Costs associated with insufficient physical activity in Germany: cross-sectional results from the baseline examination of the German national cohort (NAKO).

BACKGROUND: Insufficient physical activity (PA) is a leading risk factor for non-communicable diseases posing a significant economic burden to healthcare systems and societies. The study aimed to examine the differences in healthcare and indirect costs between sufficient and insufficient PA and the cost differences between PA intensity groups. METHODS: The cross-sectional analysis was based on data from 157,648 participants in the baseline examination of the German National Cohort (NAKO) study. Healthcare and indirect costs were calculated based on self-reported information on health-related resource use and productivity losses. PA in the domains leisure, transport, and work was assessed by the Global Physical Activity Questionnaire and categorized into sufficient/insufficient and intensity levels (very low/low/medium/high) based on PA recommendations of the World Health Organization. Two-part models adjusted for relevant covariates were used to estimate mean costs for PA groups. RESULTS AND CONCLUSION: Insufficiently active people had higher average annual healthcare costs (Δ €188, 95% CI [64, 311]) and healthcare plus indirect costs (Δ €482, 95% CI [262, 702]) compared to sufficiently active people. The difference was especially evident in the population aged 60 + years and when considering only leisure PA. An inverse association was observed between leisure PA and costs, whereas a direct association was found between PA at work and costs. Adjusting for the number of comorbidities reduced the differences between activity groups, but the trend persisted. The association between PA and costs differed in direction between PA domains. Future research may provide further insight into the temporal relationship between PA and costs.

Differential association of dietary scores with the risk of type 2 diabetes by metabotype.

PURPOSE: We aimed to examine the association between dietary patterns and type 2 diabetes mellitus (T2DM) while considering the potential effect modification by metabolic phenotypes (metabotypes). Additionally, we aimed to explore the association between dietary scores and prediabetes. METHODS: A total of 1460 participants (11.8% with T2DM) from the cross-sectional population-based KORA FF4 study were included. Participants, classified into three metabotype subgroups, had both their FSAm-NPS dietary index (underpinning the Nutri-Score) and ultra-processed foods (UPF) intake (using NOVA classification) calculated. Glucose tolerance status was assessed via oral glucose tolerance tests (OGTT) in non-diabetic participants and was classified according to the American Diabetes Association criteria. Logistic regression models were used for both the overall and metabotype-stratified analyses of dietary scores' association with T2DM, and multinomial probit models for their association with prediabetes. RESULTS: Participants who had a diet with a higher FSAm-NPS dietary index (i.e., a lower diet quality) or a greater percentage of UPF consumption showed a positive association with T2DM. Stratified analyses demonstrated a strengthened association between UPF consumption and T2DM specifically in the metabolically most unfavorable metabotype (Odds Ratio, OR 1.92; 95% Confidence Interval, CI 1.35, 2.73). A diet with a higher FSAm-NPS dietary index was also positively associated with prediabetes (OR 1.19; 95% CI 1.04, 1.35). CONCLUSION: Our study suggests different associations between poorer diet quality and T2DM across individuals exhibiting diverse metabotypes, pointing to the option for stratified dietary interventions in diabetes prevention.

Association analysis between an epigenetic risk score and blood pressure.

