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Die oralen Antidiabetika Metformin und Sitagliptin werden überwiegend renal eliminiert, weshalb während der Therapie regelmäßig die Nierenfunktion abgeschätzt werden sollte. Dies geschieht mithilfe von Serumkreatinin-basierten Formeln, zum Beispiel der Gleichung nach Cockcroft-Gault. Mit dem Ziel, zukünftig eine Möglichkeit für eine vereinfachte Kontrolle der Therapie mit Metformin und/oder Sitagliptin in Kapillarblutproben zu haben, wurde eine Methode zur Bestimmung der Konzentration von Kreatinin, Metformin und Sitagliptin aus Trockenblutproben (Dried Blood Spots, DBS) entwickelt. Als Träger zeigte Blotting Papier die besten Ergebnisse in Bezug auf die Handhabung und die Extraktionseffizienz. Aus einem einzelnen DBS gelang es, Metformin und Kreatinin mittels HPLC-UV und Sitagliptin mittels LC-MS/MS zu quantifizieren. Die flüssigchromatographischen Methoden wurden entsprechend der EMA- und FDA-Kriterien erfolgreich vollvalidiert. Die unteren Nachweisgrenzen (LLOQ) lagen bei 0,2 µg/mL für Metformin, 1,5 µg/mL für Kreatinin und 3 ng/mL für Sitagliptin. Da Referenzbereiche für Arzneistoffkonzentrationen in der Regel für Serum/Plasma angegeben werden, wurde das Verteilungsverhalten der beiden Antidiabetika zwischen Plasma (cP) und Blutzellen (cBZ) mittels in-vitro Inkubationsversuchen ermittelt. Für Metformin betrug der Verteilungskoeffizient cP/cBZ 4,65 ± 0,73, für Sitagliptin 5,58 ± 0,98. Damit lagen beide Arzneistoffe mehr als 4-fach höher im Plasma als in den Blutzellen vor. Erythrozyten waren zuvor schon als tiefes Kompartiment für Metformin beschrieben worden, für Sitagliptin waren dieses die ersten Daten die zeigten, dass der Arzneistoff ebenfalls eine relevante Verteilung in die Blutzellen zeigt. In Kooperation mit einer diabetologischen Schwerpunktpraxis wurde eine erste klinische Studie (Basisstudie) durchgeführt, die zum Ziel hatte, aus den DBS die Nierenfunktion abzuschätzen. In DBS von 70 Patienten wurden Metformin, und/oder Sitagliptin sowie Kreatinin quantifiziert. Mit Hilfe der von der Praxis übermittelten Serumkreatinin-konzentration konnte durch den Vergleich mit der Konzentration im Kapillarbut erstmalig ein Korrelationsfaktor bestimmt und verifiziert werden, um die Kapillarblut- in die Serumkonzentration des Kreatinins umzurechnen (F = cKapillarblut/cPlasma = 0,916 ± 0,088). So war es möglich, die Nierenfunktion über die Formel nach Cockcroft und Gault abzuschätzen. In der Basisstudie fiel auf, dass die Konzentration des Sitagliptins im Blut der Patienten signifikant mit steigendem Hämatokrit korrelierte (Pearson R = 0,396; p < 0,05). Die nähere Untersuchung dieser Beobachtung mittels in-vitro Verteilungsversuchen zeigte eine sehr stark inter-individuell schwankende Verteilung des Sitagliptins zwischen Plasma und den Blutzellen und eine vom Hämatokrit (Hct) linear abhängige Verteilung. In Blut mit einem höheren Hct fand sich mehr Arzneistoff in den Blutzellen als in Blut mit niedrigerem Hct, was die höheren Gesamtkonzentrationen an Sitagliptin im DBS erklärte. Dialyseversuche in-vitro bestätigten, dass die Eliminationszeit mit steigendem Hämatokrit des Blutes anstieg. Damit konnte zum ersten Mal gezeigt werden, dass die Blutzellen ein tiefes Kompartiment für Sitagliptin darstellen. Eine zweite klinische Studie (Feldstudie) wurde in Kooperation mit 14 öffentlichen Apotheken mit dem Ziel, repräsentative Konzentrationen für die Kapillarblutspiegel der beiden Medikamente unter Alltagsbedingungen zu ermitteln, durchgeführt. In DBS von 84 Patienten wurden wiederum Metformin, Sitagliptin und Kreatinin quantifiziert. Aus den Daten der beiden Studienpopulationen (n = 134) wurde für Metformin eine mittlere Konzentration von 2,22 ± 1,16 µg/mL und für Sitagliptin von 432,20 ± 268,79 ng/mL bestimmt. Mittels populationspharmakokinetischer Methoden konnten für beide Arzneistoffe zum ersten Mal Eliminationshalbwertszeiten (t1/2) aus