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Aims: A data-driven cluster analysis in a cohort of European individuals with type 2 diabetes (T2D) has previously identified four subgroups based on clinical characteristics. In the current study, we performed a comprehensive statistical assessment to (1) replicate the above-mentioned original clusters; (2) derive de novo T2D subphenotypes in the Kooperative Gesundheitsforschung in der Region Augsburg (KORA) cohort and (3) describe underlying genetic risk and diabetes complications., Methods: We used data from n = 301 individuals with T2D from KORA FF4 study (Southern Germany). Original cluster replication was assessed forcing k = 4 clusters using three different hyperparameter combinations. De novo clusters were derived by open k-means analysis. Stability of de novo clusters was assessed by assignment congruence over different variable sets and Jaccard indices. Distribution of polygenic risk scores and diabetes complications in the respective clusters were described as an indication of underlying heterogeneity., Results: Original clusters did not replicate well, indicated by substantially different assignment frequencies and cluster characteristics between the original and current sample. De novo clustering using k = 3 clusters and including high sensitivity C-reactive protein in the variable set showed high stability (all Jaccard indices >0.75). The three de novo clusters (n = 96, n = 172, n = 33, respectively) adequately captured heterogeneity within the sample and showed different distributions of polygenic risk scores and diabetes complications, that is, cluster 1 was characterized by insulin resistance with high neuropathy prevalence, cluster 2 was defined as age-related diabetes and cluster 3 showed highest risk of genetic and obesity-related diabetes., Conclusion: T2D subphenotyping based on its sample's own clinical characteristics leads to stable categorization and adequately reflects T2D heterogeneity., (Diabetes, Obesity and Metabolism© 2024 The Author(s). Diabetes, Obesity and Metabolism published by John Wiley & Sons Ltd.)

First small sample studies indicate that disturbances of spinal morphology may impair craniospinal flow of cerebrospinal fluid and result in neurodegeneration. The aim of this study was to evaluate the association of cervical spinal canal width and scoliosis with grey matter, white matter, ventricular and white matter hyperintensity volumes of the brain in a large study sample. Four hundred participants underwent whole-body 3 T magnetic resonance imaging. Grey matter, white matter and ventricular volumes were quantified using a warp-based automated brain volumetric approach. Spinal canal diameters were measured manually at the cervical vertebrae 2/3 level. Scoliosis was evaluated using manual measurements of the Cobb angle. Linear binomial regression analyses of measures of brain volumes and spine anatomy were performed while adjusting for age, sex, hypertension, cholesterol levels, body mass index, smoking and alcohol consumption. Three hundred eighty-three participants were included [57% male; age: 56.3 (±9.2) years]. After adjustment, smaller spinal canal width at the cervical vertebrae 2/3 level was associated with lower grey matter ( P = 0.034), lower white matter ( P = 0.012) and higher ventricular ( P = 0.006, inverse association) volume. Participants with scoliosis had lower grey matter ( P = 0.005), lower white matter ( P = 0.011) and larger brain ventricular ( P = 0.003) volumes than participants without scoliosis. However, these associations were attenuated after adjustment. Spinal canal width at the cervical vertebrae 2/3 level and scoliosis were not associated with white matter hyperintensity volume before and after adjustment ( P > 0.864). In our study, cohort smaller spinal canal width at the cervical vertebrae 2/3 level and scoliosis were associated with lower grey and white matter volumes and larger ventricle size. These characteristics of the spine might constitute independent risk factors for neurodegeneration., Competing Interests: The authors report no competing interests. The authors of this manuscript declare no relationships with any companies, whose products or services may be related to the subject matter of the article., (© The Author(s) 2024. Published by Oxford University Press on behalf of the Guarantors of Brain.)

Long-term exposure to traffic-related air pollution (TRAP) is associated with cardiometabolic disease; however, its role in subclinical stages of disease development is unclear. Thus, we aimed to explore this association in a cross-sectional analysis, with cardiometabolic phenotypes derived from magnetic resonance imaging (MRI). Phenotypes of the left (LV) and right cardiac ventricle, whole-body adipose tissue (AT), and organ-specific AT were obtained by MRI in 400 participants of the KORA cohort. Land-use regression models were used to estimate residential long-term exposures to TRAP, e.g., nitrogen dioxides (NO 2 ) or particle number concentration (PNC). Associations between TRAP and MRI phenotypes were modeled using linear regression. Participants' mean age was 56 ± 9 years, and 42% were female. Long-term exposure to TRAP was associated with decreased LV wall thickness; a 6.0 μg/m 3 increase in NO 2 was associated with a -1.9% [95% confidence interval: -3.7%; -0.1%] decrease in mean global LV wall thickness. Furthermore, we found associations between TRAP and increased cardiac AT. A 2,242 n/cm 3 increase in PNC was associated with a 4.3% [-1.7%; 10.4%] increase in mean total cardiac AT. Associations were more pronounced in women and in participants with diabetes. Our exploratory study indicates that long-term exposure to TRAP is associated with subclinical cardiometabolic disease states, particularly in metabolically vulnerable subgroups.

