Search

Abstract Background Protein biomarkers may contribute to the identification of vulnerable subgroups for premature mortality. This study aimed to investigate the association of plasma proteins with all-cause and cause-specific mortality among individuals with and without baseline type 2 diabetes (T2D) and evaluate their impact on the prediction of all-cause mortality in two prospective Cooperative Health Research in the Region of Augsburg (KORA) studies. Methods The discovery cohort comprised 1545 participants (median follow-up 15.6 years; 244 with T2D: 116 total, 62 cardiovascular, 31 cancer-related and 23 other-cause deaths; 1301 without T2D: 321 total, 114 cardiovascular, 120 cancer-related and 87 other-cause deaths). The validation cohort comprised 1031 participants (median follow-up 6.9 years; 203 with T2D: 76 total, 45 cardiovascular, 19 cancer-related and 12 other-cause deaths; 828 without T2D: 169 total, 74 cardiovascular, 39 cancer-related and 56 other-cause deaths). We used Cox regression to examine associations of 233 plasma proteins with all-cause and cause-specific mortality and Lasso regression to construct prediction models for all-cause mortality stratifying by baseline T2D. C-index, category-free net reclassification index (cfNRI), and integrated discrimination improvement (IDI) were conducted to evaluate the predictive performance of built prediction models. Results Thirty-five and 62 proteins, with 29 overlapping, were positively associated with all-cause mortality in the group with and without T2D, respectively. Out of these, in the group with T2D, 35, eight, and 26 were positively associated with cardiovascular, cancer-related, and other-cause mortality, while in the group without T2D, 55, 41, and 47 were positively associated with respective cause-specific outcomes in the pooled analysis of both cohorts. Regulation of insulin-like growth factor (IGF) transport and uptake by IGF-binding proteins emerged as a unique pathway enriched for all-cause and cardiovascular mortality in individuals with T2D. The combined model containing the selected proteins (five and 12 proteins, with four overlapping, in the group with and without T2D, respectively) and clinical risk factors improved the prediction of all-cause mortality by C-index, cfNRI, and IDI. Conclusions This study uncovered shared and unique mortality-related proteins in persons with and without T2D and emphasized the role of proteins in improving the prediction of mortality in different T2D subgroups.

Abstract 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

Abstract 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.

Abstract Background Metformin and sodium-glucose-cotransporter-2 inhibitors (SGLT2i) are cornerstone therapies for managing hyperglycemia in diabetes. However, their detailed impacts on metabolic processes, particularly within the citric acid (TCA) cycle and its anaplerotic pathways, remain unclear. This study investigates the tissue-specific metabolic effects of metformin, both as a monotherapy and in combination with SGLT2i, on the TCA cycle and associated anaplerotic reactions in both mice and humans. Methods Metformin-specific metabolic changes were initially identified by comparing metformin-treated diabetic mice (MET) with vehicle-treated db/db mice (VG). These findings were then assessed in two human cohorts (KORA and QBB) and a longitudinal KORA study of metformin-naïve patients with Type 2 Diabetes (T2D). We also compared MET with db/db mice on combination therapy (SGLT2i + MET). Metabolic profiling analyzed 716 metabolites from plasma, liver, and kidney tissues post-treatment, using linear regression and Bonferroni correction for statistical analysis, complemented by pathway analyses to explore the pathophysiological implications. Results Metformin monotherapy significantly upregulated TCA cycle intermediates such as malate, fumarate, and α-ketoglutarate (α-KG) in plasma, and anaplerotic substrates including hepatic glutamate and renal 2-hydroxyglutarate (2-HG) in diabetic mice. Downregulated hepatic taurine was also observed. The addition of SGLT2i, however, reversed these effects, such as downregulating circulating malate and α-KG, and hepatic glutamate and renal 2-HG, but upregulated hepatic taurine. In human T2D patients on metformin therapy, significant systemic alterations in metabolites were observed, including increased malate but decreased citrulline. The bidirectional modulation of TCA cycle intermediates in mice influenced key anaplerotic pathways linked to glutaminolysis, tumorigenesis, immune regulation, and antioxidative responses. Conclusion This study elucidates the specific metabolic consequences of metformin and SGLT2i on the TCA cycle, reflecting potential impacts on the immune system. Metformin shows promise for its anti-inflammatory properties, while the addition of SGLT2i may provide liver protection in conditions like metabolic dysfunction-associated steatotic liver disease (MASLD). These observations underscore the importance of personalized treatment strategies.

Abstract Background and aims Atherosclerosis is the main cause of stroke and coronary heart disease (CHD), both leading mortality causes worldwide. Proteomics, as a high-throughput method, could provide helpful insights into the pathological mechanisms underlying atherosclerosis. In this study, we characterized the associations of plasma protein levels with CHD and with carotid intima-media thickness (CIMT), as a surrogate measure of atherosclerosis. Methods The discovery phase included 1000 participants from the KORA F4 study, whose plasma protein levels were quantified using the aptamer-based SOMAscan proteomics platform. We evaluated the associations of plasma protein levels with CHD using logistic regression, and with CIMT using linear regression. For both outcomes we applied two models: an age-sex adjusted model, and a model additionally adjusted for body mass index, smoking status, physical activity, diabetes status, hypertension status, low density lipoprotein, high density lipoprotein, and triglyceride levels (fully-adjusted model). The replication phase included a matched case-control sample from the independent KORA F3 study, using ELISA-based measurements of galectin-4. Pathway analysis was performed with nominally associated proteins (p-value

Abstract Background Coronary heart disease (CHD) is a major global health concern, especially among individuals with type 2 diabetes (T2D). Given the crucial role of proteins in various biological processes, this study aimed to elucidate the aetiological role and predictive performance of protein biomarkers on incident CHD in individuals with and without T2D. Methods The discovery cohort included 1492 participants from the Cooperative Health Research in the Region of Augsburg (KORA) S4 study with 147 incident CHD cases (45 vs. 102 cases in the group with T2D and without T2D, respectively) during 15.6 years of follow-up. The validation cohort included 888 participants from the KORA-Age1 study with 70 incident CHD cases (19 vs. 51 cases in the group with T2D and without T2D, respectively) during 6.9 years of follow-up. We measured 233 plasma proteins related to cardiovascular disease and inflammation using proximity extension assay technology. Associations of proteins with incident CHD were assessed using Cox regression and Mendelian randomization (MR) analysis. Predictive models were developed using priority-Lasso and were evaluated on top of Framingham risk score variables using the C-index, category-free net reclassification index (cfNRI), and relative integrated discrimination improvement (IDI). Results We identified two proteins associated with incident CHD in individuals with and 29 in those without baseline T2D, respectively. Six of these proteins are novel candidates for incident CHD. MR suggested a potential causal role for hepatocyte growth factor in CHD development. The developed four-protein-enriched model for individuals with baseline T2D (ΔC-index: 0.017; cfNRI: 0.253; IDI: 0.051) and the 12-protein-enriched model for individuals without baseline T2D (ΔC-index: 0.054; cfNRI: 0.462; IDI: 0.024) consistently improved CHD prediction in the discovery cohort, while in the validation cohort, significant improvements were only observed for selected performance measures (with T2D: cfNRI: 0.633; without T2D: ΔC-index: 0.038; cfNRI: 0.465). Conclusions This study identified novel protein biomarkers associated with incident CHD in individuals with and without T2D and reaffirmed previously reported protein candidates. These findings enhance our understanding of CHD pathophysiology and provide potential targets for prevention and treatment.

