Glycaemic patterns of male professional athletes with type 1 diabetes during exercise, recovery and sleep: Retrospective, observational study over an entire competitive season.

AIMS: To analyse glycaemic patterns of professional athletes with type 1 diabetes during a competitive season. MATERIALS AND METHODS: We analysed continuous glucose monitoring data of 12 professional male cyclists with type 1 diabetes during exercise, recovery and sleep on days with competitive exercise (CE) and non-competitive exercise (NCE). We assessed whether differences exist between CE and NCE days and analysed associations between exercise and dysglycaemia. RESULTS: The mean glycated haemoglobin was 50 ± 5 mmol/mol (6.7 ± 0.5%). The athletes cycled on 280.8 ± 28.1 days (entire season 332.6 ± 18.8 days). Overall, time in range (3.9-10 mmol/L) was 70.0 ± 13.7%, time in hypoglycaemia (<3.9 mmol/L) was 6.4 ± 4.7% and time in hyperglycaemia (>10 mmol/L) was 23.6 ± 12.5%. During the nights of NCE days, athletes spent 10.1 ± 7.4% of time in hypoglycaemia, particularly after exercise in the endurance zones. The CE days were characterized by a higher time in hyperglycaemia compared with NCE days (25.2 ± 12.5% vs. 22.2 ± 12.1%, p = .012). This was driven by the CE phase, where time in range dropped to 60.4 ± 13.0% and time in hyperglycaemia was elevated (38.5 ± 12.9%). Mean glucose was higher during CE compared with NCE sessions (9.6 ± 0.9 mmol/L vs. 7.8 ± 1.1 mmol/L, p < .001). The probability of hyperglycaemia during exercise was particularly increased with longer duration, higher intensity and higher variability of exercise. CONCLUSIONS: The analysis of glycaemic patterns of professional endurance athletes revealed that overall glycaemia was generally within targets. For further improvement, athletes, team staff and caregivers may focus on hyperglycaemia during competitions and nocturnal hypoglycaemia after NCE.

Machine Learning for Predicting Micro- and Macrovascular Complications in Individuals With Prediabetes or Diabetes: Retrospective Cohort Study.

BACKGROUND: Micro- and macrovascular complications are a major burden for individuals with diabetes and can already arise in a prediabetic state. To allocate effective treatments and to possibly prevent these complications, identification of those at risk is essential. OBJECTIVE: This study aimed to build machine learning (ML) models that predict the risk of developing a micro- or macrovascular complication in individuals with prediabetes or diabetes. METHODS: In this study, we used electronic health records from Israel that contain information about demographics, biomarkers, medications, and disease codes; span from 2003 to 2013; and were queried to identify individuals with prediabetes or diabetes in 2008. Subsequently, we aimed to predict which of these individuals developed a micro- or macrovascular complication within the next 5 years. We included 3 microvascular complications: retinopathy, nephropathy, and neuropathy. In addition, we considered 3 macrovascular complications: peripheral vascular disease (PVD), cerebrovascular disease (CeVD), and cardiovascular disease (CVD). Complications were identified via disease codes, and, for nephropathy, the estimated glomerular filtration rate and albuminuria were considered additionally. Inclusion criteria were complete information on age and sex and on disease codes (or measurements of estimated glomerular filtration rate and albuminuria for nephropathy) until 2013 to account for patient dropout. Exclusion criteria for predicting a complication were diagnosis of this specific complication before or in 2008. In total, 105 predictors from demographics, biomarkers, medications, and disease codes were used to build the ML models. We compared 2 ML models: logistic regression and gradient-boosted decision trees (GBDTs). To explain the predictions of the GBDTs, we calculated Shapley additive explanations values. RESULTS: Overall, 13,904 and 4259 individuals with prediabetes and diabetes, respectively, were identified in our underlying data set. For individuals with prediabetes, the areas under the receiver operating characteristic curve for logistic regression and GBDTs were, respectively, 0.657 and 0.681 (retinopathy), 0.807 and 0.815 (nephropathy), 0.727 and 0.706 (neuropathy), 0.730 and 0.727 (PVD), 0.687 and 0.693 (CeVD), and 0.707 and 0.705 (CVD); for individuals with diabetes, the areas under the receiver operating characteristic curve were, respectively, 0.673 and 0.726 (retinopathy), 0.763 and 0.775 (nephropathy), 0.745 and 0.771 (neuropathy), 0.698 and 0.715 (PVD), 0.651 and 0.646 (CeVD), and 0.686 and 0.680 (CVD). Overall, the prediction performance is comparable for logistic regression and GBDTs. The Shapley additive explanations values showed that increased levels of blood glucose, glycated hemoglobin, and serum creatinine are risk factors for microvascular complications. Age and hypertension were associated with an elevated risk for macrovascular complications. CONCLUSIONS: Our ML models allow for an identification of individuals with prediabetes or diabetes who are at increased risk of developing micro- or macrovascular complications. The prediction performance varied across complications and target populations but was in an acceptable range for most prediction tasks.

