Complexity of continuous human blood glucose data
Diabetes is a chronic disease that occurs either when the pancreas does not produce enough insulin or when the body cannot effectively use the insulin it produces. Insulin is a hormone that regulates blood sugar (1). In 2014, 8.5% of adults aged 18 years and older had diabetes. In 2012 diabetes was the direct cause of 1.5 million deaths and high blood glucose was the cause of another 2.2 million deaths.
Currently, some important biostatistical parameters of continuous monitoring data (such as complexity) is far from fully explored. Multiscale entropy is a common used parameter to describe complexity of dynamic systems. Under healthy conditions, the fluctuation of the blood glucose data is neither random nor regular: it possesses robust multi-scale dynamic patterns. Complexity of biological single has been accepted as a parameter of human health status. This study will utilize multiscale entropy to analyze the complexity of human blood glucose and heart rate.
This is a parallel-group, open labeled, observational study. Subjects will be interviewed via phone or lab visit to determine if they meet inclusion criteria (see below Inclusion Criteria and Exclusion criteria). If the subject meets inclusion criteria, consent form will be obtained. Totally 60 patients will be involved. They will be allocated into different groups by their ages: 18 to 30, 30 to 45, and 45 to 70. A Freestyle Libre® sensor with reader and a Fitbit® wristband will be assigned to every subject to collect 14 days’ continuous blood glucose, heart rate, diet, and exercise data. The subjects will come back to PI’s lab at the end of study to export the data out of the devices. The whole study will last for 14 days.
•Healthy adults aged 18-70
•Comorbidity of thyroid disorders, nephrotic syndrome, pancreatitis, alcoholism, Lupus erythematosus, Malignant neoplasms, mental disorders, etc.
•Other medical conditions that PI considered not meet the inclusion criteria