Kapillarblut für Patienten mit einer Kreatininclearance größer und kleiner als 60 mL/min bestimmt werden. Erwartungsgemäß waren die t1/2 bei besserer Nierenfunktion kürzer, sowohl für Metformin (11,9 h versus 18,5 h) als auch für Sitagliptin (8,4 h versus 13,0 h). Für Sitagliptin waren dies erstmalige klinische Belege für eine ansteigende Eliminationszeit mit sinkender Nierenfunktion. Die gewonnenen Daten boten zudem Gelegenheit, den literaturbekannten ungünstigen Effekt einer kombinierten Einnahme von Diuretika, NSAIDs, ACE-Inhibitoren und/oder Angiotensinrezeptorantagonisten („target drugs“) auf die Nierenfunktion („triple whammy“) zu betrachten. Tatsächlich korrelierten die Anzahl der eingenommenen „target drugs“ und auch die Dosis der Diuretika mit einer sinkenden Kreatininclearance der Patienten. Mit vorliegender Arbeit wurden zum einen neue Erkenntnisse über die Pharmakokinetik des Sitagliptins gewonnen, zum anderen wurde die Grundlage geschaffen, um aus einem DBS die Blutspiegel von Metformin und Sitagliptin im Zusammenhang mit der Nierenfunktion zu betrachten. In Zukunft könnte diese Methode für ein Therapeutisches Drug Monitoring der beiden Arzneistoffe eingesetzt werden um dieses für Patienten aufgrund der minimalinvasiven Blutabnahme wesentlich angenehmer zu gestalten. / Patients´ kidney function should frequently be monitored under therapy with the oral antidiabetics metformin and sitagliptin due to their renal elimination. The creatinine clearance or the glomerular filtration rate can be estimated using serum creatinine-based equations such as the Cockcroft-Gault equation. Aiming at simplifying drug monitoring a method was developed to quantify creatinine, metformin and sitagliptin in dried blood spots (DBS). For sample collection blotting paper showed the best results regarding handling and extraction efficiency. A single DBS was used to quantify creatinine and metformin with HPLC-UV and sitagliptin with LC-MS/MS. Both chromatographic methods were subjected to a full validation following the current FDA and EMA guidelines. The lower limits of quantification (LLOQ) were 0.2 µg/mL for metformin, 1.5 µg/mL for creatinine and 3 ng/mL for sitagliptin. Since reference intervals for drug concentrations are typically reported for plasma/serum, in-vitro experiments were performed to investigate the distribution of the drugs between plasma (cP) and blood cells (cBC). For metformin the mean ratio of plasma to blood cell concentration cP/cBC was 4.65 ± 0.73, for sitagliptin 5.58 ± 0.98. Thus, both drugs were present at more than four times higher concentrations in plasma compared to blood cells. The erythrocytes had been identified as deep compartment of metformin before, but for sitagliptin these were the first data showing a drug distributing into blood cells. In collaboration with a physician specialized in diabetology a first clinical study (basic study) was performed to estimate the kidney function using DBS. In blood spots of 70 patients metformin, creatinine and sitagliptin were quantified. For the first time a correlation factor (F = ccapillary blood/cplasma = 0.916 ± 0.088) was determined and verified to translate capillary blood concentrations of creatinine into plasma concentrations. Thereby, the Cockcroft-Gault equation could be used to estimate the kidney function. In capillary blood samples of patients participating in the basic study the concentration of sitagliptin statistically significantly correlated with increasing hematocrit values (Pearson R = 0.396; p < 0,05). In-vitro experiments revealed a strong inter-individual variability of sitagliptin distribution between blood cells and plasma and also a distribution depending on the hematocrit (hct). With a higher hematocrit a bigger amount of the drug resided within the cells compared to blood with a lower hematocrit. In in-vitro dialysis experiments an increasing elimination time from venous blood was demonstrated with rising