Introduction Circulating omentin levels have been positively associated with insulin sensitivity. Although a role for adiponectin in this relationship has been suggested, underlying mechanisms remain elusive. In order to reveal the relationship between omentin and systemic metabolism, this study aimed to investigate associations of serum concentrations of omentin and metabolites.Research design and methods This study is based on 1124 participants aged 61–82 years from the population-based KORA (Cooperative Health Research in the Region of Augsburg) F4 Study, for whom both serum omentin levels and metabolite concentration profiles were available. Associations were assessed with five multivariable regression models, which were stepwise adjusted for multiple potential confounders, including age, sex, body mass index, waist-to-hip ratio, lifestyle markers (physical activity, smoking behavior and alcohol consumption), serum adiponectin levels, high-density lipoprotein cholesterol, use of lipid-lowering or anti-inflammatory medication, history of myocardial infarction and stroke, homeostasis model assessment 2 of insulin resistance, diabetes status, and use of oral glucose-lowering medication and insulin.Results Omentin levels significantly associated with multiple metabolites including amino acids, acylcarnitines, and lipids (eg, sphingomyelins and phosphatidylcholines (PCs)). Positive associations for several PCs, such as diacyl (PC aa C32:1) and alkyl-alkyl (PC ae C32:2), were significant in models 1–4, whereas those with hydroxytetradecenoylcarnitine (C14:1-OH) were significant in all five models. Omentin concentrations were negatively associated with several metabolite ratios, such as the valine-to-PC ae C32:2 and the serine-to-PC ae C32:2 ratios in most models.Conclusions Our results suggest that omentin may influence insulin sensitivity and diabetes risk by changing systemic lipid metabolism, but further mechanistic studies investigating effects of omentin on metabolism of insulin-sensitive tissues are needed.

Abstract Background Metabolic Syndrome (MetS) is characterized by risk factors such as abdominal obesity, hypertriglyceridemia, low high-density lipoprotein cholesterol (HDL-C), hypertension, and hyperglycemia, which contribute to the development of cardiovascular disease and type 2 diabetes. Here, we aim to identify candidate metabolite biomarkers of MetS and its associated risk factors to better understand the complex interplay of underlying signaling pathways. Methods We quantified serum samples of the KORA F4 study participants (N = 2815) and analyzed 121 metabolites. Multiple regression models adjusted for clinical and lifestyle covariates were used to identify metabolites that were Bonferroni significantly associated with MetS. These findings were replicated in the SHIP-TREND-0 study (N = 988) and further analyzed for the association of replicated metabolites with the five components of MetS. Database-driven networks of the identified metabolites and their interacting enzymes were also constructed. Results We identified and replicated 56 MetS-specific metabolites: 13 were positively associated (e.g., Val, Leu/Ile, Phe, and Tyr), and 43 were negatively associated (e.g., Gly, Ser, and 40 lipids). Moreover, the majority (89%) and minority (23%) of MetS-specific metabolites were associated with low HDL-C and hypertension, respectively. One lipid, lysoPC a C18:2, was negatively associated with MetS and all of its five components, indicating that individuals with MetS and each of the risk factors had lower concentrations of lysoPC a C18:2 compared to corresponding controls. Our metabolic networks elucidated these observations by revealing impaired catabolism of branched-chain and aromatic amino acids, as well as accelerated Gly catabolism. Conclusion Our identified candidate metabolite biomarkers are associated with the pathophysiology of MetS and its risk factors. They could facilitate the development of therapeutic strategies to prevent type 2 diabetes and cardiovascular disease. For instance, elevated levels of lysoPC a C18:2 may protect MetS and its five risk components. More in-depth studies are necessary to determine the mechanism of key metabolites in the MetS pathophysiology.

Abstract 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/m2) 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 (PDFFmuscle). Logistic regression models adjusted for age, sex, BMI and regular physical activity were calculated to evaluate the association between PDFFmuscle 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). PDFFmuscle 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 (PDFFautochthonous back muscles: odds ratio [OR] 2.16, 95% confidence interval [CI] [1.09, 4.3], P = 0.03; PDFFquadratus 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 (PDFFautochthonous back muscles: OR 1.97, 95% CI [0.97, 3.99], P = 0.06; PDFFquadratus lumborum: OR 1.86, 95% CI [0.92, 3.76], P = 0.09). No significant associations were shown in healthy controls (PDFFautochthonous back muscles: OR 0.62, 95% CI [0.34, 1.14], P = 0.13; PDFFquadratus 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.

BackgroundMultiple 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.MethodsWe 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.ResultsWe 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).ConclusionsOn 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.

Accurate risk prediction for myocardial infarction (MI) is crucial for preventive strategies, given its significant impact on global mortality and morbidity. Here, we propose a novel deep-learning approach to enhance the prediction of incident MI cases by incorporating metabolomics alongside clinical risk factors. We utilized data from the KORA cohort, including the baseline S4 and follow-up F4 studies, consisting of 1454 participants without prior history of MI. The dataset comprised 19 clinical variables and 363 metabolites. Due to the imbalanced nature of the dataset (78 observed MI cases and 1376 non-MI individuals), we employed a generative adversarial network (GAN) model to generate new incident cases, augmenting the dataset and improving feature representation. To predict MI, we further utilized multi-layer perceptron (MLP) models in conjunction with the synthetic minority oversampling technique (SMOTE) and edited nearest neighbor (ENN) methods to address overfitting and underfitting issues, particularly when dealing with imbalanced datasets. To enhance prediction accuracy, we propose a novel GAN for feature-enhanced (GFE) loss function. The GFE loss function resulted in an approximate 2% improvement in prediction accuracy, yielding a final accuracy of 70%. Furthermore, we evaluated the contribution of each clinical variable and metabolite to the predictive model and identified the 10 most significant variables, including glucose tolerance, sex, and physical activity. This is the first study to construct a deep-learning approach for producing 7-year MI predictions using the newly proposed loss function. Our findings demonstrate the promising potential of our technique in identifying novel biomarkers for MI prediction.

Objective: We investigated whether COVID-19 vaccination had an impact on diabetes risk. Methods: We used data of 6,198 patients (mean age 64.3 years) from the nationwide Disease Analyzer database, a representative panel of physicians’ practices in Germany. Patients received their first COVID-19 vaccination between 1 April 2021 and 31 March 2022, and all were newly diagnosed with diabetes within 183 days before or after this vaccination. Incident rates of diabetes after vaccination were compared to incident rates before vaccination. Results: The incidence rate of diabetes was lower after vaccination than before vaccination (incidence rate ratio = 0.79, 95% confidence interval: 0.75–0.83). The number of incident cases of diabetes was not greater in 2021 than in 2019. Conclusion: Our study did not confirm an increased risk of diabetes after COVID-19 vaccination. Further studies are needed to show whether the vaccination may be associated with a reduced diabetes risk.

Chronic inflammation, marked by C-reactive protein, has been associated with changes in methylation, but the causal relationship is unclear. Here, the authors perform a Epigenome-wide association meta-analysis for C-reactive protein levels and find that these methylation changes are likely the consequence of inflammation and could contribute to disease.

Abstract Background Despite strong evidence of benefit, uptake of newer glucose-lowering medications that reduce cardiovascular risk has been low. We sought to examine global trends and predictors of use of SGLT2i and GLP-1 RA in patients with type 2 diabetes. Methods DISCOVER is a global, prospective, observational study of patients with diabetes enrolled from 2014–16 at initiation of second-line glucose-lowering therapy and followed for 3 years. We used hierarchical logistic regression to examine factors associated with use of either an SGLT2i or GLP-1 RA at last follow-up and to assess country-level variability. Results Among 14,576 patients from 37 countries, 1579 (10.8%) were started on an SGLT2i (1275; 8.7%) or GLP-1 RA (318; 2.2%) at enrollment, increasing to 16.1% at end of follow-up, with large variability across countries (range 0–62.7%). Use was highest in patients treated by cardiologists (26.1%) versus primary care physicians (10.4%), endocrinologists (16.9%), and other specialists (22.0%; p

Abstract Background Skeletal muscle mass is subjected to constant changes and is considered a good predictor for outcome in various diseases. Bioelectrical‐impedance analysis (BIA) and magnetic resonance imaging (MRI) are approved methodologies for its assessment. However, muscle mass estimations by BIA may be influenced by excess intramuscular lipids and adipose tissue in obesity. The objective of this study was to evaluate the feasibility of quantitative assessment of skeletal muscle mass by MRI as compared with BIA. Methods Subjects from a population‐based cohort underwent BIA (50 kHz, 0.8 mA) and whole‐body MRI including chemical‐shift encoded MRI (six echo times). Abdominal muscle mass by MRI was quantified as total and fat‐free cross‐sectional area by a standardized manual segmentation‐algorithm and normalized to subjects' body height2 (abdominal muscle mass indices: AMMIMRI). Results Among 335 included subjects (56.3 ± 9.1 years, 56.1% male), 95 (28.4%) were obese (BMI ≥ 30 kg/m2). MRI‐based and BIA‐based measures of muscle mass were strongly correlated, particularly in non‐obese subjects [r

