Predictive Value of Steroidal Hormones to Type 2 Diabetes and Metabolic Syndrome

Date of Award


Degree Type


Degree Name

Doctor of Philosophy in Clinical-Bioanalytical Chemistry



First Advisor

Wang, Sihe

Subject Headings

Analytical Chemistry, Biochemistry, Chemistry


Metabolic syndrome (MetS) and type 2 diabetes mellitus (T2DM) are highly prevalent in the US population and major health burdens. Consensus criterion for diagnosis of MetS was proposed by the International Diabetes Federation. The American Diabetes Association (ADA) has issued diagnosis criteria for T2DM that are internationally implemented. Steroid hormones especially aldosterone, testosterone, and cortisol have been found to be correlated with these disease states. A logistic regression model is used to determine the predictive value of the measured steroid hormones.

In Chapter 1, we develop the background on steroids specifically cortisol which is directly related to the subsequent chapters. Steroid pathways and the hypothalamus pituitary adrenal axis are discussed. In the remaining chapters, solutions are proposed for measurement of various steroid panels, which involve liquid chromatography-tandem mass spectrometry (LC-MS/MS). Chapter 2 discusses LC-MS/MS technique, development, and validation of clinical assays. Chapter 3 introduces a LC-MS/MS steroid panel for the measurement of plasma testosterone, aldosterone, cortisol, and cortisone and the predictive value of these compounds for MetS and T2DM. Cortisone had a strong predictive value for T2DM based on a logistic regression model of the data. Chapter 4 introduces a new LC-MS/MS method for the measurement of salivary cortisol, which is useful for identifying Cushing’s disease. This method is simple and fast and has been used for clinical studies. A reference interval collection was performed and the salivary cortisol reference interval was verified. Chapter 5 introduces a third LC-MS/MS method that measures a panel of steroids for the detection of congenital adrenal hyperplasia that is able to identify all the possible enzymatic blockages for this disease.

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