Background: There is increasing evidence that myosteatosis, which is currently not assessed in clinical routine, plays an important role in risk estimation in individuals with impaired glucose metabolism, as it is associated with the progression of insulin resistance. With advances in artificial intelligence, automated and accurate algorithms have become feasible to fill this gap., Methods: In this retrospective study, we developed and tested a fully automated deep learning model using data from two prospective cohort studies (German National Cohort [NAKO] and Cooperative Health Research in the Region of Augsburg [KORA]) to quantify myosteatosis on whole-body T1-weighted Dixon magnetic resonance imaging as (1) intramuscular adipose tissue (IMAT; the current standard) and (2) quantitative skeletal muscle (SM) fat fraction (SMFF). Subsequently, we investigated the two measures for their discrimination of and association with impaired glucose metabolism beyond baseline demographics (age, sex and body mass index [BMI]) and cardiometabolic risk factors (lipid panel, systolic blood pressure, smoking status and alcohol consumption) in asymptomatic individuals from the KORA study. Impaired glucose metabolism was defined as impaired fasting glucose or impaired glucose tolerance (140-200 mg/dL) or prevalent diabetes mellitus., Results: Model performance was high, with Dice coefficients of ≥0.81 for IMAT and ≥0.91 for SM in the internal (NAKO) and external (KORA) testing sets. In the target population (380 KORA participants: mean age of 53.6 ± 9.2 years, BMI of 28.2 ± 4.9 kg/m 2 , 57.4% male), individuals with impaired glucose metabolism (n = 146; 38.4%) were older and more likely men and showed a higher cardiometabolic risk profile, higher IMAT (4.5 ± 2.2% vs. 3.9 ± 1.7%) and higher SMFF (22.0 ± 4.7% vs. 18.9 ± 3.9%) compared to normoglycaemic controls (all P ≤ 0.005). SMFF showed better discrimination for impaired glucose metabolism than IMAT (area under the receiver operating characteristic curve [AUC] 0.693 vs. 0.582, 95% confidence interval [CI] [0.06-0.16]; P < 0.001) but was not significantly different from BMI (AUC 0.733 vs. 0.693, 95% CI [-0.09 to 0.01]; P = 0.15). In univariable logistic regression, IMAT (odds ratio [OR] = 1.18, 95% CI [1.06-1.32]; P = 0.004) and SMFF (OR = 1.19, 95% CI [1.13-1.26]; P < 0.001) were associated with a higher risk of impaired glucose metabolism. This signal remained robust after multivariable adjustment for baseline demographics and cardiometabolic risk factors for SMFF (OR = 1.10, 95% CI [1.01-1.19]; P = 0.028) but not for IMAT (OR = 1.14, 95% CI [0.97-1.33]; P = 0.11)., Conclusions: Quantitative SMFF, but not IMAT, is an independent predictor of impaired glucose metabolism, and discrimination is not significantly different from BMI, making it a promising alternative for the currently established approach. Automated methods such as the proposed model may provide a feasible option for opportunistic screening of myosteatosis and, thus, a low-cost personalized risk assessment solution., (© 2024 The Author(s). Journal of Cachexia, Sarcopenia and Muscle published by Wiley Periodicals LLC.)

Left atrial (LA) physiology and hemodynamics are intimately connected to cardiac and lung function in health and disease. This study examined the relationship between MRI-based left atrial (LA) size and function with MRI-based lung volume and pulmonary function testing (PFT) parameters in the population-based KORA study cohort of 400 participants without overt cardiovascular disease. MRI quantification assessed LA size/function in sequences with and without ECG synchronization, alongside lung volume. Regression analysis explored the relationship of LA with MRI lung volume and PFT parameters. Among 378 participants (average age 56.3 ± 9.2 years; 42.3% women), non-gated LA size averaged 16.8 cm 2 , while maximal and minimal LA size from gated measurements were 19.6 cm 2 and 11.9 cm 2 respectively. The average MRI-derived lung volume was 4.0 L, with PFT showing a total lung capacity of 6.2 L, residual lung volume of 2.1 L, and forced vital capacity of 4.1 L. Multivariate regression analysis, adjusted for age, gender, and cardiovascular risk factors, revealed an inverse association between maximum LA size, non-gated LA, and LA area fraction with lung volume (ß = - 0.03, p = 0.006; ß = - 0.03, p = 0.021; ß = - 0.01, p = 0.012), with no significant association with PFT parameters. This suggests that MRI-based assessment may offer greater sensitivity in detecting subclinical LA impairment than PFT., (© 2024. The Author(s).)

Background: Obesity is associated with alterations in the hypothalamic-pituitary-adrenal (HPA) axis. Effects of glucocorticoids on adipose tissues appear to depend on the specific adipose depot, in which they take place. In this study, we aimed to investigate the role of MRI-based adrenal gland volume as an imaging marker in association with different adipose tissue compartments., Methods: The study cohort derives from the population-based research platform KORA (Cooperative Health Research in the Augsburg Region, Germany) MRI sub-study, a cross-sectional sub-study investigating the interactions between subclinical metabolic changes and cardiovascular disease in a study sample of 400 participants. Originally, eligible subjects underwent a whole-body MRI. MRI-based segmentations were performed manually and semi-automatically for adrenal gland volume, visceral adipose tissue (VAT), subcutaneous adipose tissue (SAT), epi- and pericardial fat and renal sinus fat. Hepatic and pancreatic lipid content were measured as pancreatic proton density fraction (PDFF) and MR-spectroscopic hepatic fat fraction (HFF). Multivariable linear regression analyses were performed., Results: A number of 307 participants (56.2 ± 9.1 years, 60.3% male, 14.3% with type 2 diabetes (T2DM), 30.6% with obesity, 34.2% with hypertension) were included. In multivariable analyses, strong positive associations between adrenal gland volume and VAT, total adipose tissue (TAT) as well as HFF persisted after extensive step-wise adjustment for possible metabolic confounders (VAT: beta = 0.31, 95%-CI [0.71, 0.81], p < 0.001; TAT: beta = 0.14, 95%-CI [0.06, 0.23], p < 0.001; HFF: beta = 1.17, 95%-CI [1.04, 1.31], p = 0.009). In contrast, associations between adrenal gland volume and SAT were attenuated in multivariate analysis after adjusting for BMI. Associations between pancreatic PDFF, epi- and pericardial fat and renal sinus fat were mediated to a great extent by VAT (pancreatic PDFF: 72%, epicardial adipose tissue: 100%, pericardial adipose tissue: 100%, renal sinus fat: 81.5%)., Conclusion: Our results found MRI-based adrenal gland volume as a possible imaging biomarker of unfavorable adipose tissue distribution, irrespective of metabolic risk factors. Thus, adrenal gland volume may serve as a potential MRI-based biomarker of metabolic changes and contributes to an individual characterization of metabolic states and individual risk stratification. Future studies should elucidate in a longitudinal study design, if and how HPA axis activation may trigger unfavorable adipose tissue distribution and whether and to which extent this is involved in the pathogenesis of manifest metabolic syndrome., (© 2024. The Author(s).)

