BackgroundFree light chain kappa (FLCκ) have emerged as a sensitive biomarker to detect intrathecal immunoglobulin synthesis. To evaluate the role of FLCκ in routine cerebrospinal fluid laboratory diagnostics we conducted a prospective nationwide real world diagnostic study for evaluating the integration of kappa free light chain into clinical routine (ORCAS - prospective multicenter validation of a new laboratory workflow integrating the free light chains kappa in CSF analysis).ObjectivesTo determine whether testing for intrathecal synthesis of FLCκ in cerebrospinal fluid (CSF) can predict intrathecal immunoglobulin (Ig) synthesis of total IgG, IgA, IgM or CSF-specific oligoclonal bands (OCB) with high sensitivity.Materials & MethodsFLCκ were measured in 1052 paired CSF and serum samples according to local laboratory standards in six laboratories in Germany. Sensitivity and negative predictive value (NPV) of intrathecal FLCκ synthesis as first line detection of intrathecal Ig synthesis were assessed.ResultsOf the 1052 samples, 624 fulfilled the inclusion criteria. The intrathecal fraction of FLCκ predicted intrathecal Ig synthesis with a sensitivity of 0.87 (CI 0.81-0.93) and a NPV of 0.97 (CI 0.95-0.98). The sensitivity for predicting CSF-specific OCB was 0.93 (CI 0.88-0.98).ConclusionsIn the real world setting this study analyzing FLCk for detecting intrathecal Ig synthesis did not reach its prespecified primary endpoint of sensitivity >0.95. Therefore, FLCκ should not yet be introduced as a stand-alone preselection marker for intrathecal Ig synthesis, but may provide additional value when combined with established diagnostic parameters.

Objective: Sphingosine-1-phosphate (S1P) is a versatile immunomodulatory lipid mediator that affects both immune and cardiovascular health. S1P has been linked to thyroid health, for example, by affecting inflammatory reactions found in Graves’ disease. In our study, we explored associations of S1P with thyroid markers obtained from laboratory tests (thyrotropin (TSH), free triiodothyronine (fT3), and free thyroxine (fT4)) and ultrasound examination. Methods: We quantified S1P by LC–MS/MS in participants from the population-based ‘Study of Health in Pomerania’ conducted in Northeast Germany. Serum levels of TSH, fT3, and fT4 were measured using chemiluminescent immunoassays. Thyroid volume, thyroid nodules, and echogenic patterns were assessed by ultrasonography. Cross-sectional associations between serum S1P and thyroid biomarkers were evaluated using multivariable linear regression models adjusted for confounding. Results: The total study population consisted of 4,034 participants (51.6% women aged 20–84 years). A 1 μmol/L higher S1P level was associated with a 0.17 mIU/L lower TSH level (95% confidence interval (CI): −0.32; −0.04) and a 0.52 pmol/L (0.42; 0.63) higher fT3 level. These associations persisted after excluding individuals with high or low serum TSH levels and those taking thyroid medication. S1P levels were not significantly associated with serum fT4 levels. Higher S1P levels were associated with a larger thyroid volume, the presence of thyroid nodules, and a hypoechogenic thyroid pattern. Conclusion: Circulating S1P is inversely associated with TSH and positively associated with fT3, suggesting a potential modulatory role of S1P in thyroid function.

Aims:Effective heart failure (HF) prevention requires early identification of high-risk individuals, yet population-wide stratification remains difficult. We evaluated whether deep learning using single-lead (lead I) electrocardiograms (ECGs), obtainable from medical systems and wearables, enables population-scale risk assessment. We developed AI-HF to estimate incident clinical HF risk using UK Biobank (UKB) data, validating in the prospective SHIP-START and SHIP-TREND cohorts. Methods and results:The analysis included 31 740 UKB participants (median age 64, 5.2 year follow-up, 243 events), 3025 SHIP-START participants (age 50, 15 year follow-up, 166 events), and 1342 SHIP-TREND participants (age 51, 9 year follow-up, 84 events). Participants with prevalent HF were excluded. Performance was evaluated at a harmonized 5-year prediction horizon. C-indices for incident clinical HF were 0.693 [95% confidence interval (CI) 0.654-0.732] in UKB, 0.715 (0.652-0.777) in SHIP-START, and 0.791 (0.749-0.833) in SHIP-TREND. Hazard ratios per standard deviation increase in AI-HF output were 1.67 (1.56-1.79), 1.43 (1.25-1.65), and 1.46 (1.34-1.59), respectively (all P < 0.001). Adding biometric variables improved discrimination modestly (C-indices: 0.714, 0.718, and 0.77). Conclusion:Across cohorts, AI-HF identified individuals at elevated 5-year incident clinical HF risk using single-lead ECGs. Given the ubiquity of wearables, this method may enable population-scale assessment to support targeted prevention and early intervention.