hct. Based on these results blood cells were identified as deep compartment for sitagliptin. A second clinical study (field study) was performed in cooperation with 14 community pharmacies to determine representative capillary blood concentrations under real-life conditions. In DBS of 84 patients metformin and/or sitagliptin and creatinine were quantified. In the cohort of both studies (n = 134) mean concentrations of 2.22 ± 1.16 µg/mL for metformin and 432.20 ± 268.79 ng/mL for sitagliptin were detected. Population pharmacokinetic methods were applied to calculate elimination half-lives (t1/2) from capillary blood for patients with creatinine clearances higher and lower than 60 mL/min. As expected, t1/2 were shorter in patients with a better renal function, both for metformin (11.9 h vs. 18.5 h) and for sitagliptin (8.4 h vs. 13. 0 h). For the first time an increasing elimination time with decreasing renal function was demonstrated for sitagliptin. The collected data provided the opportunity to examine the influence of drugs with known unfavorable impact on kidney function. The effect of the combined therapy with diuretics, NSAIDs, ACE-inhibitors and angiotensin receptor antagonists (“target drugs”) on the renal function has been termed “triple whammy”. Indeed, in the present study both the number of the target drugs as well as the dose of the diuretics correlated with a decreasing creatinine clearance. With the herein presented work new insights into the pharmacokinetics of sitagliptin were gained. Additionally, a basis was created to use a single dried-blood-spot for appraisal of the blood levels of metformin and sitagliptin in relation to the kidney function. This method might facilitate an easier and more pleasant future therapeutic drug monitoring of the compounds due to minimal invasive blood collection.

Chronic pain is a major health problem affecting more than 1.5 billion people worldwide. Specifically, neuropathic pain and chronic post-surgical pain are debilitating clinical conditions with few efficacious treatments, warranting development of novel therapeutics. Starting with the hypothesis that dysregulated translation regulation pathways may underlie these pain states, we demonstrated that there is a major reorganization of translation machinery in the peripheral nervous system of rats and mice, including enhanced mTOR and ERK activity and increased phosphorylation of mTOR and ERK downstream targets in these persistent pain states. We also hypothesized that activators of AMP-activated protein kinase (AMPK) may represent a novel treatment avenue for the treatment of neuropathic and incision-induced pain because AMPK activators inhibit ERK and mTOR signaling, two important pathways involved in the sensitization of peripheral nociceptors. The AMP activated protein kinase (AMPK) activators, metformin, resveratrol and A769662, inhibited translation regulation signaling pathways in sensory neurons, eIF4F complex formation, nascent protein synthesis in injured nerves and sodium channel-dependent excitability of sensory neurons resulting in a resolution of neuropathic allodynia. We have further demonstrated that local injection of resveratrol, metformin or A769662 and topical application of resveratrol, a potent AMPK activator, into the hindpaw following plantar incision dose-relatedly reverses incision-mediated mechanical hypersensitivity as well as hyperalgesic priming induced by incision. In addition, co-treatment with systemic metformin and local resveratrol at individually sub-efficacious doses at the time of incision blocked acute hypersensitivity and hyperalgesic priming suggesting potential super-additive effects of combined AMPK activator use. These results highlight the importance of signaling to translation control in peripheral sensitization of nociceptors and provide further evidence for activation of AMPK as a novel treatment avenue for acute and chronic pain states.

