[Prevalence of Rheumatoid Arthritis in Germany: Analysis of Longitudinal Data of Statutory Health Insurance]. / Prävalenz der Rheumatoiden Arthritis in Deutschland: Analyse von Längsschnittdaten der Gesetzlichen Krankenversicherung.

BACKGROUND: Rheumatoid arthritis (RA) is a progressive chronic inflammatory systemic autoimmune disease with destructive joint changes. Data on prevalence in adult patients are only available to a limited extent in Germany. METHODOLOGY: Anonymised diagnosis and prescription data for 2008-2013 were evaluated at patient level for approximately 2.3 million insured adults (total data set; status 2013) and approx. 1.2 million insured adults (smaller data set with additional information such as treatment by a specialist; status 2013) from 7 different statutory health insurance companies. RA was diagnosed if the code M05 (seropositive chronic polyarthritis) or M06 (other chronic polyarthritis) was present (diagnosis group M0[5,6]) and if the patient was either an outpatient at least twice (in 2 different quarters) or at least once an inpatient within a calendar year (according to the BVA criteria, inpatient diagnoses were thus classified as more reliable). The data were extrapolated to the SHI-insured and total population in Germany for 2013. RESULTS: The prevalence of RA in the total data set was 1.26% on average over all years (2008-2013). More than 90% of the diagnosis was based on the diagnosis code M06. In 88% of the cases, the classification was based exclusively on outpatient diagnoses. Taking into account a diagnosis by a specialist based on a smaller data set containing this additional information to determine a "reliable" RA diagnosis, the average RA rate for 2011-2013 was about 0.99%. Related to the diagnosis group M0[5,6] in the total data set, the prevalence of RA in 2013 was about 1.8% of women and about 0.8% of men. Only about 40% of diagnosed patients were treated with DMARDs. CONCLUSIONS: The prevalence estimates for RA derived from the larger data set correspond to those of previous surveys with partially different methodological approaches. Based on the analysis of the health insurance data presented, the prevalence of diagnosed RA in adults in Germany is 1.26%, which is within the range of 0.81-1.62% that can be found in the literature. This represents about 721,000 adult SHI-insured persons. The low rate of DMARD prescriptions may indicate inadequate care of these patients.

Epidemiology and Treatment of Patients with Rheumatoid Arthritis, Psoriatic Arthritis and Psoriasis in Germany: A Real-World Evidence Study.

INTRODUCTION: Rheumatoid arthritis (RA), psoriatic arthritis (PsA) and psoriasis (PSO) are chronic inflammatory diseases that have a substantial impact on patients' health. This retrospective database study aimed to assess the epidemiology, comorbidities, diagnosis and treatment patterns of RA, PsA and PSO in the German population. METHODS: Data were extracted from the Deutsche Forschungsdatenbank für Abrechnungsinformationen der Krankenversicherung database from 2012 to 2016 for patients aged ≥ 18 years holding full health coverage in the reporting year at least. Diagnoses were defined according to International Classification of Diseases (ICD)-10 codes. Reported outcomes included prevalence and incidence rates, pre-defined comorbidities, diagnosing and treating physicians, and treatment exposure. A subgroup analysis was performed for women of childbearing age (females aged 18-45 years). RESULTS: The prevalence rates of RA, PsA and PSO in Germany were consistent over the study period; by 2016 they were 0.4%, 0.3% and 2.1%, respectively, and in women of childbearing age they were 0.2%, 0.2% and 1.5%, respectively. RA, PsA and PSO were predominantly observed among patients aged > 45 years. RA and PsA were more prevalent in women, while PSO had an approximately equal gender distribution. Depressive episodes were the most frequently reported comorbidity in 2016 (RA: 25.7%; PsA: 25.1%; PSO: 17.8%), and this was similar in women of childbearing age (RA: 20.5%; PsA: 23.4%; PSO: 16.3%). Approximately 50% of patients with RA and PsA and 6% of patients with PSO were receiving systemic treatment in 2016, of which methotrexate (RA: 38.4%; PsA: 30.2%; PSO: 2.2%) was most common. Biologic therapies were the least frequently used treatment options (RA: 28.9%; PsA: 20.9%; PSO: 1.8%). CONCLUSIONS: This analysis provides key epidemiological information for patients with RA, PsA and PSO, including in women of childbearing age, in Germany and highlights common comorbidities and that patients were likely receiving insufficient treatment for these diagnoses.

Random Projection for Fast and Efficient Multivariate Correlation Analysis of High-Dimensional Data: A New Approach.

