Abstract Rib-cage morphology is a determinant of thoracic biomechanics, ventilation, and injury response, yet statistical shape models (SSMs) of the rib cage have relied on small cohorts (∼100s of individuals) imaged by clinical computed tomography, which over-represents injury and disease. We constructed a surface-based SSM of the complete 24-rib cage from 26,275 standardised whole-body magnetic resonance imaging (MRI) scans of adults aged 19–74 years from the population-based German National Cohort (NAKO). Ribs were segmented with a deep- learning pipeline (a rib-extended SPINEPS model), reconstructed as per-rib surface meshes, and brought into dense vertex-wise correspondence by Gaussian-process morphable registration in Scalismo; the aligned ensemble was summarised by generalised Procrustes analysis and principal component analysis (PCA). Fourteen per-rib geometric descriptors provided a quantitative cross- walk between the abstract PCA modes and named shape features, and associations with sex, age, body size and composition (including body-fat percentage), and smoking exposure were estimated by multivariable regression with Benjamini–Hochberg false-discovery-rate control. Shape variation was strongly concentrated: 28 modes captured 95% of the total variance, and the first three alone accounted for 69.4% (PC1, 42.6%; PC2, 16.3%; PC3, 10.5%) and admitted consistent anatomical readings – a sexually dimorphic axis (PC1), a slender-versus-stout body- habitus contrast (PC2), and a free-rib-size axis at ribs 11–12 (PC3). The sexes were nearly fully separated along PC1 (Cohen’s d = 2.52). Body mass and body-fat percentage were the dominant modifiable correlates of rib-cage shape, whereas the association with cumulative smoking exposure was comparatively small. The model is released as a population-representative geometric reference for benchmarking and morphing donor-derived finite-element human-body models and for further large-cohort shape analysis.

Childhood maltreatment (CM) is associated with adult cardiovascular disease (CVD) risk. Systolic (SBP) and diastolic (DBP) blood pressure (BP), key markers of CVD risk, exhibit age- and sex-dependent variability, which was insufficiently accounted for in previous studies on the CM-BP relationship. This study therefore aimed to assess age- and sex-specific associations between CM and adult BP-based outcomes and hypertension using cross-sectional data from the population based German National Cohort (NAKO). Complete data were available for 150,983 participants (49.3

Background: Observational and clinical studies involve sequential data acquisi tion over extended calendar periods. Systematic changes across the measurement sequence may compromise the validity of scientific findings. In contrast to bio logical associations, however, typically few assumptions can be made about the form, timing, or magnitude of such changes, and practical guidance for their reli able assessment remains limited. We therefore compared statistical methods for quantifying systematic deviations from stability in a sequence of measurements. Methods: We conducted a simulation study comparing seven statistical meth ods: autoregressive integrated moving average, fused lasso signal approximator (FLSA), generalized additive model (GAM), locally weighted scatterplot smooth ing (LOWESS), moving average, pruned exact linear time (PELT), and piecewise regression. Methods were evaluated for their ability to estimate the magnitude of systematic change and the number of change points. In total, 70,720 datasets were generated across 136 simulation scenarios with sample sizes ranging from 30 to 1,000 observations. Results: Method performance varied strongly by data distribution, sample size, and underlying change pattern. GAM and LOWESS provided the most stable and accurate estimates of systematic change across scenarios, with the important exception of the no-change scenario. All methods tended to underestimate the number of change points, although FLSA showed the smallest absolute bias for this estimand. Conclusions: Our results provide guidance for researchers seeking to assess systematic measurement changes in single-wave study data.Within the simulated scenarios, smooth regression-based approaches, particularly GAM and LOWESS, provided the most consistent performance for quantifying systematic changes in the measurement sequence. These findings may inform the selection of screening tools to identify variables that warrant further investigation.

