Biomarker(s) for Glucocorticoids

Study Overview
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Brief Summary
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The investigators have shown that patients with adrenal insufficiency (Addison's disease), a rare disorder, have doubled the expected mortality rate in Sweden despite Standard of Care glucocorticoid (GC) replacement. One % of the Swedish population are, however, receiving GCs for inflammatory diseases, but management is empirical and adjusted to underlying disease activity. The desired anti-inflammatory therapeutic effects cannot be differentiated from the adverse metabolic (osteoporosis, obesity, diabetes mellitus) and immunosuppressive side effects of GC. This frequently results in suboptimal GC therapy with adverse effects due to over-dosing or poor efficacy due to under-dosing. The primary aim is to identify a biomarker for the metabolic effects of GCs. Patients with Addison's disease completely lack endogenous GCs and can therefore be considered a human GC knock-out model. They can therefore be studied during near-physiological exposure and during GC starvation. This will uniquely allow a very clean biomarker identification model (using transcriptomics, proteomics and metabolomics). The secondary aim is to validate candidate biomarker(s) in a dose-response study using the same patient population. A biomarker of GC actions will make it possible to individualised therapy during pharmacological GC treatment. It would allow GC replacement to be monitored in Addison's disease and could become a specific diagnostic tool in patients with GC deficiency and excess (Cushings syndrome).

Official Title
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Protein/Metabolite Biomarker(s) for Glucocorticoid Action; an Experimental Trial in Patients With Adrenal Insufficiency

Conditions
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Addison Disease

Intervention / Treatment
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* Drug: Hydrocortisone
* Drug: Placebo


Participation Criteria
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Eligibility Criteria
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Inclusion Criteria: Primary adrenal insufficiency under stable glucocorticoid replacement therapy (15-30 mg of Hydrocortisone stable dose the last 3 months) due to autoimmune adrenalitis (disease diagnosed at least 12 months before inclusion), age 20-60 years, BMI 20-30 kg/m2, and ability to comply with the protocol procedures. Exclusion Criteria: Glucocorticoid replacement therapy for indication other than primary adrenal treatment, any treatment with sex hormones inclusive contraceptive drugs, treatment with levothyroxine, diabetes mellitus, renal or liver failure, significant and symptomatic cardiovascular disease.

Ages Eligible for Study
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Minimum Age: 20 Years
Maximum Age: 60 Years

Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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No

Study Plan
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How is the study designed?
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Design Details

Primary Purpose: Treatment
Allocation: Randomized
Intervention Model: Crossover Assignment
Masking: Single


Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| Active Comparator: Hydrocortisone<br>Near-physiologic doses of Hydrocortisone are being given to subjects. The first day between 09.00 and 12.00 0,024 mg Hydrocortisone/kg per hour. The first day between 12.00 and 20.00 0,012 mg Hydrocortisone/kg per hour. The first day between 20.00 and 24.00 0,008 mg Hydrocortisone/kg per hour. The second day between 00.00 and 11.00 0,030 mg Hydrocortisone/kg per hour. Hydrocortisone infusion: 0,4 ml Solu Cortef 100 mg (50 mg/ml) added in 999,6 ml sodium chloride 0,9% solution (1 mg Solu Cortef/ 50 ml total solution volume). | Drug: Hydrocortisone<br> <br> * Other names: SoluCortef®;|
| Placebo Comparator: Placebo<br>The same volume of sodium chloride 0,9% as in the other arm where Hydrocortisone is given in saline 0,9% solution. The given volume of sodium chloride will variate chronically as in Hydrocortisone arm. | Drug: Placebo<br> <br> * Other names: Sodium chloride 0,9%;|

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Protein profile changes between a state of GC starvation and near physiological GC exposure. | Using mass spectrometry, protein profile changes in blood, urine and adipose tissue are going to be identified between four points of time during two states: morning and midnight during near physiological GC exposure (sampling 1 and 2), morning and midnight during GC starvation (sampling 3 and 4). Quantitative measurements of all proteins will be used in the bioinformatic analysis. The bioinformatics strategic consists of a stepwise approach based on random forest analysis. Key features in the analysis include finding candidate markers that are increased during normal GC exposure (sampling 1 and 2), reduced during GC starvation (sampling 3 and 4) and exclusion of factors with high variability within normal subjects. Putative biomarkers will go through two levels of internal cross-validation. The investigators would like that this part of the project is not going to be public. | Changes in proteome (g/dl or umol/l) during 24 hours under two different states of GC exposure. | 
| Metabolite profile changes between a state of GC starvation and near physiological GC exposure. | Using mass spectrometry, metabolite profile changes in blood, urine and adipose tissue are going to be identified between four points of time during two states: morning and midnight during near physiological GC exposure (sampling 1 and 2), morning and midnight during GC starvation (sampling 3 and 4). Quantitative measurements of all metabolites will be used in the bioinformatic analysis. The bioinformatics strategic consists of a stepwise approach based on random forest analysis. Key features in the analysis include finding candidate markers that are increased during normal GC exposure (sampling 1 and 2), reduced during GC starvation (sampling 3 and 4) and exclusion of factors with high variability within normal subjects. Putative biomarkers will go through two levels of internal cross-validation. The investigators would like that this part of the project is not going to be public. | Changes in metabolome (units depending on the kind of metabolome) during 24 hours under two different states of GC exposure. | 

Secondary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| mRNA/miRNA profile changes between a state of GC starvation and near physiological GC exposure. | Using array based transcriptomics (both mRNA & miRNA), mRNA/miRNA profile changes in blood, urine and adipose tissue are going to be identified between four points of time during two states: morning and midnight during near physiological GC exposure (sampling 1 and 2), morning and midnight during GC starvation (sampling 3 and 4). Quantitative measurements of all mRNA/miRNA´s will be used in the bioinformatic analysis. The bioinformatics strategic consists of a stepwise approach based on random forest analysis. Key features in the analysis include finding candidate markers that are increased during normal GC exposure (sampling 1 and 2), reduced during GC starvation (sampling 3 and 4) and exclusion of factors with high variability within normal subjects. Putative biomarkers will go through two levels of internal cross-validation. The investigators would like that this part of the project is not going to be public. | Changes in mRNA/miRNA (Svedberg Unit, S) during 24 hours under two different states of GC exposure. | 

 Terms related to the study
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Keywords Provided by Centre Hospitalier Valida
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Metabolism, Biomarker, Glucocorticoids, Pharmacology, Metabolomics