Individuals infected with HIV have a high risk of developing metabolic comorbidities not traditionally associated with the immune dysregulation and deficiency associated with HIV infection and AIDS. Many of these comorbidities in HIV uninfected individuals have been linked to a disordered circadian clock function. The study investigators will further evaluate the circadian clock in HIV infection as a mechanism underlying the metabolic dysregulation in this population.
Study Type
OBSERVATIONAL
Enrollment
80
We will use a deep phenotyping approach to collect multidimensional datasets from individuals infected with HIV compared to healthy controls to define circadian rhythm disruptions associated with HIV infection.
Institute for Translational Medicine and Therapeutics (ITMAT), University of Pennsylvania School of Medicine
Philadelphia, Pennsylvania, United States
Time-of-day fluctuations in core clock gene expression
Relative expression normalized to housekeeping genes (GAPDH, ACTB) plotted by time of day (morning, afternoon, evening, night with target times of 08:00, 14:00, 20:00, 02:00 +/- 1 hour)
Time frame: 48 hours
Variance explained [R^2 values]
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. This approach allows to integrate multiple measurements with different units of measure. The measurements include 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, multiomics outputs (abundance of metabolites, proteins, microbiota) and markers of cellular and inflammatory function and disease state (HIV infection).
Time frame: 48 hours
Variance explained [R^2 values]
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. This approach allows to integrate multiple measurements with different units of measure. The measurements include communication (number of phone calls and text messages), mobility (miles traveled), light exposure, and sleep/wake times.
Time frame: up to 4 months
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