Der Morbus Perthes - eine MRT-basierte Studie zur frühprognostischen Analyse der Risikoparameter

Hintergrund: Das Ziel dieser retrospektiven Studie war die Darstellung verlässlicher Orientierungsparameter im Rahmen der Prognoseeinschätzung des Morbus Perthes, insbesondere anhand von magnetresonanztomographischen Analysen. Methoden: Es wurden 123 Patienten, die in den Jahren 1994-2005 aufgrund eines Morbus Perthes im Waldkrankenhaus „Rudolf Elle“ GmbH Eisenberg behandelt wurden, anhand von Patientenakten, präoperativen Röntgen- und MRT-Bildern auf mögliche relevante Risikofaktoren hin analysiert. Die 149 Endergebnisseinschätzungen der Erkrankung, die auf postoperativen Röntgenbildern ermittelt wurden, sind auf statistisch signifikante Abhängigkeit von verschiedenen Risikofaktoren untersucht wurden. Ergebnisse: Als prognostisch ungünstige Zeichen ließen sich das weibliche Geschlecht, ein Alter über 6 Jahren, die röntgenologische Ausmessung der Lateralisation des Femur über 11 mm, der lateralen Säule unter 8,5 mm sowie in der MRT-Analyse ein Extrusionsindex über 0,32, ein Labrumwinkel über 55 °, zudem das Vorhandensein lateraler Verkalkungen, der laterale und gesamte Befall der Metaphyse, ein Nachweis einer subchondralen Fraktur Salter Typ B und ein hohes Ausmaß der Nekrosegröße, außerdem der laterale, kappenförmige, metaphysennahe oder ventral und dorsal gelegene Nekrosebefall nachweisen. Keine Abhängigkeit des Endergebnisses ergab sich bei bilateraler Erkrankung, der Horizontalisierung der Wachstumsfuge und dem Gage Zeichen. Schlussfolgerung: Mit der Studie konnte gezeigt werden, dass verschiedene Faktoren zur Prognoseeinschätzung beachtet werden müssen, insbesondere die MRT-Analyse in der Diagnostik und Risikofaktorenbeurteilung früh eingesetzt werden sollte. Überdies bieten die Ergebnisse neue Ansatzpunkte für weitere Forschungsmöglichkeiten, unter anderem die nähere Betrachtung der Orte der Nekrose im MRT im Hinblick auf den Verlauf der Erkrankung

Integrating Genetics and the Plasma Proteome to Predict the Risk of Type 2 Diabetes

Funder: University of CambridgeAbstract: Purpose of the Review: Proteins are the central layer of information transfer from genome to phenome and represent the largest class of drug targets. We review recent advances in high-throughput technologies that provide comprehensive, scalable profiling of the plasma proteome with the potential to improve prediction and mechanistic understanding of type 2 diabetes (T2D). Recent Findings: Technological and analytical advancements have enabled identification of novel protein biomarkers and signatures that help to address challenges of existing approaches to predict and screen for T2D. Genetic studies have so far revealed putative causal roles for only few of the proteins that have been linked to T2D, but ongoing large-scale genetic studies of the plasma proteome will help to address this and increase our understanding of aetiological pathways and mechanisms leading to diabetes. Summary: Studies of the human plasma proteome have started to elucidate its potential for T2D prediction and biomarker discovery. Future studies integrating genomic and proteomic data will provide opportunities to prioritise drug targets and identify pathways linking genetic predisposition to T2D development

Definitions of Metabolic Health and Risk of Future Type 2 Diabetes in BMI Categories: A Systematic Review and Network Meta-analysis.