Background Circulating cellular communication network factor 1 (CCN1) improves risk stratification in patients with acute coronary syndrome. We here investigated the association of CCN1 with all-cause mortality in patients with dilated cardiomyopathy (DCM). Methods Patients with a primary diagnosis of DCM, defined as LVEF <45% and increased LVEDD (LVEDD >117%), were included in a derivation (SFB/TR19 Greifswald) and a validation (IKARUS Marburg) cohort. Exclusion criteria comprised primary valvular diseases, acute myocarditis, active infectious diseases, pulmonary diseases, cancer, chronic alcoholism, and heart failure of other origins. CCN1 levels were determined in serum from study inclusion using an enzyme-linked immunosorbent assay. An adjusted multivariable Cox regression model was used to assess the association (hazard ratios) between tertiles of CCN1 concentration and all-cause mortality. Results In the SFB/TR19 cohort and [IKARUS cohort], respectively a total of 283 [236] predominantly male (78% [75%]) DCM patients with a median age of 56 [51] years with a severely reduced LVEF (31% [30%]), increased LVEDD (67.0 [67.0]), and normal eGFR (90.9 [83.6] ml/min) were analyzed. During a median follow-up of 10.6 [14.9] years, a total of 107 (37%) [100 (42%)] patients died. Patients in the highest CCN1 tertile had a significantly higher mortality risk than those in the lower tertile (p = 0.007 [p = 0.004]). In both cohorts, CCN1 remained associated (1.92; 95% CI: 1.14–3.24; p = 0.014 [1.69; 1.00–2.85; p = 0.049]) in adjusted multivariable Cox regression models. Conclusion CCN1 is associated with all-cause mortality in DCM patients, warranting further research into the underlying pathophysiology.

BACKGROUND/OBJECTIVES:Associations between adiponectin and chemerin with the most clinically established lipid parameters for assessing cardiovascular risk - LDL-cholesterol, HDL-cholesterol, and triglycerides-are well documented. Since lipoproteins are heterogeneous with respect to size, density, and chemical composition, the examination of their subclasses could help to elucidate the complex interactions between adipokines and lipid metabolism. SUBJECTS/METHODS:Lipoprotein subclasses were quantified by nuclear magnetic resonance spectroscopy in samples from 3199 participants of the Study of Health in Pomerania (SHIP-TREND-0). Associations between adiponectin/chemerin and lipoprotein subclasses were analyzed using appropriately adjusted linear regression models. RESULTS:The analyses revealed a wide range of statistically significant associations between adiponectin/chemerin and lipoprotein subclasses, that were primarily in opposite directions. Higher adiponectin concentrations were, for example, related to a lower particle number and a lower cholesterol, phospholipid and triglyceride content in atherogenic small, dense LDL-particles, while positive associations were observed for chemerin. CONCLUSIONS:Our highly consistent results demonstrate that high adiponectin was associated with a favorable, anti-atherogenic lipoprotein profile, whereas the opposite was observed for chemerin. Whether these cross-sectional associations reflect causal mechanisms or alternatively, shared underlying metabolic processes, must be clarified by experimental and longitudinal research.

