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The German Center for Diabetes Research (DZD) established a core data set (CDS) of clinical parameters relevant for diabetes research in 2021. The CDS is central to the design of current and future DZD studies. Here, we describe the process and outcomes of FAIRifying the initial version of the CDS. We first did a baseline evaluation of the FAIRness using the FAIR Data Maturity Model. The FAIRification process and the results of this assessment led us to convert the CDS into the recommended format for spreadsheets, annotating the parameters with standardized medical codes, licensing the data set, enriching the data set with metadata, and indexing the metadata. The FAIRified version of the CDS is more suitable for data sharing in diabetes research across DZD sites and beyond. It contributes to the reusability of health research studies., (© 2024. The Author(s).)

Epidemiological studies have indicated the thyroid-disrupting effects of persistent organic pollutants (POPs). However, the association of low-exposure POPs with thyroid hormones (THs) remains unclear. Here, we aim to assess the association of low exposure of POPs, including polybrominated diphenyl ethers (PBDEs), polychlorinated biphenyls (PCBs), polychlorinated dibenzo- p -dioxins and furans (PCDD/Fs), and polybrominated dibenzo- p -dioxins and furans, with THs [total L -thyroxine (TT 4 ), total 3,3',5-triiodo- L -thyronine (TT 3 ), and total 3,3',5'-triiodo- L -thyronine (TrT 3 )] measured in human breast milk. Ninety-nine breast milk samples were collected from the LUPE cohort (2015-2016, Bavaria, Germany). Fourteen PBDEs, 17 PCBs, and 5 PCDD/Fs had quantification rates of >80%. Nonmonotonic associations were observed. In adjusted single-pollutant models, (1) TT 4 was inversely associated with BDE-99, -154, and -196; (2) TT 3 was inversely associated with BDE-47, -99, -100, -197, -203, -207, and OCDD; and (3) TrT 3 was inversely associated with BDE-47, -99, -183, and -203. Multipollutant analysis using principal component analysis and hierarchical clustering revealed inverse associations of PBDEs (BDE-28, -47, -99, -100, -154, -183, and -197) with TT 4 and TrT 3 . These results indicate that POPs at low levels might be related to reduced THs. This study shows that human breast milk might be an appropriate specimen to evaluate the thyroid disruption of POPs.

Context: Adenosine monophosphate (AMP)-activated protein kinase (AMPK) is a heterotrimeric enzyme and central regulator of cellular energy metabolism. The impact of single nucleotide polymorphisms (SNPs) in all 7 AMPK subunit genes on adiposity, glucose metabolism, and lipid metabolism has not yet been systematically studied., Objective: To analyze the associations of common SNPs in all AMPK genes, and of different scores thereof, with adiposity, insulin sensitivity, insulin secretion, blood glucose, low-density lipoprotein (LDL) cholesterol, high-density lipoprotein (HDL) cholesterol, total cholesterol, and triglycerides., Study Design and Methods: A cohort of 2789 nondiabetic participants from the Tübingen Family study of type 2 diabetes, metabolically characterized by oral glucose tolerance test and genotyped by genome-wide SNP array, was analyzed., Results: We identified 6 largely nonoverlapping SNP sets across 4 AMPK genes (PRKAA1, PRKAA2, PRKAG2, PRKAG3) associated with adiposity, insulin sensitivity, insulin secretion, blood glucose, total/LDL cholesterol, or HDL cholesterol, respectively. A genetic score of body-fat-increasing alleles revealed per-allele effect sizes on body mass index (BMI) of +0.22 kg/m2 (P = 2.3 × 10-7), insulin sensitivity of -0.12 × 1019 L2/mol2 (P = 9.9 × 10-6) and 2-hour blood glucose of +0.02 mmol/L (P = 0.0048). Similar effects on blood glucose were observed with scores of insulin-sensitivity-reducing, insulin-secretion-reducing and glucose-raising alleles, respectively. A genetic cholesterol score increased total and LDL cholesterol by 1.17 mg/dL per allele (P = 0.0002 and P = 3.2 × 10-5, respectively), and a genetic HDL score decreased HDL cholesterol by 0.32 mg/dL per allele (P = 9.1 × 10-6)., Conclusions: We describe largely nonoverlapping genetic determinants in AMPK genes for diabetes-/atherosclerosis-related traits, which reflect the metabolic pathways controlled by the enzyme. Formation of trait-specific genetic scores revealed additivity of allele effects, with body-fat-raising alleles reaching a marked effect size. (J Clin Endocrinol Metab XX: 0-0, 2019)., (© Endocrine Society 2019. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Context: Primary dysregulation of adipose tissue lipolysis caused by genetic variation and independent of insulin resistance could explain unhealthy body fat distribution and its metabolic consequences., Objective: To analyze common single nucleotide polymorphisms (SNPs) in 48 lipolysis-, but not insulin-signaling-related genes, to form polygenic risk scores of lipolysis-associated SNPs, and to investigate their effects on body fat distribution, glycemia, insulin sensitivity, insulin secretion, and proinsulin conversion., Study Design, Participants, and Methods: SNP array, anthropometric, and metabolic data were available from up to 2789 participants without diabetes of the Tübingen Family study of type 2 diabetes characterized by oral glucose tolerance tests. In a subgroup (n = 942), magnetic resonance measurements of body fat stores were available., Results: We identified insulin-sensitivity-independent nominal associations (P < 0.05) of SNPs in 10 genes with plasma free fatty acids (FFAs), in 7 genes with plasma glycerol and in 6 genes with both, plasma FFAs and glycerol. A score formed of the latter SNPs (in ADCY4, CIDEA, GNAS, PDE8B, PRKAA1, PRKAG2) was associated with plasma FFA and glycerol measurements (1.4*10-9 ≤ P ≤ 1.2*10-5), visceral adipose tissue mass (P = 0.0326), and proinsulin conversion (P ≤ 0.0272). The more lipolysis-increasing alleles a subject had, the lower was the visceral fat mass and the lower the proinsulin conversion., Conclusions: We found evidence for a genetic basis of adipose tissue lipolysis resulting from common SNPs in CIDEA, AMP-activated protein kinase subunits, and cAMP signaling components. A genetic score of lipolysis-increasing alleles determined lower visceral fat mass and lower proinsulin conversion., (Copyright © 2019 Endocrine Society.)

