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BackgroundThe German Diabetes Study (GDS) is a prospective longitudinal cohort study describing the impact of subphenotypes on the course of the disease. GDS aims at identifying prognostic factors and mechanisms underlying the development of related comorbidities.Study design and methodsThe study comprises intensive phenotyping within 12 months after clinical diagnosis, at 5-year intervals for 20 years and annual telephone interviews in between. Dynamic tests, including glucagon, mixed meal, intravenous glucose tolerance and hyperinsulinemic clamp tests, serve to assess beta-cell function and tissue-specific insulin sensitivity. Magnetic resonance imaging and multinuclei spectroscopy allow quantifying whole-body fat distribution, tissue-specific lipid deposition and energy metabolism. Comprehensive analyses of microvascular (nerve, eye, kidney) and macrovascular (endothelial, cardiorespiratory) morphology and function enable identification and monitoring of comorbidities. The GDS biobank stores specimens from blood, stool, skeletal muscle, subcutaneous adipose tissue and skin for future analyses including multiomics, expression profiles and histology. Repeated questionnaires on socioeconomic conditions, patient-reported outcomes as quality of life, health-related behavior as physical activity and nutritional habits are a specific asset of GDS. This study will recruit 3000 patients and a group of humans without familiy history of diabetes. 237 type 1 and 456 type 2 diabetes patients have been already included.Electronic supplementary materialThe online version of this article (doi:10.1186/s12933-016-0374-9) contains supplementary material, which is available to authorized users.

Purpose: Six pheochromocytoma susceptibility genes causing distinct syndromes have been identified; approximately one of three of all pheochromocytoma patients carry a predisposing germline mutation. When four major genes (VHL, RET, SDHB, SDHD) are analyzed in a clinical laboratory, costs are ∼$3,400 per patient. The aim of the study is to systematically obtain a robust algorithm to identify who should be genetically tested, and to determine the order in which genes should be tested. Experimental Design: DNA from 989 apparently nonsyndromic patients were scanned for germline mutations in the genes VHL, RET, SDHB, SDHC, and SDHD. Clinical parameters were analyzed as potential predictors for finding mutations by multiple logistic regression, validated by bootstrapping. Cost reduction was calculated between prioritized gene testing compared with that for all genes. Results: Of 989 apparently nonsyndromic pheochromocytoma cases, 187 (19%) harbored germline mutations. Predictors for presence of mutation are age <45 years, multiple pheochromocytoma, extra-adrenal location, and previous head and neck paraganglioma. If we used the presence of any one predictor as indicative of proceeding with gene testing, then 342 (34.6%) patients would be excluded, and only 8 carriers (4.3%) would be missed. We were also able to statistically model the priority of genes to be tested given certain clinical features. E.g., for patients with prior head and neck paraganglioma, the priority would be SDHD>SDHB>RET>VHL. Using the clinical predictor algorithm to prioritize gene testing and order, a 44.7% cost reduction in diagnostic process can be achieved. Conclusions: Clinical parameters can predict for mutation carriers and help prioritize gene testing to reduce costs in nonsyndromic pheochromocytoma presentations. (Clin Cancer Res 2009;15(20):6378-85)