Machine learning for non-invasive sensing of hypoglycaemia while driving in people with diabetes.

AIM: To develop and evaluate the concept of a non-invasive machine learning (ML) approach for detecting hypoglycaemia based exclusively on combined driving (CAN) and eye tracking (ET) data. MATERIALS AND METHODS: We first developed and tested our ML approach in pronounced hypoglycaemia, and then we applied it to mild hypoglycaemia to evaluate its early warning potential. For this, we conducted two consecutive, interventional studies in individuals with type 1 diabetes. In study 1 (n = 18), we collected CAN and ET data in a driving simulator during euglycaemia and pronounced hypoglycaemia (blood glucose [BG] 2.0-2.5 mmol L-1 ). In study 2 (n = 9), we collected CAN and ET data in the same simulator but in euglycaemia and mild hypoglycaemia (BG 3.0-3.5 mmol L-1 ). RESULTS: Here, we show that our ML approach detects pronounced and mild hypoglycaemia with high accuracy (area under the receiver operating characteristics curve 0.88 ± 0.10 and 0.83 ± 0.11, respectively). CONCLUSIONS: Our findings suggest that an ML approach based on CAN and ET data, exclusively, enables detection of hypoglycaemia while driving. This provides a promising concept for alternative and non-invasive detection of hypoglycaemia.

Noninvasive Hypoglycemia Detection in People With Diabetes Using Smartwatch Data.

OBJECTIVE: To develop a noninvasive hypoglycemia detection approach using smartwatch data. RESEARCH DESIGN AND METHODS: We prospectively collected data from two wrist-worn wearables (Garmin vivoactive 4S, Empatica E4) and continuous glucose monitoring values in adults with diabetes on insulin treatment. Using these data, we developed a machine learning (ML) approach to detect hypoglycemia (<3.9 mmol/L) noninvasively in unseen individuals and solely based on wearable data. RESULTS: Twenty-two individuals were included in the final analysis (age 54.5 ± 15.2 years, HbA1c 6.9 ± 0.6%, 16 males). Hypoglycemia was detected with an area under the receiver operating characteristic curve of 0.76 ± 0.07 solely based on wearable data. Feature analysis revealed that the ML model associated increased heart rate, decreased heart rate variability, and increased tonic electrodermal activity with hypoglycemia. CONCLUSIONS: Our approach may allow for noninvasive hypoglycemia detection using wearables in people with diabetes and thus complement existing methods for hypoglycemia detection and warning.

Machine Learning for Predicting the Risk of Transition from Prediabetes to Diabetes.

Traditional risk scores for the prediction of type 2 diabetes (T2D) are typically designed for a general population and, thus, may underperform for people with prediabetes. In this study, we developed machine learning (ML) models predicting the risk of T2D that are specifically tailored to people with prediabetes. We analyzed data of 13,943 individuals with prediabetes, and built a ML model to predict the risk of transition from prediabetes to T2D, integrating information about demographics, biomarkers, medications, and comorbidities defined by disease codes. Additionally, we developed a simplified ML model with only eight predictors, which can be easily integrated into clinical practice. For a forecast horizon of 5 years, the area under the receiver operating characteristic curve was 0.753 for our full ML model (79 predictors) and 0.752 for the simplified model. Our ML models allow for an early identification of people with prediabetes who are at risk of developing T2D.