Purpose Federated training is often challenging on heterogeneous datasets due to divergent data storage options, inconsistent naming schemes, varied annotation procedures, and disparities in label quality. This is particularly evident in the emerging multi-modal learning paradigms, where dataset harmonization including a uniform data representation and filtering options are of paramount importance.Methods DICOM-structured reports enable the standardized linkage of arbitrary information beyond the imaging domain and can be used within Python deep learning pipelines with highdicom. Building on this, we developed an open platform for data integration with interactive filtering capabilities, thereby simplifying the process of creation of patient cohorts over several sites with consistent multi-modal data.Results In this study, we extend our prior work by showing its applicability to more and divergent data types, as well as streamlining datasets for federated training within an established consortium of eight university hospitals in Germany. We prove its concurrent filtering ability by creating harmonized multi-modal datasets across all locations for predicting the outcome after minimally invasive heart valve replacement. The data include imaging and waveform data (i.e., computed tomography images, electrocardiography scans) as well as annotations (i.e., calcification segmentations, and pointsets), and metadata (i.e., prostheses and pacemaker dependency).Conclusion Structured reports bridge the traditional gap between imaging systems and information systems. Utilizing the inherent DICOM reference system arbitrary data types can be queried concurrently to create meaningful cohorts for multi-centric data analysis. The graphical interface as well as example structured report templates are available at https://github.com/Cardio-AI/fl-multi-modal-dataset-creation .

Journal Article Accepted manuscript Immune-metabolic response to acute exercise in patients with heart failure with reduced ejection fraction Get access Nicolle Kränkel, Nicolle Kränkel Charité–Universitätsmedizin Berlin, Department of Cardiology, Campus Benjamin Franklin, Berlin, GermanyDZHK (German Centre for Cardiovascular Research), Partner Site Berlin, Berlin, GermanyFriede Springer Cardiovascular Prevention Centre @ Charité correspondence to: Nicolle Kränkel, PD Dr. rer. nat., Dipl.-Ing. (FH), Deutsches Herzzentrum der Charité, Klinik für Kardiologie, Angiologie und Intensivmedizin, Campus Benjamin Franklin, Hindenburgdamm 30A, 12203 Berlin, Germany ph.: +49-30-450 543798 email: [email protected] https://orcid.org/0000-0002-9363-1770 Search for other works by this author on: Oxford Academic Google Scholar Aycen Koc, Aycen Koc Charité–Universitätsmedizin Berlin, Department of Cardiology, Campus Benjamin Franklin, Berlin, GermanyDZHK (German Centre for Cardiovascular Research), Partner Site Berlin, Berlin, Germany Search for other works by this author on: Oxford Academic Google Scholar Bita Astan, Bita Astan Friede Springer Cardiovascular Prevention Centre @ Charité Search for other works by this author on: Oxford Academic Google Scholar Sabine Kaczmarek, Sabine Kaczmarek University Medicine Greifswald, Department of Internal Medicine B, Greifswald, GermanyDZHK (German Centre for Cardiovascular Research), partner site Greifswald, Germany https://orcid.org/0000-0003-2215-6914 Search for other works by this author on: Oxford Academic Google Scholar Kristin Lehnert, Kristin Lehnert University Medicine Greifswald, Department of Internal Medicine B, Greifswald, GermanyDZHK (German Centre for Cardiovascular Research), partner site Greifswald, Germany https://orcid.org/0000-0003-2342-3949 Search for other works by this author on: Oxford Academic Google Scholar Anke Hannemann, Anke Hannemann DZHK (German Centre for Cardiovascular Research), partner site Greifswald, GermanyUniversity Medicine Greifswald, Institute of Clinical Chemistry and Laboratory Medicine, Greifswald, Germany Search for other works by this author on: Oxford Academic Google Scholar Nele Friedrich, Nele Friedrich DZHK (German Centre for Cardiovascular Research), partner site Greifswald, GermanyUniversity Medicine Greifswald, Institute of Clinical Chemistry and Laboratory Medicine, Greifswald, Germany Search for other works by this author on: Oxford Academic Google Scholar Kathrin Budde, Kathrin Budde DZHK (German Centre for Cardiovascular Research), partner site Greifswald, GermanyUniversity Medicine Greifswald, Institute of Clinical Chemistry and Laboratory Medicine, Greifswald, Germany Search for other works by this author on: Oxford Academic Google Scholar Ann-Kristin Henning, Ann-Kristin Henning DZHK (German Centre for Cardiovascular Research), partner site Greifswald, GermanyUniversity Medicine Greifswald, Institute of Clinical Chemistry and Laboratory Medicine, Greifswald, Germany Search for other works by this author on: Oxford Academic Google Scholar Grazyna Domanska, Grazyna Domanska Institute of Immunology, University Medicine Greifswald, Greifswald, Germany Search for other works by this author on: Oxford Academic Google Scholar ... Show more Arash Haghikia, Arash Haghikia Charité–Universitätsmedizin Berlin, Department of Cardiology, Campus Benjamin Franklin, Berlin, GermanyDZHK (German Centre for Cardiovascular Research), Partner Site Berlin, Berlin, GermanyDepartment of Cardiology and Rhythmology, St. Josef-Hospital, Ruhr University Bochum, Bochum, Germany Search for other works by this author on: Oxford Academic Google Scholar Stefan Gross, Stefan Gross University Medicine Greifswald, Department of Internal Medicine B, Greifswald, GermanyDZHK (German Centre for Cardiovascular Research), partner site Greifswald, Germany https://orcid.org/0000-0003-4121-7161 Search for other works by this author on: Oxford Academic Google Scholar Christian Templin, Christian Templin University Medicine Greifswald, Department of Internal Medicine B, Greifswald, GermanyDZHK (German Centre for Cardiovascular Research), partner site Greifswald, Germany Search for other works by this author on: Oxford Academic Google Scholar Ulf Landmesser, Ulf Landmesser Charité–Universitätsmedizin Berlin, Department of Cardiology, Campus Benjamin Franklin, Berlin, GermanyDZHK (German Centre for Cardiovascular Research), Partner Site Berlin, Berlin, GermanyFriede Springer Cardiovascular Prevention Centre @ Charité Search for other works by this author on: Oxford Academic Google Scholar Stephan B Felix, Stephan B Felix University Medicine Greifswald, Department of Internal Medicine B, Greifswald, GermanyDZHK (German Centre for Cardiovascular Research), partner site Greifswald, Germany Search for other works by this author on: Oxford Academic Google Scholar Marcus Dörr, Marcus Dörr University Medicine Greifswald, Department of Internal Medicine B, Greifswald, GermanyDZHK (German Centre for Cardiovascular Research), partner site Greifswald, Germany Search for other works by this author on: Oxford Academic Google Scholar Martin Bahls Martin Bahls University Medicine Greifswald, Department of Internal Medicine B, Greifswald, GermanyDZHK (German Centre for Cardiovascular Research), partner site Greifswald, Germany https://orcid.org/0000-0002-2016-5852 Search for other works by this author on: Oxford Academic Google Scholar European Journal of Preventive Cardiology, zwaf171, https://doi.org/10.1093/eurjpc/zwaf171 Published: 25 March 2025 Article history Received: 13 December 2024 Revision received: 04 February 2025 Accepted: 14 March 2025 Published: 25 March 2025