IntroductionThe role of endogenous androgens in kidney function and disease has not been extensively explored in men and women.Research design and methodsWe analyzed data from the observational KORA F4 study and its follow-up examination KORA FF4 (median follow-up time 6.5 years) including 1293 men and 650 peri- and postmenopausal women, not using exogenous sex hormones. We examined the associations between endogenous androgens (testosterone [T], dihydrotestosterone [DHT], free T [fT], free DHT [fDHT], and T/DHT), with estimated glomerular filtration rate (eGFR) at baseline and follow-up, prevalent, and incident chronic kidney disease (CKD) adjusting for common CKD risk factors.ResultsAt baseline, 73 men (5.7%) and 54 women (8.4%) had prevalent CKD. Cross-sectionally, no significant associations between androgens and kidney function were observed among men. In women, elevated T (β=-1.305, [95% CI -2.290; -0.320]) and fT (β=-1.423, [95% CI -2.449; -0.397]) were associated with lower eGFR. Prospectively, 81 men (8.8%) and 60 women (15.2%) developed incident CKD. In women, a reverse J-shaped associations was observed between DHT and incident CKD (Pnon-linear=0.029), while higher fDHT was associated with lower incident CKD risk (odds ratio per 1 standard deviation=0.613, [95% CI 0.369; 0.971]. Among men, T/DHT (β=-0.819, [95% CI -1.413; -0.226]) and SHBG (Pnon-linear=0.011) were associated with eGFR at follow-up but not with incident CKD. Some associations appeared to be modified by type 2 diabetes (T2D).ConclusionSuggestive associations are observed of androgens and SHBG with kidney impairment among men and women. However, larger well-phenotyped prospective studies are required to further elucidate the potential of androgens, SHBG, and T2D as modifiable risk factors for kidney function and CKD.

Abstract Background Biomarkers may contribute to improved cardiovascular risk estimation. Glycated hemoglobin A1c (HbA1c) is used to monitor the quality of diabetes treatment. Its strength of association with cardiovascular outcomes in the general population remains uncertain. This study aims to assess the association of HbA1c with cardiovascular outcomes in the general population. Methods Data from six prospective population-based cohort studies across Europe comprising 36,180 participants were analyzed. HbA1c was evaluated in conjunction with classical cardiovascular risk factors (CVRFs) for association with cardiovascular mortality, cardiovascular disease (CVD) incidence, and overall mortality in subjects without diabetes (N = 32,496) and with diabetes (N = 3684). Results Kaplan–Meier curves showed higher event rates with increasing HbA1c levels (log-rank-test: p

Abstract Background Despite the worldwide burden of diverticular disease, the connections between diverticular disease and dietary habits remain poorly understood, particularly in an asymptomatic representative sample. We investigated the association between asymptomatic diverticular disease as assessed by magnetic resonance imaging (MRI) and dietary habits in a Western study cohort. Methods Participants from a cross-sectional sample of a population-based cohort study underwent whole-body 3T-MRI including an isotropic VIBE-Dixon sequence. The presence and extent of diverticular disease was assessed in blinded fashion. Habitual dietary intake was recorded using a blended approach, applying 24-h food lists and a food-frequency questionnaire. Traditional cardiometabolic risk factors were obtained by interviews and medical examination. Univariate and multivariate associations were calculated. Results A total of 308 subjects were included in this analysis (56% male, 56.4 ± 9.1 years). 39.9% had any form of diverticular disease and 15.3% had advanced asymptomatic diverticular disease. After adjustment for age, sex and total energy intake a higher intake of fiber and vegetables was associated with a lower odds for asymptomatic diverticular disease (fiber: OR 0.68 95% CI [0.48, 0.95]; vegetables: OR 0.72 95% CI [0.53, 0.97]) and an increased intake of meat was associated with an approximately two-fold higher odds for advanced asymptomatic diverticular disease (OR 1.84 95% CI [1.13, 2.99]). However, after additional adjustment for body-mass-index (BMI), alcohol consumption, smoking behavior and physical activity only a high fiber and vegetables intake remained significantly associated with lower odds of asymptomatic diverticular disease. Conclusion Our results indicate that a high-fiber diet and increased intake of vegetables is associated with lower odds of having asymptomatic diverticular disease, independent of age, sex, total energy intake, BMI and other life-style factors.

Abstract The aim of our study was to investigate the association of hepatic steatosis derived from quantitative ultrasound and magnetic resonance imaging (MRI) with prediabetes in a large population-based study conducted in Northeast Germany. Hepatic steatosis was assessed through transabdominal ultrasound and quantitative MRI. For analysis we included 1622 subjects with MRI who participated in an oral glucose tolerance test and reported no known type 2 diabetes mellitus (T2DM). We classified participants as proposed by the American Diabetes Association: isolated impaired fasting glucose (i-IFG), isolated impaired glucose tolerance (i-IGT), combined IFG and IGT (IFG + IGT), and undiagnosed T2DM. Regression models were adjusted for age, sex body mass index and alcohol consumption. We observed positive associations of hepatic steatosis with glycated hemoglobin, fasting glucose and insulin, 2-h glucose and insulin, as well as homeostasis model assessment-insulin resistance index. Similarly, individuals having hepatic steatosis as defined by MRI had a higher relative risk ratio (RR) to be in the prediabetes groups i-IFG (RR = 1.6; 95% confidence interval (CI) 1.2; 2.2), i-IGT (RR = 3.3, 95% CI 2.0; 5.6) and IFG + IGT (RR = 2.5, 95% CI 1.6; 3.9) or to have undiagnosed T2DM (RR = 4.8, 95% CI 2.6; 9.0). All associations were attenuated when defining hepatic steatosis by ultrasound. Hepatic steatosis is associated with prediabetes and undiagnosed T2DM in the general population. Quantitative liver MRI revealed stronger associations with prediabetes and undiagnosed T2DM compared to ultrasound, which indicates the higher sensitivity and specificity of MRI to determine hepatic steatosis.

Abstract Background The metabolic syndrome (MetS), defined by the simultaneous clustering of cardio-metabolic risk factors, is a significant worldwide public health burden with an estimated 25% prevalence worldwide. The pathogenesis of MetS is not entirely clear and the use of molecular level data could help uncover common pathogenic pathways behind the observed clustering. Methods Using a highly multiplexed aptamer-based affinity proteomics platform, we examined associations between plasma proteins and prevalent and incident MetS in the KORA cohort (n = 998) and replicated our results for prevalent MetS in the HUNT3 study (n = 923). We applied logistic regression models adjusted for age, sex, smoking status, and physical activity. We used the bootstrap ranking algorithm of least absolute shrinkage and selection operator (LASSO) to select a predictive model from the incident MetS associated proteins and used area under the curve (AUC) to assess its performance. Finally, we investigated the causal effect of the replicated proteins on MetS using two-sample Mendelian randomization. Results Prevalent MetS was associated with 116 proteins, of which 53 replicated in HUNT. These included previously reported proteins like leptin, and new proteins like NTR domain-containing protein 2 and endoplasmic reticulum protein 29. Incident MetS was associated with 14 proteins in KORA, of which 13 overlap the prevalent MetS associated proteins with soluble advanced glycosylation end product-specific receptor (sRAGE) being unique to incident MetS. The LASSO selected an eight-protein predictive model with an (AUC = 0.75; 95% CI = 0.71–0.79) in KORA. Mendelian randomization suggested causal effects of three proteins on MetS, namely apolipoprotein E2 (APOE2) (Wald-Ratio = − 0.12, Wald-p = 3.63e−13), apolipoprotein B (APOB) (Wald-Ratio = − 0.09, Wald-p = 2.54e−04) and proto-oncogene tyrosine-protein kinase receptor (RET) (Wald-Ratio = 0.10, Wald-p = 5.40e−04). Conclusions Our findings offer new insights into the plasma proteome underlying MetS and identify new protein associations. We reveal possible casual effects of APOE2, APOB and RET on MetS. Our results highlight protein candidates that could potentially serve as targets for prevention and therapy.