Aim of this study was to analyse the associations of cardiovascular health and adrenal gland volume as a rather new imaging biomarker of chronic hypothalamic-pituitary-adrenal (HPA) axis activation. The study population originates from the KORA population-based cross-sectional prospective cohort. 400 participants without known cardiovascular disease underwent a whole-body MRI. Manual segmentation of adrenal glands was performed on VIBE-Dixon gradient-echo sequence. MRI based evaluation of cardiac parameters was achieved semi-automatically. Cardiometabolic risk factors were obtained through standardized interviews and medical examination. Univariate and multivariate associations were derived. Bi-directional causal mediation analysis was performed. 351 participants were eligible for analysis (56 ± 9.1 years, male 58.7%). In multivariate analysis, significant associations were observed between adrenal gland volume and hypertension (outcome hypertension: Odds Ratio = 1.11, 95% CI [1.01, 1.21], p = 0.028), left ventricular remodelling index (LVRI) (outcome LVRI: β = 0.01, 95% CI [0.00, 0.02], p = 0.011), and left ventricular (LV) wall thickness (outcome LV wall thickness: β = 0.06, 95% CI [0.02, 0.09], p = 0.005). In bi-directional causal mediation analysis adrenal gland volume had a borderline significant mediating effect on the association between hypertension and LVRI (p = 0.052) as well as wall thickness (p = 0.054). MRI-based assessment of adrenal gland enlargement is associated with hypertension and LV remodelling. Adrenal gland volume may serve as an indirect cardiovascular imaging biomarker., (© 2024. The Author(s).)

Objectives: To assess the association of serum magnesium with prevalent and incident metabolic syndrome (MetS) and its individual components in the general population and to examine any effect modification by chronic kidney disease (CKD) status., Methods: We analysed longitudinal data from the population-based KORA F4/FF4 study, including 2996 participants (387 with CKD) for cross-sectional analysis and 1446 participants (88 with CKD) for longitudinal analysis. Associations with MetS, as well as single components of MetS, were assessed by adjusted regression models. Nonlinearity was tested by restricted cubic splines and analyses were stratified by CKD. Causality was evaluated by two-sample Mendelian randomization (MR)., Results: Serum magnesium (1 SD) was inversely associated with prevalent MetS (odds ratio [OR] 0.90, 95% confidence interval [CI] 0.83, 0.98). The association was more pronounced in individuals with CKD (OR 0.75, 95% CI 0.59, 0.94). Among MetS components, serum magnesium was negatively associated with elevated fasting glucose (OR 0.78, 95% CI 0.71, 0.88) and, again, this association was more pronounced in individuals with CKD (OR 0.67, 95% CI 0.53, 0.84). Serum magnesium was not associated with incident MetS or its components. Restricted cubic spline analysis revealed a significant nonlinear inverse relationship of serum magnesium with MetS and elevated fasting glucose. MR analysis suggested an inverse causal effect of serum magnesium on MetS (OR 0.91, 95% CI 0.85, 0.97)., Conclusion: Serum magnesium is associated with prevalent, but not incident MetS, and this effect is stronger in individuals with CKD. MR analysis implies a potential, albeit weak, causal role of magnesium in MetS., (© 2024 The Authors. Diabetes, Obesity and Metabolism published by John Wiley & Sons Ltd.)

