A Systematic Review of Longitudinal Studies. Am J Prev Med. 10.1016/j.amepre.2015.11.013 Souza BB, Monteze NM, Oliveira FLPD, Oliveira JMD, Nascimento SDF, Neto RMDN, Sales ML, Souza GGL (2015) Lifetime shift work exposure: association with anthropometry, body composition, blood pressure, glucose and heart rate variability. Occup Environ Med. 10.1136/oemed-2014-102429 Wong PM, Hasler BP, Kamarck TW, Muldoon MF, Manuck SB (2015) Social Jetlag, Chronotype, and Cardiometabolic Risk. J Clin Endocrinol Metab. 10.1210/jc.2015-2923 Yu J, Yun CH, Ahn JH, Suh S, Cho HJ, Lee SK, Yoo HJ, Seo JA, Kim SG, Choi KM, Baik SH, Choi DS, Shin C, Kim N (2015) Evening chronotype is associated with metabolic disorders and body composition in middle-aged adults. J Clin Endocrinol Metab. 10.1210/jc.2014-3754 Tables Tables 1 to 3 are available in the Supplementary Files section. Additional Declarations The authors declare no competing interests. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5826797","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":401994721,"identity":"ea6839d0-82f7-4657-8ffc-ccf6acd63c75","order_by":0,"name":"Amanda S. Wanigasinghe","email":"","orcid":"","institution":"Department of Applied Nutrition, Faculty of Livestock, Fisheries \u0026 Nutrition, Wayamba University of Sri Lanka, NWP, Sri Lanka 60170","correspondingAuthor":false,"prefix":"","firstName":"Amanda","middleName":"S.","lastName":"Wanigasinghe","suffix":""},{"id":401997846,"identity":"70c90ec4-5487-4ac4-9188-337204af9db8","order_by":1,"name":"Dilki S. Perera","email":"","orcid":"","institution":"Department of Applied Nutrition, Faculty of Livestock, Fisheries \u0026 Nutrition, Wayamba University of Sri Lanka, NWP, Sri Lanka 60170","correspondingAuthor":false,"prefix":"","firstName":"Dilki","middleName":"S.","lastName":"Perera","suffix":""},{"id":401997847,"identity":"80365022-ecda-498e-8d5e-e007ad71a7e0","order_by":2,"name":"Kumari M. Rathnayake","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA0klEQVRIiWNgGAWjYBACCTBpwMDAz8DABmbz8RCrRbIBqoWNOC0gXQeI1SI5Izvxc0XBtnzjGzlmDxhq7IBaDuDXIi2Ru1nyjMFty203cswNGI4lM7DxNuDXIieRu0GyweC2gdntHDMJBjag8/gJOAyoZfNPkBbj2SAt/4jQAnTYNrAtBtJALYxtBwg7TLLn7TZLkBaJ+8/KDRL7knkIel/ieO7mmw1/bhvw9xze9uDDNzs5fp4EAi5DAA4DBqBiQrGCAtgfkKJ6FIyCUTAKRhAAABd1O4byD4QHAAAAAElFTkSuQmCC","orcid":"","institution":"Department of Applied Nutrition, Faculty of Livestock, Fisheries \u0026 Nutrition, Wayamba University of Sri Lanka, NWP, Sri Lanka 60170","correspondingAuthor":true,"prefix":"","firstName":"Kumari","middleName":"M.","lastName":"Rathnayake","suffix":""}],"badges":[],"createdAt":"2025-01-14 11:52:24","currentVersionCode":1,"declarations":{"humanSubjects":true,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":true,"humanSubjectConsent":true,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-5826797/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5826797/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":73934010,"identity":"82bdca80-39ff-415b-b57b-ca9538cfc505","added_by":"auto","created_at":"2025-01-16 06:38:46","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":679560,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5826797/v1/e02eb740-d6e9-4c2a-9198-cefa9ed2cfed.pdf"},{"id":73933782,"identity":"216ce4f9-0b6d-4fed-945e-f314f15b93b6","added_by":"auto","created_at":"2025-01-16 06:38:26","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":62691,"visible":true,"origin":"","legend":"","description":"","filename":"ChronoStudyBMCNutritionTablesSubmittedversion.docx","url":"https://assets-eu.researchsquare.com/files/rs-5826797/v1/76d19ea44b66a987baae94e0.docx"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eElevated cardiometabolic risk markers associated with shift work and evening chronotype \u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCircadian rhythms play crucial roles in governing the physiological \u0026amp; behavioral functions of the human body. Numerous observational studies have established a connection between circadian disruption \u0026amp; the onset of cardiometabolic diseases [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Circadian disruption can result from various lifestyle \u0026amp; environmental factors. Multiple factors, including shift work, late chronotype, late sleep timing, sleep irregularity, and late meal timing, have been identified as disruptors of circadian rhythm alignment [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. These factors are associated with potential adverse effects on cardiometabolic health, such as increased BMI/obesity, increased blood pressure, increased dyslipidemia, inflammation, and diabetes [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eShift work is a risk factor for conditions like overweight, obesity, Type 2 diabetes, increased blood pressure, and metabolic syndrome. It also influences eating behavior, food choices, energy intake, and macronutrient consumption [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Night-shift work, in particular, not only leads to a misalignment between the body's internal circadian system and the external light-dark cycle but also induces internal desynchronization among various levels of the circadian system and disrupts the expression of clock genes in various tissues. Metabolomics studies revealed shifts in metabolite timing during night work, further misaligning with the circadian system [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eChronotype has been shown to play a role in the effect of shift work on health [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. To date, multiple studies have reported that morning types may be less able to adapt to shift work than evening types [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. The chronotype is hypothesized to influence the relationship linking shift-induced circadian disruption to cardiometabolic outcomes [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. It was observed that individuals with an evening chronotype presented elevated levels of proteins previously associated with cardiometabolic risk [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Few studies have explored this connection, and the findings are inconclusive. Some studies propose that both morning-oriented and evening-oriented chronotypes could contribute to the risk of cardiometabolic outcomes. [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. However, research into the role of chronotype in the effect of shift work on metabolic risk factors is still lacking [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe influence of circadian misalignment on cardiometabolic health has been extensively studied, but there exists a notable gap in understanding how these dynamics manifest among adults specially security officers, particularly those engaged in night shift work. Therefore, the aim of our present study was to determine the impact of the chrono effect on cardiometabolic risk markers in shift workers using night work parameters and chronotype as key variables. This case control study would be beneficial in designing effective interventions to prevent cardiometabolic diseases among shift workers.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design and participants\u003c/h2\u003e \u003cp\u003eFor this study, participants were recruited from an export processing zone in Sri Lanka. The inclusion criterion was healthy male individuals aged between 40\u0026ndash;60 years working full time on rotating shifts. Non-shift workers, comprising regular day-time workers, served as the reference group. Recruitment strategies involve various approaches such as posters, telephone calls, messages, and notices, that target workers from fire and security units. Shift workers were defined as those engaging in rotating shifts, alternating between day shifts (mainly between 07:30 am\u0026ndash;4:00 pm), night shifts (mainly between 11:00 pm\u0026ndash;7:45 am), and/or night shifts from 06:00 am\u0026ndash;6:00 am or 4:00 pm \u0026ndash; 6:00 am. Conversely, non-shift workers were individuals not participating in rotating and night shifts in their work life. The exclusion criteria included females, individuals using statins, antihypertensives, or treatments for diabetes, those with higher smoking and alcoholic status, individuals with psychological disorders, and participants who had lost their legs. The presentation outlined the study's objectives and extended invitations for participation without offering financial incentives. The participants had access to group nutrition education on the basis of the study's findings. Before the study commenced, participants signed an informed consent form outlining objectives, methodology, and the intent to publish results. Among the 123 initially recruited participants, 5 were excluded because of the use of antihypertensives and treatments for diabetes, 11 were excluded because they were females, 2 were excluded because they were lost to follow-up, and 1 was excluded he had the highest alcoholic status. The main analysis included a total of 104 participants (52 shift workers \u0026amp; 52 non-shift workers). This case-control study received approval from the Ethics Review Committee of the Faculty of Livestock, Fisheries \u0026amp; Nutrition, Wayamba University of Sri Lanka.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eSample size\u003c/h3\u003e\n\u003cp\u003eSample size was calculated to detect a difference in cardiometabolic risk markers between shift and non-shift workers. A medium effect size was assumed (Cohen's d\u0026thinsp;=\u0026thinsp;0.5), based on previous studies [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. The power calculation through G*Power indicated that, a minimum of 52 participants in each group was required for 80% power level and a significance level of α\u0026thinsp;=\u0026thinsp;0.05. The study aimed to have a total sample size of 104 participants, with an allocation of 52 individuals in each group, based on an anticipated response rate of 75%. This calculation ensures adequate statistical power to detect accurate significant differences in cardiometabolic risk markers between the two groups.\u003c/p\u003e\n\u003ch3\u003eData collection\u003c/h3\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eShift work\u003c/h2\u003e \u003cp\u003eInformation about participants' current shift work status (including work schedules and types of shifts), the frequency of (night) shifts (number of each shift type per month), and the duration of (night) shift work, as well as the total number of years of (night) shift work, was collected during the baseline and follow-up assessments. An interviewer administered questionnaire was used to collect information from the workers. The interviews were conducted in person. The questionnaire comprehensively covered all major aspects of the shift work.