Sleep in pituitary insufficient patients compared to patients with depression and healthy controls at baseline and after challenge with CRH.

Sleep disturbances are prevalent in both patients with pituitary insufficiency and with depression. The role of corticotropin releasing hormone (CRH), involved in sleep regulation, has not been fully clarified. Pituitary insufficiency is an ideal model for studying sleep-endocrine effects since no consecutive hormone releases and feedback effects occur after hormone administration. 11 male patients with a chronic insufficiency of the anterior pituitary gland (PI) and under stable hormonal substitution were studied during three consecutive nights in the sleep laboratory. The first night served for adapting to laboratory setting, during the second night placebo was administered and during the third night 4 × 50 µg CRH were injected in pulsatile fashion. Sleep parameters were additionally compared with those of 15 healthy male controls (C) and 15 male patients with depression (D). CRH administration was associated with a numerical increase of wake time (115 ± 15 to 131 ± 13 min) and a decrease of REM sleep (89 ± 8 to 80 ± 8 min), REM latency (69 ± 14 to 55 ± 9 min) and slow wave sleep (66 ± 16 to 57 ± 15 min). Yet, none of these changes reached statistical significance. PI showed a worse sleep profile as compared to both control groups, e.g. indicated by a significantly lower sleep efficiency index (PI:0.80 ± 0.03 vs. C:0.94 ± 0.01 vs. D:0.87 ± 0.03). In conclusion sleep-EEG changes after CRH in PI patients resemble those found in in part in patients with depression. Sleep in anterior pituitary insufficiency was impaired despite full hormonal substitution possibly suggesting an alteration of the receptor organisation of brain structures involved in sleep regulation.

Computer Vision Technology in the Differential Diagnosis of Cushing's Syndrome.

OBJECTIVE: Cushing's syndrome is a rare disease characterized by clinical features that show morphological similarity with the metabolic syndrome. Distinguishing these diseases in clinical practice is challenging. We have previously shown that computer vision technology can be a potentially useful diagnostic tool in Cushing's syndrome. In this follow-up study, we addressed the described problem by increasing the sample size and including controls matched by body mass index. METHODS: We enrolled 82 patients (22 male, 60 female) and 98 control subjects (32 male, 66 female) matched by age, gender and body-mass-index. The control group consisted of patients with initially suspected, but biochemically excluded Cushing's syndrome. Standardized frontal and profile facial digital photographs were acquired. The images were analyzed using specialized computer vision and classification software. A grid of nodes was semi-automatically placed on disease-relevant facial structures for analysis of texture and geometry. Classification accuracy was calculated using a leave-one-out cross-validation procedure with a maximum likelihood classifier. RESULTS: The overall correct classification rates were 10/22 (45.5%) for male patients and 26/32 (81.3%) for male controls, and 34/60 (56.7%) for female patients and 43/66 (65.2%) for female controls. In subgroup analyses, correct classification rates were higher for iatrogenic than for endogenous Cushing's syndrome. CONCLUSION: Regarding the advanced problem of detecting Cushing's syndrome within a study sample matched by body mass index, we found moderate classification accuracy by facial image analysis. Classification accuracy is most likely higher in a larger sample with healthy control subjects. Further studies might pursue a more advanced analysis and classification algorithm.

[Prevalence, drug treatment and metabolic control of diabetes mellitus in primary care]. / Prävalenz, medikamentöse Behandlung und Einstellung des Diabetes mellitus in der Hausarztpraxis.

BACKGROUND AND PURPOSE: The primary care sector is of key importance for the management of patients with diabetes mellitus. The authors investigated (a) the prevalence of diabetes mellitus type 1 and type 2, (b) the type and frequency of non-drug and drug treatment and its association with the presence of diabetic complications, and (c) the quality of metabolic control by HbA1c. METHODS: Using a nationwide probability sample of 3,188 general practices (response rate [RR] 50.6%), a total of 55,518 (RR 93.5%) patients were assessed in a prospective cross-sectional study by their physicians in September 2003 in a standardized manner using questionnaires, physician interview, and laboratory assessments. In addition to diabetes mellitus, 28 diseases were explicitly screened for, among them typical macrovascular (coronary heart disease, cerebrovascular disease, peripheral arterial disease) and microvascular disease (neuropathy, nephropathy, retinopathy, diabetic foot) complications. RESULTS: The prevalence of diabetes mellitus was 0.5% (type 1) and 14.7% (type 2), respectively. 49.5% (type 1) and 50.2% (type 2) of patients had micro- or macrovascular complications. 6.8% did not receive any treatment, 13.5% received non-drug treatment, and 75.3% received oral antidiabetic drugs and/or insulin (26.6% a combination of two or more). Compared to diabetics without any complications, treatment intensity was significantly higher in patients with microvascular complications (odds ratio [OR] 3.02), but not in those with macrovascular complications only (OR 0.98). An HbA1c value>or=7.0% was recorded in 39.6% of patients. CONCLUSION: Compared to previous studies in this setting, the proportion of diabetics with drug treatment has increased. More patients receive antidiabetic drug combinations. Quality of blood sugar control appears to have improved as well.