Non-cellular blood circulating microRNAs (plasma miRNAs) represent a promising source for the development of prognostic and diagnostic tools owing to their minimally invasive sampling, high stability, and simple quantification by standard techniques such as RT-qPCR. So far, the majority of association studies involving plasma miRNAs were disease-specific case-control analyses. In contrast, in the present study, plasma miRNAs were analysed in a sample of 372 individuals from a population-based cohort study, the Study of Health in Pomerania (SHIP). Quantification of miRNA levels was performed by RT-qPCR using the Exiqon Serum/Plasma Focus microRNA PCR Panel V3.M covering 179 different miRNAs. Of these, 155 were included in our analyses after quality-control. Associations between plasma miRNAs and the phenotypes age, body mass index (BMI), and sex were assessed via a two-step linear regression approach per miRNA. The first step regressed out the technical parameters and the second step determined the remaining associations between the respective plasma miRNA and the phenotypes of interest. After regressing out technical parameters and adjusting for the respective other two phenotypes, 7, 15, and 35 plasma miRNAs were significantly (q < 0.05) associated with age, BMI, and sex, respectively. Additional adjustment for the blood cell parameters identified 12 and 19 miRNAs to be significantly associated with age and BMI, respectively. Most of the BMI-associated miRNAs likely originate from liver. Sex-associated differences in miRNA levels were largely determined by differences in blood cell parameters. Thus, only 7 as compared to originally 35 sex-associated miRNAs displayed sex-specific differences after adjustment for blood cell parameters. These findings emphasize that circulating miRNAs are strongly impacted by age, BMI, and sex. Hence, these parameters should be considered as covariates in association studies based on plasma miRNA levels. The established experimental and computational workflow can now be used in future screening studies to determine associations of plasma miRNAs with defined disease phenotypes.

The impact of risk factors on poor outcome after ischemic stroke is well known, but estimating the amount of poor outcome attributable to single factors is challenging in presence of multimorbidity. We aim to compare population attributable risk estimates obtained from different statistical approaches regarding their consistency. We use a real-life data set from the PROSCIS study to identify predictors for mortality and functional impairment one year after first-ever ischemic stroke and quantify their contribution to poor outcome using population attributable risks. The PROSpective Cohort with Incident Stroke (PROSCIS) is a prospective observational hospital-based cohort study of patients after first-ever stroke conducted independently in Berlin (PROSCIS-B) and Munich (PROSCIS-M). The association of baseline factors with poor outcome one year after stroke in PROSCIS-B was analysed using multiple logistic regression analysis and population attributable risks were calculated, which were estimated using sequential population attributable risk based on a multiple generalized additive regression model, doubly robust estimation, as well as using average sequential population attributable risk. Findings were reproduced in an independent validation sample from PROSCIS-M. Out of 507 patients with available outcome information after 12 months in PROSCIS-B, 20.5% suffered from poor outcome. Factors associated with poor outcome were age, pre-stroke physical disability, stroke severity (NIHSS), education, and diabetes mellitus. The order of risk factors ranked by magnitudes of population attributable risk was almost similar for all methods, but population attributable risk estimates varied markedly between the methods. In PROSCIS-M, incidence of poor outcome and distribution of baseline parameters were comparable. The multiple logistic regression model could be reproduced for all predictors, except pre-stroke physical disability. Similar to PROSCIS-B, the order of risk factors ranked by magnitudes of population attributable risk was almost similar for all methods, but magnitudes of population attributable risk differed markedly between the methods. Ranking of risk factors by population impact is not affected by the different statistical approaches. Thus, for a rational decision on which risk factor to target in disease interventions, population attributable risk is a supportive tool. However, population attributable risk estimates are difficult to interpret and are not comparable when they origin from studies applying different methodology. The predictors for poor outcome identified in PROSCIS-B have a relevant impact on mortality and functional impairment one year after first-ever ischemic stroke.