Use and Perception of Telemedicine in People with Type 1 Diabetes During the COVID-19 Pandemic: A 1-Year Follow-Up.

Background: In the spring of 2020, our research group circulated a worldwide survey with the aim of gathering information on the use and perception of telemedicine in people living with type 1 diabetes at the start of the COVID-19 pandemic. The data suggested that a large number of respondents had rapidly adopted to telemedicine, as in-person visits were not possible, and that this was perceived positively by many. In this study, we conducted a 1-year follow-up to investigate changes in opinions and experiences to telemedicine over the past year of the pandemic. Methods: An anonymous questionnaire was distributed through social media (Twitter, Facebook, and Instagram) between May 9 and May 15, 2021, using an open-access web-based platform (SurveyMonkey.com). The survey was identical to that used in the original study, covering questions relating to the use and perception of telemedicine, diabetes treatment and control, and medical supplies during the COVID-19 pandemic. The questionnaire was available in English, Spanish, German, French, and Italian. We compared the results from the two surveys descriptively and statistically, results were stratified according to age, gender, and HbA1c. Results: There were 531 survey responses from 40 countries (Europe 54%, North America 36%, South America 2%, and Africa and Asia 2%). A large percentage of respondents (67%) reported meeting with their health care provider remotely since the beginning of the pandemic, a significant increase compared with the 28% in the 2020 survey (P < 0.001). Eighty-three percent of respondents found remote appointments to be somewhat-to-extremely useful, similar to the 86% satisfaction rate in the previous survey (P = 0.061). Remote appointments were most frequently undertaken through telephone (50%) and video call (45%), which are significant changes compared with those in 2020 (72% and 28%, respectively, P < 0.001). Forty-five percent of respondents in 2021 were likely to consider remote appointments instead of in-person appointments in the future-being significantly lower than the 75% in the initial survey (P < 0.001)-whereas 37% indicated they would not. The majority of respondents (84%) reported no issues in their access to diabetes supplies and medication over the past year. Conclusions: This study showed that the use of telemedicine in the form of remote appointments increased during the COVID-19 pandemic in people living with type 1 diabetes, with high levels of satisfaction. However, a remarkable decline took place in the past year in the proportion of patients stating a willingness to continue with remote appointments beyond the pandemic. It seems that a personalized approach is needed since a substantial proportion of respondents in this follow-up still indicated a preference for in-person diabetes care, hence the use of telemedicine should be considered on an individual basis.

Differences in Physiological Responses to Cardiopulmonary Exercise Testing in Adults With and Without Type 1 Diabetes: A Pooled Analysis.

OBJECTIVE: To investigate physiological responses to cardiopulmonary exercise (CPX) testing in adults with type 1 diabetes compared with age-, sex-, and BMI-matched control participants without type 1 diabetes. RESEARCH DESIGN AND METHODS: We compared results from CPX tests on a cycle ergometer in individuals with type 1 diabetes and control participants without type 1 diabetes. Parameters were peak and threshold variables of VO2, heart rate, and power output. Differences between groups were investigated through restricted maximum likelihood modeling and post hoc tests. Differences between groups were explained by stepwise linear regressions (P < 0.05). RESULTS: Among 303 individuals with type 1 diabetes (age 33 [interquartile range 22; 43] years, 93 females, BMI 23.6 [22; 26] kg/m2, HbA1c 6.9% [6.2; 7.7%] [52 (44; 61) mmol/mol]), VO2peak (32.55 [26.49; 38.72] vs. 42.67 ± 10.44 mL/kg/min), peak heart rate (179 [170; 187] vs. 184 [175; 191] beats/min), and peak power (216 [171; 253] vs. 245 [200; 300] W) were lower compared with 308 control participants without type 1 diabetes (all P < 0.001). Individuals with type 1 diabetes displayed an impaired degree and direction of the heart rate-to-performance curve compared with control participants without type 1 diabetes (0.07 [-0.75; 1.09] vs. 0.66 [-0.28; 1.45]; P < 0.001). None of the exercise physiological responses were associated with HbA1c in individuals with type 1 diabetes. CONCLUSIONS: Individuals with type 1 diabetes show altered responses to CPX testing, which cannot be explained by HbA1c. Intriguingly, the participants in our cohort were people with recent-onset type 1 diabetes; heart rate dynamics were altered during CPX testing.

