Depression als komorbide Störung in der primärärztlichen Versorgung

Auf der Grundlage der DETECT-Studie wird die querschnittliche Assoziation depressiver Störungen mit einem weiten Spektrum körperlicher Erkrankungen in einer bundesweit repräsentativen Stichprobe von 51.000 Patienten aus der primärärztlichen Versorgung in Deutschland sowie der Zusammenhang mit gesundheitsbezogener Lebensqualität und Arbeitsunfähigkeit untersucht. Das Vorliegen einer Depression wurde über den Depression Screening Questionnaire (DSQ) mit seinem ICD-10 Algorithmus ermittelt. Bei einer Gesamt-Querschnittsprävalenz depressiver Störungen von 7,5 % ergaben sich erhöhte Depressionsraten und signifikante Assoziationen für nahezu alle untersuchten Krankheitsgruppen. (1) Ko- und Multimorbidität somatischer als auch somatischer mit depressiven Störungen sind die Regel: „Reine“ (nicht komorbide) Depressionen sind ebenso wie reine somatische Erkrankungen die Ausnahme. (2) Das Depressionsrisiko steigt stetig mit der Anzahl komorbider Krankheiten. (3) Besonders ausgeprägte Assoziationen ergaben sich für schwergradige Herzinsuffizienzen (OR: 5,8), diabetische Folgekomplikationen (OR: 1,7–2,0), koronare Herzerkrankungen (KHK) (OR: 1,7), zerebrale Insulte (OR: 2,5) sowie muskuloskelettäre Erkrankungen (OR: 1,5). Demgegenüber waren z. B. die Raten bei Hyperlipidämie (OR: 1,1) nur leicht erhöht. (4) Komorbide Depression und steigende Multimorbidität waren mit stetig zunehmenden Arbeitsunfähigkeits- raten und absinkender gesundheitsbezogener Lebensqualität assoziiert. Angesichts der quantitativen Bedeutung der Depression sowie des mit Multimorbidität drastisch ansteigenden Depressionsrisikos und der damit verbundenen hohen direkten und indirekten Krankheitslast für das Gesundheitssystem und die Gesellschaft ist das hohe Ausmaß der Unterschätzung von Depression in der Routineversorgung besorgniserregend.As part of the DETECT study, a nationwide representative clinical-epidemiological study, the frequency and associated problems of comorbid depression with a wide range of somatic illnesses were studied in N = 51,000 primary care patients. Further the association with health related quality of life and disability is examined. Depression was assessed with the Depression Screening Questionnaire (DSQ) with an ICD-10 algorithm. Results: (1) 7.5 % of all primary care patients met criteria for ICD-10 depressive disorders. (2) Depression risk was increased whenever any somatic disorder was present and increased in a dose-response relationship by number of comorbid conditions. (3) Elevation of depression risk was fairly independent of type of diagnosis, although associations with coronary heart disease (OR: 1.7), diabetic complications (OR: 1.7– 2.0), stroke (OR: 2.5) and pain-related chronic disorders (OR: 1.5) were particularly pronounced. Moderate associations were found for hyperlipidaemia (OR: 1.1). (4) Associated with the increasing number of comorbid conditions, patients with comorbid depression had increasingly more disability days and lower health related quality of life. It is concluded that the degree to which the frequency and the deleterious effects of comorbid depression is underestimated and unrecognized is alarming. The use of comorbidity indices might improve recognition

Using the posterior distribution of deviance to measure evidence of association for rare susceptibility variants

Aitkin recently proposed an integrated Bayesian/likelihood approach that he claims is general and simple. We have applied this method, which does not rely on informative prior probabilities or large-sample results, to investigate the evidence of association between disease and the 16 variants in the KDR gene provided by Genetic Analysis Workshop 17. Based on the likelihood of logistic regression models and considering noninformative uniform prior probabilities on the coefficients of the explanatory variables, we used a random walk Metropolis algorithm to simulate the distributions of deviance and deviance difference. The distribution of probability values and the distribution of the proportions of positive deviance differences showed different locations, but the direction of the shift depended on the genetic factor. For the variant with the highest minor allele frequency and for any rare variant, standard logistic regression showed a higher power than the novel approach. For the two variants with the strongest effects on Q1 under a type I error rate of 1%, the integrated approach showed a higher power than standard logistic regression. The advantages and limitations of the integrated Bayesian/likelihood approach should be investigated using additional regions and considering alternative regression models and collapsing methods

Erysense, a Lab-on-a-Chip-Based Point-of-Care Device to Evaluate Red Blood Cell Flow Properties With Multiple Clinical Applications