Cognitive deficits are a core symptom of schizophrenia, but research on their neural underpinnings has been challenged by the heterogeneity in deficits' severity among patients. Here, we address this issue by combining logistic regression and random forest to classify two neuropsychological profiles of patients with high (HighCog) and low (LowCog) cognitive performance in two independent samples. We based our analysis on the cortical features grey matter volume (VOL), cortical thickness (CT), and mean curvature (MC) of N = 57 patients (discovery sample) and validated the classification in an independent sample (N = 52). We investigated which cortical feature would yield the best classification results and expected that the 10 most important features would include frontal and temporal brain regions. The model based on MC had the best performance with area under the curve (AUC) values of 76% and 73%, and identified fronto-temporal and occipital brain regions as the most important features for the classification. Moreover, subsequent comparison analyses could reveal significant differences in MC of single brain regions between the two cognitive profiles. The present study suggests MC as a promising neuroanatomical parameter for characterizing schizophrenia cognitive subtypes., Competing Interests: Declaration of competing interest IP, SW, BP, UK, IM, TK, SR, BM declare no conflict of interest. PF is a co-editor of the German (DGPPN) schizophrenia treatment guidelines and a co-author of the WFSBP schizophrenia treatment guidelines; he is on the advisory boards and receives speaker fees from Janssen, Lundbeck, Otsuka, Servier, Boehringer-Ingelheim and Richter. AH is editor of the German (DGPPN) schizophrenia treatment guidelines and first author of the WFSBP schizophrenia treatment guidelines; he has been on the advisory boards of and has received speaker fees from Janssen-Cilag, Lundbeck, Recordati, Rovi and Otsuka. AS was an honorary speaker for TAD Pharma and Roche and a member of Roche advisory boards., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Background: Multiple risk factors contribute jointly to the development and progression of cardiometabolic diseases. Therefore, joint longitudinal trajectories of multiple risk factors might represent different degrees of cardiometabolic risk., Methods: We analyzed population-based data comprising three examinations (Exam 1: 1999-2001, Exam 2: 2006-2008, Exam 3: 2013-2014) of 976 male and 1004 female participants of the KORA cohort (Southern Germany). Participants were followed up for cardiometabolic diseases, including cardiovascular mortality, myocardial infarction and stroke, or a diagnosis of type 2 diabetes, until 2016. Longitudinal multivariate k-means clustering identified sex-specific trajectory clusters based on nine cardiometabolic risk factors (age, systolic and diastolic blood pressure, body-mass-index, waist circumference, Hemoglobin-A1c, total cholesterol, high- and low-density lipoprotein cholesterol). Associations between clusters and cardiometabolic events were assessed by logistic regression models., Results: We identified three trajectory clusters for men and women, respectively. Trajectory clusters reflected a distinct distribution of cardiometabolic risk burden and were associated with prevalent cardiometabolic disease at Exam 3 (men: odds ratio (OR)ClusterII = 2.0, 95% confidence interval: (0.9-4.5); ORClusterIII = 10.5 (4.8-22.9); women: ORClusterII = 1.7 (0.6-4.7); ORClusterIII = 5.8 (2.6-12.9)). Trajectory clusters were furthermore associated with incident cardiometabolic cases after Exam 3 (men: ORClusterII = 3.5 (1.1-15.6); ORClusterIII = 7.5 (2.4-32.7); women: ORClusterII = 5.0 (1.1-34.1); ORClusterIII = 8.0 (2.2-51.7)). Associations remained significant after adjusting for a single time point cardiovascular risk score (Framingham)., Conclusions: On a population-based level, distinct longitudinal risk profiles over a 14-year time period are differentially associated with cardiometabolic events. Our results suggest that longitudinal data may provide additional information beyond single time-point measures. Their inclusion in cardiometabolic risk assessment might improve early identification of individuals at risk., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Niedermayer et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Background: Changes in serum metabolites in individuals with altered cardiac function and morphology may exhibit information about cardiovascular disease (CVD) pathway dysregulations and potential CVD risk factors. We aimed to explore associations of cardiac function and morphology, evaluated using magnetic resonance imaging (MRI) with a large panel of serum metabolites., Methods: Cross-sectional data from CVD-free individuals from the population-based KORA cohort were analyzed. Associations between 3T-MRI-derived left ventricular (LV) function and morphology parameters (e.g., volumes, filling rates, wall thickness) and markers of carotid plaque with metabolite profile clusters and single metabolites as outcomes were assessed by adjusted multinomial logistic regression and linear regression models., Results: In 360 individuals (mean age 56.3 years; 41.9% female), 146 serum metabolites clustered into three distinct profiles that reflected high-, intermediate- and low-CVD risk. Higher stroke volume (relative risk ratio (RRR): 0.53, 95%-CI [0.37; 0.76], p-value < 0.001) and early diastolic filling rate (RRR: 0.51, 95%-CI [0.37; 0.71], p-value < 0.001) were most strongly protectively associated against the high-risk profile compared to the low-risk profile after adjusting for traditional CVD risk factors. Moreover, imaging markers were associated with 10 metabolites in linear regression. Notably, negative associations of stroke volume and early diastolic filling rate with acylcarnitine C5, and positive association of function parameters with lysophosphatidylcholines, diacylphosphatidylcholines, and acylalkylphosphatidylcholines were observed. Furthermore, there was a negative association of LV wall thickness with alanine, creatinine, and symmetric dimethylarginine. We found no significant associations with carotid plaque., Conclusions: Serum metabolite signatures are associated with cardiac function and morphology even in individuals without a clinical indication of CVD., (© 2024. The Author(s).)

Development of back pain is multifactorial, and it is not well understood which factors are the main drivers of the disease. We therefore applied a machine-learning approach to an existing large cohort study data set and sought to identify and rank the most important contributors to the presence of back pain amongst the documented parameters of the cohort. Data from 399 participants in the KORA-MRI (Cooperative health research in the region Augsburg-magnetic resonance imaging) (Cooperative Health Research in the Region Augsburg) study was analyzed. The data set included MRI images of the whole body, including the spine, metabolic, sociodemographic, anthropometric, and cardiovascular data. The presence of back pain was one of the documented items in this data set. Applying a machine-learning approach to this preexisting data set, we sought to identify the variables that were most strongly associated with back pain. Mediation analysis was performed to evaluate the underlying mechanisms of the identified associations. We found that depression and anxiety were the 2 most selected predictors for back pain in our model. Additionally, body mass index, spinal canal width and disc generation, medium and heavy physical work as well as cardiovascular factors were among the top 10 most selected predictors. Using mediation analysis, we found that the effects of anxiety and depression on the presence of back pain were mainly direct effects that were not mediated by spinal imaging. In summary, we found that psychological factors were the most important predictors of back pain in our cohort. This supports the notion that back pain should be treated in a personalized multidimensional framework. PERSPECTIVE: This article presents a wholistic approach to the problem of back pain. We found that depression and anxiety were the top predictors of back pain in our cohort. This strengthens the case for a multidimensional treatment approach to back pain, possibly with a special emphasis on psychological factors., (Copyright © 2024 United States Association for the Study of Pain, Inc. Published by Elsevier Inc. All rights reserved.)