Abstract Background The gut microbiome impacts human health through various mechanisms and is involved in the development of a range of non-communicable diseases. Diet is a well-known factor influencing microbe-host interaction in health and disease. However, very few findings are based on large-scale analysis using population-based studies. Our aim was to investigate the cross-sectional relationship between habitual dietary intake and gut microbiota structure in the Cooperative Health Research in the Region of Augsburg (KORA) FF4 study. Results Fecal microbiota was analyzed using 16S rRNA gene amplicon sequencing. Latent Dirichlet allocation (LDA) was applied to samples from 1992 participants to identify 20 microbial subgroups within the study population. Each participant’s gut microbiota was subsequently described by a unique composition of these 20 subgroups. Associations between habitual dietary intake, assessed via repeated 24-h food lists and a Food Frequency Questionnaire, and the 20 subgroups, as well as between prevalence of metabolic diseases/risk factors and the subgroups, were assessed with multivariate-adjusted Dirichlet regression models. After adjustment for multiple testing, eight of 20 microbial subgroups were significantly associated with habitual diet, while nine of 20 microbial subgroups were associated with the prevalence of one or more metabolic diseases/risk factors. Subgroups 5 (Faecalibacterium, Lachnospiracea incertae sedis, Gemmiger, Roseburia) and 14 (Coprococcus, Bacteroides, Faecalibacterium, Ruminococcus) were particularly strongly associated with diet. For example, participants with a high probability for subgroup 5 were characterized by a higher Alternate Healthy Eating Index and Mediterranean Diet Score and a higher intake of food items such as fruits, vegetables, legumes, and whole grains, while participants with prevalent type 2 diabetes mellitus were characterized by a lower probability for subgroup 5. Conclusions The associations between habitual diet, metabolic diseases, and microbial subgroups identified in this analysis not only expand upon current knowledge of diet-microbiota-disease relationships, but also indicate the possibility of certain microbial groups to be modulated by dietary intervention, with the potential of impacting human health. Additionally, LDA appears to be a powerful tool for interpreting latent structures of the human gut microbiota. However, the subgroups and associations observed in this analysis need to be replicated in further studies. Video abstract

Abstract Background The aim of this study was to determine the prevalence of cardiovascular disease in persons with type 2 diabetes mellitus (T2D) in Germany. Methods A claims database with an age- and sex-stratified sample of nearly 4 million individuals insured within the German statutory health system was used. All patients aged ≥18 years with T2D documented between 1 January 2015 and 31 December 2015 and complete retrospective documentation of ≥5 years (continuous enrollment in the German statutory health system) before 2015 were selected based on a validated algorithm. Cardiovascular disease (CVD) events were identified based on ICD-10 and OPS codes according to a previous clinical study (EMPA-REG OUTCOME trial). Results The prevalence of T2D in Germany in 2015 was 9.9% (n = 324,708). Using a narrow definition of CVD, the 6-year observation period prevalence of CVD was estimated as 46.7% [95% CI: 46.52%;46.86%]. Applying a wider CVD definition, the proportion of T2D patients who showed a history of CVD was 57.1% [95% CI: 56.9%;57.24%]. The prevalence of CVD in patients with T2D ranged from 36.3 to 57.1%, depending on the observation period and definition of CVD. Conclusions The results underline the need for a population-based registration of cardiovascular complications in T2D.

Blood circulating proteins reflect biological processes, thus providing insight into complex traits. Here the authors study the relationship between 1000 plasma proteins and body mass index (BMI), highlighting widespread proteome changes and causal relationships between BMI and specific proteins.

Abstract Background The association of longitudinal trajectories of cardiovascular risk factors with cardiovascular magnetic resonance (CMR)-measures of cardiac structure and function in the community is not well known. Therefore we aimed to relate risk factor levels from different examination cycles to CMR-measures of the left ventricle (LV) and right ventricle in a population-based cohort. Methods We assessed conventional cardiovascular disease risk factors in 349 participants (143 women; aged 25–59 years) at three examination cycles (Exam 1 [baseline], at Exam 2 [7-years follow-up] and at Exam 3 [14-years follow-up]) of the KORA S4 cohort and related single-point measurements of individual risk factors and longitudinal trajectories of these risk factors to various CMR-measures obtained at Exam 3. Results High levels of diastolic blood pressure, waist circumference, and LDL-cholesterol at the individual exams were associated with worse cardiac function and structure. Trajectory clusters representing higher levels of the individual risk factors were associated with worse cardiac function and structure compared to low risk trajectory clusters of individual risk factors. Multivariable (combining different risk factors) trajectory clusters were associated with different cardiac parameters in a graded fashion (e.g. decrease of LV stroke volume for middle risk cluster β = − 4.91 ml/m2, 95% CI − 7.89; − 1.94, p

Abstract To identify the most important parameters associated with cerebral white matter hyperintensities (WMH), in consideration of potential collinearity, we used a data-driven machine-learning approach. We analysed two independent cohorts (KORA and SHIP). WMH volumes were derived from cMRI-images (FLAIR). 90 (KORA) and 34 (SHIP) potential determinants of WMH including measures of diabetes, blood-pressure, medication-intake, sociodemographics, life-style factors, somatic/depressive-symptoms and sleep were collected. Elastic net regression was used to identify relevant predictor covariates associated with WMH volume. The ten most frequently selected variables in KORA were subsequently examined for robustness in SHIP. The final KORA sample consisted of 370 participants (58% male; age 55.7 ± 9.1 years), the SHIP sample comprised 854 participants (38% male; age 53.9 ± 9.3 years). The most often selected and highly replicable parameters associated with WMH volume were in descending order age, hypertension, components of the social environment (i.e. widowed, living alone) and prediabetes. A systematic machine-learning based analysis of two independent, population-based cohorts showed, that besides age and hypertension, prediabetes and components of the social environment might play important roles in the development of WMH. Our results enable personal risk assessment for the development of WMH and inform prevention strategies tailored to the individual patient.

DISCOVER is a 3-year observational study program of 15,983 people with type 2 diabetes initiating second-line glucose-lowering therapy in 38 countries. We investigated the association between socioeconomic status and both the availability of a baseline glycated hemoglobin (HbA1c) measurement and poor glycemic control (HbA1c level ≥ 9.0%) in participants enrolled in DISCOVER. Factors associated with a lack of baseline HbA1c measurement or an HbA1c level ≥ 9.0% were assessed using three-level hierarchical logistic models. Overall, 19.1% of participants did not have a baseline HbA1c measurement recorded. Lower-middle country income (vs. high) and primary/no formal education (vs. university education) were independently associated with a reduced likelihood of having a baseline HbA1c measurement (odds ratio [95% confidence interval]: 0.11 [0.03–0.49] and 0.81 [0.66–0.98], respectively. Of the participants with an available HbA1c measurement, 26.9% had an HbA1c level ≥ 9.0%; 68.7% of these individuals were from lower- or upper-middle-income countries. Factors associated with an increased likelihood of poor glycemic control included low country income, treatment at a site with public and/or governmental funding (vs. private funding) and having public or no health insurance (vs. private). A substantial proportion of DISCOVER participants did not have an HbA1c measurement; more than one-quarter of these participants had poorly controlled type 2 diabetes. Both individual- and country-level socioeconomic factors are associated with the quality of care regarding glycemic control. Awareness of these factors could help improve the management of patients with type 2 diabetes.

Abstract Background High N-terminal pro-brain-type natriuretic peptide levels have been associated with a lower risk of type 2 diabetes mellitus (T2D). However, less is known about other cardiac stress biomarkers in this context. Here we evaluated the association of mid-regional pro-atrial natriuretic peptide (MR-proANP), C-terminal pro-arginine vasopressin (copeptin), C-terminal pro-endothelin-1 (CT-proET-1) and mid-regional pro-adrenomedullin (MR-proADM) with incident T2D and changes in glucose metabolism. Methods We performed a prospective cohort study using data from the population-based KORA F4/FF4 study. 1773 participants (52.3% women) with MR-proANP measurements and 960 (52.7% women) with copeptin, CT-proET-1 and MR-proADM measurements were included. We examined associations of circulating plasma levels of MR-proANP, copeptin, CT-proET-1 and MR-proADM with incident T2D, the combined endpoint of incident prediabetes/T2D and with fasting and 2 h-glucose, fasting insulin, HOMA-IR, HOMA-B and HbA1c at follow-up. Logistic and linear regression models adjusted for age, sex, waist circumference, height, hypertension, total/HDL cholesterol ratio, triglycerides, smoking, physical activity and parental history of diabetes were used to compute effect estimates. Results During a median follow-up time of 6.4 years (25th and 75th percentiles: 6.0 and 6.6, respectively), 119 out of the 1773 participants and 72 out of the 960 participants developed T2D. MR-proANP was inversely associated with incident T2D (odds ratio [95% confidence interval]: 0.75 [0.58; 0.96] per 1-SD increase of log MR-proANP). Copeptin was positively associated with incident prediabetes/T2D (1.29 [1.02; 1.63] per 1-SD increase of log copeptin). Elevated levels of CT-proET-1 were associated with increased HOMA-B at follow-up, while elevated MR-proADM levels were associated with increased fasting insulin, HOMA-IR and HOMA-B at follow-up. These associations were independent of previously described diabetes risk factors. Conclusions High plasma concentrations of MR-proANP contributed to a lower risk of incident T2D, whereas high plasma concentrations of copeptin were associated with an increased risk of incident prediabetes/T2D. Furthermore, high plasma concentrations of CT-proET-1 and MR-proADM were associated with increased insulin resistance. Our study provides evidence that biomarkers implicated in cardiac stress are associated with incident T2D and changes in glucose metabolism.