Metformin (MET) is a widely used drug indicated for type-2 diabetes management. Interestingly, numerous epidemiological studies show that MET may confer protective benefits from overall cancer risk and cancer-related mortality. Various pre-clinical studies show that MET also exerts chemotherapeutic properties using doses that far exceed those for glycemic control. Currently, there are numerous ongoing clinical trials testing the chemotherapeutic advantages of MET treatment in various cancer types using doses that are within the therapeutic range for diabetes management. We sought out to determine whether therapeutic doses of MET for diabetes management is chemopreventive or chemotherapeutic. We have shown that such doses are chemopreventive at best since they were unable to decrease cell viability in BxPC-3 (wild type K-RAS) and MIA PaCa-2 (mutant K-RAS) pancreatic cancer (PDAC) cells. Through a targeted metabolomics approach using 1,2-¹³C₂-D-glucose as the sole tracer, we have shown that a therapeutic dose (100 μM) of MET that is prescribed for diabetes management inhibits glucose-derived new palmitate synthesis when acetyl-CoA is dedicated towards de novo fatty acid synthesis. This occurs when a) K-RAS mutation is present (in MIA PaCa-2 cells) and b) cholesterol in the form of the more water-soluble derivative cholesteryl hemisuccinate (CHS) is supplemented in the media to prevent acetyl-CoA from being directed towards cholesterol synthesis. Immnunoblot analyses showed that this phenomenon is regulated by decreased protein expression of the fatty acid synthase (FAS) enzyme. We also showed that chronic (every two days in 30 days) CHS and MET treatment decreased triglyceride intracellular concentrations in BxPC-3 and MIA PaCa-2 cells. These treatments exerted no effect on FASN gene expression, indicating that the lipid-inhibitory effects of MET in PDAC is regulated at the metabolic flux and protein expression levels. Finally, we interrogated the metabolic effects of MET treatment using uniformly-labeled glucose tracer (¹³C₆-D-glucose) and gas chromatography/mass spectrometry (GC/MS) using a LSL-K-rasᴳ¹²ᴰ/⁺;LSL-Trp53ᴿ¹⁷²ᴴ/⁺;Pdx-1-Cre (KPC) mouse model of PDAC. We showed that acute (5 days), high-dose treatment (250 mg/kg body weight administered intraperitoneally) of MET reversed the glycolytic metabolism of pancreatic tumor into an oxidative one in the presence of K-ras and Tp53 mutation. MET, regardless of K-ras status, inhibited glucose-derived acetate enrichment towards palmitate synthesis. Immunohistochemistry analysis of KPC pancreases revealed that a decrease in the tumor cell proliferation marker Ki67 and in the FAS protein was observed in KPC mice treated with MET. In summary, we have identified a mechanism of action of the popular anti-hyperglycemic drug MET against PDAC. It is an inhibitor of de novo fatty acid synthesis that is dependent on the K-RAS and metabolic status of the tumor. Future pre-clinical and clinical studies should take this into consideration when performing mechanistic studies on MET and when investigating the potential chemotherapeutic effects of this drug. It is also important to determine what dose of MET is chemotherapeutic in the clinic. As shown by other pre-clinical studies and ours as well, this dose exceeds the range for diabetes management.