Background: Epigenome-wide association studies have revealed multiple DNA methylation sites (CpGs) associated with alcohol consumption, an important lifestyle risk factor for cardiovascular diseases. Results: We generated an alcohol consumption epigenetic risk score (ERS) based on previously reported 144 alcohol-associated CpGs and examined the association of the ERS with systolic blood pressure (SBP), diastolic blood pressure (DBP), and hypertension (HTN) in 3,898 Framingham Heart Study (FHS) participants. We found an association of alcohol intake with the ERS in the meta-analysis with 0.09 units higher ERS per drink consumed per day (p < 0.0001). Cross-sectional analyses in FHS revealed that a one-unit increment of the ERS was associated with 1.93 mm Hg higher SBP (p = 4.64E-07), 0.68 mm Hg higher DBP (p = 0.006), and an odds ratio of 1.78 for HTN (p < 2E-16). Meta-analysis of the cross-sectional association of the ERS with BP traits in eight independent external cohorts (n = 11,544) showed similar relationships with blood pressure levels, i.e., a one-unit increase in ERS was associated with 0.74 (p = 0.002) and 0.50 (p = 0.0006) mm Hg higher SBP and DBP, but could not confirm the association with hypertension. Longitudinal analyses in FHS (n = 3,260) and five independent external cohorts (n = 4,021) showed that the baseline ERS was not associated with a change in blood pressure over time or with incident HTN. Conclusions: Our findings provide proof-of-concept that utilizing an ERS is a useful approach to capture the recent health consequences of lifestyle behaviors such as alcohol consumption.

Nocturnal heat exposure and stroke risk.

BACKGROUND AND AIMS: In recent decades, nighttime temperatures have increased faster than daytime temperatures. The increasing prevalence of nocturnal heat exposure may pose a significant risk to cardiovascular health. This study investigated the association between nighttime heat exposure and stroke risk in the region of Augsburg, Germany, and examined its temporal variations over 15 years. METHODS: Hourly meteorological parameters, including mean temperature, relative humidity, and barometric pressure, were acquired from a local meteorological station. A data set was obtained consisting of 11 037 clinical stroke cases diagnosed during warmer months (May to October) between the years 2006 and 2020. The average age of cases was 71.3 years. Among these cases, 642 were identified as haemorrhagic strokes, 7430 were classified as ischaemic strokes, and 2947 were transient ischaemic attacks. A time-stratified case-crossover analysis with a distributed lag non-linear model was used to estimate the stroke risk associated with extreme nighttime heat, as measured by the hot night excess (HNE) index after controlling for the potential confounding effects of daily maximum temperature and other climatic variables. Subgroup analyses by age group, sex, stroke subtype, and stroke severity were performed to identify variations in susceptibility to nighttime heat. RESULTS: Results suggested a significant increase in stroke risk on days with extreme nighttime heat (97.5% percentile of HNE) (odds ratio 1.07, 95% confidence interval 1.01-1.15) during the full study period. When comparing the results for 2013-20 with the results for 2006-12, there was a significant increase (P < .05) in HNE-related risk for all strokes and specifically for ischaemic strokes during the more recent period. Furthermore, older individuals, females, and patients with mild stroke symptoms exhibited a significantly increased vulnerability to nighttime heat. CONCLUSIONS: This study found nocturnal heat exposure to be related to elevated stroke risk after controlling for maximum daytime temperature, with increasing susceptibility between 2006 and 2020. These results underscore the importance of considering nocturnal heat as a critical trigger of stroke events in a warming climate.

Prediction of Myocardial Infarction Using a Combined Generative Adversarial Network Model and Feature-Enhanced Loss Function.

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.

Large-scale assessment of physical activity in a population using high-resolution hip-worn accelerometry: the German National Cohort (NAKO).

Large population-based cohort studies utilizing device-based measures of physical activity are crucial to close important research gaps regarding the potential protective effects of physical activity on chronic diseases. The present study details the quality control processes and the derivation of physical activity metrics from 100 Hz accelerometer data collected in the German National Cohort (NAKO). During the 2014 to 2019 baseline assessment, a subsample of NAKO participants wore a triaxial ActiGraph accelerometer on their right hip for seven consecutive days. Auto-calibration, signal feature calculations including Euclidean Norm Minus One (ENMO) and Mean Amplitude Deviation (MAD), identification of non-wear time, and imputation, were conducted using the R package GGIR version 2.10-3. A total of 73,334 participants contributed data for accelerometry analysis, of whom 63,236 provided valid data. The average ENMO was 11.7 ± 3.7 mg (milli gravitational acceleration) and the average MAD was 19.9 ± 6.1 mg. Notably, acceleration summary metrics were higher in men than women and diminished with increasing age. Work generated in the present study will facilitate harmonized analysis, reproducibility, and utilization of NAKO accelerometry data. The NAKO accelerometry dataset represents a valuable asset for physical activity research and will be accessible through a specified application process.