Background: Early stroke complications (ESCs) impair recovery and provide an important therapeutic target. Our multicenter prospective cohort study examined the prevalence and amount of poor outcomes attributable to ESCs. Methods and Results: In patients with stroke, 16 ESCs were monitored by clinical assessment (“basic definition”) and by inclusion of laboratory values and clinical scales (“extended definition”). The association of complications with poor outcome (death or functional impairment: modified Rankin Scale score >3 or Barthel Index <60) at 3 months was analyzed using multiple logistic regression adjusted for stroke severity, age, and sex. Complications were stratified by their early treatment options, and average sequential population‐attributable fractions were estimated for treatable early complications. Of 1202 patients, outcome data were available for 1105 (91.9%; 43.0% women; mean age 68.3 years; baseline median National Institutes of Health Stroke Scale score on admission 3 points). Poor outcome was recorded for 100 patients (9.0%). By basic definition, recurrent stroke, fever, pneumonia, depression, urinary tract infection, and delirium were significantly associated with poor outcomes. The occurrence of 1 or more of these ESCs was associated with up to 19.6% of poor outcomes. By extended definition, urinary tract infection, depression, and delirium were significantly associated with poor outcomes. The occurrence of 1 or more of these ESCs was associated with up to 15.9% of poor outcomes. Conclusions: In our cohort of patients with mild to moderate stroke, we identified a set of treatable ESCs with clinically relevant impact on outcome that may be targeted in future interventional trials. Registration: URL: https://www.bfarm.de ; Unique identifier: DRKS00004712.

Background: The integration of artificial intelligence (AI) into radiology aims to improve diagnostic accuracy and efficiency, particularly in settings with limited access to expert radiologists and in times of personnel shortage. However, challenges such as insufficient validation in actual real-world settings or automation bias should be addressed before implementing AI software in clinical routine. Methods: This cross-sectional study in a maximum care hospital assesses the concordance between diagnoses made by a commercial AI-based software and conventional radiological methods augmented by AI for four major thoracic pathologies in chest X-ray: fracture, pleural effusion, pulmonary nodule and pneumonia. Chest radiographs of 1506 patients (median age 66 years, 56.5% men) consecutively obtained between January and August 2023 were re-evaluated by the AI software InferRead DR Chest^®. Results: Overall, AI software detected thoracic pathologies more often than radiologists (18.5% vs. 11.1%). In detail, it detected fractures, pneumonia, and nodules more frequently than radiologists, while radiologists identified pleural effusions more often. Reliability was highest for pleural effusions (0.63, 95%-CI 0.58–0.69), indicating good agreement, and lowest for fractures (0.39, 95%-CI 0.32–0.45), indicating moderate agreement. Conclusions: The tested software shows a high detection rate, particularly for fractures, pneumonia, and nodules, but hereby produces a nonnegligible number of false positives. Thus, AI-based software shows promise in enhancing diagnostic accuracy; however, cautious interpretation and human oversight remain crucial.

Objective: Back pain is a multifactorial condition commonly associated with degenerative spinal changes. Spondylophytes are frequent outgrowths of the vertebral bodies that may be influenced by arterial hypertension via a possible increased pulsation of the aorta and its effects on bone remodeling. If it can be demonstrated that an increased pulse pressure in the aorta due to hypertension promotes the growth of spondylophytes and thereby increases the likelihood of back pain, future studies may investigate how the effectiveness of blood pressure management can be improved in order to reduce the prevalence of degenerative changes in the spine and, consequently, prevent back pain. This study investigated the association between arterial hypertension and thoracic spondylophyte formation using whole-body MRI data from the population-based Study of Health in Pomerania (SHIP). Materials and Methods: Spondylophyte presence and area were assessed for their association with hypertension status in 859 SHIP-START-3 participants who underwent whole-body MRI. Right-sided spondylophytes at T8-T11 were measured on axial T2-weighted sequences. Hypertension was defined by self-report or antihypertensive medication use; a sensitivity analysis was conducted using the 2024 European Society of Cardiology definition (systolic blood pressure ≥ 140 mmHg). Multivariate regression models adjusted for age, sex, obesity, and smoking were used to assess associations. Machine learning algorithms were applied for validation. Results: Spondylophytes were present in 87.7% of participants. Hypertension was significantly associated with spondylophyte presence (OR = 2.07, 95% CI: 1.15–3.81) but not consistently associated with spondylophyte size. Spondylophyte size increased from T8 to T11, and was associated with age, male sex, and obesity. Sensitivity analyses widely confirmed robustness of the analysis. Conclusions: This population-based MRI study investigates the still insufficiently studied relationship between arterial hypertension and the formation of thoracic spondylophytes. The findings are consistent with the hypothesis that hypertension may be associated with spinal bone remodelling, though causal inference remains limited by the cross-sectional study design. Further longitudinal studies are needed to clarify causality and clinical relevance for spinal degeneration and back pain.