Background: This thesis is composed of three studies. In the first paper, we tested the association of metformin use with prostate cancer incidence. In the second paper, we examined the association of metformin use with all-cause and prostate cancer specific mortality. The final paper explored the benefit of detailed pathology review to predict mortality among diabetic men with prostate cancer. Methods: A total of 5306 incident diabetic men older than 66 who subsequently developed prostate cancer were identified using the Ontario Diabetes Database and the Ontario Cancer Registry between 1994-2008. The association of metformin use and risk of prostate cancer and its grade was tested with a nested case-control design using a conditional logistic regression model. We used a cohort design with a time dependent Cox-proportional hazard model to examine the association of metformin use and mortality. Finally, we employed a c-statistic and Net Reclassification Improvement analysis to study the impact of pathology abstraction on predicting mortality. Results: The data suggest metformin use was not associated with the risk of prostate cancer or its grade at presentation. However, each additional 6 month of metformin use was associated with a 24% decrease in prostate-cancer-specific and 8% decrease in all-cause mortality. Pathology abstraction improved the accuracy in predicting all-cause and prostate-cancer specific mortality. Conclusions: In our study metformin use was not associated with a decreased risk of prostate cancer, but had a significant impact on all-cause and prostate cancer specific mortality. These results may serve as proof of concept in designing an interventional study of metformin to delay progression in prostate cancer.

Background: This thesis is composed of three studies. In the first paper, we tested the association of metformin use with prostate cancer incidence. In the second paper, we examined the association of metformin use with all-cause and prostate cancer specific mortality. The final paper explored the benefit of detailed pathology review to predict mortality among diabetic men with prostate cancer. Methods: A total of 5306 incident diabetic men older than 66 who subsequently developed prostate cancer were identified using the Ontario Diabetes Database and the Ontario Cancer Registry between 1994-2008. The association of metformin use and risk of prostate cancer and its grade was tested with a nested case-control design using a conditional logistic regression model. We used a cohort design with a time dependent Cox-proportional hazard model to examine the association of metformin use and mortality. Finally, we employed a c-statistic and Net Reclassification Improvement analysis to study the impact of pathology abstraction on predicting mortality. Results: The data suggest metformin use was not associated with the risk of prostate cancer or its grade at presentation. However, each additional 6 month of metformin use was associated with a 24% decrease in prostate-cancer-specific and 8% decrease in all-cause mortality. Pathology abstraction improved the accuracy in predicting all-cause and prostate-cancer specific mortality. Conclusions: In our study metformin use was not associated with a decreased risk of prostate cancer, but had a significant impact on all-cause and prostate cancer specific mortality. These results may serve as proof of concept in designing an interventional study of metformin to delay progression in prostate cancer.

OBJECTIVE: T2D is a metabolic disease that is a significant health problem throughout many populations. Increased incidence of T2D across the age spectrum makes preventive measures for this disease a top healthcare priority. Physiological changes such as expression of pro-inflammatory T cell cytokines, insulin resistance, and pancreatic beta cell dysfunction play major roles in the onset of T2D. Current treatments include lifestyle changes with oral medications and/or synthetic insulin therapy. While treatments aim to normalize blood glucose and increase insulin sensitivity in patients diagnosed with T2D, efforts are growing to find preventative therapies for prediabetes, a condition where blood glucose levels are higher