In recent years, the advent of great technological advances has produced a wealth of very high-dimensional data, and combining high-dimensional information from multiple sources is becoming increasingly important in an extending range of scientific disciplines. Partial Least Squares Correlation (PLSC) is a frequently used method for multivariate multimodal data integration. It is, however, computationally expensive in applications involving large numbers of variables, as required, for example, in genetic neuroimaging. To handle high-dimensional problems, dimension reduction might be implemented as pre-processing step. We propose a new approach that incorporates Random Projection (RP) for dimensionality reduction into PLSC to efficiently solve high-dimensional multimodal problems like genotype-phenotype associations. We name our new method PLSC-RP. Using simulated and experimental data sets containing whole genome SNP measures as genotypes and whole brain neuroimaging measures as phenotypes, we demonstrate that PLSC-RP is drastically faster than traditional PLSC while providing statistically equivalent results. We also provide evidence that dimensionality reduction using RP is data type independent. Therefore, PLSC-RP opens up a wide range of possible applications. It can be used for any integrative analysis that combines information from multiple sources.

Comparison of variants of canonical correlation analysis and partial least squares for combined analysis of MRI and genetic data.

The standard analysis approach in neuroimaging genetics studies is the mass-univariate linear modeling (MULM) approach. From a statistical view, however, this approach is disadvantageous, as it is computationally intensive, cannot account for complex multivariate relationships, and has to be corrected for multiple testing. In contrast, multivariate methods offer the opportunity to include combined information from multiple variants to discover meaningful associations between genetic and brain imaging data. We assessed three multivariate techniques, partial least squares correlation (PLSC), sparse canonical correlation analysis (sparse CCA) and Bayesian inter-battery factor analysis (Bayesian IBFA), with respect to their ability to detect multivariate genotype-phenotype associations. Our goal was to systematically compare these three approaches with respect to their performance and to assess their suitability for high-dimensional and multi-collinearly dependent data as is the case in neuroimaging genetics studies. In a series of simulations using both linearly independent and multi-collinear data, we show that sparse CCA and PLSC are suitable even for very high-dimensional collinear imaging data sets. Among those two, the predictive power was higher for sparse CCA when voxel numbers were below 400 times sample size and candidate SNPs were considered. Accordingly, we recommend Sparse CCA for candidate phenotype, candidate SNP studies. When voxel numbers exceeded 500 times sample size, the predictive power was the highest for PLSC. Therefore, PLSC can be considered a promising technique for multivariate modeling of high-dimensional brain-SNP-associations. In contrast, Bayesian IBFA cannot be recommended, since additional post-processing steps were necessary to detect causal relations. To verify the applicability of sparse CCA and PLSC, we applied them to an experimental imaging genetics data set provided for us. Most importantly, application of both methods replicated the findings of this data set.

Body weight status, eating behavior, sensitivity to reward/punishment, and gender: relationships and interdependencies.

Behavioral and personality characteristics are factors that may jointly regulate body weight. This study explored the relationship between body mass index (BMI) and self-reported behavioral and personality measures. These measures included eating behavior (based on the Three-Factor Eating Questionnaire; Stunkard and Messick, 1985), sensitivity to reward and punishment (based on the Behavioral Inhibition System/Behavioral Activation System (BIS/BAS) scales) (Carver and White, 1994) and self-reported impulsivity (based on the Barratt Impulsiveness Scale-11; Patton et al., 1995). We found an inverted U-shaped relationship between restrained eating and BMI. This relationship was moderated by the level of disinhibited eating. Independent of eating behavior, BIS and BAS responsiveness were associated with BMI in a gender-specific manner with negative relationships for men and positive relationships for women. Together, eating behavior and BIS/BAS responsiveness accounted for a substantial proportion of BMI variance (men: ∼25%, women: ∼32%). A direct relationship between self-reported impulsivity and BMI was not observed. In summary, our results demonstrate a system of linear and non-linear relationships between the investigated factors and BMI. Moreover, body weight status was not only associated with eating behavior (cognitive restraint and disinhibition), but also with personality factors not inherently related to an eating context (BIS/BAS). Importantly, these relationships differ between men and women.

Transcript co-variance with Nestin in two mouse genetic reference populations identifies Lef1 as a novel candidate regulator of neural precursor cell proliferation in the adult hippocampus.

Adult neurogenesis, the lifelong production of new neurons in the adult brain, is under complex genetic control but many of the genes involved remain to be identified. In this study, we have integrated publicly available gene expression data from the BXD and CXB recombinant inbred mouse lines to discover genes co-expressed in the adult hippocampus with Nestin, a common marker of the neural precursor cell population. In addition, we incorporated spatial expression information to restrict candidates to genes with high differential gene expression in the hippocampal dentate gyrus. Incorporating data from curated protein-protein interaction databases revealed interactions between our candidate genes and those already known to be involved in adult neurogenesis. Enrichment analysis suggested a link to the Wnt/ß-catenin pathway, known to be involved in adult neurogenesis. In particular, our candidates were enriched in targets of Lef1, a modulator of the Wnt pathway. In conclusion, our combination of bioinformatics approaches identified six novel candidate genes involved in adult neurogenesis; Amer3, Eya3, Mtdh, Nr4a3, Polr2a, and Tbkbp1. Further, we propose a role for Lef1 transcriptional control in the regulation of adult hippocampal precursor cell proliferation.