Lower Daily Carbohydrate Intake Is Associated With Improved Glycemic Control in Adults With Type 1 Diabetes Using a Hybrid Closed-Loop System.

OBJECTIVE: To assess the association between daily carbohydrate (CHO) intake and glycemic control in adult hybrid closed-loop (HCL) users with type 1 diabetes (T1D). RESEARCH DESIGN AND METHODS: Mean individual daily CHO intake (MIDC) and relative deviation from MIDC (≤80% low, 81-120% medium, >120% high CHO consumption) were compared with parameters of glycemic control assessed by continuous glucose monitoring. RESULTS: Records from 36 patients (26 male, 10 female; age 36.9 ± 13.5 years; HbA1c 7.1 ± 0.9% [54 ± 10 mmol/mol]) provided 810 days of data (22.5 ± 6.7 days per patient). Time in range (70-180 mg/dL) for low, medium, and high CHO consumption was 77.4 ± 15.4%, 75.2 ± 16.7%, and 70.4 ± 17.8%, respectively (P < 0.001). Time above range (>180 mg/dL) was 20.1 ± 14.7%, 22.0 ± 16.9%, and 27.2 ± 18.4%, respectively (P < 0.001). There was no between-group difference for time in hypoglycemia (<70 mg/dL; P = 0.50). CONCLUSIONS: Daily CHO intake was inversely associated with glycemic control in adults with T1D using an HCL system. Lower CHO intake may be a strategy to optimize glucose control in HCL users.

White coat adherence effect on glucose control in adult individuals with diabetes.

BACKGROUND: White coat adherence (WCA) is defined as an increased adherence to treatment regimens directly before a visit with a healthcare provider. Little is known on the effect of WCA on glucose control in adult patients with diabetes mellitus. METHODS: The present study is based on 618 CGM-observations of 276 patients with diabetes treated between January 2013 and July 2018. The analysis compares data from the 3 days prior to a visit (p1) with the preceding 25 days (p2). RESULTS: Sensor use was higher during p1 than p2 (92.8 ± 7.3% vs 88.8 ± 7.5%; p < 0.001). Mean glucose [MG] and coefficient of variation [CV] were lower in p1 compared to p2 (MG 163.9 ± 39.2 mg/dL vs 166.9 ± 35.7 mg/dL, p = 0.001; CV 33.5 ± 8.4% vs 36.0 ± 7.0%, p < 0.001; respectively). Time in range (70-180 mg/dL) was higher in p1 than p2 (61.4 ± 21.2% vs 60.0 ± 18.4%, p = 0.002). Sensitivity-analysis showed that WCA effect was mainly detected in patients with HbA1c > 7% [53 mmol/mol]. CONCLUSION: This study reveals a WCA effect on pre-visit glucose control in adult patients with diabetes. The effect was most pronounced in patients with moderate to poor glycemic control. In these patients, analysis of CGM data should encompass a minimum of 1 to 2 weeks prior to a consultation.

Ominous triad triggered by high-dose glucocorticosteroid therapy.