than normal but are under the threshold determined for a diabetes diagnosis. Metformin, a well-known first-line recommendation for treating T2D, in conjunction with lifestyle modification may be a viable preventative measure to delay the onset of T2D. Previous study results have created momentum to generate data promoting metformin use as an off-label preventative drug for T2D. To identify a therapeutic intervention that may help to shift T cell cytokine profiles from being pro-inflammatory and diabetogenic to anti-inflammatory, we investigated the effects of metformin on immune cell function in prediabetic patients. It is known that one effect of metformin is activating AMPK, which secondarily decreases inflammation. We therefore hypothesized metformin affects immune cell function by modulating genes in the AMPK pathway. METHODS: We recruited 49 subjects using EPIC database to screen patients with appointments at the Nutrition and Weight Management Center at Boston Medical Center. Forty-nine pre-metformin and 13 post- metformin blood samples were collected from subjects at baseline and after three months of taking metformin, respectively. Ficoll was utilized to separate and extract PBMCs. I activated PBMCs with LPS or CpG for 24 hours, and anti-CD3/CD28 for 24 or 40 hours. Then I isolated and reverse transcribed RNA, producing cDNA. We ran a human AMPK signaling qRT-PCR array on the 40-hour anti-CD3/CD28 activated PBMCs from 4 randomly chosen subjects and analyzed data to investigate candidate targets in the AMPK pathway possibly modulated by metformin. I designed primers for six chosen targets and ran qRT-PCR comparing the pre- and post-metformin dataset of 13 subjects, using the generated human gene-specific primers to see if these genes were affected across the dataset. RESULTS: Total sample population was n=13. The majority of subjects were African American females. The study participants were considered prediabetic when A1C measured between 5.7-6.4%. Median A1C and BMI averaged at 5.8% and 38.6 kg/m2  2.48 (mean  SEM), respectively. There was an expected decrease in BMI as metformin is associated with weight loss. To understand how metformin may affect genes in the AMPK pathway, qRT-PCR array analysis of the 40-hour anti-CD3/CD28 activated PBMCs in a subset of 4 subjects was used to create a volcano plot. The plot demonstrated that out of the possible gene candidates, SLC2A4, LIPE, INSR, CRY1, GAPDH, and STK11 had the greatest log2 fold change and –log (p-value). Further analysis on the 4 subjects compared delta Ct values and relative gene expression showing CRY1, a circadian function gene, had a significant decrease in expression (p=0.03, n=4, paired t-test). Primers were designed for the six candidate genes and used to run qRT-PCR on the entire dataset of 13 subjects. There was a significant decrease in expression of STK11 in 24-hour non-stimulated PBMCs (p=0.008, n=12, paired t-test) and CRY1 in 24-hour anti-CD3/CD28 activated PBMCs (p=0.04, n=12, paired t-test). There was a significant increase in expression of SLC2A4 in 24-hour CpG activated PBMCs (p=0.02, n=12, paired t-test). Furthermore, GLUT4 was detected in CpG activated immune cells and gene expression was increased in cells from subjects post-metformin treatment. CONCLUSIONS: Further investigation is required to examine how metformin decreases the expression of CRY1 and how this decrease associates with pro-inflammatory cytokine expression. STK11 expression was decreased in non-stimulated cells but did not show any trend in the activated conditions. Additional research is warranted to see if these results can be repeated, and if so, more work will be needed to define the link between CRY1/STK11 and metformin-driven AMPK activation in immune cells. Protein expression analysis will be required to support our gene expression data. Overall, these findings initiate our understanding of how AMPK activation and changes in cellular metabolism activate pathways leading to cytokine secretion by immune cells. Further study of the downstream effects of metformin and how it may change inflammatory cytokine profiles will strengthen the evidence identifying metformin as a viable preventative therapy for prediabetic patients.