Childhood and adolescence factors and multiple sclerosis: results from the German National Cohort (NAKO).

BACKGROUND: Multiple Sclerosis (MS) represents the most common inflammatory neurological disease causing disability in early adulthood. Childhood and adolescence factors might be of relevance in the development of MS. We aimed to investigate the association between various factors (e.g., prematurity, breastfeeding, daycare attendance, weight history) and MS risk. METHODS: Data from the baseline assessment of the German National Cohort (NAKO) were used to calculate adjusted hazard ratios (HR) and 95% confidence intervals (CI) for the association between childhood and adolescence factors and risk of MS. Analyses stratified by sex were conducted. RESULTS: Among a total of 204,273 participants, 858 reported an MS diagnosis. Male sex was associated with a decreased MS risk (HR 0.48; 95% CI 0.41-0.56), while overweight (HR 2.03; 95% CI 1.41-2.94) and obesity (HR 1.89; 95% CI 1.02-3.48) at 18 years of age compared to normal weight were associated with increased MS risk. Having been breastfed for ≤ 4 months was associated with a decreased MS risk in men (HR 0.59; 95% CI 0.40-0.86) compared to no breastfeeding. No association with MS risk was observed for the remaining factors. CONCLUSIONS: Apart from overweight and obesity at the age of 18 years, we did not observe considerable associations with MS risk. The proportion of cases that can be explained by childhood and adolescence factors examined in this study was low. Further investigations of the association between the onset of overweight and obesity in childhood and adolescence and its interaction with physical activity and MS risk seem worthwhile.

Air pollution, traffic noise, greenness, and temperature and the risk of incident type 2 diabetes: Results from the KORA cohort study.

Introduction: Type 2 diabetes (T2D) is a major public health concern, and various environmental factors have been associated with the development of this disease. This study aimed to investigate the longitudinal effects of multiple environmental exposures on the risk of incident T2D in a German population-based cohort. Methods: We used data from the KORA cohort study (Augsburg, Germany) and assessed exposure to air pollutants, traffic noise, greenness, and temperature at the participants' residencies. Cox proportional hazard models were used to analyze the associations with incident T2D, adjusting for potential confounders. Results: Of 7736 participants included in the analyses, 10.5% developed T2D during follow-up (mean: 15.0 years). We found weak or no association between environmental factors and the risk of T2D, with sex and education level significantly modifying the effects of air pollutants. Conclusion: Our study contributes to the growing body of literature investigating the impact of environmental factors on T2D risks and suggests that the impact of environmental factors may be small.

Sex-specific associations of environmental exposures with prevalent diabetes and obesity - Results from the KORA Fit study.