Glucocorticosteroids (CS) play a key role in the treatment of numerous diseases. Nonetheless, they can be accompanied by several adverse effects. We present the case of a 51-year-old woman who was treated with high-dose CS for a relapse of her multiple sclerosis. After 5 days of treatment, the patient developed severe diabetic ketoacidosis, hypertriglyceridemia and acute pancreatitis-a potentially life-threatening triad which has previously been described, in our case, however, for the first time as a complication of CS therapy. Our patient's condition was further aggravated by a circulatory shock, haemodynamic relevant bleeding from a duodenal ulcer and psychotic symptoms. In the intensive care unit, intravenous insulin infusion, fluid resuscitation, catecholamine support, electrolyte supplementation, endoscopic haemoclipping and antibiotic and antipsychotic treatment were administered, leading to a continuous improvement of the patient's health state.

Metabolic Effects of Glucose-Fructose Co-Ingestion Compared to Glucose Alone during Exercise in Type 1 Diabetes.

This paper aims to compare the metabolic effects of glucose-fructose co-ingestion (GLUFRU) with glucose alone (GLU) in exercising individuals with type 1 diabetes mellitus. Fifteen male individuals with type 1 diabetes (HbA1c 7.0% ± 0.6% (53 ± 7 mmol/mol)) underwent a 90 min iso-energetic continuous cycling session at 50% VO2max while ingesting combined glucose-fructose (GLUFRU) or glucose alone (GLU) to maintain stable glycaemia without insulin adjustment. GLUFRU and GLU were labelled with 13C-fructose and 13C-glucose, respectively. Metabolic assessments included measurements of hormones and metabolites, substrate oxidation, and stable isotopes. Exogenous carbohydrate requirements to maintain stable glycaemia were comparable between GLUFRU and GLU (p = 0.46). Fat oxidation was significantly higher (5.2 ± 0.2 vs. 2.6 ± 1.2 mg·kg-1·min-1, p < 0.001) and carbohydrate oxidation lower (18.1 ± 0.8 vs. 24.5 ± 0.8 mg·kg-1·min-1p < 0.001) in GLUFRU compared to GLU, with decreased muscle glycogen oxidation in GLUFRU (10.2 ± 0.9 vs. 17.5 ± 1.0 mg·kg-1·min-1, p < 0.001). Lactate levels were higher (2.2 ± 0.2 vs. 1.8 ± 0.1 mmol/L, p = 0.012) in GLUFRU, with comparable counter-regulatory hormones between GLUFRU and GLU (p > 0.05 for all). Glucose and insulin levels, and total glucose appearance and disappearance were comparable between interventions. Glucose-fructose co-ingestion may have a beneficial impact on fuel metabolism in exercising individuals with type 1 diabetes without insulin adjustment, by increasing fat oxidation whilst sparing glycogen.

Regulation of fuel metabolism during exercise in hypopituitarism with growth hormone-deficiency (GHD).

OBJECTIVE: Growth hormone (GH) has a strong lipolytic action and its secretion is increased during exercise. Data on fuel metabolism and its hormonal regulation during prolonged exercise in patients with growth hormone deficiency (GHD) is scarce. This study aimed at evaluating the hormonal and metabolic response during aerobic exercise in GHD patients. DESIGN: Ten patients with confirmed GHD and 10 healthy control individuals (CI) matched for age, sex, BMI, and waist performed a spiroergometric test to determine exercise capacity (VO2max). Throughout a subsequent 120-minute exercise on an ergometer at 50% of individual VO2max free fatty acids (FFA), glucose, GH, cortisol, catecholamines and insulin were measured. Additionally substrate oxidation assessed by indirect calorimetry was determined at begin and end of exercise. RESULTS: Exercise capacity was lower in GHD compared to CI (VO2max 35.5±7.4 vs 41.5±5.5ml/min∗kg, p=0.05). GH area under the curve (AUC-GH), peak-GH and peak-FFA were lower in GHD patients during exercise compared to CI (AUC-GH 100±93.2 vs 908.6±623.7ng∗min/ml, p<0.001; peak-GH 1.5±1.53 vs 12.57±9.36ng/ml, p<0.001, peak-FFA 1.01±0.43 vs 1.51±0.56mmol/l, p=0.036, respectively). There were no significant differences for insulin, cortisol, catecholamines and glucose. Fat oxidation at the end of exercise was higher in CI compared to GHD patients (295.7±73.9 vs 187.82±103.8kcal/h, p=0.025). CONCLUSION: A reduced availability of FFA during a 2-hour aerobic exercise and a reduced fat oxidation at the end of exercise may contribute to the decreased exercise capacity in GHD patients. Catecholamines and cortisol do not compensate for the lack of the lipolytic action of GH in patients with GHD.