BackgroundWild-type transthyretin cardiac amyloidosis (ATTRwt) is an infiltrative disease leading to restrictive cardiomyopathy. We aimed to characterise exercise capacity in ATTRwt and to identify predictors of cardiopulmonary fitness, focusing on echocardiographic and clinical parameters.MethodsWe studied 110 ATTRwt patients from a prospective single-centre registry (2020-2024) by cardiopulmonary exercise testing (CPET). Besides CPET, all patients underwent comprehensive clinical assessment including follow-up for mortality. In 32 patients follow-up CPET after 1 year was available.ResultsIn ATTRwt, reduced aerobic capacity (pVO2 16 [13-18] ml/kg/min), and ventilatory inefficiency (VE/VCO2 slope 35 [30-43]) were common. In the multivariable regression analysis, we identified TAPSE/sPAP ratio as predictive for pVO2 (p = 0.019) and ventilatory efficiency (p = 0.004), while left ventricular ejection fraction or measures of left ventricular hypertrophy were not predictive. Concordantly, TAPSE/sPAP ratio assessed at baseline predicted pVO2 at 1-year follow-up (p = 0.009). Furthermore, patients with a TAPSE/sPAP ratio below the median of 0.38 mm/mmHg presented a higher risk of all-cause death (p = 0.009).ConclusionIn ATTRwt the TAPSE/sPAP ratio, a marker of right ventricular coupling, was an independent predictor of aerobic capacity assessed by CPET, at baseline and after 1 year, highlighting the importance of right ventricular assessment for risk stratification.