Promising evidence suggests a link between environmental factors, particularly air pollution, and diabetes and obesity. However, it is still unclear whether men and women are equally susceptible to environmental exposures. Therefore, we aimed to assess sex-specific long-term effects of environmental exposures on metabolic diseases. We analyzed cross-sectional data from 3,034 participants (53.7% female, aged 53-74 years) from the KORA Fit study (2018/19), a German population-based cohort. Environmental exposures, including annual averages of air pollutants [nitrogen oxides (NO2, NOx), ozone, particulate matter of different diameters (PM10, PMcoarse, PM2.5), PM2.5abs, particle number concentration], air temperature and surrounding greenness, were assessed at participants' residences. We evaluated sex-specific associations of environmental exposures with prevalent diabetes, obesity, body-mass-index (BMI) and waist circumference using logistic or linear regression models with an interaction term for sex, adjusted for age, lifestyle factors and education. Further effect modification, in particular by urbanization, was assessed in sex-stratified analyses. Higher annual averages of air pollution, air temperature and greenness at residence were associated with diabetes prevalence in men (NO2: Odds Ratio (OR) per interquartile range increase in exposure: 1.49 [95% confidence interval (CI): 1.13, 1.95], air temperature: OR: 1.48 [95%-CI: 1.15, 1.90]; greenness: OR: 0.78 [95%-CI: 0.59, 1.01]) but not in women. Conversely, higher levels of air pollution, temperature and lack of greenness were associated with lower obesity prevalence and BMI in women. After including an interaction term for urbanization, only higher greenness was associated with higher BMI in rural women, whereas higher air pollution was associated with higher BMI in urban men. To conclude, we observed sex-specific associations of environmental exposures with metabolic diseases. An additional interaction between environmental exposures and urbanization on obesity suggests a higher susceptibility to air pollution among urban men, and higher susceptibility to greenness among rural women, which needs corroboration in future studies.

Long-term exposure to air pollution and chronic kidney disease-associated mortality-Results from the pooled cohort of the European multicentre ELAPSE-study.

Despite the known link between air pollution and cause-specific mortality, its relation to chronic kidney disease (CKD)-associated mortality is understudied. Therefore, we investigated the association between long-term exposure to air pollution and CKD-related mortality in a large multicentre population-based European cohort. Cohort data were linked to local mortality registry data. CKD-death was defined as ICD10 codes N18-N19 or corresponding ICD9 codes. Mean annual exposure at participant's home address was determined with fine spatial resolution exposure models for nitrogen dioxide (NO2), black carbon (BC), ozone (O3), particulate matter ≤2.5 µm (PM2.5) and several elemental constituents of PM2.5. Cox regression models were adjusted for age, sex, cohort, calendar year of recruitment, smoking status, marital status, employment status and neighbourhood mean income. Over a mean follow-up time of 20.4 years, 313 of 289,564 persons died from CKD. Associations were positive for PM2.5 (hazard ratio (HR) with 95% confidence interval (CI) of 1.31 (1.03-1.66) per 5 µg/m3, BC (1.26 (1.03-1.53) per 0.5 × 10- 5/m), NO2 (1.13 (0.93-1.38) per 10 µg/m3) and inverse for O3 (0.71 (0.54-0.93) per 10 µg/m3). Results were robust to further covariate adjustment. Exclusion of the largest sub-cohort contributing 226 cases, led to null associations. Among the elemental constituents, Cu, Fe, K, Ni, S and Zn, representing different sources including traffic, biomass and oil burning and secondary pollutants, were associated with CKD-related mortality. In conclusion, our results suggest an association between air pollution from different sources and CKD-related mortality.

Air pollution changes due to COVID-19 lockdowns and attributable mortality changes in four countries.

COVID-19 lockdowns reduced nitrogen dioxide (NO2) and fine particulate matter (PM2.5) emissions in many countries. We aim to quantify the changes in these pollutants and to assess the attributable changes in mortality in Jiangsu, China; California, U.S.; Central-southern Italy; and Germany during COVID-19 lockdowns in early 2020. Accounting for meteorological impacts and air pollution time trends, we use a machine learning-based meteorological normalization technique and the difference-in-differences approach to quantify the changes in NO2 and PM2.5 concentrations due to lockdowns. Using region-specific estimates of the association between air pollution and mortality derived from a causal modeling approach using data from 2015 to 2019, we assess the changes in mortality attributable to the air pollution changes caused by the lockdowns in early 2020. During the lockdowns, NO2 reductions avoided 1.41 (95% empirical confidence interval [eCI]: 0.94, 1.88), 0.44 (95% eCI: 0.17, 0.71), and 4.66 (95% eCI: 2.03, 7.44) deaths per 100,000 people in Jiangsu, China; California, U.S.; and Central-southern Italy, respectively. Mortality benefits attributable to PM2.5 reductions were also significant, albeit of a smaller magnitude. For Germany, the mortality benefits attributable to NO2 changes were not significant (0.11; 95% eCI: -0.03, 0.25), and an increase in PM2.5 concentrations was associated with an increase in mortality of 0.35 (95% eCI: 0.22, 0.48) deaths per 100,000 people during the lockdown. COVID-19 lockdowns overall improved air quality and brought attributable health benefits, especially associated with NO2 improvements, with notable heterogeneity across regions. This study underscores the importance of accounting for local characteristics when policymakers adapt successful emission control strategies from other regions.