Accuracy of continuous glucose monitoring during differing exercise conditions.

AIM: Depending on intensity, exercise may induce a strong hormonal and metabolic response, including acid-base imbalances and changes in microcirculation, potentially interfering with the accuracy of continuous glucose monitoring (CGM). The present study aimed at comparing the accuracy of the Dexcom G4 Platinum (DG4P) CGM during continuous moderate and intermittent high-intensity exercise (IHE) in adults with type 1 diabetes (T1DM). METHODS: Ten male individuals with well-controlled T1DM (HbA1c 7.0 ± 0.6% [54 ± 6 mmol/mol]) inserted the DG4P sensor 2 days prior to a 90 min cycling session (50% VO2peak) either with (IHE) or without (CONT) a 10s all-out sprint every 10 min. Venous blood samples for reference glucose measurement were drawn every 10 min and euglycemia (target 7 mmol/l) was maintained using an oral glucose solution. Additionally, lactate and venous blood gas variables were determined. RESULTS: Mean reference blood glucose was 7.6 ± 0.2 mmol/l during IHE and 6.7 ± 0.2 mmol/l during CONT (p<0.001). IHE resulted in significantly higher levels of lactate (7.3 ± 0.5 mmol/l vs. 2.6 ± 0.3 mmol/l, p<0.001), while pH values were significantly lower in the IHE group (7.27 vs. 7.38, p=0.001). Mean absolute relative difference (MARD) was 13.3 ± 2.2% for IHE and 13.6 ± 2.8% for CONT suggesting comparable accuracy (p=0.90). Using Clarke Error Grid Analysis, 100% of CGM values during both IHE and CONT were in zones A and B (IHE: 77% and 23%; CONT: 78% and 22%). CONCLUSIONS: The present study revealed good and comparable accuracy of the DG4P CGM system during intermittent high intensity and continuous moderate intensity exercise, despite marked differences in metabolic conditions. This corroborates the clinical robustness of CGM under differing exercise conditions. CLINICAL TRIAL REGISTRATION NUMBER: ClinicalTrials.gov NCT02068638.

Metabolic and hormonal response to intermittent high-intensity and continuous moderate intensity exercise in individuals with type 1 diabetes: a randomised crossover study.

AIMS/HYPOTHESIS: To investigate exercise-related fuel metabolism in intermittent high-intensity (IHE) and continuous moderate intensity (CONT) exercise in individuals with type 1 diabetes mellitus. METHODS: In a prospective randomised open-label cross-over trial twelve male individuals with well-controlled type 1 diabetes underwent a 90 min iso-energetic cycling session at 50% maximal oxygen consumption ([Formula: see text]), with (IHE) or without (CONT) interspersed 10 s sprints every 10 min without insulin adaptation. Euglycaemia was maintained using oral (13)C-labelled glucose. (13)C Magnetic resonance spectroscopy (MRS) served to quantify hepatocellular and intramyocellular glycogen. Measurements of glucose kinetics (stable isotopes), hormones and metabolites complemented the investigation. RESULTS: Glucose and insulin levels were comparable between interventions. Exogenous glucose requirements during the last 30 min of exercise were significantly lower in IHE (p = 0.02). Hepatic glucose output did not differ significantly between interventions, but glucose disposal was significantly lower in IHE (p < 0.05). There was no significant difference in glycogen consumption. Growth hormone, catecholamine and lactate levels were significantly higher in IHE (p < 0.05). CONCLUSIONS/INTERPRETATION: IHE in individuals with type 1 diabetes without insulin adaptation reduced exogenous glucose requirements compared with CONT. The difference was not related to increased hepatic glucose output, nor to enhanced muscle glycogen utilisation, but to decreased glucose uptake. The lower glucose disposal in IHE implies a shift towards consumption of alternative substrates. These findings indicate a high flexibility of exercise-related fuel metabolism in type 1 diabetes, and point towards a novel and potentially beneficial role of IHE in these individuals. TRIAL REGISTRATION: ClinicalTrials.gov NCT02068638 FUNDING: Swiss National Science Foundation (grant number 320030_149321/) and R&A Scherbarth Foundation (Switzerland).