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eAnthropometrics\u003c/h3\u003e\n\u003cp\u003eHeight was measured using a stadiometer to the nearest 0.5 cm. While participants were wearing light clothing, weight and BMI were determined by using the multi-frequency segmental body composition analyser Tanita MC-780 (Tanita, Japan) with the following settings: standard body type and \u0026minus;\u0026thinsp;1 kg for clothing. Waist \u0026amp; hip circumferences were measured using a measuring tape following a standardized protocol that provided detailed instructions for accurate measurement. All measurements were taken prior to the start of the work shift, with participants in light clothing and no shoes. If there was a difference of more than one unit (in centimeters/kilograms) between the first two measurements, a third measurement was taken. The average of the three measurements was subsequently calculated. Body mass index (BMI) was calculated as the ratio of weight (in kg) to the square of height (in m\u003csup\u003e2\u003c/sup\u003e).\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eCardiometabolic risk markers\u003c/h2\u003e \u003cp\u003eBlood samples (3.5 mL) were collected into serum separator tubes (BD vacutainer, UK) from participants following a 12-hour overnight fast (before the commencement of a day shift for both day and night workers) and left at room temperature for 30 min until centrifugation at 1700 \u0026times; g for 15 min at 20\u0026deg;C to obtain serum which was stored at \u0026minus;\u0026thinsp;20\u0026deg;C until analysis. These serum samples were used to determine lipids [total cholesterol (TC), HDL cholesterol (HDL-C), and triglycerides (TAG)], and glucose, with the use of an automated biochemistry analyzer (Thermo Scientific, Indiko, Finland) with reagents from Thermo Scientific (Thermo Scientific, Finland). Fasting LDL cholesterol (LDL-C) was estimated by using the Friedewald formula [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Blood pressure was measured in triplicate by using an OMRON M6 automatic digital blood pressure monitor. A trained research student performed these measurements, ensuring that each measurement was taken at least 5 minutes apart, and the average of the three readings was used in the data analysis.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eDietary assessment\u003c/h3\u003e\n\u003cp\u003eDietary intake was assessed using a single 24-hour dietary recall (representing both normal days and work days). Participants were provided with a common guidance during group sessions at their workplace on how to provide dietary information. This guidance included instructions on measuring food quantities (using glasses, cups, spoons), specifying food preparation details (such as recipes, commercial brands, and restaurant names), and recording meal starting times on research forms. Researchers estimated the amounts of food and beverages consumed based on the information provided by participants in household measurements, aided by photographs of common household utensils. Participants were questioned by researchers trained in food measurement units about their eating habits. All meal and snack intakes were reported along with their respective intake times. A trained dietitian reviewed the forms to ensure completeness and consistency.\u003c/p\u003e\n\u003ch3\u003eChronotype\u003c/h3\u003e\n\u003cp\u003eThe assessment of chronotype was conducted via the Munich Chronotype Questionnaire (MCTQ) which is a validated tool for assessing individual chronotypes, which reflect preferences in circadian rhythms (Ref). The chronotype was determined on the basis of participants\u0026rsquo; reported mid-sleep time (MSF) on free days. In the questionnaire, the participants provided information regarding the time they spent before sleeping (sleep onset) and the time they woke up. Sleep duration (SD) was calculated as the difference between the reported sleep onset and awakening times. The MSF was computed via the following formula:\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e\u0026#119872;\u0026#119878;\u0026#119865; = \u0026#119878;\u0026#119874; \u0026ndash; \u0026#119878;\u0026#119863;/ 2\u003c/h2\u003e \u003cp\u003eA higher MSF indicates an evening-oriented chronotype, whereas a lower MSF indicates a morning-oriented chronotype. Individuals who reported using an alarm clock on free days were excluded from the exploratory analysis, as their MSF values may be influenced by external factors and may not accurately reflect their internal circadian rhythm [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eThe statistical analysis aimed to explore associations, trends, and differences in the collected data, employing various tests and models to assess the study's objectives. All analyses were performed via SPSS version 26.0. Descriptive statistics were calculated to present the basic features of the study variables. The means and standard deviations were reported for continuous variables, whereas frequencies and percentages were reported for categorical variables. Independent t-tests were used to compare demographic characteristics between shift workers and non-shift workers. Multiple linear regression analyses, adjusting for potential confounding factors such as age and family history of cardiovascular disease, were conducted to investigate associations between night work