Sex and statin-related genetic associations at the PCSK9 gene locus: results of genome-wide association meta-analysis.

BACKGROUND: Proprotein convertase subtilisin/kexin type 9 (PCSK9) is a key player of lipid metabolism with higher plasma levels in women throughout their life. Statin treatment affects PCSK9 levels also showing evidence of sex-differential effects. It remains unclear whether these differences can be explained by genetics. METHODS: We performed genome-wide association meta-analyses (GWAS) of PCSK9 levels stratified for sex and statin treatment in six independent studies of Europeans (8936 women/11,080 men respectively 14,825 statin-free/5191 statin-treated individuals). Loci associated in one of the strata were tested for statin- and sex-interactions considering all independent signals per locus. Independent variants at the PCSK9 gene locus were then used in a stratified Mendelian Randomization analysis (cis-MR) of PCSK9 effects on low-density lipoprotein cholesterol (LDL-C) levels to detect differences of causal effects between the subgroups. RESULTS: We identified 11 loci associated with PCSK9 in at least one stratified subgroup (p < 1.0 × 10-6), including the PCSK9 gene locus and five other lipid loci: APOB, TM6SF2, FADS1/FADS2, JMJD1C, and HP/HPR. The interaction analysis revealed eight loci with sex- and/or statin-interactions. At the PCSK9 gene locus, there were four independent signals, one with a significant sex-interaction showing stronger effects in men (rs693668). Regarding statin treatment, there were two significant interactions in PCSK9 missense mutations: rs11591147 had stronger effects in statin-free individuals, and rs11583680 had stronger effects in statin-treated individuals. Besides replicating known loci, we detected two novel genome-wide significant associations: one for statin-treated individuals at 6q11.1 (within KHDRBS2) and one for males at 12q24.22 (near KSR2/NOS1), both with significant interactions. In the MR of PCSK9 on LDL-C, we observed significant causal estimates within all subgroups, but significantly stronger causal effects in statin-free subjects compared to statin-treated individuals. CONCLUSIONS: We performed the first double-stratified GWAS of PCSK9 levels and identified multiple biologically plausible loci with genetic interaction effects. Our results indicate that the observed sexual dimorphism of PCSK9 and its statin-related interactions have a genetic basis. Significant differences in the causal relationship between PCSK9 and LDL-C suggest sex-specific dosages of PCSK9 inhibitors.

The protein "proprotein convertase subtilisin/kexin type 9" (PCSK9) regulates the levels of low-density lipoprotein cholesterol (LDL-C) in blood, and thus, contributes to the risk of cardio-vascular diseases. Women tend to have higher PCSK9 plasma levels throughout their life, although the difference is smaller in patients under LDL-C lowering medication (e.g., statins). We investigated the interplay of genetics, statin-treatment and sex, using combined data from six European studies. We detected 11 genetic regions associated with PCSK9 levels, of which one was specific for women (at SLCO1B3, a statin-transporter gene), and three were specific for men (e.g., ALOX5, encoding a protein linked to chronic inflammatory diseases such as atherosclerosis). We also tested if statin use changed the genetic effect and found five genes only associated with PCSK9 levels in untreated participants. Variants in the gene encoding PCSK9 were most strongly associated and had heterogeneous effects in dependence on statin treatment and sex: On one hand, there were genetic variants with stronger effects in men than women. Those variants are also linked to sex-differential gene expression of PCSK9. On the other hand, there were also variants with treatment-depending effects, linked to protein structure and functionality of PCSK9. This indicates that the observed sexual and treatment-related effects on PCSK9 levels have a genetic basis. In addition, we compared the causal effects of PCSK9 on LDL-C levels between men and women and found a different response to statin treatment. This highlights the need for sex-sensitive dosages of lipid-lowering medication.