Computed exercise plasma lactate concentrations: A conversion formula.

OBJECTIVES: Blood lactate measurements are common as a marker of skeletal muscle metabolism in sport medicine. Due to the close equilibrium between the extracellular and intramyocellular space, plasma lactate is a more accurate estimate of muscle lactate. However, whole blood-based lactate measurements are more convenient in field use. The purpose of this investigation was therefore (1) to establish a plasma-converting lactate formula for field use, and (2) to validate the computed plasma lactate levels by comparison to a laboratory standard method. DESIGN AND METHODS: A total of 91 venous samples were taken from 6 individuals with type 1 diabetes during resting and exercise conditions and assessed for whole blood and plasma lactate using the YSI 2300 analyzer. A linear model was applied to establish a formula for converting whole blood lactate to plasma lactate. The validity of computed plasma lactate values was assessed by comparison to a laboratory standard method. RESULTS: Whole blood YSI lactate could be converted to plasma YSI values (slope 1.66, intercept 0.12) for samples with normal hematocrit. Computed plasma levels compared to values determined by the laboratory standard method using Passing-Bablok regression yielded a slope of 1.03 (95%CI:0.99:1.08) with an intercept of -0.11 (95%CI:-0.18:-0.06). CONCLUSIONS: Whole blood YSI lactate values can be reliably converted into plasma values which are in line with laboratory determined plasma measurements.

Utility of 30 and 60 minute cortisol samples after high-dose synthetic ACTH-1-24 injection in the diagnosis of adrenal insufficiency.

BACKGROUND: In clinical practise the high dose ACTH stimulation test (HDT) is frequently used in the assessment of adrenal insufficiency (AI). However, there is uncertainty regarding optimal time-points and number of blood samplings. The present study compared the utility of a single cortisol value taken either 30 or 60 minutes after ACTH stimulation with the traditional interpretation of the HDT. METHODS: Retrospective analysis of 73 HDT performed at a single tertiary endocrine centre. Serum cortisol was measured at baseline, 30 and 60 minutes after intravenous administration of 250 µg synthetic ACTH1-24. Adrenal insufficiency (AI) was defined as a stimulated cortisol level <550 nmol/l. RESULTS: There were twenty patients (27.4%) who showed an insufficient rise in serum cortisol using traditional HDT criteria and were diagnosed to suffer from AI. There were ten individuals who showed insufficient cortisol values after 30 minutes, rising to sufficient levels at 60 minutes. All patients revealing an insufficient cortisol response result after 60 minutes also had an insufficient result after 30 minutes. The cortisol value taken after 30 minutes did not add incremental diagnostic value in any of the cases under investigation compared with the 60 minutes' sample. CONCLUSIONS: Based on the findings of the present analysis the utility of a cortisol measurement 30 minutes after high dose ACTH injection was low and did not add incremental diagnostic value to a single measurement after 60 minutes.

Peak oxygen uptake test in the assessment of growth hormone deficiency.

This study aimed at evaluating a peak oxygen uptake test as a simple diagnostic tool to assess growth-hormone deficiency (GHD) in adults. Based on the findings of multiple growth hormone (GH) samplings after the exercise, a single GH sample taken 15 min postexercise revealed high accuracy in the diagnosis of GHD in the present study. A standardized peak oxygen uptake test may, therefore, provide an accurate alternative to more invasive tests of GHD.

The effect of a single 2 h bout of aerobic exercise on ectopic lipids in skeletal muscle, liver and the myocardium.