Arterial stiffness, a risk factor for cardiovascular disease, can be measured using pulse wave velocity (PWV) and augmentation index (AIx). We studied sex-specific associations between carotid-femoral PWV (cfPWV), brachial-ankle PWV (baPWV), aortic PWV (aoPWV), aortic (aoAIx), and brachial (baAIx) AIx with echocardiographic parameters. Data of 1150 participants of the Study of Health in Pomerania (SHIP-Trend 1; 530 men; median age 53 years; inter quartile range (IQR) 44 to 64) were used. Echocardiography assessed common structural and functional cardiac parameters. PWV and AIx were measured using the Vascular Explorer. Multivariable linear regression models were applied. In men, a higher brAIx was related to a greater right ventricular diameter (RV) (β 0.037; CI 0.003 to 0.148). A one m/s higher baPWV was associated with a smaller RV (β −0.037; CI −0.168 to −0.021) and right ventricular outflow tract (RVOT; β −0.029; CI −0.141 to −0.026). In men, a higher aoAIx (β 0.028; CI 0.01 to 0.122) and brAIx (β 0.029; CI 0.017 to 0.13) were associated with a greater RVOT. In women, a one m/s higher aoPWV (β 0.025; CI 0.006 to 0.105) was associated with a larger RV and a one m/s higher baPWV (β −0.031; CI −0.124 to −0.001) was inversely related to RVOT. In women, PWV associated with right ventricular dimensions, while in men, baPWV and AIx were related to right ventricular parameters. This suggests potentially sex-specific relations between PWV and cardiac structure and function.

The relationship between muscular strength and metabolic markers is poorly understood. Previous studies demonstrated that higher muscular strength was associated with lower prevalence of diabetes mellitus possibly due to better glycemic metabolism. We investigated the relations of handgrip strength (HGS) with oral glucose tolerance test (OGTT) markers: fasting glucose (FG) and insulin (FI), the homeostasis model assessment-insulin resistance index (HOMA-IR), 2-hour postload glucose (2HG) and insulin (2HI), and glucose tolerance categories in the general population. We analyzed data from 3,125 women and men, aged 20-82 years, from the population-based Study of Health in Pomerania (SHIP-TREND-0). HGS, in kilograms, was assessed by a hand-grip dynamometer. Plasma FG was measured using a hexokinase method, and serum FI through electrochemiluminescence immunoassay. For OGTT examination, 75 grams of anhydrous glucose was given to study participants without known type 2 diabetes or taking glucose-lowering agents. Following the criteria of the American Diabetes Association, we classified individuals as having normal glucose tolerance (NGT), isolated impaired fasting glucose (i-IFG), isolated impaired glucose tolerance (i-IGT), combined IFG and IGT (IFG + IGT), and unknown type 2 diabetes (UT2D). The associations of HGS with glucose and insulin parameters were analyzed using multivariable linear regression models adjusted for appropriate confounders. HGS was inversely associated with glycemic and insulin levels. A 10 kg lower HGS was associated with a 0.07 mmol/L (95% confidence interval [CI]: 0.02 to 0.12; p=0.003) higher FG, a 0.57 µlU/mL (95% CI: 0.23 to 0.92; p=0.001) higher FI, a 0.23 mmol/L (95% CI: 0.11 to 0.35; p<0.001) higher 2HG, and a 0.12 (95% CI: 0.03 to 0.21; p=0.007) higher HOMA-IR. There was no association with 2HI. HGS was lower in individuals with metabolic impairments compared to NGT. Specifically, a 1kg (95% CI: 0.38 to 1.61; p=0.045), a 0.99kg (95% CI: 0.53 to 1.45; p=0.021) and 1.92kg (95% CI: 1.35 to 2.49; p<0.001) lower HGS was found in study participants with i-IGT, IFG+IGT and UDM, respectively. In a general population, HGS was inversely associated with glucose and insulin levels and resistance. The postprandial hyperglycemia seems to be associated the strongest with HGS when compared with the nocturnal hepatic gluconeogenesis dependent on hepatic insulin sensitivity.Figure 1