Background and aimDespite its vasodilatory effect, adrenomedullin and its surrogate mid-regional pro-adrenomedullin (MR-proADM) have been found to be positively associated with all-cause and cardiovascular mortality. However, the underlying mechanisms thereof remain unclear and the associations were mostly shown in geriatric cohorts or in patients with chronic diseases. Therefore, we aimed to investigate the possible involvement of abdominal obesity, selected adipokines, and biomarkers of subclinical inflammation in the association of MR-proADM with mortality in a population based study cohort.MethodsProspective analysis of the KORA F4 study; median follow-up 9.1 (8.8-9.4) years. Complete data on MR-proADM and mortality was available for 1551 participants, aged 56.9±12.9 years (mean±SD). Correlation and regression analyses of MR-proADM with overall (BMI) and abdominal obesity (waist circumference), selected adipokines and biomarkers of subclinical inflammation. Cox proportional hazard models on the association of MR-proADM with all-cause and cardiovascular mortality with adjustment for cardiovascular risk factors and selected biomarkers in study subgroups (n = 603-1551).ResultsMR-proADM associated with all-cause (HR (95%CI): 2.37 (1.72-3.26) and 2.31 (1.67-3.20)) and cardiovascular mortality (4.28 (2.19-8.39) and 4.44 (2.25-8.76)) after adjustment for traditional cardiovascular risk factors including BMI or waist circumference, respectively. MR-proADM was further associated with four out of seven examined adipokines (leptin, retinol-binding protein-4, chemerin, and adiponectin) and with five out of eleven examined biomarkers of subclinical inflammation (high-sensitivity C-reactive protein, interleukin-6, myeloperoxidase, interleukin-22, and interleukin-1 receptor antagonist) after multivariable adjustment and correction for multiple testing. However, only IL-6 substantially attenuated the association of MR-proADM with all-cause mortality.ConclusionsWe found an association of MR-proADM with (abdominal) obesity, selected adipokines, and biomarkers of subclinical inflammation. However, the association of MR-proADM with mortality was independent of these parameters. Future studies should investigate the role of IL-6 and further characteristics of subclinical inflammation in the association between MR-proADM and all-cause mortality.

Obesity plays an important role in the development of insulin resistance and diabetes, but the molecular mechanism that links obesity and diabetes is still not completely understood. Here, we used 146 targeted metabolomic profiles from the German KORA FF4 cohort consisting of 1715 participants and associated them with obesity and type 2 diabetes. In the basic model, 83 and 51 metabolites were significantly associated with body mass index (BMI) and T2D, respectively. Those metabolites are branched-chain amino acids, acylcarnitines, lysophospholipids, or phosphatidylcholines. In the full model, 42 and 3 metabolites were significantly associated with BMI and T2D, respectively, and replicate findings in the previous studies. Sobel mediation testing suggests that the effect of BMI on T2D might be mediated via lipids such as sphingomyelin (SM) C16:1, SM C18:1 and diacylphosphatidylcholine (PC aa) C38:3. Moreover, mendelian randomization suggests a causal relationship that BMI causes the change of SM C16:1 and PC aa C38:3, and the change of SM C16:1, SM C18:1, and PC aa C38:3 contribute to T2D incident. Biological pathway analysis in combination with genetics and mice experiments indicate that downregulation of sphingolipid or upregulation of phosphatidylcholine metabolism is a causal factor in early-stage T2D pathophysiology. Our findings indicate that metabolites like SM C16:1, SM C18:1, and PC aa C38:3 mediate the effect of BMI on T2D and elucidate their role in obesity related T2D pathologies.

Introduction There are conflicting results on the association between type 2 diabetes and chronic low back pain (CLBP). Therefore, the goal was to investigate the relationship between type 2 diabetes and CLBP in individuals followed in general practices in Germany.Research design and methods Adults diagnosed for the first time with type 2 diabetes in 809 general practices in Germany between 2005 and 2018 (index date) were included. Adults without type 2 diabetes were matched (1:1) to those with type 2 diabetes by sex, age, index year, and the annual number of medical consultations (index date: a randomly selected visit date). The association between type 2 diabetes and the 10-year incidence of CLBP was analyzed in conditional Cox regression models adjusted for a wide range of comorbidities, including hypertension, lipid metabolism disorders, and obesity.Results There were 139 002 individuals included in this study (women: 58.0%; mean (SD) age 62.5 (13.4) years). There was a positive association between type 2 diabetes and the incidence of CLBP in the overall sample (HR=1.23, 95% CI: 1.13 to 1.35). Sex-stratified analyses showed a higher risk of CLBP in women (HR=1.68, 95% CI: 1.43 to 1.90) and a lower risk in men with than in their counterparts without type 2 diabetes (HR=0.83, 95% CI: 0.71 to 0.97).Conclusions Newly diagnosed type 2 diabetes was associated with an increased risk of CLBP. There were important sex differences in the type 2 diabetes-CLBP relationship, and more research is warranted to investigate the underlying factors explaining these differences.

The aim of metabotyping is to categorize individuals into metabolically similar groups. Earlier studies that explored metabotyping used numerous parameters, which made it less transferable to apply. Therefore, this study aimed to identify metabotypes based on a set of standard laboratory parameters that are regularly determined in clinical practice. K-means cluster analysis was used to group 3001 adults from the KORA F4 cohort into three clusters. We identified the clustering parameters through variable importance methods, without including any specific disease endpoint. Several unique combinations of selected parameters were used to create different metabotype models. Metabotype models were then described and evaluated, based on various metabolic parameters and on the incidence of cardiometabolic diseases. As a result, two optimal models were identified: a model composed of five parameters, which were fasting glucose, HDLc, non-HDLc, uric acid, and BMI (the metabolic disease model) for clustering; and a model that included four parameters, which were fasting glucose, HDLc, non-HDLc, and triglycerides (the cardiovascular disease model). These identified metabotypes are based on a few common parameters that are measured in everyday clinical practice. These metabotypes are cost-effective, and can be easily applied on a large scale in order to identify specific risk groups that can benefit most from measures to prevent cardiometabolic diseases, such as dietary recommendations and lifestyle interventions.