parameters and various cardiometabolic risk factors. Night work parameters included current work status, years of current shift work, hours duration of current shift work, and intensity of night work. Multiple linear regression analyses were also performed to examine cardiometabolic risk factors among current non-shift workers and shift workers, stratified by chronotype (morning-oriented and evening-oriented). The significance level for all analyses was set at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eCharacteristics of the study population\u003c/h2\u003e \u003cp\u003eThe study included a total of 104 participants, shift workers (n\u0026thinsp;=\u0026thinsp;52) and non-shift workers (n\u0026thinsp;=\u0026thinsp;52). Table\u0026nbsp;1 elucidates distinct differences in demographic characteristics, physiological measures, and dietary intake between shift workers and non-shift workers. Compared with non-shift workers, shift workers presented a mean age of 43.3 years (\u0026plusmn;\u0026thinsp;SD 10.2), whereas non-shift workers presented a mean age of 41.2 years (\u0026plusmn;\u0026thinsp;SD 9.8) (p\u0026thinsp;=\u0026thinsp;0.451). In terms of physiological measures, shift workers presented a higher pulse rate with a mean of 78.7 beats per minute (\u0026plusmn;\u0026thinsp;SD 15.2) than non-shift workers with a mean of 72.3 beats per minute (\u0026plusmn;\u0026thinsp;SD 11.2) (p-value\u0026thinsp;=\u0026thinsp;0.015). Body fat percentage revealed a significant difference, showing a higher mean percentage in shift workers (31.7% \u0026plusmn;SD 41.6) compared to non-shift workers (22.7% \u0026plusmn;SD 5.8) with a p-value of 0.031. Additionally, LDL-C level (p\u0026thinsp;=\u0026thinsp;0.081) showed trends towards significance, suggesting potential differences between shift and non-shift workers. Dietary intake variables indicated minor distinctions between the two groups. The percentage of energy from added sugar was slightly higher in shift workers (6.76% \u0026plusmn;SD 0.79) compared to non-shift workers (5.98% \u0026plusmn;SD 0.75), with a significant p-value of 0.001.\u003c/p\u003e \u003cp\u003eSeveral other variables included in the analysis had higher p-values, suggesting that there were no significant differences between the two groups. However, it is important to note that given the limitations of unadjusted analysis, the results based on independent t-tests require further investigation to provide conclusive findings. Adjustments for potential confounders in subsequent analyzes in Table\u0026nbsp;2 and Table\u0026nbsp;3 may provide a more nuanced understanding of the observed differences.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eCardiometabolic risk factors by shift work parameters among shift workers and non-shift workers\u003c/h2\u003e \u003cp\u003eThe investigation delving into the association between night work parameters and cardiometabolic risk factors in Table\u0026nbsp;2. In the comparison of current work status, where non-shift workers served as the reference group, notable disparities surfaced. Shift workers presented a significantly higher waist: hip by 0.02 cm (95% CI: -0.05, 0.01), elevated systolic blood pressure (SBP) by 10.10 mmHg (95% CI: -17.28, -2.98), an increased pulse rate by 6.46 (95% CI: -11.66, -1.26), an increased fasting triglycerides level by 0.25mmol/l (%95 CI: (-0.03, 0.54), higher LDL-C level by 0.35mmol/l (%95 CI: -0.75, 0.04) and a higher body fat percentage by 9.0% (95% CI: -20.5, 2.53). Exploring the impact of years of current shift work, significant mean differences emerged. For those engaged in 5\u0026ndash;10 years of shift work, a notable increase in waist: hip ratio (0.05, 95% CI: -0.09, -0.01) and SBP (12.56 mmHg, 95% CI: -21.9, -3.15) was evident. Individuals with over 10 years of current shift work displayed a pronounced rise in pulse rate (8.38, 95% CI: -14.39, -2.38), fasting total cholesterol (0.40, 95% CI: -0.05, 0.86), fasting triglycerides (0.34, 95% CI: 0.01, 0.68) and visceral fat (2.89 g, 95% CI: -5.06, -0.72).\u003c/p\u003e \u003cp\u003eIn the examination of hours duration of current shift work, significant mean differences were identified. Notably, individuals working 8\u0026ndash;12 hours manifested an increased waist: hip (0.05, 95% CI: -0.94, -0.16) and SBP (12.56 mmHg, 95% CI: -21.98, -3.15). Those working 12\u0026ndash;24 hours demonstrated a marginal increase in SBP (1.36 mmHg, 95% CI: -9.67, 6.94) and a higher pulse rate (8.38, 95% CI: -14.39, -2.38). The visceral fat content also exhibited notable differences, with 8\u0026ndash;12 hours (2.89 g, 95% CI: -5.06, -0.72) and 12\u0026ndash;24 hours (0.09 g, 95% CI: -1.81, 2.01). Examining the intensity of night work, with non-shift work as the reference, significant mean differences surfaced. Those working fewer than 15 nights showcased elevated SBP (9.88 mmHg, 95% CI: 1.32, 18.44), triglycerides (0.32, 95% CI: (-0.68, 0.02), pulse rate (6.37, 95% CI: 0.14, 12.61), visceral fat content (3.71 g, 95% CI: 1.79, 5.62) and body fat percentage (15.21%, 95% CI: 1.64, 28.77). Notably, individuals working more than 15 nights manifested an increased LDL-C level (0.55, 95% CI: (0.05, 1.05).