Aim: Diabetes is a global health challenge, and many individuals are undiagnosed and not aware of their increased risk of morbidity/mortality although dedicated tests are available, which indicates the need for novel population-wide screening approaches. Here, we developed a deep learning pipeline for opportunistic screening of impaired glucose metabolism using routine magnetic resonance imaging (MRI) of the liver and tested its prognostic value in a general population setting., Methods: In this retrospective study a fully automatic deep learning pipeline was developed to quantify liver shape features on routine MR imaging using data from a prospective population study. Subsequently, the association between liver shape features and impaired glucose metabolism was investigated in individuals with prediabetes, type 2 diabetes and healthy controls without prior cardiovascular diseases. K-medoids clustering (3 clusters) with a dissimilarity matrix based on Euclidean distance and ordinal regression was used to assess the association between liver shape features and glycaemic status., Results: The deep learning pipeline showed a high performance for liver shape analysis with a mean Dice score of 97.0±0.01. Out of 339 included individuals (mean age 56.3±9.1 years; males 58.1%), 79 (23.3%) and 46 (13.6%) were classified as having prediabetes and type 2 diabetes, respectively. Individuals in the high risk cluster using all liver shape features (n = 14) had a 2.4 fold increased risk of impaired glucose metabolism after adjustment for cardiometabolic risk factors (age, sex, BMI, total cholesterol, alcohol consumption, hypertension, smoking and hepatic steatosis; OR 2.44 [95% CI 1.12-5.38]; p = 0.03). Based on individual shape features, the strongest association was found between liver volume and impaired glucose metabolism after adjustment for the same risk factors (OR 1.97 [1.38-2.85]; p<0.001)., Conclusions: Deep learning can estimate impaired glucose metabolism on routine liver MRI independent of cardiometabolic risk factors and hepatic steatosis., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Michel et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

In magnetic resonance imaging (MRI), the perception of substandard image quality may prompt repetition of the respective image acquisition protocol. Subsequently selecting the preferred high-quality image data from a series of acquisitions can be challenging. An automated workflow may facilitate and improve this selection. We therefore aimed to investigate the applicability of an automated image quality assessment for the prediction of the subjectively preferred image acquisition. Our analysis included data from 11,347 participants with whole-body MRI examinations performed as part of the ongoing prospective multi-center German National Cohort (NAKO) study. Trained radiologic technologists repeated any of the twelve examination protocols due to induced setup errors and/or subjectively unsatisfactory image quality and chose a preferred acquisition from the resultant series. Up to 11 quantitative image quality parameters were automatically derived from all acquisitions. Regularized regression and standard estimates of diagnostic accuracy were calculated. Controlling for setup variations in 2342 series of two or more acquisitions, technologists preferred the repetition over the initial acquisition in 1116 of 1396 series in which the initial setup was retained (79.9%, range across protocols: 73-100%). Image quality parameters then commonly showed statistically significant differences between chosen and discarded acquisitions. In regularized regression across all protocols, 'structured noise maximum' was the strongest predictor for the technologists' choice, followed by 'N/2 ghosting average'. Combinations of the automatically derived parameters provided an area under the ROC curve between 0.51 and 0.74 for the prediction of the technologists' choice. It is concluded that automated image quality assessment can, despite considerable performance differences between protocols and anatomical regions, contribute substantially to identifying the subjective preference in a series of MRI acquisitions and thus provide effective decision support to readers., (© 2023. The Author(s).)

Background: Identifying high-risk individuals is crucial for preventing cardiovascular diseases (CVDs). Currently, risk assessment is mostly performed by physicians. Mobile health apps could help decouple the determination of risk from medical resources by allowing unrestricted self-assessment. The respective test results need to be interpretable for laypersons., Objective: Together with a patient organization, we aimed to design a digital risk calculator that allows people to individually assess and optimize their CVD risk. The risk calculator was integrated into the mobile health app HerzFit, which provides the respective background information., Methods: To cover a broad spectrum of individuals for both primary and secondary prevention, we integrated the respective scores (Framingham 10-year CVD, Systematic Coronary Risk Evaluation 2, Systematic Coronary Risk Evaluation 2 in Older Persons, and Secondary Manifestations Of Arterial Disease) into a single risk calculator that was recalibrated for the German population. In primary prevention, an individual's heart age is estimated, which gives the user an easy-to-understand metric for assessing cardiac health. For secondary prevention, the risk of recurrence was assessed. In addition, a comparison of expected to mean and optimal risk levels was determined. The risk calculator is available free of charge. Data safety is ensured by processing the data locally on the users' smartphones., Results: Offering a risk calculator to the general population requires the use of multiple instruments, as each provides only a limited spectrum in terms of age and risk distribution. The integration of 4 internationally recommended scores allows risk calculation in individuals aged 30 to 90 years with and without CVD. Such integration requires recalibration and harmonization to provide consistent and plausible estimates. In the first 14 months after the launch, the HerzFit calculator was downloaded more than 96,000 times, indicating great demand. Public information campaigns proved effective in publicizing the risk calculator and contributed significantly to download numbers., Conclusions: The HerzFit calculator provides CVD risk assessment for the general population. The public demonstrated great demand for such a risk calculator as it was downloaded up to 10,000 times per month, depending on campaigns creating awareness for the instrument., (©Fabian Starnecker, Lara Marie Reimer, Leon Nissen, Marko Jovanović, Maximilian Kapsecker, Susanne Rospleszcz, Moritz von Scheidt, Johannes Krefting, Nils Krüger, Benedikt Perl, Jens Wiehler, Ruoyu Sun, Stephan Jonas, Heribert Schunkert. Originally published in JMIR Cardio (https://cardio.jmir.org), 08.12.2023.)

