American journal of human biology : the official journal of the Human Biology Council · 2026
Work-Influenced Circadian Disruption Connected to Disease Risk but Not Microbiomes in a Cohort of Philadelphia Nurses.
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Super Clare, Asif Maira, Compher Charlene, Schurr Theodore G, Hoke Morgan K
Аннотация
OBJECTIVES: Circadian rhythms influence activity cycles in humans, and circadian rhythm disruption (CRD) can negatively impact cardiometabolic disease risk and microbiome composition. This study documented CRD in a sample of nurses in Philadelphia, examining connections between CRD, disease risk, and work environment factors, proposing the concept of work-influenced circadian disruption (WICD).
MATERIALS AND METHODS: A total of 75 nurses were recruited for the study. Disease risk indicators included triglycerides (TRG), C-reactive protein (hs-CRP), blood pressure (BP), and fecal gut microbiome composition. Surveys recorded CRD via sleep/exhaustion levels, weekly exercise, mealtime timing/length, shift diet, and night shift work. Work environment variables included staff/resources, physician-nurse relations, break times, and patient care assignments. Mixed multiple regression models assessed CRD's associations with cardiometabolic disease risk indicators (H1) and work environment variables (H2). Microbiome similarity by CRD variables was tested using PERMANOVAs.
RESULTS: Lower exercise levels were associated with higher hs-CRP (β = -4.2, p = 0.05), TRG (β = -9.8, p = 0.03), BP (β = -3.6, p = 0.01), and less break time (β = 0.1, p = 0.01). Higher exhaustion was linked to elevated hs-CRP (β = 6.0, p = 0.02) and fewer staff/resources (β = -0.7, p = 0.01). Less mealtime was associated with higher BP (β = -0.2, p = 0.05) and shorter breaks (β = 0.2, p = 0.04). Night shifts were linked to higher BP (β = 6.3, p = 0.01). CRD variables were not significantly associated with microbiome profiles.
CONCLUSIONS: CRD is associated with disease risk indicators and with work environment variables but not with microbiome profiles, suggesting these relationships may operate through other pathways. Further studies should explore biobehavioral networks in WICD.
MATERIALS AND METHODS: A total of 75 nurses were recruited for the study. Disease risk indicators included triglycerides (TRG), C-reactive protein (hs-CRP), blood pressure (BP), and fecal gut microbiome composition. Surveys recorded CRD via sleep/exhaustion levels, weekly exercise, mealtime timing/length, shift diet, and night shift work. Work environment variables included staff/resources, physician-nurse relations, break times, and patient care assignments. Mixed multiple regression models assessed CRD's associations with cardiometabolic disease risk indicators (H1) and work environment variables (H2). Microbiome similarity by CRD variables was tested using PERMANOVAs.
RESULTS: Lower exercise levels were associated with higher hs-CRP (β = -4.2, p = 0.05), TRG (β = -9.8, p = 0.03), BP (β = -3.6, p = 0.01), and less break time (β = 0.1, p = 0.01). Higher exhaustion was linked to elevated hs-CRP (β = 6.0, p = 0.02) and fewer staff/resources (β = -0.7, p = 0.01). Less mealtime was associated with higher BP (β = -0.2, p = 0.05) and shorter breaks (β = 0.2, p = 0.04). Night shifts were linked to higher BP (β = 6.3, p = 0.01). CRD variables were not significantly associated with microbiome profiles.
CONCLUSIONS: CRD is associated with disease risk indicators and with work environment variables but not with microbiome profiles, suggesting these relationships may operate through other pathways. Further studies should explore biobehavioral networks in WICD.