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eCardiometabolic risk factors among current non-shift workers and shift workers, stratified by chronotype\u003c/h2\u003e \u003cp\u003eThe investigation of cardiometabolic risk factors among current non-shift workers and shift workers, stratified by chronotype, is shown in Table\u0026nbsp;3. The comprehensive array of selected cardiometabolic risk factors included BMI (kg/m\u0026sup2;), SBP (mmHg), DBP (mmHg), pulse rate, visceral fat (g), body fat analysis (%), fasting glucose (mmol/l), total cholesterol (mmol/l), triglycerides (mmol/l), HDL-C (mmol/l) \u0026amp; LDL-C (mmol/l). The analysis was divided into two distinct sections on the basis of chronotype: to examine circadian rhythm patterns. Within each section, participants were further categorized into non-shift workers and shift workers, with non-shift workers considering as the reference group.\u003c/p\u003e \u003cp\u003eIn the morning-oriented chronotype section, none of the cardiometabolic risk factors presented a p-value below the 0.05 significance threshold. Conversely, within the evening-oriented chronotype section, several notable mean differences were observed. Shift workers with an evening-oriented chronotype demonstrated a significant mean difference of 9.39 mmHg (95% CI: -19.19, 0.33) in SBP, 0.53 mmol/l (95% CI: -1.18, 0.05) in LDL-C, 9.44 (95% CI: -16.49, -1.95) in pulse rate and 3.88g (95% CI: -6.54, -1.27) in visceral fat content were noted, indicating distinct cardiometabolic profiles among shift workers with an Evening-oriented chronotype.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe research results presented provide a comprehensive overview of the complex relationships among demographic data, physiological parameters, dietary intake and cardiometabolic risk factors in the context of shift work. Thorough investigation of cardiometabolic risk factors in relation to various shift work parameters expands our understanding of the diverse effects of irregular work schedules on cardiovascular and metabolic health. The increased waist-to-hip ratio, systolic blood pressure (SBP), pulse rate, triglycerides, LDL-C, and body fat percentage in shift workers represent potential health risks that require comprehensive attention. When analyzing the correlation between night shifts and cardiometabolic risk factors, our results are consistent with previous research indicating an association between current night work and increased cardiometabolic risk markers [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. However, it is important to note that there is not complete uniformity across all studies. Discrepancies may be attributed to methodological differences, including possible over adjustment of confounding factors, different definitions of night shift work, and the influence of selection bias [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe recognized increased waist-to-hip ratio in shift workers implies severe consequences for cardiovascular health, as an increased ratio is often linked to an increased risk of cardiovascular diseases. A cross-sectional study of female hospital employees established that those engaged in rotating shift systems exhibited an increased waist circumference compared to their non-shift working counterparts [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Investigating the underlying mechanisms contributing to this relationship is important for the development of targeted interventions. Shift workers exhibit significantly elevated SBP and pulse rate compared with non-shift workers. The elevated pulse rate might indicate increased activity of the sympathetic nervous system, which reflects the physiological stressors due to irregular working schedules [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Blood pressure levels tend to rise in periods of wakefulness, especially during work shifts, in contrast to non-working hours. Considering that working hours often coincide with the night-time; a period during which blood pressure is normally supposed to fall; engagement in shift work, particularly at night, could disrupt the normal pattern of blood pressure, leading the risk of cardiovascular disease (CVD) [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] A variety of epidemiological studies has continually shown higher blood pressure levels in shift workers compared to those working regular daytime shifts [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eOur analysis of fasting lipid profiles, which includes parameters such as total cholesterol, triglycerides, and LDL-C, showed some interesting trends among the shift workers. Despite changes in the overall levels of total cholesterol not being statistically significant, the trends in the levels of LDL-C suggests potential implications in cardiovascular risk. Shift workers revealed a tendency toward higher LDL-C levels compared to non-shift workers. High levels of LDL-C have often been associated with an increased risk of cardiovascular disease [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMoreover, increased body fat percentage leads to questions about the metabolic consequences of circadian disruptions. Investigating these aspects can possibly lead the way toward tailored interventions that could reduce potential health risks associated with shift work. Further unraveling the paths relating shift work, body composition, and metabolic health will be important in developing targeted intervention strategies. (OR: 1.44; 95% CI 1.06\u0026ndash;1.95) and higher body mass index (BMI) (β: 0.56 kg m\u0026thinsp;\u0026minus;\u0026thinsp;2, 95% CI 0.10\u0026ndash;1.03) in shift workers [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], compared with day workers. Further research on the long-term health effects of these demographic and physiological differences is warranted to appropriately develop health interventions for those with prolonged exposure to shift work.