Background: Access to high-quality, FAIR health data is essential for advancing epidemiological, public health and clinical research. NFDI4Health, one of the consortia of Germany’s National Research Data Infrastructure (NFDI), was established in 2020 to address this need by improving data FAIRness for the scientific community. Methods: During the initial funding phase, NFDI4Health developed key infrastructure components and services focusing on interoperability, data sharing and research support. Our approach was guided by user needs and real-world use cases in alignment with (inter)national FAIR standards and infrastructures. Results: We advanced findability of health data by establishing a central Health Study Hub and connecting it to local infrastructures, e.g., through the Local Data Hub software, to facilitate transfer of metadata from the local infrastructures to the Health Study Hub. Accessibility was improved by expanding the German Research Data Portal for Health (FDPG) to provide central access to study data and by providing tools to support anonymisation and synthetic data generation. We also developed an interoperable metadata schema for publishing study data and implemented it in the Health Study Hub. To further improve interoperability, a NFDI4Health FAIR sharing collection and AI support for metadata annotation and harmonisation workflows were established. To enhance reusability, data quality assessment tools were further developed, and two frameworks for federated analysis of sensitive health data were piloted. Intensive engagement with our communities through training, advisory services, and collaborative development ensured user relevance. Conclusion/outlook: NFDI4Health has established a scalable, interoperable, and user-centred infrastructure to support FAIR data sharing in health research. Future work will focus on further developing and consolidating the infrastructure, expanding cross-domain data integration, fostering broader adoption within our research community, and strengthening national and international collaboration.

BACKGROUND:Synthetic data hold substantial potential to address practical challenges in epidemiology due to restricted data access and privacy concerns. However, many current methods suffer from limited quality, high computational demands, and complexity for non-experts. Furthermore, common evaluation strategies for synthetic data often fail to directly reflect statistical utility and measure privacy risks sufficiently. Against this background, a critical underexplored question is whether synthetic data can reliably reproduce key findings from epidemiological research while preserving privacy. METHODS:We propose adversarial random forests (ARF) as an efficient and convenient method for synthesizing tabular epidemiological data. To evaluate its performance, we replicated statistical analyses from six epidemiological publications covering blood pressure, anthropometry, myocardial infarction, accelerometry, loneliness, and diabetes, from the German National Cohort (NAKO Gesundheitsstudie), the Bremen STEMI Registry U45 Study, and the Guelph Family Health Study. We further assessed how dataset dimensionality and variable complexity affect the quality of synthetic data, and contextualized ARF's performance by comparison with commonly used tabular data synthesizers in terms of utility, privacy, generalization, and runtime. RESULTS:Across all replicated studies, results on ARF-generated synthetic data consistently aligned with original findings. Even for datasets with relatively low sample size-to-dimensionality ratios, replication outcomes closely matched the original results across descriptive and inferential analyses. Reduced dimensionality and variable complexity further enhanced synthesis quality. ARF demonstrated favourable performance regarding utility, privacy preservation, and generalization relative to other synthesizers and superior computational efficiency. CONCLUSIONS:In summary, ARF reliably generates high-quality synthetic data that replicate diverse epidemiological analyses while offering a competitive privacy-utility trade-off.

BACKGROUND:Both all-cause and alcohol mortality follow a social gradient. Studies have provided evidence of interaction between socioeconomic status (SES) and alcohol use in predicting alcohol-attributable mortality. It is unclear whether this holds true for all-cause mortality. This study examines interaction effects and investigates the role of biomarkers in all-cause mortality. METHODS:We included data from 4307 participants of the baseline survey (1997-2001) of SHIP (Study of Health in Pomerania)-START and a mortality follow-up from baseline until January 2023. All-cause mortality was the outcome; exposure variables were income or education as SES proxies, alcohol use, and biomarkers related to chronic alcohol consumption. Confounders were age, sex, marital status, smoking, and Body Mass Index (BMI). We used Cox proportional hazards regression (CPHR) in main analyses and CPHR and Aalen additive hazards regression (AAHR) in interaction analyses. RESULTS:Our final samples (either with income or education) consisted of 3771/3939 individuals of whom 981 (26.0%) and 1047 (28.6%) died during the follow-up period. Adjusting for confounders, individuals with low income (HR = 1.31, CI = 1.11, 1.56; compared to high income) and both current abstainers that formerly consumed alcohol (HR = 1.56, CI = 1.27, 1.92) and individuals with high alcohol intake (HR = 1.31, CI = 1.03, 1.66; compared to individuals with low alcohol intake) showed increased mortality. Biomarkers were positively associated with mortality. There was evidence of statistical interaction effects, for example, increased risks in low-income current abstainers as well as individuals with moderate or high alcohol use. CONCLUSIONS:We found a social gradient in all-cause mortality that was attenuated, but not fully explained, by risk factors. There was some evidence of statistical interaction effects between alcohol use and SES, but small subgroup counts limit the certainty of related conclusions.

