Longitudinal associations of circadian eating patterns with sleep quality, fatigue, and inflammation in colorectal cancer survivors up to 24 months post-treatment

Fatigue and insomnia, potentially induced by inflammation, are distressing symptoms experienced by colorectal cancer (CRC) survivors. Emerging evidence suggests that besides the nutritional quality and quantity, also the timing, frequency, and regularity of dietary intake (chrono-nutrition) could be important for alleviating these symptoms. We investigated longitudinal associations of circadian eating patterns with sleep quality, fatigue, and inflammation in CRC survivors.In a prospective cohort of 459 stage I-III CRC survivors, four repeated measurements were performed between 6 weeks and 24 months post-treatment. Chrono-nutrition variables included meal energy contribution, frequency (a maximum of six meals could be reported each day), irregularity, and time-window of caloric intake, operationalized based on 7-day dietary records. Outcomes included sleep quality, fatigue, and plasma concentrations of inflammatory markers. Longitudinal associations of chrono-nutrition variables with outcomes from 6 weeks until 24 months post-treatment were analyzed by confounder-adjusted linear mixed models, including hybrid models to disentangle intra-individual changes from inter-individual differences over time.An hour longer time-window of caloric intake between individuals was associated with less fatigue (beta:-6.1; 95%CI: -8.8,-3.3) and insomnia (-4.8;-7.4,-2.1). A higher meal frequency of on average 0.6 meals/day between individuals was associated with less fatigue (-3.7;-6.6,-0.8). An hour increase in time-window of caloric intake within individuals was associated with less insomnia (-3.0;-5.2,-0.8) and inflammation (-0.1;-0.1,0.0).Our results suggest that longer time-windows of caloric intake and higher meal frequencies may be associated with less fatigue, insomnia, and inflammation among CRC survivors. Future studies with larger contrasts in chrono-nutrition variables are needed to confirm these findings.