\u003c/p\u003e \u003cp\u003eThe significantly large increment in SBP among shift workers with 5\u0026ndash;10 years of working underscores the cumulative nature of cardiovascular risk associated with long-term exposure to non-normal working hours. Similarly, one study shows that those who had a history of night work for 10 or more years also showed a trend toward larger differences in cardiometabolic risk factors [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Knowing the temporal dynamics of these changes and modifiable factors would help design targeted interventions. The steep increase in pulse rate among those with more than 10 years of current shift work raises questions about the adaptability of the cardiovascular system to prolonged exposure to irregular work schedules. Investigating whether this is a transient response or a sustained adaptation will provide insights into the long-term consequences of shift work on cardiovascular function. The study shows significant differences in total cholesterol levels, among individuals with more than 10 years of current shift work. Elevated triglyceride levels and visceral fat level were observed among shift workers, particularly those with more than 10 years of current shift work, and this may indicate that night work parameters could be related to lipid metabolism.\u003c/p\u003e \u003cp\u003eMoreover, this study of visceral fat content in relation to various shift work parameters helps to understand the potential risk factors of metabolic disorders. The differences in visceral fat and SBP between those working 8\u0026ndash;12 h and 12\u0026ndash;24 h of current shift work are notable, indicating the influence of work duration on metabolic outcomes. Longitudinal studies, following changes in SBP and visceral fat over time, coupled with serial dietary assessment and physical activity measurements, will add much-needed detail to the understanding of metabolic implications of irregular work schedules.\u003c/p\u003e \u003cp\u003eThe higher levels of LDL-C in people who work at night more than 15 nights per month suggest a potential dose-response relationship between the frequency of night work and adverse lipid profiles. Few studies have explored the length and frequency of night shifts and how these are associated with cardiometabolic risk factors, and this heterogeneity makes direct comparisons difficult [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Also, it remains unclear why perpetual night work frequency is associated with higher levels of SBP, pulse rate, visceral fat, percentage body fat, and TAG levels [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Notably, a recent study suggested that there is no clear association between a history of night work or the frequency of night shifts and cardiometabolic risk factors [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. On the other hand, it has been observed by a longitudinal study that more years of night shift work were associated with an increased risk of CVD [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eShift work, characterized by circadian rhythm disturbances, may lead to variations in diet, insulin sensitivity, and hormonal control, which may elevate triglycerides [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The relationship of increased triglyceride, total cholesterol, and LDL-C levels in certain subgroups of shift workers points toward a complex interplay between the nature of night work and lipid metabolism. This complex interplay of these associations is probably influenced by a variety of lifestyle factors, disturbances in circadian rhythms, and physiological stressors. Further research is needed to identify the underlying mechanisms that give rise to the observed lipid-related differences and to develop targeted interventions that address the specific problems faced by shift workers.\u003c/p\u003e \u003cp\u003eStratifying the analysis by chronotype adds a layer of complexity to our understanding of the interplay between circadian rhythms and shift work. Morning-oriented types did not differ significantly in cardiometabolic risk factors whether they worked non-shift or shift work. In the evening-oriented chronotype category, however, some significant differences in mean values emerged, especially in the shift worker group. Notable mean differences in SBP, LDL-C, pulse rate, and visceral fat content further underlined the distinct cardiometabolic profiles associated with evening-oriented shift workers. Recent studies also suggest that for shift workers, an evening chronotype is related to a higher risk of obesity [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Moreover, some findings from studies not in the setting of shift work point out that having an evening chronotype may be associated with a higher likelihood of cardiometabolic diseases [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Notably, a large difference in BMI between day and shift workers was particularly pronounced among those with evening chronotypes (β: 0.97 kg m-2, 95% CI 0.21\u0026ndash;1.73) while no such difference was found among