Objective: The prevalence of diabetes mellitus and associated complications is steadily increasing. As a resource for studying systemic consequences of chronic insulin insufficiency and hyperglycemia, we established a comprehensive biobank of long-term diabetic INS C94Y transgenic pigs, a model of mutant INS gene-induced diabetes of youth (MIDY), and of wild-type (WT) littermates., Methods: Female MIDY pigs (n = 4) were maintained with suboptimal insulin treatment for 2 years, together with female WT littermates (n = 5). Plasma insulin, C-peptide and glucagon levels were regularly determined using specific immunoassays. In addition, clinical chemical, targeted metabolomics, and lipidomics analyses were performed. At age 2 years, all pigs were euthanized, necropsied, and a broad spectrum of tissues was taken by systematic uniform random sampling procedures. Total beta cell volume was determined by stereological methods. A pilot proteome analysis of pancreas, liver, and kidney cortex was performed by label free proteomics., Results: MIDY pigs had elevated fasting plasma glucose and fructosamine concentrations, C-peptide levels that decreased with age and were undetectable at 2 years, and an 82% reduced total beta cell volume compared to WT. Plasma glucagon and beta hydroxybutyrate levels of MIDY pigs were chronically elevated, reflecting hallmarks of poorly controlled diabetes in humans. In total, ∼1900 samples of different body fluids (blood, serum, plasma, urine, cerebrospinal fluid, and synovial fluid) as well as ∼17,000 samples from ∼50 different tissues and organs were preserved to facilitate a plethora of morphological and molecular analyses. Principal component analyses of plasma targeted metabolomics and lipidomics data and of proteome profiles from pancreas, liver, and kidney cortex clearly separated MIDY and WT samples., Conclusions: The broad spectrum of well-defined biosamples in the Munich MIDY Pig Biobank that will be available to the scientific community provides a unique resource for systematic studies of organ crosstalk in diabetes in a multi-organ, multi-omics dimension.

Under the label of the German Mouse Clinic (GMC), a concept has been developed and implemented that allows the better understanding of human diseases on the pathophysiological and molecular level. This includes better understanding of the crosstalk between different organs, pleiotropy of genes, and the systemic impact of envirotypes and drugs. In the GMC, experts from various fields of mouse genetics and physiology, in close collaboration with clinicians, work side by side under one roof. The GMC is an open-access platform for the scientific community by providing phenotypic analysis in bilateral collaborations ("bottom-up projects") and as a partner and driver in international large-scale biology projects ("top-down projects"). Furthermore, technology development is a major topic in the GMC. Innovative techniques for primary and secondary screens are developed and implemented into the phenotyping pipelines (e.g., detection of volatile organic compounds, VOCs).

