Author
Listed:
- Yangyang Cui
(Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China
Department of Mechanical Engineering, Tsinghua University, Beijing 100084, China
Biomechanics and Biotechnology Lab, Research Institute of Tsinghua University in Shenzhen, Shenzhen 518057, China
These authors contributed equally to this work.)
- Hankun Zhang
(Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China
Biomechanics and Biotechnology Lab, Research Institute of Tsinghua University in Shenzhen, Shenzhen 518057, China
These authors contributed equally to this work.)
- Jia Zhu
(Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China
Department of Mechanical Engineering, Tsinghua University, Beijing 100084, China
Biomechanics and Biotechnology Lab, Research Institute of Tsinghua University in Shenzhen, Shenzhen 518057, China)
- Zhenhua Liao
(Biomechanics and Biotechnology Lab, Research Institute of Tsinghua University in Shenzhen, Shenzhen 518057, China)
- Song Wang
(Biomechanics and Biotechnology Lab, Research Institute of Tsinghua University in Shenzhen, Shenzhen 518057, China)
- Weiqiang Liu
(Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China
Department of Mechanical Engineering, Tsinghua University, Beijing 100084, China
Biomechanics and Biotechnology Lab, Research Institute of Tsinghua University in Shenzhen, Shenzhen 518057, China)
Abstract
Background: Saliva has been studied as a better indicator of disorders and diseases than blood. Specifically, the salivary glucose level is considered to be an indicator of diabetes mellitus (DM). However, saliva collection methods can affect the salivary glucose level, thereby affecting the correlation between salivary glucose and blood glucose. Therefore, this study aims to identify an ideal saliva collection method and to use this method to determine the population and individual correlations between salivary glucose and blood glucose levels in DM patients and healthy controls. Finally, an analysis of the stability of the individual correlations is conducted. Methods: This study included 40 age-matched DM patients and 40 healthy controls. In the fasting state, saliva was collected using six saliva collection methods, venous blood was collected simultaneously from each study participant, and both samples were analyzed at the same time using glucose oxidase peroxidase. A total of 20 DM patients and 20 healthy controls were arbitrarily selected from the above participants for one week of daily testing. The correlations between salivary glucose and blood glucose before and after breakfast were analyzed. Finally, 10 DM patients and 10 healthy controls were arbitrarily selected for one month of daily testing to analyze the stability of individual correlations. Results: Salivary glucose levels were higher in DM patients than healthy controls for the six saliva collection methods. Compared with unstimulated saliva, stimulated saliva had decreased glucose level and increased salivary flow. In addition, unstimulated parotid salivary glucose was most correlated with blood glucose level (R 2 = 0.9153), and the ROC curve area was 0.9316, which could accurately distinguish DM patients. Finally, it was found that the correlations between salivary glucose and blood glucose in different DM patients were quite different. The average correlation before breakfast was 0.83, and the average correlation after breakfast was 0.77. The coefficient of variation of the correlation coefficient before breakfast within 1 month was less than 5%. Conclusion: Unstimulated parotid salivary glucose level is the highest and is most correlated with blood glucose level, which can be accurately used to distinguish DM patients. Meanwhile, the correlation between salivary glucose and blood glucose was found to be relatively high and stable before breakfast. In general, the unstimulated parotid salivary glucose before breakfast presents an ideal saliva collecting method with which to replace blood-glucose use to detect DM, which provides a reference for the prediction of DM.
Suggested Citation
Yangyang Cui & Hankun Zhang & Jia Zhu & Zhenhua Liao & Song Wang & Weiqiang Liu, 2022.
"Correlations of Salivary and Blood Glucose Levels among Six Saliva Collection Methods,"
IJERPH, MDPI, vol. 19(7), pages 1-15, March.
Handle:
RePEc:gam:jijerp:v:19:y:2022:i:7:p:4122-:d:783748
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Cited by:
- Anming Chen & Jia Zhu & Qunxiong Lin & Weiqiang Liu, 2022.
"A Comparative Study of Forehead Temperature and Core Body Temperature under Varying Ambient Temperature Conditions,"
IJERPH, MDPI, vol. 19(23), pages 1-18, November.
- Yangyang Cui & Jia Zhu & Zhili Duan & Zhenhua Liao & Song Wang & Weiqiang Liu, 2022.
"Artificial Intelligence in Spinal Imaging: Current Status and Future Directions,"
IJERPH, MDPI, vol. 19(18), pages 1-21, September.
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