those with morning chronotypes (β: 0.04 kg m-2, 95% CI -0.85 to 0.93) [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFurther research is needed to understand the mechanisms by which chronotype, shift work, and cardiometabolic outcomes are linked, in order to develop effective targeted interventions. Chronotype-based intervention, for example, personalized sleep hygiene recommendations or optimized work schedules, could be used to try to minimize the health risks associated with shift work. Observed chronotype differences between those assigned to the evening-oriented chronotype group could imply a need for tailored interventions and health management strategies among shift workers, especially for individuals presenting different chronotypes. Furthermore, the call for more research indicates the dynamic nature of occupational health research and the constant search for better preventive measures. It is also imperative to find out whether cardiometabolic profiles in evening-oriented chronotype shift workers are reversible or open to intervention. That being said, longitudinal studies on the effectiveness of chronotype-tailored interventions in preventing adverse health effects of shift work would be very informative and helpful in providing valuable evidence for occupational health practice.\u003c/p\u003e \u003cp\u003eWhen interpreting the results of this study, several limitations must be considered. Sample size may limit the statistical power and generalizability of findings. The intermediate chronotype group was excluded due to an insufficient number of participants in this category. Expanding the sample size could have strengthened the observed associations with shift work, chronotype, and cardiometabolic risk markers. Efforts were made to control collinearity by performing the variance inflation factor analysis, but some degree of residual multicollinearity may not be completely avoided. Some confounding factors such as stress and quality of sleep may have impacted findings. Additionally, since this is a case-control study, some risk factors may have changed by the time of data collection due to participants' awareness of their condition. This may have influenced the true relationship of certain current factors.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eMCTQ \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Munich chronotype questionnaire\u003c/p\u003e\n\u003cp\u003eBMI \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Body mass index\u003c/p\u003e\n\u003cp\u003eMSF \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Mid sleep Time\u003c/p\u003e\n\u003cp\u003eCVD \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Cardiovascular disease\u003c/p\u003e\n\u003cp\u003eLDL-C \u0026nbsp; \u0026nbsp; \u0026nbsp;Low density lipoprotein cholesterol\u003c/p\u003e\n\u003cp\u003eHDL-C\u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;High density lipoprotein cholesterol\u003c/p\u003e\n\u003cp\u003eTAG\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Triglycerides\u003c/p\u003e\n\u003cp\u003eTC\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Total cholesterol\u003c/p\u003e\n\u003cp\u003eSBP\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Systolic blood pressure\u003c/p\u003e\n\u003cp\u003eDBP \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Diastolic blood pressure\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthical approval was obtained from the Ethics Review Committee of the Faculty of Livestock, Fisheries and Nutrition, Wayamba University of Sri Lanka, Makandura, NWP, Sri Lanka 60170 (202311H16).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication:\u003c/strong\u003e Not Applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDisclaimer\u003c/strong\u003e: There are no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e:\u003c/p\u003e\n\u003cp\u003eThe data presented in this study are available upon reasonable request from the first author.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e: The authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e:\u003c/p\u003e\n\u003cp\u003eASW designed the study, conducted the research, analyzed the data, conducted the statistical analysis, prepared Tables, and drafted the main manuscript under the guidance of KMR. DSP assisted in the acquisition and analysis of data. KMR guided in designing the research, data interpretation, and statistical analyses. All authors critically reviewed the manuscript and approved the final submitted version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e:\u003c/p\u003e\n\u003cp\u003eThis research received no specific grant from any funding agency in the public, commercial or not-for-profit sectors. We express appreciation to the participants for their enthusiastic cooperation with this study and the nutrition research team of the Department of Applied Nutrition, Wayamba University of Sri Lanka, for their valuable support.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; information (optional\u003c/strong\u003e):\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors and affiliations\u003c/strong\u003e:\u003c/p\u003e\n\u003cp\u003eDepartment of Applied Nutrition, Faculty of Livestock, Fisheries \u0026amp; Nutrition, Wayamba University of Sri Lanka, Makandura, Gonawila NWP, Sri Lanka\u003c/p\u003e\n\u003cp\u003eAmanda S. Wanigasinghe, Dilki S. Perera and Kumari M. Rathnayake\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCorresponding author:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCorrespondence to Kumari M. Rathnayake.