Background: Metabolomics is the rapidly evolving field of the comprehensive measurement of ideally all endogenous metabolites in a biological fluid. However, no single analytic technique covers the entire spectrum of the human metabolome. Here we present results from a multiplatform study, in which we investigate what kind of results can presently be obtained in the field of diabetes research when combining metabolomics data collected on a complementary set of analytical platforms in the framework of an epidemiological study., Methodology/principal Findings: 40 individuals with self-reported diabetes and 60 controls (male, over 54 years) were randomly selected from the participants of the population-based KORA (Cooperative Health Research in the Region of Augsburg) study, representing an extensively phenotyped sample of the general German population. Concentrations of over 420 unique small molecules were determined in overnight-fasting blood using three different techniques, covering nuclear magnetic resonance and tandem mass spectrometry. Known biomarkers of diabetes could be replicated by this multiple metabolomic platform approach, including sugar metabolites (1,5-anhydroglucoitol), ketone bodies (3-hydroxybutyrate), and branched chain amino acids. In some cases, diabetes-related medication can be detected (pioglitazone, salicylic acid)., Conclusions/significance: Our study depicts the promising potential of metabolomics in diabetes research by identification of a series of known and also novel, deregulated metabolites that associate with diabetes. Key observations include perturbations of metabolic pathways linked to kidney dysfunction (3-indoxyl sulfate), lipid metabolism (glycerophospholipids, free fatty acids), and interaction with the gut microflora (bile acids). Our study suggests that metabolic markers hold the potential to detect diabetes-related complications already under sub-clinical conditions in the general population.

The German Mouse Clinic (GMC) is a large scale phenotyping center where mouse mutant lines are analyzed in a standardized and comprehensive way. The result is an almost complete picture of the phenotype of a mouse mutant line--a systemic view. At the GMC, expert scientists from various fields of mouse research work in close cooperation with clinicians side by side at one location. The phenotype screens comprise the following areas: allergy, behavior, clinical chemistry, cardiovascular analyses, dysmorphology, bone and cartilage, energy metabolism, eye and vision, host-pathogen interactions, immunology, lung function, molecular phenotyping, neurology, nociception, steroid metabolism, and pathology. The German Mouse Clinic is an open access platform that offers a collaboration-based phenotyping to the scientific community (www.mouseclinic.de). More than 80 mutant lines have been analyzed in a primary screen for 320 parameters, and for 95% of the mutant lines we have found new or additional phenotypes that were not associated with the mouse line before. Our data contributed to the association of mutant mouse lines to the corresponding human disease. In addition, the systemic phenotype analysis accounts for pleiotropic gene functions and refines previous phenotypic characterizations. This is an important basis for the analysis of underlying disease mechanisms. We are currently setting up a platform that will include environmental challenge tests to decipher genome-environmental interactions in the areas nutrition, exercise, air, stress and infection with different standardized experiments. This will help us to identify genetic predispositions as susceptibility factors for environmental influences.

Much of our understanding of physiology and metabolism is derived from investigating mouse mutants and transgenic mice, and open-access platforms for standardized mouse phenotyping such as the German Mouse Clinic (GMC) are currently viewed as one powerful tool for identifying novel gene-function relationships. Phenotyping or phenotypic screening involves the comparison of wild-type control mice with their mutant or transgenic littermates. In our study, we explored the extent to which standardized phenotyping will succeed in detecting biologically relevant phenotypic differences in mice generated and provided by different collaborators. We analyzed quantitative metabolic data (body mass, energy intake, and energy metabolized) collected at the GMC under the current workflow, and used them for statistical power considerations. Our results demonstrate that there is substantial variability in these parameters among lines of wild-type C57BL/6 (B6) mice from different sources. Given this variable background noise in mice that serve as controls, subtle phenotypes in mutant or transgenic littermates may be overlooked. Furthermore, a phenotype observed in one cohort of a mutant line may not be reproducible (to the same extent) in mice coming from a different environment or supplier. In the light of these constraints, we encourage researchers to incorporate information on intrastrain variability into future study planning, or to perform advanced hierarchical analyses. Both will ultimately improve the detectability of novel phenotypes by phenotypic screening.