Your email was sent successfully. Check your inbox.

An error occurred while sending the email. Please try again.

Proceed reservation?

Export
  • 1
    facet.materialart.
    Unknown
    German Medical Science GMS Publishing House; Düsseldorf
    In:  HEC 2016: Health - Exploring Complexity; Joint Conference of GMDS, DGEpi, IEA-EEF, EFMI; 20160828-20160902; München; DOCAbstr. 392 /20160808/
    Publication Date: 2016-08-11
    Keywords: ddc: 610
    Language: German
    Type: conferenceObject
    Signatur Availability
    BibTip Others were also interested in ...
  • 2
    Keywords: PATHWAY ; PATHWAYS ; DISEASE ; RISK ; PROTEIN ; ASSOCIATION ; VARIANTS ; DESIGN ; genetics ; meta-analysis ; inflammation ; interaction ; CORONARY-HEART-DISEASE ; METAANALYSIS ; metabolic syndrome ; myocardial infarction ; COMMON VARIANTS ; ALPHA-GENE ; CRP GENE ; EPIDEMIOLOGIC APPLICATIONS ; FRAMINGHAM ; GENETICALLY ISOLATED POPULATION ; genome-wide association study ; NETHERLANDS TWIN REGISTER
    Abstract: Background-C-reactive protein (CRP) is a heritable marker of chronic inflammation that is strongly associated with cardiovascular disease. We sought to identify genetic variants that are associated with CRP levels. Methods and Results-We performed a genome-wide association analysis of CRP in 66 185 participants from 15 population-based studies. We sought replication for the genome-wide significant and suggestive loci in a replication panel comprising 16 540 individuals from 10 independent studies. We found 18 genome-wide significant loci, and we provided evidence of replication for 8 of them. Our results confirm 7 previously known loci and introduce 11 novel loci that are implicated in pathways related to the metabolic syndrome (APOC1, HNF1A, LEPR, GCKR, HNF4A, and PTPN2) or the immune system (CRP, IL6R, NLRP3, IL1F10, and IRF1) or that reside in regions previously not known to play a role in chronic inflammation (PPP1R3B, SALL1, PABPC4, ASCL1, RORA, and BCL7B). We found a significant interaction of body mass index with LEPR (P 〈 2.9 x 10(-6)). A weighted genetic risk score that was developed to summarize the effect of risk alleles was strongly associated with CRP levels and explained approximate to 5% of the trait variance; however, there was no evidence for these genetic variants explaining the association of CRP with coronary heart disease. Conclusions-We identified 18 loci that were associated with CRP levels. Our study highlights immune response and metabolic regulatory pathways involved in the regulation of chronic inflammation.
    Type of Publication: Journal article published
    PubMed ID: 21300955
    Signatur Availability
    BibTip Others were also interested in ...
  • 3
    facet.materialart.
    Unknown
    German Medical Science GMS Publishing House; Düsseldorf
    In:  GMS Medizinische Informatik, Biometrie und Epidemiologie; VOL: 15; DOC08 /20191108/
    Publication Date: 2019-11-09
    Description: High data quality is fundamental for valid inferences in health research. Metadata, i.e. "data that describe other data", are essential to implement data quality assessments but more guidance on which metadata to use is needed. Similarly, the selection and use of variables describing the measurement process should be exemplified to improve the design and conduct of observational health studies. This work provides a conceptual framework and overview of metadata and process information for systematic data quality reports based on implementations within the population-based cohort Study of Health in Pomerania (SHIP). In previous years, a prerequisite for automated data quality checks has been established by the augmentation of the data dictionary; the added information of up to 20 different characteristics for each variable is used for data quality assessments and triggers diverse data quality checks. Conceptually we distinguish static metadata, variable metadata, and process variables. Examples for static metadata are the expected probability distribution, plausibility limits, and the data type. Variable metadata may be reference limits of a laboratory marker. Information inherent to these metadata allows for the detection of data quality flaws by comparing observed with expected data characteristics. In contrast, process variables, such as the observer or device ID, also allow for the identification of sources of data quality issues. This is the case even if characteristics defined in metadata were not violated. Metadata and process variables can be used alone or in combination to implement a versatile and efficient data quality assessment. A comprehensive setup of metadata and process variables is an extensive task, particularly in studies involving large data collections. Nonetheless, the gain in transparency and efficacy of data curation and quality reporting after this setup is considerable.
    Description: Eine hohe Datenqualität ist eine wesentliche Voraussetzung für valide Entscheidungen in der Gesundheitsforschung. Metadaten bzw. "Daten über andere Daten" sind für die Implementierung eines Datenqualitätsmonitorings essentiell. Klare Empfehlungen und Benennungen von Metadaten für spezifische Aspekte von Datenqualität werden in relevanter Literatur jedoch nicht gegeben. Gleichfalls ist nicht klar, welche Informationen über den datengenerierenden Prozess gesammelt werden sollten, um Studiendesign und -durchführung zu verbessern. In dieser Arbeit wird unter konzeptioneller Perspektive ein Überblick zu Metadaten und Prozessinformationen gegeben, welche in der Kohortenstudie Study of Health in Pomerania (SHIP) verwendet werden. Zurückliegend wurde in SHIP das allgemein gebräuchliche Data Dictionary um Informationen erweitert, welche für Datenqualitätsbewertungen verwendet werden und diese auch steuern können; bis zu 20 unterschiedliche Charakteristika von Variablen können spezifiziert werden. Konzeptionell werden hierfür statische von variablen Metadaten sowie Prozessvariablen unterschieden. Zum Beispiel sind die Verteilungsform, Plausibilitäts- und Zulässigkeitsgrenzen sowie der Dateneingabetyp statische Metadaten. Variierende Referenzgrenzen von z.B. Laborparametern werden als variable Metadaten betrachtet. Diese Information erlaubt die Identifizierung von Beeinträchtigungen der Datenqualität durch einen Vergleich von beobachteten und erwarteten Charakteristika der Daten. Prozessvariablen wie die ID des Untersuchers oder des Messgeräts erlauben hingegen die Identifikation von möglichen Quellen für Fehler, selbst wenn keine Metadaten verletzt wurden. Metadaten und Prozessvariablen können jeweils allein oder in Kombination verwendet werden, um vielseitige und effiziente Qualitätsbewertungen umzusetzen. Die Erstellung notwendiger Metadaten und die Definition von Prozessvariablen bedeuten einen erheblichen Aufwand, insbesondere für größere Studien. Der Zugewinn an Transparenz und Effektivität bei der Qualitätsberichterstellung ist jedoch erheblich.
    Keywords: data quality ; metadata ; process variables ; data monitoring ; health research ; cohort studies ; Datenqualität ; Metadaten ; Prozessvariablen ; Datenmonitoring ; Gesundheitsforschung ; Kohortenstudien ; ddc: 610
    Language: English
    Type: article , info:eu-repo/semantics/article
    Signatur Availability
    BibTip Others were also interested in ...
Close ⊗
This website uses cookies and the analysis tool Matomo. More information can be found here...