Glucocorticoid excess as a result of Cushing’s syndrome or pharmacological treatment can result in the development of obesity and type 2 diabetes mellitus (T2DM). The reactivation of cortisone to cortisol is catalysed by 11βHSD1 which is expressed widely but notably in adipose tissue and liver. Studies have shown dysregulation of cortisol in these tissues with obesity potentially promoting the development of T2DM. Inhibition of 11βHSD1 has been attempted as a novel treatment for T2DM with observed improvement in glycaemic control, body weight and blood pressure. The efficacy of such agents has been disappointing with few reaching phase 2 trials. With recent evidence of bidirectional activity of 11βHSD1 in vivo it becomes apparent that dysregulation may occur at an intracellular rather than tissue level. In this thesis I address several key outstanding questions concerning the physiology and regulation of 11βHSD1 including: 1. Whether combined therapy with metformin alters 11βHSD1 activity and obscures the efficacy of 11βHSD1inhibitors; 2. Whether the contribution of 11βHSD1 to local cortisol concentrations has been under-estimated by considering total rather than free cortisol turnover; and 3. Whether recycling between cortisol and cortisone in adipose tissue and skeletal muscle in obesity is a neglected feature of 11βHSD1 biochemistry and function. Eight obese healthy men with and without type 2 diabetes were recruited to a randomised placebo controlled cross over trial. They received 4 weeks treatment with metformin and placebo. Participants with T2DM additionally received gliclazide as a further control. Using the deuterated tracer D4-cortisol 11βHSD1 activity was measured. Metformin treatment increased whole body 11βHSD1 in both groups postulated as a result of improved insulin sensitivity. 11βHSD1 is located within cells and so contributes to free tissue cortisol concentrations but perhaps less so to total (protein-bound) cortisol in plasma. It has been shown that 11βHSD1 contributes almost half of total circulating cortisol concentrations at rest. This measurement relied upon blood sampling during steady state deuterated cortisol (D4-cortisol) infusion with measurements of total (free plus protein bound) cortisol which may have underestimated true 11βHSD1 activity. This was therefore investigated by comparing 11βHSD1 activity as calculated using total compared with free cortisol tracer enrichments. Equilibrium dialysis was performed separating free from bound portions in plasma samples taken from healthy volunteers who received D4-cortisol infusion. Analysis revealed similar measurements of 11βHSD1 activity using free compared with total cortisol implicating rapid turnover of glucocorticoids between the free and bound pools. On first discovery 11βHSD1 was seen to be a dehydrogenase enzyme in vitro. Later work recognised reductase activity in vivo and up until recently 11βHSD1 has been viewed as a predominantly reductase enzyme. As with other enzymes in the same family, the ability to catalyse both reductase and dehydrogenase depends upon the availability of substrate and co substrate. Whether dysregulation of 11βHSD1 in the settings of obesity and T2DM is the result of alteration in directionality at a cellular level is not known. Firstly bidirectional activity of 11βHSD1 was confirmed in vitro using HEK-293 cells stably transfected with 11βHSD1. The influence of obesity and acute perturbation with hyperinsulinaemia was subsequently investigated in vivo in a random order cross over single blinded case control study involving ten normal weight and ten obese healthy male volunteers. D4-cortisol and deuterated cortisone (D2-cortisone) were infused for the measurement of reductase and dehydrogenase activity of 11βHSD1 respectively with measurements taken across forearm muscle and abdominal subcutaneous adipose tissue. Across whole body, lean and obese individuals displayed similar 11β-reductase and 11β-dehydrogenase activity. Across tissue, 11β-reductase and 11β-dehydrogenase activity was different from zero across adipose tissue in obese individuals and across skeletal muscle in lean individuals providing further evidence of tissue specific differences in 11βHSD1 with obesity. With the addition of hyperinsulinaemia, reductase and dehydrogenase activity was somewhat increased in lean individuals although there was no statistically significant difference between lean and obese individuals. Across tissue there was a trend for obese individuals to display increased 11β-reductase activity across adipose tissue with hyperinsulinaemia. Comparing the rates of reductase and dehydrogenase activity revealed predominantly reductase activity across tissue in obese and dehydrogenase activity in lean individuals. The development of direction specific inhibitors targeting reductase activity by 11βHSD1 may prove efficacious for the treatment of obesity. In conclusion, 11βHSD1 acts as a bidirectional enzyme in vitro and in vivo. Overall directionality of enzyme activity is altered in a tissue specific manner in the setting of obesity. We have shown that this intracellular regulation of cortisol is reflected equally in the metabolically active free pool and total plasma pool. The efficacy of 11βHSD1 inhibitors as novel agents for the treatment of T2DM and coexisting obesity is not diminished by co-prescription with metformin but may prove more efficacious through the development of reductase specific inhibitors.

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