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe detailed analysis of cardiometabolic risk factors exhibits increased waist-to-hip ratio, systolic blood pressure, pulse rate, and percentage of total body fat in shift workers. Distinct trends in the lipid profile, such as elevated levels of triglycerides, total cholesterol, and LDL-C, were prominent in shift workers with more than 10 years of shift work and those who worked more than 8 hours per shift. The findings indicate that the number of working hours and the length of current shift work were associated with cardiometabolic risk indicators.\u003c/p\u003e \u003cp\u003eStratification by chronotype indicated that evening chronotype shift workers showed significantly more elevated levels of cardiometabolic risk markers, including systolic blood pressure, LDL-C levels, pulse rate, and visceral fat content, compared to morning chronotype shift workers. This highlights the greater susceptibility of evening chronotypes to adverse cardiometabolic risk markers. The findings presented in this paper give a base for future research studies in the development of better preventive measures in promoting health and well-being among shift workers.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBaidoo VA, Knutson KL (2023) Associations between circadian disruption and cardiometabolic disease risk: A review. Obesity. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/oby.23666\u003c/span\u003e\u003cspan address=\"10.1002/oby.23666\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHemmer A, Marescha J, Dibner C, Dorribo V, Perrig S, Genton L, Collet TH (2021) The Effects of Shift Work on Cardio-Metabolic Diseases and Eating Patterns. 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J Clin Endocrinol Metab. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1210/jc.2014-3754\u003c/span\u003e\u003cspan address=\"10.1210/jc.2014-3754\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTables 1 to 3 are available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Wayamba University of Sri Lanka","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Cardiometabolic risk factors, chronobiology, chrono-nutrition, chronotype, circadian misalignment","lastPublishedDoi":"10.21203/rs.3.rs-5826797/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5826797/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e: Shift work-induced circadian disruption has been strongly linked to various cardiometabolic diseases including obesity, diabetes \u0026amp; cardiovascular disease. Limited studies have explored the impact of different variables such as night work durations, intensities and chronotype on cardiometabolic risk.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e: This study aimed to determine the impact of circadian disruption on cardiometabolic risk markers in shift workers. This case-control study was conducted with 104 male workers (shift workers; n=52, mean age ±SD; 43.3±10.2 and non-shift workers; n=52, mean age ±SD; 41.2±9.8). Shift work status, durations and intensity of night shifts were determined via an interviewer administered questionnaire. Cardiometabolic risk was evaluated through anthropometric (height, weight, waist circumference and body composition), biochemical (fasting glucose and lipid profile), clinical (blood pressure) and dietary assessment (24-hr recalls from normal days and from work days). The chronotype was determined via the Munich Chronotype Questionnaire (MCTQ).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003eShift-workers had significantly higher mean body fat percentage (31.7, 22.7% p=0.031), systolic blood pressure (138.6, 128.5 mmHg p=0.009), pulse rate (78.7, 72.3 bpm p=0.015), triglycerides (1.60, 1.30mmol/l p=0.021) and LDL-C (3.90, 3.40 mmol/l p=0.012) than non-shift workers. Evening chronotype shift workers had significantly higher visceral fat levels (12.8, 8.90 p=0.001), systolic blood pressure (137.0, 127.6 mmHg p=0.006), pulse rate (82.7, 73.3 bpm p=0.005) and LDL-C (4.00,3.40 mmol/l p=0.039) than shift workers with a morning chronotype.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003eThe number of working hours and the duration of current shift work were associated with cardiometabolic risk markers and the evening chronotype was significantly associated with cardiometabolic risk markers. Further research is warranted to elucidate the underlying mechanisms and inform targeted interventions for individuals engaged in shift work, considering chronotypes.\u003c/p\u003e","manuscriptTitle":"Elevated cardiometabolic risk markers associated with shift work and evening chronotype","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-01-16 06:22:21","doi":"10.21203/rs.3.rs-5826797/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"b843327c-8f69-474b-a2e6-98fde07df9d7","owner":[],"postedDate":"January 16th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":42844703,"name":"Nutrition \u0026 Dietetics"}],"tags":[],"updatedAt":"2025-01-17T08:23:27+00:00","versionOfRecord":[],"versionCreatedAt":"2025-01-16 06:22:21","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5826797","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5826797","identity":"rs-5826797","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}