Structural brain alterations associated with depression and anxiety are subtle, heterogeneous, and difficult to characterize. We applied autoencoder-based normative modeling to contrastively learned structural MRI representations from two large population-based cohorts (German National Cohort, N ≈ 29,000; UK Biobank, N ≈ 25,000) to quantify individual deviations from normative brain structure across symptom dimensions of depression, anxiety, and, for contextualization, alcohol use.Deviation magnitude increased with symptom severity for depressive and anxiety symptoms and was most pronounced in individuals with high alcohol use. Directional analyses revealed shared deviation patterns for depression and anxiety that were largely distinct from alcohol-related deviations, and these patterns generalized across cohorts. These affective-symptom-related patterns implicated distributed regional brain-structural variation. Individual deviation profiles improved classification of symptomatic status beyond demographic covariates, with gains concentrated at higher symptom severity.Together, these findings indicate that affective symptoms are associated with reproducible, dimensional patterns of regional brain-structural deviation that extend beyond normative population variability, supporting transdiagnostic models of internalizing psychopathology.

BACKGROUND:Claims data are often used to investigate the quality of care for patients with low back pain (LBP). However, there is no standard regarding the preferred choice of ICD-10 codes for identifying patients with LBP, and guidelines for the treatment of LBP differ in their interpretation of ICD-10 codes. Furthermore, for some indicators measuring the quality of care, such as the appropriate use of imaging, it is necessary to differentiate between cases with specific, treatable causes and those without. This study therefore investigates coding practices for LBP in outpatient care and the use of imaging across specialist groups over a six-year period. METHODS:Based on the TREND cohort of the population-based Study of Health in Pomerania (SHIP), coding practices in claims data were analysed using data from 3,837 statutorily insured participants for the years 2014-2019. In total, eleven ICD-10 categories of relevance to LBP were included. We evaluated the findings based on two German guidelines: one for specific and one for non-specific LBP. RESULTS:At least one LBP diagnosis was coded for 2,474 participants (64%) during the entire observation period. The predominant ICD-10 category was M54 (dorsalgia, 87% of patients with LBP). Around half of the participants with M54 diagnoses also had diagnoses from other LBP-related categories in the same year. Diagnoses that can be assigned to specific LBP according to the respective German guideline occurred in 86% of patients with LBP. Participants who consulted only general practitioners during the observation period were more likely to receive only an M54 diagnosis and less likely to undergo imaging procedures. CONCLUSIONS:The results underline the high epidemiologic relevance of LBP. Using the German guideline on specific LBP as a reference, we categorized most LBP diagnoses as specific, contrary to common international assumptions. Most patients with LBP received multiple ICD-10 codes, complicating the distinction between non-specific and specific LBP based on claims data. Health care analyses on LBP require transparent reporting of the ICD codes used, along with a detailed discussion of the data's limitations.

This article introduces dqrep, a Stata package designed for conducting comprehensive data quality assessments, focusing on data from observational health studies. A single command call flexibly scales from small "on-the-fly" assessments involving only a few variables to extensive tasks, such as generating and comparing quality reports for thousands of variables across multiple examinations within or across studies. To do so, dqrep activates an analytical pipeline that evaluates the requested data quality aspects, such as data integrity, missingness, range violations, outliers, temporal trends, observer or device effects. Detailed information and expectations about the data can be provided via the numerous dqrep options or MS Excel sheets. The package generates single or series of reports in PDF and DOCX formats, detailing data properties as well as the type, number, and severity of data quality issues. In addition, HTML dashboards may be requested to browse images. dqrep offers standardized machine-readable result summaries, facilitating downstream tasks such as benchmarking data quality across studies and examinations. The package is online available from https://dataquality.qihs.uni-greifswald.de/vignettes.html#STATA ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement This work was partially supported by the German Research Foundation (DFG: SCHM 2744/3-4, NNFDI4Health project (www.nfdi4health.de), Project Number 442326535, by the European Union's Horizon 2020 research and innovation programme under grant agreement No 825903 (euCanSHare project), and the German National Cohort (NAKO) as funded by the Federal Ministry of Education and Research (BMBF: 01ER1301A and 01ER1801A). ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes This work does not contain original data but is a guidance work on appropriately assessing data