AIMS/HYPOTHESIS: Ectopic lipids are fuel stores in non-adipose tissues (skeletal muscle [intramyocellular lipids; IMCL], liver [intrahepatocellular lipids; IHCL] and heart [intracardiomyocellular lipids; ICCL]). IMCL can be depleted by physical activity. Preliminary data suggest that aerobic exercise increases IHCL. Data on exercise-induced changes on ICCL is scarce. Increased IMCL and IHCL have been related to insulin resistance in skeletal muscles and liver, whereas this has not been documented in the heart. The aim of this study was to assess the acute effect of aerobic exercise on the flexibility of IMCL, IHCL and ICCL in insulin-sensitive participants in relation to fat availability, insulin sensitivity and exercise capacity. METHODS: Healthy physically active men were included. VO(2max) was assessed by spiroergometry and insulin sensitivity was calculated using the HOMA index. Visceral and subcutaneous fat were separately quantified by MRI. Following a standardised dietary fat load over 3 days, IMCL, IHCL and ICCL were measured using MR spectroscopy before and after a 2 h exercise session at 50-60% of VO(2max). Metabolites were measured during exercise. RESULTS: Ten men (age 28.9 ± 6.4 years, mean ± SD; VO(2max) 56.3 ± 6.4 ml kg(-1) min(-1); BMI 22.75 ± 1.4 kg/m(2)) were recruited. A 2 h exercise session resulted in a significant decrease in IMCL (-17 ± 22%, p = 0.008) and ICCL (-17 ± 14%, p = 0.002) and increase in IHCL (42 ± 29%, p = 0.004). No significant correlations were found between the relative changes in ectopic lipids, fat availability, insulin sensitivity, exercise capacity or changes of metabolites during exercise. CONCLUSIONS/INTERPRETATION: In this group, physical exercise decreased ICCL and IMCL but increased IHCL. Fat availability, insulin sensitivity, exercise capacity and metabolites during exercise are not the only factors affecting ectopic lipids during exercise.

Glucocorticoid replacement and mortality in patients with nonfunctioning pituitary adenoma.

CONTEXT: Current treatment guidelines generally suggest using lower and weight-adjusted glucocorticoid replacement doses in patients with insufficiency of the hypothalamic-pituitary-adrenal (HPA) axis. Although data in patients with acromegaly revealed a positive association between glucocorticoid dose and mortality, no comparable results exist in patients with nonfunctioning pituitary adenomas (NFPA). OBJECTIVE: Our objective was to assess whether higher glucocorticoid replacement doses are associated with increased mortality in patients with NFPA and HPA axis insufficiency. DESIGN, PARTICIPANTS, AND INTERVENTION: We included 105 patients receiving glucocorticoid replacement after pituitary surgery due to NFPA and concomitant HPA axis insufficiency. Patients were grouped according weight-adapted and absolute hydrocortisone (HC) replacement doses. Mortality was assessed using Kaplan-Meier methodology as well as multivariable Cox regression models. SETTING: This was a retrospective analysis conducted at a tertiary referral center. MAIN OUTCOME: We evaluated overall mortality based on HC replacement doses. RESULTS: Average age at inclusion was 58.9±14.8 yr, and mean follow-up was 12.7±9.4 yr. The groups did not differ according to age, follow-up time, pattern of hypopituitarism, and comorbidities. Kaplan-Meier survival probabilities differed significantly when comparing individuals with differing weight-adjusted HC dose (P=0.001) as well as absolute HC dose (5-19, 20-29, and ≥30 mg, P=0.009). Hazard ratios for mortality increased from 1 (0.05-0.24 mg/kg) to 2.62 (0.25-0.34 mg/kg) to 4.56 (≥0.35 mg/kg, P for trend=0.006) and from 1 (5-19 mg) to 2.03 (20-29 mg) to 4 (≥30 mg, P for trend=0.029), respectively. CONCLUSION: Higher glucocorticoid replacement doses are associated with increased overall mortality in patients with NFPA and insufficiency of HPA axis. This further substantiates the importance of a balanced and adjusted glucocorticoid replacement therapy in these patients.