Mapping the Shift Worker's Microbiome
Study Details
Study Description
Brief Summary
The investigators hypothesize that disruptions to the microbiome of shift-workers represent a hitherto unexamined factor contributing to disease risk. The investigators will therefore define time-of-day dependent fluctuations of the microbiome in night shift workers and matched daytime workers deeply phenotyped for behavioral, clinical, and metabolomic outputs using integrated remote sensing.
| Condition or Disease | Intervention/Treatment | Phase |
|---|---|---|
|
N/A |
Detailed Description
Though several epidemiological studies have demonstrated that working night shift schedules are a risk factor for developing metabolic and cardiovascular diseases, the mechanisms through which this is conferred is not yet understood. Shift-work schedules alter employee's patterns of activity, light exposure and dietary intake in a manner incongruent with the endogenous clock. This circadian clock ensures that our metabolic activity occurs at maximally beneficial times of the day, but is largely unable to adapt to rapidly shifting schedules or sustained night-work. In mice, the investigators' lab has previously shown that genes relevant to all aspects of the metabolic syndrome are subject to circadian oscillation and that the gut microbiome is also subject to control by the host molecular clock. Despite the large contribution of our microbiome to host metabolism, the microbiome has been scarcely studied in the shift-working population. The investigators hypothesize that disruptions to the microbiome of shift-workers represent a hitherto unexamined factor contributing to disease risk. The investigators will therefore define time-of-day dependent fluctuations of the microbiome in night shift workers and matched daytime workers deeply phenotyped for behavioral, clinical, and metabolomic outputs using integrated remote sensing. The investigators will assess core body temperature, sleep/activity cycles, cortisol and melatonin as outputs determined by the host clock, and postprandial glucose and insulin levels as well as nocturnal blood pressure dipping as risk-related outputs. Through antibiotic-induced suppression, The investigators will determine the microbiome's specific contribution to these outputs. This has major implications for refining shift-work schedules and exploring therapeutic strategies in this population.
Study Design
Arms and Interventions
| Arm | Intervention/Treatment |
|---|---|
| Experimental: Cohort 1 Shift workers receive a standardized meal with a glucose challenge test |
Other: Standardized meal with a glucose challenge test
Postprandial glucose and insulin response
|
| Experimental: Cohort 2 Matched healthy controls receive a standardized meal with a glucose challenge test |
Other: Standardized meal with a glucose challenge test
Postprandial glucose and insulin response
|
Outcome Measures
Primary Outcome Measures
- Area under the glucose over time curve [12 hour]
Area under the curve (AUC) will be calculated from serial, timed glucose measurements
Secondary Outcome Measures
- Time-of-day dependent fluctuations of the microbiome [48 hours]
Relative abundances assessed several times of day (morning, afternoon, evening, night with target times of 08:00, 14:00, 20:00, 02:00 +/- 1 hour)
- Compound outcome derived from percent variance explained in communication (number of phone calls and text messages), mobility (miles traveled), light exposure, blood pressure, heart rate, heart rate variability, sleep/wake times, body core temperature [48 hours]
To evaluate the linear relationships between every pairwise combination of variables in the integrated dataset, the R^2, or coefficient of determination, will be calculated for each pair using linear regression. A heat map of the proportion of variance in each variable (e.g. mobility, light exposure, systolic blood pressure) explained by each other variable will then be constructed to allow an integrative exploration of these data. Here, the advantage is that multiple assessments with different units of measure can be integrated to generate deep phenotypes.
- Compound outcome derived from variance observed in multiomics outputs (metabolites, microbiota). [48 hours]
To explore factors contributing to the variance observed using principal components analysis
Eligibility Criteria
Criteria
Inclusion Criteria:
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Cohort 1: healthy un-medicated males (to limit gender-induced variability similar to our pilot study), shift-work schedule (>3 shifts per month outside 7am-6pm (9)) for the past ≥10 years, 40-59 years old (increased prevalence of the metabolic syndrome at ≥60 years of age (20));
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Cohort 2: day workers who work 7am-6pm for ≥10 years matched for line of work, age, gender, and BMI;
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Volunteers are capable of giving informed consent;
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40-59 years of age;
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Own an android smartphone which installs the remote sensing applications (those with apple smartphones will not be recruited);
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Non-smoking;
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Male subjects
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The use of contraception will NOT be required for male participants.
Exclusion Criteria:
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Recent travel across more than two (2) time zones (within the past month);
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Planned travel across more than two (2) time zones during the planned study activities;
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Use of illicit drugs;
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High dose vitamins (Vitamin A, Vitamin C, Vitamin E, Beta Carotene, Folic Acid and Selenium), alcohol and any over-the counter NSAID in the (2) two weeks before the start of the 48 hour deep phenotyping;
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High fat foods and caffeine in the past 24 hours prior to the 48-hour deep chronotyping session;
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History of abdominal surgery;
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Known allergy or intolerance to Vancomycin, and/or Neomycin;
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Use of anticholinergics in the week prior to the 48-hour sessions;
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Use of laxatives or anti-diarrhea medications in the two weeks prior to the 48-hour sessions;
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Subjects, who have received an experimental drug, used an experimental medical device within 30 days prior to screening, or who gave a blood donation of ≥ one pint within 8 weeks prior to screening;
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Subjects with any abnormal laboratory value or physical finding that according to the investigator may interfere with interpretation of the study results, be indicative of an underlying disease state, or compromise the safety of a potential subject;
Contacts and Locations
Locations
| Site | City | State | Country | Postal Code | |
|---|---|---|---|---|---|
| 1 | Institute for Translational Medicine and Therapeutics (ITMAT), University of Pennsylvania School of Medicine | Philadelphia | Pennsylvania | United States | 19104 |
Sponsors and Collaborators
- University of Pennsylvania
Investigators
- Principal Investigator: Carsten Skarke, MD, University of Pennsylvania
Study Documents (Full-Text)
None provided.More Information
Publications
None provided.- 826117