BACKGROUND:Despite wide acceptance in medical research, implementation of the FAIR (findability, accessibility, interoperability, and reusability) principles in certain health domains and interoperability across data sources remain a challenge. While clinical trial registries collect metadata about clinical studies, numerous epidemiological and public health studies remain unregistered or lack detailed information about relevant study documents. Making valuable data from these studies available to the research community could improve our understanding of various diseases and their risk factors. The National Research Data Infrastructure for Personal Health Data (NFDI4Health) seeks to optimize data sharing among the clinical, epidemiological, and public health research communities while preserving privacy and ethical regulations. OBJECTIVE:We aimed to develop a tailored metadata schema (MDS) to support the standardized publication of health studies' metadata in NFDI4Health services and beyond. This study describes the development, structure, and implementation of this MDS designed to improve the FAIRness of metadata from clinical, epidemiological, and public health research while maintaining compatibility with metadata models of other resources to ease interoperability. METHODS:Based on the models of DataCite, ClinicalTrials.gov, and other data models and international standards, the first MDS version was developed by the NFDI4Health Task Force COVID-19. It was later extended in a modular fashion, combining generic and NFDI4Health use case-specific metadata items relevant to domains of nutritional epidemiology, chronic diseases, and record linkage. Mappings to schemas of clinical trial registries and international and local initiatives were performed to enable interfacing with external resources. The MDS is represented in Microsoft Excel spreadsheets. A transformation into an improved and interactive machine-readable format was completed using the ART-DECOR (Advanced Requirement Tooling-Data Elements, Codes, OIDs, and Rules) tool to facilitate editing, maintenance, and versioning. RESULTS:The MDS is implemented in NFDI4Health services (eg, the German Central Health Study Hub and the Local Data Hub) to structure and exchange study-related metadata. Its current version (3.3) comprises 220 metadata items in 5 modules. The core and design modules cover generic metadata, including bibliographic information, study design details, and data access information. Domain-specific metadata are included in use case-specific modules, currently comprising nutritional epidemiology, chronic diseases, and record linkage. All modules incorporate mandatory, optional, and conditional items. Mappings to the schemas of clinical trial registries and other resources enable integrating their study metadata in the NFDI4Health services. The current MDS version is available in both Excel and ART-DECOR formats. CONCLUSIONS:With its implementation in the German Central Health Study Hub and the Local Data Hub, the MDS improves the FAIRness of data from clinical, epidemiological, and public health research. Due to its generic nature and interoperability through mappings to other schemas, it is transferable to services from adjacent domains, making it useful for a broader user community.

Objectives: The German National Research Data Infrastructure for Personal Health Data (NFDI4Health) has developed a metadata schema (MDS) for harmonizing health study descriptions, along with a platform for content discovery. This work evaluates how well the MDS promotes FAIR data principles (Findability, Accessibility, Interoperability, Reusability) and serves as a proxy for assessing study FAIRness. Materials and Methods: Using the Research Data Alliance's 'FAIR data maturity model', we assessed the scope of evaluable FAIRness indicators for NFDI4Health's MDS (version 3.3). Results: 29 of 41 FAIRness indicators can be evaluated (Findability: 7/7, Interoperability: 7/12, Accessibility: 7/12, Reusability: 8/10). The remaining indicators relate to the research data's format and access procedures. Discussion: The MDS provides a viable basis for evaluating study FAIRness. To enable full indicator coverage, additional metadata elements should be incorporated. Conclusion: NFDI4Health's MDS effectively promotes FAIR-compliant study descriptions, although adjustments could strengthen its utility for assessing research data FAIRness. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement This work was done as part of the NFDI4Health Consortium (www.nfdi4health.de). We gratefully acknowledge the financial support of the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) - project number 442326535. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes All data produced in the present work are contained in the manuscript.