BACKGROUND & AIMS:Liver-related mortality represents a growing public health concern, disproportionately affecting younger subjects. Because there are no established tools for early detection of individuals at risk for liver-related death (LRD), we analyzed LRD predictors in the UK Biobank (UKB) data and validated the usefulness of serum insulin-like growth factor-1 (IGF-1). METHODS:The UKB dataset encompassing 325,981 participants, a median follow-up of 13.5 years, and 846 LRDs was used as a training cohort. IGF-1 was validated in several independent cohorts of different liver disease etiologies and fibrosis stages. A Cox proportional hazard model was used to develop the gamma-glutamyl transferase (GGT)-IGF-1 score that was validated in an independent UKB cohort with 83,528 subjects and 237 LRDs. RESULTS:Among 59 variables in the UKB training cohort, GGT and IGF-1 were identified as the LRD predictors with time-dependent area under the curve (AUROC) >80%. Phenome-wide association study demonstrated the higher liver specificity of IGF-1 compared with GGT. In validation cohorts, IGF-1 levels: (1) increased in subjects with alcohol misuse after alcohol detoxification; (2) were reduced in individuals with alcohol-related/steatotic liver disease or severe alpha-1 antitrypsin deficiency and higher fibrosis stages; and (3) were diminished in participants with more advanced liver cirrhosis and lower levels associated with higher mortality. In the UKB training and validation cohorts, the novel GGT-IGF-1 score achieved an AUROC of 0.87 for LRD and was significantly better than established risk scores (AUROC = 0.77-0.81). CONCLUSIONS:The study highlights the usefulness of IGF-1 as a reliable predictor of LRD and identifies a novel, population-based screening tool outperforming the currently used scores.

BACKGROUND:Ceramides are complex sphingolipids with pleiotropic effects. The ratio of specific ceramides (plasma C24:0/C16:0) is inversely related to incident heart failure (HF) and all-cause death in large, community-based cohorts without pre-existing HF. Whether plasma C24:0/C16:0 relates to outcomes in patients with HF with preserved ejection fraction (HFpEF) is unclear. We hypothesized plasma C24:0/C16:0 would be inversely related to, and independently predict, outcomes in HFpEF patients. OBJECTIVES:To test our hypothesis, we used plasma samples, baseline, and outcomes data from the TOPCAT (Treatment of Preserved Cardiac Function Heart Failure with an Aldosterone Antagonist) trial. Findings were extended to a community-based cohort of HFpEF patients from the SHIP (Study of Health in Pomerania). METHODS:Plasma C24:0/C16:0 was measured using well-validated liquid chromatography/tandem mass spectrometry. For TOPCAT, our primary endpoint was the composite of time to cardiovascular disease (CVD) death, hospitalization for HF, or aborted cardiac death episode. Secondary endpoints were time to CVD death and to HF hospitalization. For SHIP, CVD death was the primary endpoint and total mortality a secondary endpoint. RESULTS:In 419 TOPCAT subjects (mean follow-up 3.3 years), lower plasma C24:0/C16:0 was associated with a higher risk of the primary endpoint; and HF hospitalization. In SHIP (N = 292; median follow-up 15.7 years), lower plasma C24:0/C16:0 was associated with a higher risk of CVD death and all-cause mortality in the SHIP cohort. CONCLUSIONS:Low plasma C24:0/C16:0 is independently associated with CVD death, all-cause mortality, and HF hospitalization in patients with HFpEF and may provide insight into novel treatment targets.

Heart failure (HF) is a major contributor to global morbidity and mortality. While distinct clinical subtypes, defined by etiology and left ventricular ejection fraction, are well recognized, their genetic determinants remain inadequately understood. In this study, we report a genome-wide association study of HF and its subtypes in a sample of 1.9 million individuals. A total of 153,174 individuals had HF, of whom 44,012 had a nonischemic etiology (ni-HF). A subset of patients with ni-HF were stratified based on left ventricular systolic function, where data were available, identifying 5,406 individuals with reduced ejection fraction and 3,841 with preserved ejection fraction. We identify 66 genetic loci associated with HF and its subtypes, 37 of which have not previously been reported. Using functionally informed gene prioritization methods, we predict effector genes for each identified locus, and map these to etiologic disease clusters through phenome-wide association analysis, network analysis and colocalization. Through heritability enrichment analysis, we highlight the role of extracardiac tissues in disease etiology. We then examine the differential associations of upstream risk factors with HF subtypes using Mendelian randomization. These findings extend our understanding of the mechanisms underlying HF etiology and may inform future approaches to prevention and treatment.