Abstract Background Mortality attributable to heart failure remains high. The prevalence of heart failure in patients with diabetes mellitus ranges from 19 to 26%. It is estimated that up to 21.1 million adults in the United States have diagnosed diabetes mellitus and around 80.8 million have impaired fasting glucose. We investigated the associations of fasting glucose (FG) and fasting insulin (FI), the homeostasis model assessment-insulin resistance index (HOMA-IR) and 2-h postload glucose (2HG) and insulin (2HI) with parameters of left ventricular geometry and function and arterial stiffness determined by magnetic resonance imaging in individuals without diagnosed type 2 diabetes. Methods Cross-sectional analyses of 1001 individuals (453 women, 45.3%), aged 21 to 80 years, from two independent population-based studies, the Study of Health in Pomerania (SHIP-TREND-0) and KORA FF4 Study. FG, FI, HOMA-IR, 2HG and 2HI, as well as glucose tolerance categories, were analyzed for associations with heart and arterial parameters using multivariable-adjusted linear regression models. Results In total, 390 individuals (39%) had prediabetes (isolated impaired fasting glucose, isolated glucose tolerance or both), and 49 (4.9%) were found to have unknown type 2 diabetes. In the multivariable-adjusted analysis, positive linear associations of FG, FI, HOMA-IR, 2HG and 2HI with arterial stiffness index and left ventricular wall-thickness and concentricity and inverse linear associations with left ventricular end-diastolic volume were observed. A 1 mmol/l higher FG was associated with a 1.18 ml/m2.7 (1.80 to 0.57; p

Objective: Metabolic syndrome and obesity are risk factors for chronic kidney disease. However, early kidney alterations may escape diagnosis in these conditions due to glomerular hyperfiltration. Uromodulin, a glycoprotein exclusively synthesized in tubular cells of the thick ascending limb of Henle´s loop, is a novel t issue-specific biomarker for kidney function. In contrast to the commonly used markers creatinine and cystatin C, serum uromodulin does not primarily depend on glomerular filtrat ion. We hypothesized that serum uromodulin is a marker for metabolic syndrome and related components. Design: The analyses included 1088 participants of the population-based KORA F4 study aged 62–81 years. Metabolic syndrome was present in 554 participants. After a mean follow-up time of 6.5 years, 621 participants were reevaluated, of which 92 had developed incident metabolic syndrome. Methods: The association of serum uromodulin with metabolic syndrome and its components were assessed using multivariable logistic regression models. Results: Serum uromodulin was inversely associated with metabolic syndrome after adjustment for sex, age, estimated glomerular filtration rate, p hysical activity, smoking, alcohol consumption and high-sensitivity C-reactive protein (OR 0.65; 95% CI 0.56–0.76 per standard deviation uromodulin; P < 0.001). Serum uromodulin was inversely associated with all single components of metabolic syndrome. However, serum uromodulin was not associated with new-onset metabolic syndrome after the follow-up period of 6.5 ± 0.3 years (OR 1.18; 95% CI 0.86–1.60). Conclusions: Serum uromodulin is independently associated with prevalent, but not with incident metabolic syndrome. Low serum uromodulin may indicate a decreased renal reserve in the metabolic syndrome.

Background: Associations between several persistent organic pollutants (POPs) and type 2 diabetes have been found in humans, but the relationship has rarely been investigated in the general population. The current nested case-control study examined internal exposure to polychlorinated biphenyls (PCB) and pesticides and the incidence of type 2 diabetes among participants of two population-based German cohort studies. Methods: We retrospectively selected 132 incident cases of type 2 diabetes and 264 age- and sex-matched controls from the CARdiovascular Living and Aging in Halle (CARLA) study (2002–2006, East Germany) and the Cooperative Health Research in the Region of Augsburg (KORA) study (1999–2001, South Germany) based on diabetes status at follow-up examinations in 2007–2010 and 2006–08, respectively (60% male, mean age 63 and 54 years). We assessed the association between baseline POP concentrations and incident diabetes by conditional logistic regression adjusted for cohort, BMI, cholesterol, alcohol, smoking, physical activity, and parental diabetes. Additionally, we examined effect modification by sex, obesity, parental diabetes and cohort. Results: In both cohorts, diabetes cases showed a higher BMI, a higher frequency of parental diabetes, and higher levels of POPs. We observed an increased chance for incident diabetes for PCB-138 and PCB-153 with an odds ratio (OR) of 1.50 (95%CI: 1.07–2.11) and 1.53 (1.15–2.04) per interquartile range increase in the respective POP. In addition, explorative results suggested higher OR for women and non-obese participants. Conclusions: Our results add to the evidence on diabetogenic effects of POPs in the general population, and warrant both policies to prevent human exposure to POPs and additional research on the adverse effects of more complex chemical mixtures. Keywords: Type 2 diabetes, Incidence, Environmental contaminates, Case-control study, Epidemiology

Associations between diet and DNA methylation may vary among subjects with different metabolic states, which can be captured by clustering populations in metabolically homogenous subgroups, called metabotypes. Our aim was to examine the relationship between habitual consumption of various food groups and DNA methylation as well as to test for effect modification by metabotype. A cross-sectional analysis of participants (median age 58 years) of the population-based prospective KORA FF4 study, habitual dietary intake was modeled based on repeated 24-h diet recalls and a food frequency questionnaire. DNA methylation was measured using the Infinium MethylationEPIC BeadChip providing data on >850,000 sites in this epigenome-wide association study (EWAS). Three metabotype clusters were identified using four standard clinical parameters and BMI. Regression models were used to associate diet and DNA methylation, and to test for effect modification. Few significant signals were identified in the basic analysis while many significant signals were observed in models including food group-metabotype interaction terms. Most findings refer to interactions of food intake with metabotype 3, which is the metabotype with the most unfavorable metabolic profile. This research highlights the importance of the metabolic characteristics of subjects when identifying associations between diet and white blood cell DNA methylation in EWAS.

Abstract Background Previous studies found regional differences in the prevalence and incidence of type 2 diabetes between Northeast and South of Germany. The aim of this study was to investigate if regional variations are also present for macrovascular disease in people with type 2 diabetes and in the general population. A further aim was to investigate if traditional risk factors of macrovascular complications can explain these regional variations. Methods Data of persons aged 30–79 from two regional population-based studies, SHIP-TREND (Northeast Germany, 2008–2012, n = 2539) and KORA-F4 (South Germany, 2006–2008, n = 2932), were analysed. Macrovascular disease was defined by self-reported previous myocardial infarction, stroke or coronary angiography. Multivariable logistic regression was performed to estimate odds ratios (OR) and 95% confidence intervals (CI) for prevalence of macrovascular disease in persons with type 2 diabetes and in the general population. Results The prevalence of macrovascular disease in persons with type 2 diabetes and in the general population was considerably higher in the Northeast (SHIP-TREND: 32.8 and 12.0%) than in the South of Germany (KORA-F4: 24.9 and 8.8%), respectively. The odds of macrovascular disease in persons with type 2 diabetes was 1.66 (95% CI: 1.11–2.49) in the Northeast in comparison to the South after adjustment for sex, age, body mass index, hypertension, hyperlipidemia and smoking. In the general population, SHIP-TREND participants also had a significantly increased odds of macrovascular disease compared to KORA-F4 participants (OR = 1.63, 95% CI: 1.33–2.00). After excluding coronary angiography (myocardial infarction or stroke only), the ORs for region decreased in all models, but the difference between SHIP-TREND and KORA-F4 participants was still significant in the age- and sex-adjusted model for the general population (OR = 1.34, 95% CI: 1.01–1.78). Conclusions This study provides an indication for regional differences in macrovascular disease, which is not explained by traditional risk factors. Further examinations of other risk factors, such as regional deprivation or geographical variations in medical care services are needed.

Background: DNA methylation at the GFI1-locus has been repeatedly associated with exposure to smoking from the foetal period onwards. We explored whether DNA methylation may be a mechanism that links exposure to maternal prenatal smoking with offspring's adult cardio-metabolic health. Methods: We meta-analysed the association between DNA methylation at GFI1-locus with maternal prenatal smoking, adult own smoking, and cardio-metabolic phenotypes in 22 population-based studies from Europe, Australia, and USA (n = 18,212). DNA methylation at the GFI1-locus was measured in whole-blood. Multivariable regression models were fitted to examine its association with exposure to prenatal and own adult smoking. DNA methylation levels were analysed in relation to body mass index (BMI), waist circumference (WC), fasting glucose (FG), high-density lipoprotein cholesterol (HDL—C), triglycerides (TG), diastolic, and systolic blood pressure (BP). Findings: Lower DNA methylation at three out of eight GFI1-CpGs was associated with exposure to maternal prenatal smoking, whereas, all eight CpGs were associated with adult own smoking. Lower DNA methylation at cg14179389, the strongest maternal prenatal smoking locus, was associated with increased WC and BP when adjusted for sex, age, and adult smoking with Bonferroni-corrected P

Objectives: An inverse relationship between education and cardiovascular risk has been described, however, the combined association of education, income, and neighborhood socioeconomic status with macrovascular disease is less clear. The aim of this study was to evaluate the association of educational level, equivalent household income and area deprivation with macrovascular disease in Germany.Methods: Cross-sectional data from two representative German population-based studies, SHIP-TREND (n = 3,731) and KORA-F4 (n = 2,870), were analyzed. Multivariable logistic regression models were applied to estimate odds ratios and 95% confidence intervals for the association between socioeconomic determinants and macrovascular disease (defined as self-reported myocardial infarction or stroke).Results: The study showed a higher odds of prevalent macrovascular disease in men with low and middle educational level compared to men with high education. Area deprivation and equivalent income were not related to myocardial infarction or stroke in any of the models.Conclusion: Educational level, but not income or area deprivation, is significantly related to the macrovascular disease in men. Effective prevention of macrovascular disease should therefore start with investing in individual education.