The German National Cohort (NAKO) is the largest population-based epidemiologic cohort study in Germany and investigates the causes of the most common chronic diseases. Between 2014 and 2019, a total of 1.3 million residents aged 20-69 years from 16 German regions were randomly selected from the general population and invited to participate following a highly standardized recruitment protocol. The overall response was 15.6% and differed considerably across study centers (7.6-30.7%). Females were more likely to participate than males (17.5% vs. 14.1%) and participation increased with age (10.2% in age group " < 29 years" up to 20.7% in age group " > 60 years"). Across all study regions, response was highest in rural areas (22.3%), followed by towns and suburbs (17.2%), and was lowest in cities (14.5%). Compared with the general population in the respective study regions, participants with low and medium education are underrepresented in the NAKO sample, while highly educated participants are overrepresented. Participants with non-German nationality and with a migration background are also underrepresented. Participants living in single households are underrepresented, while participants from larger households (2 or more persons) are overrepresented compared to the general population. Survey weights are made available to researchers along with the study data that account for the sampling design and adjust for differences in the distribution of age, sex, nationality (German vs. non-German), migration status, education, and household size.

Purpose:The geometry of the Notch within the knee joint is highly discussed to influence the overall knee stability and can predict the rupture of the cruciate ligaments. Therefore, associations between anthropometric measurements and notch geometry may help to identify patients at risk for soft tissue knee trauma or inferior surgical outcome. To better describe the normal anatomy of the notch geometry, the primary objective of this study was to examine the notch geometry in a large general population cohort and to define reference values. Furthermore, associations of anthropometric parameters on the notch geometry were examined. Methods:Notch Depth, Notch Angle, Notch Width and Notch Width Index were measured on bilateral knee magnetic resonance imaging (MRI) of 1043 participants of the Study of Health in Pomerania (SHIP), aged 28-89 years. SHIP drew a sample of the adult general population of Northeastern Germany. Reference values for Notch parameters were assessed by quantile regression models. Associations of sex, age, body height and body weight with the Notch parameters were calculated by linear regression models. Results:Significantly higher values for men were present for all Notch parameters (p = <0.001-0.037) as well as a positive association with age (p = 0.001-0.025). Increasing body height was positively associated with Notch Depth and Notch Width (p = <0.001), whereas Notch Angle showed an inverse relation to body height (p = 0.001). Additionally, Notch Depth showed a significant association with body weight (p = 0.009). Based on these associations, adjusted reference values were calculated. Conclusion:Knee notch geometry is influenced by sex and anthropometric factors. Therefore, individual reference values were provided to enable patient-specific diagnostics. Level of Evidence:Level II.

To semantically enrich the laboratory data dictionary of the Study of Health in Pomerania (SHIP), a population-based cohort study, with LOINC to achieve better compliance with the FAIR principles for data stewardship. We employed a workflow that maps codes from the SHIP-START-4 laboratory data dictionary to LOINC codes following the terminology mapping principles and best practices recommended by the World Health Organization Family of International Classifications (WHO-FIC) Network. We were able to annotate 71 out of 72 (98.6%) of the source codes in the SHIP-START-4 laboratory data dictionary with LOINC codes. 32 source codes were mapped to a single LOINC code (cardinality 1:1) and 39 resulted in a complex mapping. All of the successful mappings are equivalent (=) matches. We increased the FAIRness of the SHIP laboratory data dictionary by semantically enriching laboratory items with links to an accessible, established, and machine-readable language for knowledge representation (LOINC). Our mapping improves semantic data retrieval and integration. However, not all clinically and significantly relevant data are included in the LOINC code. Therefore, these missing aspects have to be considered in data interpretation as well. Semantically enriching the SHIP-START-4 laboratory data dictionary has contributed to its improved data interoperability and reuse. We recommend that data owners and standardization experts collaboratively perform annotations before data collection starts instead of doing this retrospectively. These experiences may inform the development of standard operating procedures for annotating data dictionaries developed for other population-based cohort studies.