Metabolic syndrome, systemic inflammation and sedentary behaviour are known as three major important risk factors for cardiometabolic diseases, where systemic inflammation may be the link of metabolic syndrome with cardiovascular diseases. Physical activity is inversely related with systemic inflammation, which can be characterised by markers such as high-sensitive C-reactive protein (hs-CRP) or white blood cell count (WBC). This inverse association of physical activity with inflammation might be partly explained by reduction of abdominal obesity. Moreover, physical activity also induces anti-inflammatory cytokines such as IL-10 and an increase of high-density lipoprotein cholesterol (HDL-C), which is a protective factor of cardiometabolic diseases. An active lifestyle is also associated with other protective behavioural and psychological risk factors. Nevertheless, the relationship between cardio-respiratory fitness (CRF) and inflammation should to be further elucidated. We Investigated the relationship between CRF and body fat with inflammatory markers (i.e. hs-CRP and WBC) and potential effect modifications on those associations through the presence of the metabolic syndrome in the general population. We performed a cross-sectional multivariable linear regression analysis on the association of CRF parameters determined by cardiopulmonary exercise testing, and body fat (assessed through body impedance analysis and magnetic resonance imaging) as exposures and inflammatory markers as outcomes, based on data from two independent cohorts of the population-based Study of Health in Pomerania (SHIP-START-2, SHIP-TREND-0). After exclusion of missing data as well as subjects with anti-rheumatic/steroid/anti-inflammatory medication, chronic inflammatory and hepatic diseases, gastritis, severe renal disease, chronic lung disease, asthma, previous myocardial infarction, left ventricular ejection fraction <40% and previous cancer the pooled sample resulted in n=1,500. Models were adjusted for age, sex, current smoking, body lean mass, body height, renal function, alcohol consumption and cohort-ID to account for confounding. Interaction terms with metabolic syndrome status were included as well. We found an inverse association between CRF (maximum workload and peak oxygen consumption) and resting inflammatory status (hs-CRP and WBC) which was more pronounced in participants with metabolic syndrome. Furthermore, a notable difference was identified in the association between total body fat and WBC between individuals with and without metabolic syndrome (Table 1). Overall, our findings suggest that the potential biological mechanisms underlying the athero- and cardioprotective effects of higher CRF may be particularly significant in patients with metabolic syndrome. Hence, patients with metabolic syndrome could benefit more from a higher CRF which can be improved by physical exercise training.

Federated learning is a renowned technique for utilizing decentralized data while preserving privacy. However, real-world applications often face challenges like partially labeled datasets, where only a few locations have certain expert annotations, leaving large portions of unlabeled data unused. Leveraging these could enhance transformer architectures ability in regimes with small and diversely annotated sets. We conduct the largest federated cardiac CT analysis to date (n=8,104) in a real-world setting across eight hospitals. Our two-step semi-supervised strategy distills knowledge from task-specific CNNs into a transformer. First, CNNs predict on unlabeled data per label type and then the transformer learns from these predictions with label-specific heads. This improves predictive accuracy and enables simultaneous learning of all partial labels across the federation, and outperforms UNet-based models in generalizability on downstream tasks. Code and model weights are made openly available for leveraging future cardiac CT analysis.

Handgrip strength (HGS), cardiorespiratory fitness (CRF) and body size, shape, and composition are all related to cardiometabolic health and are associated in cross-sectional settings. Their longitudinal relationship is less clear. We used observational data from the Study of Health in Pomerania at baseline (SHIP-TREND-0; 2008–2012) and follow-up (SHIP-TREND-1; 2016–2019) with 1,214 men and 1,293 women. HGS was measured with a hand dynamometer. CRF was assessed using cardiopulmonary exercise testing. Linear regression models were adjusted appropriately. Several sensitivity analyses were performed. From baseline to follow-up (7 years) HGS decreased in men (3.5 kg) and women (0.8 kg). VO2peak lessened in men (36 ml/min) and increased in women (53 ml/min). We only found significant relations in men where a 1 l decline in VO2peak was associated with a 0.87 kg larger decrease in fat free mass and with a 1.15 kg stronger decline in body weight. All other analysis revealed non-significant findings. This longitudinal analysis suggests that age related changes in strength and CRF are not related to body size and shape but only composition (in men). A novelty of our findings are the sex-specific aspects given that strength decreased much stronger in men compared to women.

