Sleep, diet, and physical activity are increasingly recognized as modifiable factors for improved health. Modern lifestyles disrupt sleep patterns, linking sleep disturbances to impaired health, cardiovascular disease, increased BMI, and metabolic issues. Beyond significant circadian shifts like shift work, smaller irregularities such as social jetlag (SJL) – a discrepancy in mid-sleep time between weekdays and weekends – may affect health. SJL is more common in individuals with late chronotypes and fluctuates with age. Existing research suggests links between SJL and poor diet, adiposity, and metabolic disturbances, although the mechanisms are unclear. Proposed mechanisms include disrupted appetite regulation, altered reward center activity, and dysregulation of metabolic and endocrine functions. Studies also suggest sleep disturbances impact inflammatory markers and oxidative stress, potentially increasing cardiovascular disease risk. A bidirectional relationship exists between sleep quality and gut microbiome composition, with gut dysbiosis and circadian rhythm disruption linked to similar diseases. This study investigates the relationship between SJL, gut microbiome, dietary patterns, and cardiometabolic health, exploring potential mediating effects of diet.

Numerous studies have explored the relationship between sleep disturbances and various health outcomes. Shift work has been consistently linked to an increased risk of cardiovascular disease, higher BMI, and metabolic disruptions. Emerging evidence suggests that even smaller sleep irregularities, such as social jetlag (SJL), can have detrimental effects on health. Previous research indicates associations between SJL and poorer diet quality, increased adiposity, and metabolic disturbances, particularly in specific populations like adolescents and individuals with diabetes or metabolic syndrome. For instance, SJL has been linked to lower adherence to the Mediterranean diet and higher sugar-sweetened beverage consumption in young people. However, it remains unclear whether these associations are simply a reflection of reduced sleep duration or are independent effects of SJL. Studies have also reported associations between SJL and glycaemic dysregulation, altered lipid profiles, increased insulin resistance, adiposity, and metabolic syndrome. These effects might be mediated by dysregulation of metabolic and endocrine functions that are normally synchronized with the circadian rhythm. The impact of sleep disturbances on inflammatory markers and oxidative stress has also been observed, although the precise mechanisms require further investigation. The gut microbiome's role in sleep quality is increasingly recognized. Changes in gut microbiota composition are associated with various sleep disorders. However, the relationship between SJL and the gut microbiome remains largely unexplored.

This secondary analysis used data from the ZOE PREDICT 1 study (NCT034798866), a single-arm, single-blinded intervention study of 1002 UK adults (18-65 years) designed to investigate diet-microbiome-cardiometabolic interactions. Data included demographics, diet, cardiometabolic markers, stool metagenomics, and postprandial metabolic measures. Social jetlag (SJL) was calculated using self-reported sleep data (n=934), defining SJL as a ≥1.5-hour difference in mid-sleep time between weekdays and weekends. Participants also completed the Pittsburgh Sleep Quality Index (PSQI) and provided information on chronotype. Stool samples (n=1001) were subjected to shotgun metagenomic sequencing for gut microbiome analysis. Blood samples were collected for fasting and postprandial biomarker analysis (glucose, insulin, C-peptide, triglycerides, lipid profile, GlycA, IL-6). Continuous glucose monitoring (CGM) data (14 days) were used to assess glucose variability and time in range (TIR). Dietary intake was assessed using the EPIC Food-Frequency Questionnaire (FFQ) and free-living dietary data. Statistical analyses included χ² tests, Kruskal-Wallis tests, ANCOVA (adjusted for sex, age, BMI, ethnicity, education), Mann-Whitney U tests (FDR-corrected), Random Forest machine learning for microbiome classification, and mediation analysis (to assess the mediating effect of diet on the SJL-microbiome relationship).

The study included 934 participants, predominantly white (90%) and female (72%). 16% (n=145) were classified as having SJL. The SJL group had a higher proportion of males, were younger (38.4 ± 11.3 years vs 46.8 ± 11.7 years), and had a greater percentage of short sleepers. Seventeen bacterial species showed significantly different abundances between the SJL and no-SJL groups. Nine species were more abundant in the SJL group, and eight were less abundant. Machine learning analysis did not reveal significant differences in overall microbiome composition. Diet quality partially mediated the association between SJL and the abundance of two microbiome species (SGB71759 and Ruminococcaceae bacterium). Nut intake also significantly mediated the relationship between SJL and these two species, as well as two additional species (Flavonifractor plautii and Clostridia bacterium SGB14253). Participants with SJL had slightly poorer diet quality scores, higher intakes of fish and seafood, sugar-sweetened beverages, and potatoes, and lower intakes of fruits and nuts. They also had fewer eating occasions and main meals and a later first main meal time. Fasting GlycA (inflammation marker) and IL-6 were slightly higher in the SJL group, though not significant after multiple testing correction. No significant differences were found in other fasting or postprandial biomarkers or CGM data. Subgroup analyses revealed interactions between SJL and GlycA based on sex (males with SJL exhibiting higher inflammation) and age. Perimenopausal women were more susceptible to SJL-related inflammation.

This study provides novel evidence of multiple associations between SJL, diet quality, dietary habits, inflammation, and gut microbial composition in a large, deeply phenotyped cohort. While the overall microbiome composition did not predict SJL status, specific species showed significant differences independent of age. The partial mediation effects of diet and nut intake suggest modifiable dietary factors influence the SJL-microbiome relationship. This aligns with existing knowledge that individuals with SJL tend to consume less healthy diets. The findings of slightly elevated inflammation markers, although not significant after correction, warrant further investigation, especially the interactions observed across sex and menopausal status. The lack of significant differences in fasting and postprandial metabolic markers in this largely healthy cohort contrasts with some previous findings in metabolically unhealthy populations. This may reflect the overall health of the PREDICT 1 cohort, which has a lower prevalence of inadequate sleepers compared to the general population. The results suggest that even minor circadian misalignment may have health implications, possibly through its influence on diet and the gut microbiome.

This study demonstrates associations between social jetlag (SJL), gut microbiome composition, dietary patterns, and inflammation. Dietary factors partially mediate the relationship between SJL and the gut microbiome. Maintaining a healthy diet and consistent sleep-wake timing are recommended lifestyle changes. Further research is needed to confirm causality and long-term effects of SJL, potentially exploring microbial therapies as clinical interventions.

The cross-sectional design limits causal inference. Lack of information on shift work, employment status, sleep latency, and sleep medication use are limitations. Subjective sleep measures were used due to the impact of the study protocol on actigraphy data. The week versus weekend sleep comparison might not fully capture the impact of work versus work-free days. The relatively low prevalence of SJL and short sleepers in the cohort may limit generalizability to broader populations.