Background Accurate diagnosis of bipolar disorder (BPD) is difficult in clinical practice, with an average delay between symptom onset and diagnosis of about 7 years. A depressive episode often precedes the first manic episode, making it difficult to distinguish BPD from unipolar major depressive disorder (MDD). Aims We use genome-wide association analyses (GWAS) to identify differential genetic factors and to develop predictors based on polygenic risk scores (PRS) that may aid early differential diagnosis. Method Based on individual genotypes from case-control cohorts of BPD and MDD shared through the Psychiatric Genomics Consortium, we compile case-case-control cohorts, applying a careful quality control procedure. In a resulting cohort of 51 149 individuals (15 532 BPD patients, 12 920 MDD patients and 22 697 controls), we perform a variety of GWAS and PRS analyses. Results Although our GWAS is not well powered to identify genome-wide significant loci, we find significant chip heritability and demonstrate the ability of the resulting PRS to distinguish BPD from MDD, including BPD cases with depressive onset (BPD-D). We replicate our PRS findings in an independent Danish cohort (iPSYCH 2015, N = 25 966). We observe strong genetic correlation between our case-case GWAS and that of case-control BPD. Conclusions We find that MDD and BPD, including BPD-D are genetically distinct. Our findings support that controls, MDD and BPD patients primarily lie on a continuum of genetic risk. Future studies with larger and richer samples will likely yield a better understanding of these findings and enable the development of better genetic predictors distinguishing BPD and, importantly, BPD-D from MDD.

We applied deep normative modeling to structural MRI data from two large cohorts (German National Cohort, N ≈ 29,000 and UK Biobank, N ≈ 25,000) to characterize individual-level brain deviations along symptom dimensions of depression, anxiety, and alcohol use. Each brain was embedded into a 256-dimensional latent space, allowing us to quantify both the magnitude and direction of deviation from a normative reference trained on the non/low-symptomatic subpopulation. Deviation magnitude increased with symptom severity, and directional patterns separated mood-anxiety and alcohol-use tendencies. These deviation axes generalized across cohorts and supported individual-level classification of symptomatic group membership, especially at higher symptom levels. Combining deviations with polygenic risk scores improved classification performance, particularly for depressive and anxiety measures, indicating complementary contributions of imaging and genetics. Our findings demonstrate that structural brain deviations reflect meaningful, continuous variation in affective and behavioral symptoms. ### Competing Interest Statement HJG has received travel grants and speakers honoraria from Neuraxpharm, Servier, Indorsia and Janssen Cilag. ES received speaker fees from bfd buchholz-fachinformationsdienst GmbH, Lundbeckfonden, and Janssen-Cilag GmbH, as well as editorial fees from Lundbeckfonden and the Wellcome Trust. AML has received consultancy honoraria from AbbVie, Janssen-Cilag GmbH, Boehringer-Ingelheim, Daimler und Benz Stiftung, Helmut Horten Stiftung, Neurotorium/Lundbeckfonden, Hector Stiftung, Endosane Pharmaceuticals, Elsevier, von Behring-Roentgen-Stiftung, The LOOP Zuerich, ECNP, Teva, Medical Research Council/UKRI, Heinrich-Lanz-Stiftung, Johnson & Johnson, Lundbeckfonden, and the Wellcome Trust. He has receivedvlecture honoraria from pro Mente Akademie GmbH, Schoen Klinik, Janssen-Cilag, Evangelische Hochschule Ludwigsburg, Landesaerztekammer Baden-Wuerttemberg, Klinikum Ingolstadt, PSY (Psychiatrie und Psychotherapie Update Refresher, FOMF), Consorcio Mexicano de Neuropsico-farmacologia (MCNP), Universitaet Klagenfurt, and Universitaet Norwalk/USA. He has received editorial honoraria (as editor, etc.) from ECNP/Neuroscience Applied and JSPS. He has received authorship honoraria from Beltz Verlag, Thieme Verlag, and Kohlhammer Verlag. He has received project funding from BMBF, DFG, Hector Stiftung, Klaus Tschira Stiftung, and MWK. ### Funding Statement The project was conducted with data (NAKO-711) from the German National Cohort (NAKO) (http://www.nako.de/). The NAKO is funded by the Federal Ministry of Education and Research (BMBF) [project funding reference numbers: 01ER1301A/B/C, 01ER1511D, and 01ER1801A/B/C/D and 01ER2301A/B/C], federal states of Germany and the Helmholtz Association, the participating universities, and the institutes of the Leibniz Association. We thank all participants who took part in the NAKO study and the staff of this research initiative. We also thank the participants and scientists involved in making the UK Biobank resource available (http://www.ukbiobank.ac.uk/). This study was conducted under UK Biobank application number 162313. The project was supported by the DZPG (German Centre for Mental Health Research) and by the BMBF (German Ministry of Education and Research) grant 01EE2303E. Fabian Streit is supported by a 2023 NARSAD Young Investigator Grant (#31537) from the Brain & Behavior Research Foundation with support from the Families for Borderline Personality Disorder Research. This work was supported by the Hector foundation II and was endorsed by German Center for Mental Health (DZPG). ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: Ethics Committee II of Ruprecht-Karls-Universitaet Heidelberg (Medizinische Fakultaet Mannheim) gave ethical approval for this work I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes Access to and use of NAKO data and biosamples can be obtained via the electronic application portal (https://transfer.nako.de). Access to UK Biobank data requires application through the registration and application portal (http://ukbiobank.ac.uk/register-apply).