BACKGROUND:Due to the prevalence and impact of cognitive impairment on quality of life, regular screening for changes in cognition is the recommended standard care for PwMS. The shift from PASAT3 to SDMT as the recommended screening test poses challenges, especially for long-term analyses of patient cohorts, necessitating the development of an algorithm converting PASAT3 into SDMT scores. METHODS:Concurrent SDMT and PASAT3 scores of 459 PwMS and 147 HC from four clinical centers were analyzed retrospectively together with the demographic variables age, sex and education. Twelve linear (mixed) models were built and the dataset was stratified, split and bootstrapped for model training, evaluation and validation. The best performing model was refit to the full dataset to derive a practical formula estimating SDMT from PASAT3 scores. RESULTS:Demographic variables influence SDMT scores differently than PASAT3 scores. The final model includes a random effect for clinical center and estimates main effects of PASAT3 score (p<0.001), patient/control group (p<0.001), age (p<0.05) and education (p<0.01). Furthermore, significant interactions are found between PASAT3 score and age (p<0.001), and between sex and patient/control group (p<0.05). Predicted SDMT scores correlate significantly with observed SDMT scores (R = 0.77, p<0.001) and are well calibrated across the distribution of the data. CONCLUSION:A conversion of PASAT3 into SDMT scores can be performed based on our formula, accounting for demographic and test-performance interactions. Our approach not only addresses a methodological challenge by aligning past and present cognitive assessments, but also provides deeper insights into the demographic influences on cognitive performance in PwMS.

Sphingosine-1-phosphate (S1P) and derivatives of arginine (i.e. symmetric [SDMA] and asymmetric [ADMA] dimethylarginine), have been linked to endothelial dysfunction and cardiovascular diseases. However, the relationship between S1P and arginine derivates is currently unclear. We investigated the relation between serum S1P concentrations and serum SDMA as well as ADMA levels. We further explored a potential interaction for age and sex in the regression models. We used data from the population-based Study of Health in Pomerania (SHIP-TREND-0) (n=4,420). Subjects with pacemakers (n = 63), left branch bundle block (n = 30), missing values for S1P, SDMA or ADMA (n = 231) and extreme values for ADMA or SDMA (> 2μmol/l; n = 2) were excluded. SDMA and ADMA were measured using LC/MS. S1P was determined using LC-MS/MS. Linear regression models adjusted for age, sex, and waist circumference were used to assess the relation between S1P and SDMA and ADMA. The final study population comprised of n=4,094 participants (48% male). The median age was 52 year (interquartile range 40 – 64). A one µM higher S1P concentration was linearly related to higher ADMA in women (0.06 μmol/l; 95% CI: 0.01 – 0.11) but not men (0.03 μmol/l; 95% CI -0.02 – 0.07). S1P was non-linearly associated with SDMA in both sexes (p < .01; see Figure A). Age significantly influenced the association of S1P with ADMA/SDMA. The effect sizes decreased in older individuals compared to young study participants (see Figure B and C). We report significant relations between S1P and ADMA/SDMA in a large study sample from the general population. Importantly, the relationship was significantly influenced by age and sex (for ADMA only).Figures A, B, C

Reactive oxygen species (ROS) are important secondary messengers involved in a variety of cellular processes, including activation, proliferation, and differentiation. Hydrogen peroxide (H2O2) is a major ROS typically kept in low nanomolar range that causes cell and tissue damage at supraphysiological concentrations. While ROS have been studied in detail at molecular scale, little is known about their impact on cell mechanical properties as label-free biomarker for stress response. Here, we exposed human myeloid precursor cells, T-lymphoid cells and neutrophils to varying concentrations of H2O2 and show that elevated levels of mitochondrial superoxide are accompanied by an increased Young's modulus. Mechanical alterations do not originate from global modifications in filamentous actin and microtubules but from cytosolic acidification due to lysosomal degradation. Finally, we demonstrate our findings to be independent of the presence of H2O2 and that stiffening seems to be a general response of cells to stress factors lowering cytosolic pH. Reactive oxygen species (ROS), such as hydrogen peroxide, play a key role in cellular processes but can cause damage at high concentrations. The authors demonstrate that elevated ROS levels are accompanied by an increased cell stiffness that is explained by cytosolic acidification due to lysosomal degradation and not by alterations in the cytoskeleton.