Background: Non-alcoholic fatty liver disease (NAFLD) represents a major disease burden in the population. While the bidirectional association between NAFLD and diabetes is established, little is known about the association of hepatic iron content and glycaemia. Moreover, analyses of sex-specific effects and of dynamic changes in glycaemia are scarce., Methods: We investigated 7-year sex-specific trajectories of glycaemia and related traits (HbA1c, fasting glucose, fasting insulin, HOMA-IR, 2-h glucose and cross-sectional 2-h insulin) in a sample from a population-based cohort (N = 365; 41.1% female). Hepatic iron and fat content were assessed by 3T-Magnetic Resonance Imaging (MRI). Two-step multi-level models adjusted for glucose-lowering medication and confounders were applied., Results: In women and men, markers of glucose metabolism correlated with hepatic iron and fat content. Deterioration of glycaemia was associated with increased hepatic iron content in men (normoglycaemia to prediabetes: beta = 2.21 s -1 , 95% CI [0.47, 3.95]). Additionally, deterioration of glycaemia (e.g. prediabetes to diabetes: 1.27 log(%), [0.84, 1.70]) and trajectories of glucose, insulin and HOMA-IR were significantly associated with hepatic fat content in men. Similarly, deterioration of glycaemia as well as trajectories of glucose, insulin and HOMA-IR was significantly associated with increased hepatic fat content in women (e.g. trajectory of fasting insulin: 0.63 log(%), [0.36, 0.90])., Conclusions: Unfavourable 7-year trajectories of markers of glucose metabolism are associated with increased hepatic fat content, particularly in women, whereas the association with hepatic iron content was less clear. Monitoring changes of glycaemia in the sub-diabetic range might enable early identification of hepatic iron overload and steatosis., (© 2023 The Authors. Liver International published by John Wiley & Sons Ltd.)

Obesity is characterized by the accumulation of adipose tissue in different body compartments. Whether adipose tissue directly affects kidney function is still unknown. We aimed to investigate the role of the adipose tissue and circulating creatinine, cystatin C and kidney function in subjects free of cardio-renal diseases. In the KORA-MRI population-based study, 377 subjects (mean age 56.2 ± 9.2 years; 41.6% female) underwent whole-body 3T-MRI examination. Adipose tissue defined as visceral adipose tissue (VAT) and subcutaneous adipose tissue (SAT) were quantified from T1-DIXON sequence using a semi-automatic algorithm. Serum creatinine and cystatin C were measured using standard laboratory and estimated glomerular filtration rate (e-GFR) was performed based on creatinine (e-GFR crea ), cystatin C (e-GFR cys ) and creatinine-cystatin C (e-GFR cc ). Linear regression analysis, adjusted for risk factors, was used to investigate the relationship between adipose tissue and circulating creatinine, cystatin C, and kidney function. In multivariate analyses VAT was inversely associated with eGFR cys (ß = - 4.88, p =  < 0.001), and positively associated with serum cystatin C (ß = 0.05, p =  < 0.001), respectively. No association was found between other adipose parameters such as total adipose tissue (TAT) and subcutaneous adipose tissue (SAT) and serum creatinine, urine microalbumin and eGFR crea . Stratified analyses according to BMI revealed confirmatory results for category of BMI > 30. VAT is positively associated with serum cystatin C and inversely with eGFR based on cystatin C, suggesting a direct involvement of visceral adipose tissue in increased metabolism of cystatin C and consequently decreased kidney function., (© 2023. The Author(s).)

Background: Intervertebral disc degeneration (IVDD) may be linked to dysregulations of skeletal muscle glucose metabolism and fatty alterations of muscle composition (Myosteatosis). Our aim was to evaluate the different associations of magnetic resonance imaging (MRI)-based paravertebral myosteatosis with lumbar disc degeneration in individuals with impaired glucose metabolism and normoglycaemic controls., Methods: In total, 304 individuals (mean age: 56.3 ± 9.1 years, 53.6% male sex, mean body mass index [BMI]: 27.6 ± 4.7 kg/m 2 ) from a population-based cohort study who underwent 3-Tesla whole-body chemical-shift-encoded (six echo times) and T2-weighted single-shot-fast-spin-echo MRI were included. Lumbar disc degeneration was assessed at motion segments L1 to L5, categorized according to the Pfirrmann score and defined as Pfirrmann grade > 2 and/or disc bulging/herniation on at least one segment. Fat content of the autochthonous back muscles and the quadratus lumborum muscle was quantified as proton density fat fraction (PDFF muscle ). Logistic regression models adjusted for age, sex, BMI and regular physical activity were calculated to evaluate the association between PDFF muscle and outcome IVDD., Results: The overall prevalence of IVDD was 79.6%. There was no significant difference in the prevalence or severity distribution of IVDD between participants with or without impaired glucose metabolism (77.7% vs. 80.7%, P = 0.63 and P = 0.71, respectively). PDFF muscle was significantly and positively associated with an increased risk for the presence of IVDD in participants with impaired glycaemia when adjusted for age, sex and BMI (PDFF autochthonous back muscles : odds ratio [OR] 2.16, 95% confidence interval [CI] [1.09, 4.3], P = 0.03; PDFF quadratus lumborum : OR 2.01, 95% CI [1.04, 3.85], P = 0.04). After further adjustment for regular physical activity, the results attenuated, albeit approaching statistical significance (PDFF autochthonous back muscles : OR 1.97, 95% CI [0.97, 3.99], P = 0.06; PDFF quadratus lumborum : OR 1.86, 95% CI [0.92, 3.76], P = 0.09). No significant associations were shown in healthy controls (PDFF autochthonous back muscles : OR 0.62, 95% CI [0.34, 1.14], P = 0.13; PDFF quadratus lumborum : OR 1.06, 95% CI [0.6, 1.89], P = 0.83)., Conclusions: Paravertebral myosteatosis is positively associated with intervertebral disc disease in individuals with impaired glucose metabolism, independent of age, sex and BMI. Regular physical activity may confound these associations. Longitudinal studies will help to better understand the pathophysiological role of skeletal muscle in those with concomitant disturbed glucose haemostasis and intervertebral disc disease, as well as possible underlying causal relationships., (© 2023 The Authors. Journal of Cachexia, Sarcopenia and Muscle published by John Wiley & Sons Ltd on behalf of Society on Sarcopenia, Cachexia and Wasting Disorders.)

