Background and Aims: Associations of hyperlipidaemia and inflammation with the risk for incident major adverse cardiovascular events (MACEs) were analysed in individuals with and without cholesterol-lowering medication therapy. Methods: Data from 322,922 participants (55.9% women) aged 38 to 73 years from the UK Biobank were included. Longitudinal associations of low-density lipoprotein cholesterol (LDL-C), lipoprotein(a) [Lp(a)], and high-sensitivity C-reactive protein (Hs-CRP), both individually and in combination, were analysed with the risk for incident MACEs using Cox regression models, stratified by cholesterol-lowering medication use. Results: During a median follow-up of 13.7 years, 31,295 (9.69%) participants had incident MACEs. The incidence was 8.32% in non-users and 18.6% in users of cholesterol-lowering medication. Higher LDL-C levels were associated with the highest risk for MACEs, followed by Lp(a) and Hs-CRP. One higher standard deviation in LDL-C, Lp(a), and Hs-CRP was associated with a 13%, 8%, and 6% greater risk for MACEs in non-users and 11%, 7%, and 6% in cholesterol-lowering medication users, respectively. When combined, LDL-C, Lp(a), and Hs-CRP demonstrated a synergistic effect. Compared with individuals with all three biomarkers at or below the 75th percentile, those with all three biomarkers above the 75th percentile had a 77% higher risk for incident MACEs among non-users and a 58% higher risk among those on cholesterol-lowering medications. Conclusions: Hyperlipidaemia and inflammation independently and synergistically contribute to an increased risk for incident cardiovascular events. The magnitude of risk is more closely related to serum biomarker concentrations than to the use or not of cholesterol-lowering medications.

Data quality assessments (DQA) are necessary to ensure valid research results. Despite the growing availability of tools of relevance for DQA in the R language, a systematic comparison of their functionalities is missing. Therefore, we review R packages related to data quality (DQ) and assess their scope against a DQ framework for observational health studies. Based on a systematic search, we screened more than 140 R packages related to DQA in the Comprehensive R Archive Network. From these, we selected packages which target at least three of the four DQ dimensions (integrity, completeness, consistency, accuracy) in a reference framework. We evaluated the resulting 27 packages for general features (e.g., usability, metadata handling, output types, descriptive statistics) and the possible assessment’s breadth. To facilitate comparisons, we applied all packages to a publicly available dataset from a cohort study. We found that the packages’ scope varies considerably regarding functionalities and usability. Only three packages follow a DQ concept, and some offer an extensive rule-based issue analysis. However, the reference framework does not include a few implemented functionalities, and it should be broadened accordingly. Improved use of metadata to empower DQA and user-friendliness enhancement, such as GUIs and reports that grade the severity of DQ issues, stand out as the main directions for future developments.

DataquieR version 2 is a major update to the dataquieR package (Richter et al., 2021). It enables extensive, highly standardized, and accessible data quality assessments related to data integrity (such as data type errors or duplicates), completeness (for example, missing values), consistency (for instance, range violations or contradictions), and accuracy (such as time trends or examiner effects) on tabular form data. This update extends the coverage of data quality indicators from the underlying framework (Schmidt et al., 2021) based on a substantially improved information model. It furthermore comprises performance enhancements, interactive output, and versatile options to grade data quality issues. The broader framework coverage is achieved by integrating dataquieR 2 with a differentiated metadata schema. This spreadsheet-type schema includes descriptions, expectations and requirements about the following data objects in a machine-readable way: (1) single variables and data fields, (2) combinations of variables, (3) segments of the data, (4) data tables, (5) representation and classification of missing data, and (6) identifier codes. The new metadata schema makes additional assumptions underlying data quality assessments explicit, thereby improving reproducible data quality reporting in compliance with the FAIR (Findable, Accessible, Interoperable, and Reusable) principles (Wilkinson et al., 2016). While dataquieR has primarily been designed for observational health studies, such as population-based cohort studies (Peters et al., 2022; Schmidt et al., 2023), it can also be applied to all sorts of tabular data from other sources, for instance, electronic health record (EHR) data, or registries.

Thalassia testudinum and Halodule wrigthii are the dominant seagrasses in the Caribbean, being common across shallow shorelines, either as monospecific or as intermixed meadows. Among the macrofauna associated with these beds, crustaceans are considered essential for the whole ecosystem functioning. However, knowledge about the associated community of decapod crustaceans in assemblages of T. testudinum and H. wrigthii is still scarce, particularly outside of protected areas. Here we report eight new decapod species for the Paraguaná Peninsula (Falcón State, Venezuela) in association with intermixed seagrass beds: Achelous tumidulus Stimpson, 1871, Alpheus aff. floridanus Kingsley, 1878, Chorinus heros (Herbst, 1790), Clibanarius antillensis (Stimpson, 1859), Clibanarius sclopetarius (Herbst, 1796), Latreutes parvulus (Stimpson, 1866), Panopeus occidentalis Saussure, 1857, and Processa fimbriata Manning & Chace, 1971. These records represent habitat extensions and fill gaps in the geographical distribution of the species along the northern coast of South America. Furthermore, we found that statistical differences in decapod species abundance and composition are likely to be caused by the joint action of coverage and heterogeneity of the beds. Our results indicate that typical Caribbean species were the most influential in the community; nevertheless, the abundance of juvenile Penaeus schmitti Burkenroad, 1936 was notable, since they have rarely been found in these habitats. This finding highlights the role of non-protected areas as nursery habitats for economically important species. Our results show that seagrass meadows in the Paraguaná Peninsula reflect overall good health when compared to other Caribbean zones, representing an important habitat for the maintenance of crustacean populations.

Conducting large-scale epidemiologic studies requires powerful software for electronic data capture, data management, data quality assessments, and participant management. There is also an increasing need to make studies and the data collected findable, accessible, interoperable, and reusable (FAIR). However, reusable software tools from major studies, underlying such needs, are not necessarily known to other researchers. Therefore, this work gives an overview on the main tools used to conduct the internationally highly networked population-based project Study of Health in Pomerania (SHIP), as well as approaches taken to improve its FAIRness. Deep phenotyping, formalizing processes from data capture to data transfer, with a strong emphasis on cooperation and data exchange have laid the foundation for a broad scientific impact with more than 1500 published papers to date.