Background: A growing number of epidemiological studies show associations between environmental factors and impaired cardiometabolic health. However, evidence is scarce concerning these risk factors and their impact on metabolic syndrome (MetS). This analysis aims to investigate associations between long-term exposure to air pollution, road traffic noise, residential greenness, and MetS. Methods: We used data of the first (F4, 2006–2008) and second (FF4, 2013–2014) follow-up of the population-based KORA S4 survey in the region of Augsburg, Germany, to investigate associations between exposures and MetS prevalence at F4 (N = 2883) and MetS incidence at FF4 (N = 1192; average follow-up: 6.5 years). Residential long-term exposures to air pollution – including particulate matter (PM) with a diameter

Numerous predictive models for the risk of type 2 diabetes mellitus (T2DM) exist, but a minority of them has implemented nutrition data so far, even though the significant effect of nutrition on the pathogenesis, prevention and management of T2DM has been established. Thus, in the present study, we aimed to build a predictive model for the risk of T2DM that incorporates nutrition data and calculates its predictive performance. We analysed cross-sectional data from 1591 individuals from the population-based Cooperative Health Research in the Region of Augsburg (KORA) FF4 study (2013–14) and used a bootstrap enhanced elastic net penalised multivariate regression method in order to build our predictive model and select among 193 food intake variables. After selecting the significant predictor variables, we built a logistic regression model with these variables as predictors and T2DM status as the outcome. The values of area under the receiver operating characteristic (ROC) curve, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and accuracy of our predictive model were calculated. Eleven out of the 193 food intake variables were selected for inclusion in our model, which yielded a value of area under the ROC curve of 0⋅79 and a maximum PPV, NPV and accuracy of 0⋅37, 0⋅98 and 0⋅91, respectively. The present results suggest that nutrition data should be implemented in predictive models to predict the risk of T2DM, since they improve their performance and they are easy to assess.

Summary: Background: Impaired insulin sensitivity could be an intermediate step that links exposure to air pollution to the development of type 2 diabetes. However, longitudinal associations of air pollution with insulin sensitivity remain unclear. Our study investigated the associations of long-term air pollution exposure with the degree and rate of change of insulin sensitivity. Methods: In this longitudinal study, we analysed data from the Cooperative Health Research in the Region of Augsburg (KORA) cohort from Augsburg, Germany, which recruited participants aged 25–74 years in the survey between 1999 and 2001 (KORA S4), with two follow-up examinations in 2006–08 (KORA F4) and 2013–14 (KORA FF4). Serum concentrations of fasting insulin and glucose, and homoeostasis model assessment of insulin resistance (HOMA-IR, a surrogate measure of insulin sensitivity) and β-cell function (HOMA-B, a surrogate marker for fasting insulin secretion) were assessed at up to three visits between 1999 and 2014. Annual average air pollutant concentrations at the residence were estimated by land-use regression models. We examined the associations of air pollution with repeatedly assessed biomarker levels using mixed-effects models, and we assessed the associations with the annual rate of change in biomarkers using quantile regression models. Findings: Among 9620 observations from 4261 participants in the KORA cohort, we included 6008 (62·5%) observations from 3297 (77·4%) participants in our analyses. Per IQR increment in annual average air pollutant concentrations, HOMA-IR significantly increased by 2·5% (95% CI 0·3 to 4·7) for coarse particulate matter, by 3·1% (0·9 to 5·3) for PM2·5, by 3·6% (1·0 to 6·3) for PM2·5absorbance, and by 3·2% (0·6 to 5·8) for nitrogen dioxide, and borderline significantly increased by 2·2% (–0·1 to 4·5) for ozone, whereas it did not significantly increase for the whole range of ultrafine particles. Similar positive associations in slightly smaller magnitude were observed for HOMA-B and fasting insulin levels. In addition, air pollutant concentrations were positively associated with the annual rate of change in HOMA-IR, HOMA-B, and fasting insulin. Neither the level nor the rate of change of fasting glucose were associated with air pollution exposure. Interpretation: Our study indicates that long-term air pollution exposure could contribute to the development of insulin resistance, which is one of the key factors in the pathogenesis of type 2 diabetes. Funding: German Federal Ministry of Education and Research.

Objectives To evaluate the extent to which patients with type 2 diabetes discontinue metformin therapy when initiating second-line treatment and factors associated with metformin discontinuation, using baseline data from the DISCOVER study programme.Design DISCOVER is a 3-year, prospective, observational study programme including data from 38 countries across 6 continents from 2014 to 2019.Setting Primary and secondary healthcare centres, hospitals and specialist diabetes centres in both urban and rural locations.Participants A total of 15 992 patients with type 2 diabetes initiating second-line glucose-lowering therapy.Primary and secondary outcome measures The proportion of patients who discontinued metformin as a second-line therapy and the factors associated with this treatment change.Results Of the 14 668 patients (from 37 countries) with valid treatment data, 11 837 (80.7%) received metformin as first-line glucose-lowering therapy; 8488 (71.7%) received metformin monotherapy and 3349 (28.3%) received metformin as part of a combination therapy. Overall, treatment with metformin was discontinued in 15.1% (1782) of patients who received first-line metformin (14.1% (1194) and 17.6% (588) in those who received metformin as monotherapy and as part of a combination, respectively); this proportion varied across regions from 6.9% (54) in Africa to 20.6% (628) in South-East Asia. On metformin discontinuation, 73.6% (1311) of patients received a non-insulin monotherapy at second line. Factors associated with an increased odds of metformin discontinuation were older age (≥75 years) and having a history of chronic kidney disease. The probability of metformin monotherapy discontinuation was lower in patients from Africa than in those from Europe.Conclusions A substantial number of patients discontinued taking metformin when beginning second-line therapy. Most of these patients subsequently received a non-insulin monotherapy at second line, in contradiction to international guidelines and potentially leaving them at an increased risk of hyperglycaemia and associated adverse outcomes.Trial registration numbers NCT02322762 and NCT02226822.

PURPOSE:We hypothesize that MRI-based renal compartment volumes, particularly renal sinus fat as locally and potentially independently acting perivascular fat tissue, increase with glucose intolerance. We therefore analyze the distribution of renal volumes in individuals with normal glucose levels and prediabetic and diabetic individuals and investigate potential associations with other typical cardiometabolic biomarkers. MATERIAL AND METHODS:The sample comprised N = 366 participants who were either normoglycemic (N = 230), had prediabetes (N = 87) or diabetes (N = 49), as determined by Oral Glucose Tolerance Test. Other covariates were obtained by standardized measurements and interviews. Whole-body MR measurements were performed on a 3 Tesla scanner. For assessment of the kidneys, a coronal T1w dual-echo Dixon and a coronal T2w single shot fast spin echo sequence were employed. Stepwise semi-automated segmentation of the kidneys on the Dixon-sequences was based on thresholding and geometric assumptions generating volumes for the kidneys and sinus fat. Inter- and intra-reader variability were determined on a subset of 40 subjects. Associations between glycemic status and renal volumes were evaluated by linear regression models, adjusted for other potential confounding variables. Furthermore, the association of renal volumes with visceral adipose tissue was assessed by linear regression models and Pearson's correlation coefficient. RESULTS:Renal volume, renal sinus volume and renal sinus fat increased gradually from normoglycemic controls to individuals with prediabetes to individuals with diabetes (renal volume: 280.3±64.7 ml vs 303.7±67.4 ml vs 320.6±77.7ml, respectively, p < 0.001). After adjustment for age and sex, prediabetes and diabetes were significantly associated to increased renal volume, sinus volume (e.g. βPrediabetes = 10.1, 95% CI: [6.5, 13.7]; p