This research addresses the assessment of adipose tissue (AT) and spatial distribution of visceral (VAT) and subcutaneous fat (SAT) in the trunk from standardized magnetic resonance imaging at 3 T, thereby demonstrating the feasibility of deep learning (DL)-based image segmentation in a large population-based cohort in Germany (five sites). Volume and distribution of AT play an essential role in the pathogenesis of insulin resistance, a risk factor of developing metabolic/cardiovascular diseases. Cross-validated training of the DL-segmentation model led to a mean Dice similarity coefficient of >0.94, corresponding to a mean absolute volume deviation of about 22 ml. SAT is significantly increased in women compared to men, whereas VAT is increased in males. Spatial distribution shows age- and body mass index-related displacements. DL-based image segmentation provides robust and fast quantification of AT (≈15 s per dataset versus 3 to 4 hours for manual processing) and assessment of its spatial distribution from magnetic resonance images in large cohort studies.

Objective: To assess the association of lumbar bone marrow adipose tissue fat fraction (BMAT-FF) and paraspinal muscle proton density fat fraction (PDFF) and their interplay with intervertebral disc degeneration (IVDD)., Methods: In this retrospective cross-sectional study based on a prospective population-based cohort, BMAT-FF and PDFF of asymptomatic individuals were calculated based on 3T-MRI dual-echo and multi-echo Dixon VIBE sequences. IVDD was assessed at motion segments L1 to L5 and dichotomized based on Pfirrmann grade ≥ 4 and/or presence of other severe degenerative changes or spinal abnormalities at least at one segment. Pearson's correlation coefficients were calculated for BMAT-FF and PDFF. Univariable and multivariable logistic regression models for IVDD were calculated., Results: Among 335 participants (mean age: 56.2 ± 9.0 years, 43.3% female), the average BMI was 27.7 ± 4.5 kg/m 2 and the prevalence of IVDD was high (69.9%). BMAT-FF and PDFF were significantly correlated (r = 0.31-0.34; p < 0.001). The risk for IVDD increased with higher PDFF (OR = 1.45; CI 1.03, 2.04) and BMAT-FF (OR = 1.56; CI 1.16, 2.11). Pairwise combinations of PDFF and BMAT-FF quartiles revealed a lower risk for IVDD in individuals in the lowest BMAT-FF and PDFF quartile (OR = 0.21; CI 0.1, 0.48). Individuals in the highest BMAT-FF and PDFF quartile showed an increased risk for IVDD (OR = 5.12; CI 1.17, 22.34) CONCLUSION: Lumbar BMAT-FF and paraspinal muscle PDFF are correlated and represent both independent and additive risk factors for IVDD. Quantitative MRI measurements of paraspinal myosteatosis and vertebral bone marrow fatty infiltration may serve as imaging biomarkers to assess the individual risk for IVDD., Key Points: • Fat composition of the lumbar vertebral bone marrow is positively correlated with paraspinal skeletal muscle fat. • Higher fat-fractions of lumbar vertebral bone marrow and paraspinal muscle are both independent as well as additive risk factors for intervertebral disc degeneration. • Quantitative magnetic resonance imaging measurements of bone marrow and paraspinal muscle may serve as imaging biomarkers for intervertebral disc degeneration., (© 2022. The Author(s).)

Purpose: Hip osteoarthritis (HOA) is known to have a multifactorial pathogenesis. Recent studies suggest that spinopelvic alignment may represent an important additional pathogenic abnormality resulting in HOA. This study aims to assess the correlation between spinopelvic parameters (pelvic incidence (PI), pelvic tilt (PT), sacral slope (SS) and lumbar lordosis (LL)) obtained in the supine position on MRI and HOA, lateral center edge (LCE) angle, and patient reported back pain., Methods: Asymptomatic participants from the whole-body MRI cohort (FF4) from the cross-sectional case-control "Cooperative Health Research in the Region of Augsburg" study (KORA) were included. Whole-body MRI was performed in a standardized fashion in each case, on which hip osteoarthritis (HOA), anatomical spinopelvic parameters and lateral center edge angle were measured. Presence of back pain was assessed using a standardized questionnaire. Correlations were estimated by logistic regression models providing odds ratio., Results: Among 340 subjects (mean age 56.3 ± 9.3 years; 56.5% male), HOA was present in 89.1% (male: 87.0%, female: 91.7%, p = 0.17). The LCE angle was 30.0° ± 5.5 (men: 29.8° ± 5.9; women: 30.1° ± 5.1; p = 0.696). Mean PI was 54.0° ± 11.3°, PT was 13.7° ± 5.9°, SS was 40.3° ± 8.8° (significantly smaller in women p<0.05) and LL was 36.4° ± 9.6° (significantly greater in women p<0.05). None of the spinopelvic parameters correlated significantly with hip osteoarthritis or LCE angle. HOA was not correlated with back pain., Conclusion: Spinopelvic parameters as measured in the supine position on MRI, do not correlate with hip osteoarthritis or lateral center edge angle., Competing Interests: Declaration of Competing Interest The authors state that there is no conflict of interests., (Copyright © 2022. Published by Elsevier Ltd.)