In many medical disciplines, red blood cells are discovered to be biomarkers since they “experience” various conditions in basically all organs of the body. Classical examples are diabetes and hypercholesterolemia. However, recently the red blood cell distribution width (RDW), is often referred to, as an unspecific parameter/marker (e.g., for cardiac events or in oncological studies). The measurement of RDW requires venous blood samples to perform the complete blood cell count (CBC). Here, we introduce Erysense, a lab-on-a-chip-based point-of-care device, to evaluate red blood cell flow properties. The capillary chip technology in combination with algorithms based on artificial neural networks allows the detection of very subtle changes in the red blood cell morphology. This flow-based method closely resembles in vivo conditions and blood sample volumes in the sub-microliter range are sufficient. We provide clinical examples for potential applications of Erysense as a diagnostic tool [here: neuroacanthocytosis syndromes (NAS)] and as cellular quality control for red blood cells [here: hemodiafiltration (HDF) and erythrocyte concentrate (EC) storage]. Due to the wide range of the applicable flow velocities (0.1–10 mm/s) different mechanical properties of the red blood cells can be addressed with Erysense providing the opportunity for differential diagnosis/judgments. Due to these versatile properties, we anticipate the value of Erysense for further diagnostic, prognostic, and theragnostic applications including but not limited to diabetes, iron deficiency, COVID-19, rheumatism, various red blood cell disorders and anemia, as well as inflammation-based diseases including sepsis

Big Data in Transfusion Medicine and Artificial Intelligence Analysis for Red Blood Cell Quality Control

peer reviewedBackground: ``Artificial intelligence'' and ``big data'' increasingly take the step from just being interesting concepts to being relevant or even part of our lives. This general statement holds also true for transfusion medicine. Besides all advancements in transfusion medicine, there is not yet an established red blood cell quality measure, which is generally applied. Summary: We highlight the usefulness of big data in transfusion medicine. Furthermore, we emphasize in the example of quality control of red blood cell units the application of artificial intelligence. Key Messages: A variety of concepts making use of big data and artificial intelligence are readily available but still await to be implemented into any clinical routine. For the quality control of red blood cell units, clinical validation is still required

Quantifying Missing Heritability at Known GWAS Loci

Recent work has shown that much of the missing heritability of complex traits can be resolved by estimates of heritability explained by all genotyped SNPs. However, it is currently unknown how much heritability is missing due to poor tagging or additional causal variants at known GWAS loci. Here, we use variance components to quantify the heritability explained by all SNPs at known GWAS loci in nine diseases from WTCCC1 and WTCCC2. After accounting for expectation, we observed all SNPs at known GWAS loci to explain 1.29 X more heritability than GWAS-associated SNPs on average (P = 3.3 X 10[superscript -5]). For some diseases, this increase was individually significant:2.07 X for Multiple Sclerosis (MS) (P = 6.5 X 10 [superscript -9]) and for Crohn's Disease (CD) (P = 1.3 X 10[superscript -3]); all analyses of autoimmune diseases excluded the well-studied MHC region. Additionally, we found that GWAS loci from other related traits also explained significant heritability. The union of all autoimmune disease loci explained 7.15 X more MS heritability than known MS SNPs (P 20,000 Rheumatoid Arthritis (RA) samples typed on ImmunoChip, with 2.37 X more heritability from all SNPs at GWAS loci (P = 2.3 X 10[superscript -6]) and more heritability from all autoimmune disease loci (P < 1 X 10[superscript -16]) compared to known RA SNPs (including those identified in this cohort). Our methods adjust for LD between SNPs, which can bias standard estimates of heritability from SNPs even if all causal variants are typed. By comparing adjusted estimates, we hypothesize that the genome-wide distribution of causal variants is enriched for low-frequency alleles, but that causal variants at known GWAS loci are skewed towards common alleles. These findings have important ramifications for fine-mapping study design and our understanding of complex disease architecture.National Institutes of Health (U.S.) (Grant R03HG006731)National Institutes of Health (U.S.) (Fellowship F32GM106584

Modulation of gene expression in U251 glioblastoma cells by binding of mutant p53 R273H to intronic and intergenic sequences

Missense point mutations in the TP53 gene are frequent genetic alterations in human tumor tissue and cell lines derived thereof. Mutant p53 (mutp53) proteins have lost sequence-specific DNA binding, but have retained the ability to interact in a structure-selective manner with non-B DNA and to act as regulators of transcription. To identify functional binding sites of mutp53, we established a small library of genomic sequences bound by p53R273H in U251 human glioblastoma cells using chromatin immunoprecipitation (ChIP). Mutp53 binding to isolated DNA fragments confirmed the specificity of the ChIP. The mutp53 bound DNA sequences are rich in repetitive DNA elements, which are dispersed over non-coding DNA regions. Stable down-regulation of mutp53 expression strongly suggested that mutp53 binding to genomic DNA is functional. We identified the PPARGC1A and FRMD5 genes as p53R273H targets regulated by binding to intronic and intra-genic sequences. We propose a model that attributes the oncogenic functions of mutp53 to its ability to interact with intronic and intergenic non-B DNA sequences and modulate gene transcription via re-organization of chromatin