OBJECTIVE: Various definitions of metabolic health have been proposed to explain differences in the risk of type 2 diabetes within BMI categories. The goal of this study was to assess their predictive relevance. RESEARCH DESIGN AND METHODS: We performed systematic searches of MEDLINE records for prospective cohort studies of type 2 diabetes risk in categories of BMI and metabolic health. In a two-stage meta-analysis, relative risks (RRs) specific to each BMI category were derived by network meta-analysis and the resulting RRs of each study were pooled using random-effects models. Hierarchical summary receiver operating characteristic curves were used to assess predictive performance. RESULTS: In a meta-analysis of 140,845 participants and 5,963 incident cases of type 2 diabetes from 14 cohort studies, classification as metabolically unhealthy was associated with higher RR of diabetes in all BMI categories (lean RR compared with healthy individuals 4.0 [95% CI 3.0-5.1], overweight 3.4 [2.8-4.3], and obese 2.5 [2.1-3.0]). Metabolically healthy obese individuals had a high absolute risk of type 2 diabetes (10-year cumulative incidence 3.1% [95% CI 2.6-3.5]). Current binary definitions of metabolic health had high specificity (pooled estimate 0.88 [95% CI 0.84-0.91]) but low sensitivity (0.40 [0.31-0.49]) in lean individuals and satisfactory sensitivity (0.81 [0.76-0.86]) but low specificity (0.42 [0.35-0.49]) in obese individuals. However, positive (0.4) likelihood ratios were consistent with insignificant to small improvements in prediction. CONCLUSIONS: Although individuals classified as metabolically unhealthy have a higher RR of type 2 diabetes compared with individuals classified as healthy in all BMI categories, current binary definitions of metabolic health have limited relevance to the prediction of future type 2 diabetes.The research leading to these results has received support from the Innovative Medicines Initiative Joint Undertaking under EMIF grant agreement n° 115372, resources of which are composed of financial contribution from the European Union's Seventh Framework Programme (FP7/2007-2013) and EFPIA companies’ in kind contribution. This work was supported by the Netherlands Organization for Scientific Research (NWO), and the Medical Research Council UK (grant no. MC_U106179471). A.A. is supported by a Rubicon grant from the NWO (Project no. 825.13.004).This is an author-created, uncopyedited electronic version of an article accepted for publication in Diabetes Care. The American Diabetes Care Association (ADA), publisher of Diabetes Care, is not responsible for any errors or omissions in this version of the manuscript or any version derived from it by third parties. The definitive publisher-authenticated version will be available in a future issue of Diabetes Care in print and online at http://care.diabetesjournals.org

Synergistic insights into human health from aptamer- and antibody-based proteomic profiling.

Funder: Wellcome TrustAffinity-based proteomics has enabled scalable quantification of thousands of protein targets in blood enhancing biomarker discovery, understanding of disease mechanisms, and genetic evaluation of drug targets in humans through protein quantitative trait loci (pQTLs). Here, we integrate two partly complementary techniques-the aptamer-based SomaScan® v4 assay and the antibody-based Olink assays-to systematically assess phenotypic consequences of hundreds of pQTLs discovered for 871 protein targets across both platforms. We create a genetically anchored cross-platform proteome-phenome network comprising 547 protein-phenotype connections, 36.3% of which were only seen with one of the two platforms suggesting that both techniques capture distinct aspects of protein biology. We further highlight discordance of genetically predicted effect directions between assays, such as for PILRA and Alzheimer's disease. Our results showcase the synergistic nature of these technologies to better understand and identify disease mechanisms and provide a benchmark for future cross-platform discoveries.The Fenland Study (10.22025/2017.10.101.00001) is funded by the Medical Research Council (MC_UU_12015/1). We are grateful to all the volunteers and to the General Practitioners and practice staff for assistance with recruitment. We thank the Fenland Study Investigators, Fenland Study Co-ordination team and the Epidemiology Field, Data and Laboratory teams. We further acknowledge support for genomics from the Medical Research Council (MC_PC_13046). Proteomic measurements were supported and governed by a collaboration agreement between the University of Cambridge and Somalogic. JCZ is supported by a 4-year Wellcome Trust PhD Studentship and the Cambridge Trust, CL, EW, and NJW are funded by the Medical Research Council (MC_UU_12015/1). NJW is a NIHR Senior Investigator. ADH is an NIHR Senior Investigator and supported by the UCL Hospitals NIHR Biomedical Research Centre and the UCL BHF Research Accelerator (AA/18/6/34223). We thank Philippa Pettingill, Ida Grundberg, Klev Diamanti, and Andrea Ballagi for advice and comments on an earlier draft of this manuscript. We thank Vladimir Saudek for generating a 3D-model of variant GDF-15 protein