Several studies have implied a role of magnesium in the development of cardiovascular disease (CVD). Thus, magnesium might serve as a potential risk marker for early CVD. Therefore, we investigated the association of serum magnesium and dietary magnesium intake with markers of subclinical CVD in a population-based study. We used cross-sectional data from the sub-study of the Cooperative Health Research in the Region of Augsburg (KORA-FF4). Markers of subclinical CVD, namely, left and right ventricular structure and function and carotid plaque and carotid wall thickness, were derived by magnetic resonance imaging (MRI). Multivariable-adjusted regression models were applied to assess the relationship between serum and dietary magnesium and MRI-derived subclinical CVD markers. Among 396 included participants (mean age: 56.3 ± 9.2 years; 57.8% male), 181 (45.7%) had low serum magnesium levels (<2.07 mg/dL). Among 311 subjects with complete dietary data (mean age: 56.3 ± 9.1 years; 56.3% male), 154 (49.5%) had low dietary magnesium intake (≤155.2 mg/1000 kcal/day). Serum and dietary magnesium were not correlated (p-value = 0.5). Serum magnesium was significantly associated with presence of carotid plaque (OR 1.62, p-value 0.033). Dietary magnesium was associated with higher left ventricular end-systolic and end-diastolic volume (0.04 mL/m2, 0.06 mL/m2; p-value 0.011, 0.013, respectively), and also with a decrease in left ventricular remodeling index and mean diastolic wall thickness (−0.001 g/mL/m2, −0.002 mm/m2; p-value 0.004, 0.029, respectively). In summary, there was no consistent association of serum and dietary magnesium with imaging markers of subclinical CVD.

Background & Aims: Non-alcoholic fatty liver disease (NAFLD) and alcohol-related liver disease frequently coexist. While several blood-based indices exist for the detection of NAFLD, few studies have examined how alcohol use possibly impacts their diagnostic performance. We analysed the effects of alcohol use on the performance of indices for detecting fatty liver disease (FLD)., Methods: We included participants from the Cardiovascular Risk in Young Finns Study (Finnish sample) and KORA study (German sample) who underwent abdominal ultrasound or magnetic resonance imaging, respectively, for detection of FLD and had serum analyses available for calculation of Fatty Liver Index (FLI), Hepatic Steatosis Index (HSI), Lipid Accumulation Product (LAP), and Dallas Steatosis Index (DSI). Alcohol use was estimated by questionnaires as mean daily consumption and binge drinking (Finnish sample only). Predictive performance for FLD was assessed according to alcohol consumption., Results: The study included 1426 (Finnish sample) and 385 (German sample) individuals, of which 234 (16%) and 168 (44%) had FLD by imaging. When alcohol consumption was <50 g/day, all indices discriminated FLD with area under the receiver operating characteristics (AUROC) of 0.82-0.88. AUROCs were 0.61-0.66 among heavy drinkers (>50 g/day). AUROCs decreased to 0.74-0.80 in the highest binge-drinking category (>2 times/week). Alcohol use correlated with FLI and LAP ( r -range 0.09-0.16, p -range <.001-.02) in both samples and with DSI ( r  = 0.13, p  < .001) in the Finnish sample., Conclusions: Indices perform well and comparably for detection of FLD with alcohol consumption <50 g/day and with different binge-drinking behaviour.

Progressive dilation of the infrarenal aortic diameter is a consequence of the ageing process and is considered the main determinant of abdominal aortic aneurysm (AAA). We aimed to investigate the genetic and clinical determinants of abdominal aortic diameter (AAD). We conducted a meta-analysis of genome-wide association studies in 10 cohorts (n = 13 542) imputed to the 1000 Genome Project reference panel including 12 815 subjects in the discovery phase and 727 subjects [Partners Biobank cohort 1 (PBIO)] as replication. Maximum anterior-posterior diameter of the infrarenal aorta was used as AAD. We also included exome array data (n = 14 480) from seven epidemiologic studies. Single-variant and gene-based associations were done using SeqMeta package. A Mendelian randomization analysis was applied to investigate the causal effect of a number of clinical risk factors on AAD. In genome-wide association study (GWAS) on AAD, rs74448815 in the intronic region of LDLRAD4 reached genome-wide significance (beta = -0.02, SE = 0.004, P-value = 2.10 × 10-8). The association replicated in the PBIO1 cohort (P-value = 8.19 × 10-4). In exome-array single-variant analysis (P-value threshold = 9 × 10-7), the lowest P-value was found for rs239259 located in SLC22A20 (beta = 0.007, P-value = 1.2 × 10-5). In the gene-based analysis (P-value threshold = 1.85 × 10-6), PCSK5 showed an association with AAD (P-value = 8.03 × 10-7). Furthermore, in Mendelian randomization analyses, we found evidence for genetic association of pulse pressure (beta = -0.003, P-value = 0.02), triglycerides (beta = -0.16, P-value = 0.008) and height (beta = 0.03, P-value < 0.0001), known risk factors for AAA, consistent with a causal association with AAD. Our findings point to new biology as well as highlighting gene regions in mechanisms that have previously been implicated in the genetics of other vascular diseases., (© The Author(s) 2022. Published by Oxford University Press.)