OBJECTIVE:To investigate the impact of metabolic syndrome and its components on osteoarthritis of the hip joints compared to a healthy cohort in the KORA MRI-study. METHODS:Randomly selected men and women from the general population were classified as having metabolic syndrome, defined as presence of central obesity plus two of the following four components: elevated blood pressure (BP), elevated fasting glucose, elevated triglycerides (TG) and low HDL-cholesterol (HDL-c), or as controls without metabolic syndrome. Therefore, each subject underwent detailed assessment of waist circumference as well as fasting glucose, systolic and diastolic BP, TG, and HDL-c concentrations as well as a full-body MR scan. MR measurements were performed on a 3 Tesla scanner (Magnetom Skyra, Siemens) including a dual-echo Dixon and a T2 SS-FSE sequence for anatomical structures. In order to quantify osteoarthritis of the hip, assessment was performed by two independent, experienced radiologists for joint gap narrowing, osteophytic lipping and subchondral changes (e.g. sclerosis, pseudocysts). Associations between metabolic syndrome components and hip degeneration were estimated by logistic regression models providing odds ratios. RESULTS:Among 354 included participants (mean age: 56.1 ± 9.2 years; 55.4% male), 119 (34%) had metabolic syndrome, while 235 (66%) were part of the control group. Except for elevated blood glucose (p = 0.02), none of the metabolic syndromes' component was independently associated with osteoarthritis. Multivariable adjusted ORs for osteoarthritis of the right hip were 1.00 (95% CI 0.98;1.03), 1.00 (95% CI 0.99;1.00), 1.01 (95% CI 0.99;1.03), 1.00 (95% CI 0.97;1.04) and 1.01 (95% CI 0.96;1.06), and for the left hip 1.00 (95% CI 0.98;1.03), 1.00 (95% CI 1.00;1.01), 1.01 (95% CI 0.99;1.03), 0.99 (95% CI 0.96;1.02) and 1.04 (95% CI 0.99;1.09) for waist circumference, triglyceride, HDL-c and systolic and diastolic BP, respectively. Blood glucose was a borderline non-dependent factor for osteoarthritis of the right hip (OR: 1.02 (95% CI 1.0;1.04); p = 0.05). Furthermore, the compound metabolic syndrome was not significantly associated (OR left hip: 1.53 (95% CI 0.8;2.92), p = 0.20; OR right hip: 1.33 (95% CI 0.72;2.45), p = 0.37) with osteoarthritis of the hip joint. Age as well as gender (left hip) were the only parameters in univariate and multivariate analysis to be significantly associated with osteoarthritis of the hip joint. CONCLUSION:The compound metabolic syndrome showed no association with osteoarthritis of the hip joint. Age was the only parameter to be dependently and independently associated to osteoarthritis of both hip joints, while elevated blood glucose was independently associated with degeneration of the right hip joint.

ObjectivesUromodulin has been associated with arterial hypertension in genome-wide association studies, but data from clinical and preclinical studies are inconsistent. We here analyzed the association of serum uromodulin (sUmod) with arterial hypertension and vasoactive hormones in a population-based study.MethodsIn 1108 participants of the KORA F4 study aged 62-81 years, sUmod was measured and the association of sUmod with arterial hypertension was assessed using logistic regression models. The associations of sUmod with renin and aldosterone and with the vasoconstrictive prohormone C-terminal pro-endothelin-1 (CT-proET-1) were analyzed in 1079 participants and in 618 participants, respectively, using linear regression models.ResultsAfter multivariable adjustment including sex, age, eGFR, BMI, fasting glucose, current smoking, previous stroke and myocardial infarction, sUmod was inversely associated with arterial hypertension (OR 0.78; 95% CI 0.68-0.91; p = 0.001). SUmod was not significantly associated with renin and aldosterone after adjustment for sex, age and eGFR. However, sUmod was inversely associated with CT-proET-1 (β -0.19 ± 0.04; p < 0.001) after adjustment for sex, age, eGFR, BMI, arterial hypertension, fasting glucose, current smoking, previous stroke and myocardial infarction. The association with CT-proET-1 was stronger in participants with hypertension (β -0.22 ± 0.04) than in normotensive participants (β -0.13 ± 0.06; p for interaction hypertension = 0.003 in the model adjusted for hypertension).ConclusionsSUmod was inversely associated with arterial hypertension and the vasoconstrictive prohormone CT-proET-1, suggesting direct or indirect effects of sUmod on blood pressure regulation.

Abstract Background The global prevalence of type 2 diabetes-related complications is not well described. We assessed prevalence of vascular complications at baseline in DISCOVER (NCT02322762; NCT02226822), a global, prospective, observational study program of 15,992 patients with type 2 diabetes initiating second-line therapy, conducted across 38 countries. Methods Patients were recruited from primary and specialist healthcare settings. Data were collected using a standardized case report form. Prevalence estimates of microvascular and macrovascular complications at baseline were assessed overall and by country and region, and were standardized for age and sex. Modified Poisson regression was used to assess factors associated with the prevalence of complications. Results The median duration of type 2 diabetes was 4.1 years (interquartile range [IQR]: 1.9–7.9 years), and the median glycated hemoglobin (HbA1c) level was 8.0% (IQR: 7.2–9.1%). The crude prevalences of microvascular and macrovascular complications were 18.8% and 12.7%, respectively. Common microvascular complications were peripheral neuropathy (7.7%), chronic kidney disease (5.0%), and albuminuria (4.3%). Common macrovascular complications were coronary artery disease (8.2%), heart failure (3.3%) and stroke (2.2%). The age- and sex-standardized prevalence of microvascular complications was 17.9% (95% confidence interval [CI] 17.3–18.6%), ranging from 14.2% in the Americas to 20.4% in Europe. The age- and sex-standardized prevalence of macrovascular complications was 9.2% (95% CI 8.7–9.7%), ranging from 4.1% in South-East Asia to 18.8% in Europe. Factors positively associated with vascular complications included age (per 10-year increment), male sex, diabetes duration (per 1-year increment), and history of hypoglycemia, with rate ratios (95% CIs) for microvascular complications of 1.14 (1.09–1.19), 1.30 (1.20–1.42), 1.03 (1.02–1.04) and 1.45 (1.25–1.69), respectively, and for macrovascular complications of 1.41 (1.34–1.48), 1.29 (1.16–1.45), 1.02 (1.01–1.02) and 1.24 (1.04–1.48), respectively. HbA1c levels (per 1.0% increment) were positively associated with microvascular (1.05 [1.02–1.08]) but not macrovascular (1.00 [0.97–1.04]) complications. Conclusions The global burden of microvascular and macrovascular complications is substantial in these patients with type 2 diabetes who are relatively early in the disease process. These findings highlight an opportunity for aggressive early risk factor modification, particularly in regions with a high prevalence of complications. Trial registration ClinicalTrials.gov; NCT02322762. Registered 23 December 2014. https://clinicaltrials.gov/ct2/show/NCT02322762. ClinicalTrials.gov; NCT02226822. Registered 27 August 2014. https://clinicaltrials.gov/ct2/show/NCT02226822

Abstract Background Little evidence exists on the impact of diabetes risk scores, e.g. on physicians and patient’s behavior, perceived risk of persons, shared-decision making and particularly on patient’s health. The aim of this study is to investigate the impact of a non-invasive type 2 diabetes risk prediction model in the primary health care setting as component of routine health checks on change in physical activity. Methods Parallel group cluster randomized controlled trial including 30 primary care physicians (PCPs) and 300 participants in the region of Düsseldorf and surrounding urban and rural municipalities, West Germany. On cluster level, PCPs will be randomized into intervention or control group using a biased coin minimization technique. Participants in the control group are going to have a routine health check “Check-up 35” which is recommended biannually for all people ≥35 years of age in Germany. In the intervention group, the routine health check is expanded by usage of a non-invasive diabetes risk prediction model (German Diabetes Risk Score). Primary outcome is change in physical activity after 1 year. Secondary outcomes include aspects of targeted counseling, motivation of participant’s to change lifestyle, perceived and objectively measured diabetes risk, acceptance of diabetes risk scores, quality of life, depression and anxiety. Patients will be followed over 12 months. Hierarchical or mixed models will be conducted, including a random intercept to adjust for cluster, the respective baseline value, and covariates to compare the groups. Discussion This pragmatic cluster randomized controlled trial will enhance our knowledge on the clinical impact of diabetes risk scores for the first time in the real-life primary health care setting. Trial registration ClinicalTrials.gov NCT03234322, registered on July 28, 2017.