Proteomic signatures for identification of impaired glucose tolerance

The implementation of recommendations for type 2 diabetes (T2D) screening and diagnosis focuses on the measurement of glycated hemoglobin (HbA1c) and fasting glucose. This approach leaves a large number of individuals with isolated impaired glucose tolerance (iIGT), who are only detectable through oral glucose tolerance tests (OGTTs), at risk of diabetes and its severe complications. We applied machine learning to the proteomic profiles of a single fasted sample from 11,546 participants of the Fenland study to test discrimination of iIGT defined using the gold-standard OGTTs. We observed significantly improved discriminative performance by adding only three proteins (RTN4R, CBPM and GHR) to the best clinical model (AUROC = 0.80 (95% confidence interval: 0.79–0.86), P = 0.004), which we validated in an external cohort. Increased plasma levels of these candidate proteins were associated with an increased risk for future T2D in an independent cohort and were also increased in individuals genetically susceptible to impaired glucose homeostasis and T2D. Assessment of a limited number of proteins can identify individuals likely to be missed by current diagnostic strategies and at high risk of T2D and its complications

Infectious Prions in Pre-Clinical Deer and Transmission of Chronic Wasting Disease Solely by Environmental Exposure

Key to understanding the epidemiology and pathogenesis of prion diseases, including chronic wasting disease (CWD) of cervids, is determining the mode of transmission from one individual to another. We have previously reported that saliva and blood from CWD-infected deer contain sufficient infectious prions to transmit disease upon passage into naïve deer. Here we again use bioassays in deer to show that blood and saliva of pre-symptomatic deer contain infectious prions capable of infecting naïve deer and that naïve deer exposed only to environmental fomites from the suites of CWD-infected deer acquired CWD infection after a period of 15 months post initial exposure. These results help to further explain the basis for the facile transmission of CWD, highlight the complexities associated with CWD transmission among cervids in their natural environment, emphasize the potential utility of blood-based testing to detect pre-clinical CWD infection, and could augur similar transmission dynamics in other prion infections

Exome Sequencing Identifies Genes and Gene Sets Contributing to Severe Childhood Obesity, Linking PHIP Variants to Repressed POMC Transcription.

Obesity is genetically heterogeneous with monogenic and complex polygenic forms. Using exome and targeted sequencing in 2,737 severely obese cases and 6,704 controls, we identified three genes (PHIP, DGKI, and ZMYM4) with an excess burden of very rare predicted deleterious variants in cases. In cells, we found that nuclear PHIP (pleckstrin homology domain interacting protein) directly enhances transcription of pro-opiomelanocortin (POMC), a neuropeptide that suppresses appetite. Obesity-associated PHIP variants repressed POMC transcription. Our demonstration that PHIP is involved in human energy homeostasis through transcriptional regulation of central melanocortin signaling has potential diagnostic and therapeutic implications for patients with obesity and developmental delay. Additionally, we found an excess burden of predicted deleterious variants involving genes nearest to loci from obesity genome-wide association studies. Genes and gene sets influencing obesity with variable penetrance provide compelling evidence for a continuum of causality in the genetic architecture of obesity, and explain some of its missing heritability

Proteomic analysis of 92 circulating proteins and their effects in cardiometabolic diseases

Background: Human plasma contains a wide variety of circulating proteins. These proteins can be important clinical biomarkers in disease and also possible drug targets. Large scale genomics studies of circulating proteins can identify genetic variants that lead to relative protein abundance. Methods: We conducted a meta-analysis on genome-wide association studies of autosomal chromosomes in 22,997 individuals of primarily European ancestry across 12 cohorts to identify protein quantitative trait loci (pQTL) for 92 cardiometabolic associated plasma proteins. Results: We identified 503 (337 cis and 166 trans) conditionally independent pQTLs, including several novel variants not reported in the literature. We conducted a sex-stratified analysis and found that 118 (23.5%) of pQTLs demonstrated heterogeneity between sexes. The direction of effect was preserved but there were differences in effect size and significance. Additionally, we annotate trans-pQTLs with nearest genes and report plausible biological relationships. Using Mendelian randomization, we identified causal associations for 18 proteins across 19 phenotypes, of which 10 have additional genetic colocalization evidence. We highlight proteins associated with a constellation of cardiometabolic traits including angiopoietin-related protein 7 (ANGPTL7) and Semaphorin 3F (SEMA3F). Conclusion: Through large-scale analysis of protein quantitative trait loci, we provide a comprehensive overview of common variants associated with plasma proteins. We highlight possible biological relationships which may serve as a basis for further investigation into possible causal roles in cardiometabolic diseases