To present a publicly available deep learning-based torso segmentation model that provides comprehensive voxel-wise coverage, including delineations that extend to the boundaries of anatomical compartments. We extracted preliminary segmentations from TotalSegmentator, spine, and body composition models for magnetic resonance tomography (MR) images, then improved them iteratively and retrained an nnUNet model. Using a random retrospective subset of German National Cohort (NAKO), UK Biobank, internal MR and computed tomography (CT) data (Training: 2897 series from 626 subjects, 290 female; mean age 53 ± 16; 3-fold-cross validation (20

This study aimed to establish normal knee cartilage T2-values at 1.5-Tesla, assess the influence of age and sex on T2-values, and compares T2-times between subjects with and without morphological cartilage changes. A sagittal 2D T2-weighted multi-slice multi-echo sequence (MSME) sequence with automatic generation of a color-coded T2-map was acquired at 1.5-Tesla in 929 volunteers (ages 28–89) from the Study-of-Health-in-Pomerania TREND-1 cohort. Knee morphology was assessed with the modified Noyes Score in eight cartilage regions. T2 measurements were performed manually in seven cartilage regions, including superficial and deep cartilage layers. Subjects with normal cartilage morphology (300 subjects) showed significant T2-value differences across cartilage regions (p ≤ 0.001), with higher values in femoral cartilage and superficial layers. T2-values increased with age (p ≤ 0.001), and women had higher T2-values in the femoral, tibial, and medial femorotibial compartments. The subjects with evidence of pathological cartilage morphology changes (629 subjects) had higher T2-values compared to the subjects with structurally normal knee cartilage in MRI (p ≤ 0.001). This study provides population-based 1.5-Tesla knee cartilage T2-values, showing age-related increases and higher values in superficial and femoral layers. Pathological cartilage morphology was associated with elevated T2-values. Question This study examines early cartilage degeneration by establishing normal T2-values and analyzing how demographics and morphological cartilage changes impact these values. Findings T2-times were higher in superficial femoral cartilage but lower in retropatellar, tibial cartilage, and deep layers, increasing with age and pathological cartilage changes. Clinical relevance This study establishes normal T2-values for knee cartilage at 1.5-Tesla, identifies age- and sex-related variations, and associates elevated T2-values to morphological cartilage changes, enhancing cartilage health understanding and early diagnostic precision.

Background The investigation of prevalence trends of metabolic cardiovascular risk factors is important for appropriate planning of future health programs aiming to prevent cardiovascular morbidity and mortality. In a previous study, we demonstrated an increase in the prevalence of type 2 diabetes (T2D) between 2000 and 2010 in Northeast Germany. The purpose of this study is to investigate prevalence trends of T2D treatment, dyslipidemia and hepatic steatosis in Northeast Germany. Methods The baseline examinations of the first Study of Health in Pomerania (SHIP) project were carried out from 1997 to 2001 (SHIP-START-0, 4308 subjects). A second, independent random sample of the same region was enrolled between 2008 and 2012 (SHIP-TREND-0, 4420 subjects). All data were standardized with post-stratification weighting derived from the adult population of the German federal state of Mecklenburg-West Pomerania. Results The prevalence of metformin intake increased from 2.1% to 4.1% and insulin use from 2.0% to 2.8%. While the prevalence of statin intake increased from 6.8% to 12.2%, the prevalence of dyslipidemia decreased slightly from 49.0% in SHIP-START-0 to 45.5% in SHIP-TREND-0. The prevalence of hepatic steatosis increased from 29.7% to 37.3%. This increase was most prominently observed in women and younger age groups. Conclusions T2D, dyslipidemia and hepatic steatosis are common and increasing health problems among adults in Northeast Germany. Reassuring healthy diet and controlling obesity may result in prevention of above-mentioned health problems.