Associations of Proteomics With Hypertension and Systolic Blood Pressure: KORA S4/F4/FF4 and KORA Age1/Age2 Cohort Studies.

BACKGROUND: Hypertension, a complex condition, is primarily defined based on blood pressure readings without involving its pathophysiological mechanisms. We aimed to identify biomarkers through a proteomic approach, thereby enhancing the future definition of hypertension with insights into its molecular mechanisms. METHODS: The discovery analysis included 1560 participants, aged 55 to 74 years at baseline, from the KORA (Cooperative Health Research in the Region of Augsburg) S4/F4/FF4 cohort study, with 3332 observations over a median of 13.4 years of follow-up. Generalized estimating equations were used to estimate the associations of 233 plasma proteins with hypertension and systolic blood pressure (SBP). For validation, proteins significantly associated with hypertension or SBP in the discovery analysis were validated in the KORA Age1/Age2 cohort study (1024 participants, 1810 observations). A 2-sample Mendelian randomization analysis was conducted to infer causalities of validated proteins with SBP. RESULTS: Discovery analysis identified 49 proteins associated with hypertension and 99 associated with SBP. Validation in the KORA Age1/Age2 study replicated 7 proteins associated with hypertension and 23 associated with SBP. Three proteins, NT-proBNP (N-terminal pro-B-type natriuretic peptide), KIM1 (kidney injury molecule 1), and OPG (osteoprotegerin), consistently showed positive associations with both outcomes. Five proteins demonstrated potential causal associations with SBP in Mendelian randomization analysis, including NT-proBNP and OPG. CONCLUSIONS: We identified and validated 7 hypertension-associated and 23 SBP-associated proteins across 2 cohort studies. KIM1, NT-proBNP, and OPG demonstrated robust associations, and OPG was identified for the first time as associated with blood pressure. For NT-proBNP (protective) and OPG, causal associations with SBP were suggested.

Imaging Markers Derived From MRI-Based Automated Kidney Segmentation­an Analysis of Data From the German National Cohort (NAKO Gesundheitsstudie)

BACKGROUND: Population-wide research on potential new imaging biomarkers of the kidney depends on accurate automated segmentation of the kidney and its compartments (cortex, medulla, and sinus). METHODS: We developed a robust deep-learning framework for kidney (sub-)segmentation based on a hierarchical, three-dimensional convolutional neural network (CNN) that was optimized for multi-scale problems of combined localization and segmentation. We applied the CNN to abdominal magnetic resonance images from the population-based German National Cohort (NAKO) study. RESULTS: There was good to excellent agreement between the model predictions and manual segmentations. The median values for the body-surface normalized total kidney, cortex, medulla, and sinus volumes of 9934 persons were 158, 115, 43, and 24 mL/m2. Distributions of these markers are provided both for the overall study population and for a subgroup of persons without kidney disease or any associated conditions. Multivariable adjusted regression analyses revealed that diabetes, male sex, and a higher estimated glomerular filtration rate (eGFR) are important predictors of higher total and cortical volumes. Each increase of eGFR by one unit (i.e., 1 mL/min per 1.73 m2 body surface area) was associated with a 0.98 mL/m2 increase in total kidney volume, and this association was significant. Volumes were lower in persons with eGFR-defined chronic kidney disease. CONCLUSION: The extraction of image-based biomarkers through CNN-based renal sub-segmentation using data from a population-based study yields reliable results, forming a solid foundation for future investigations.

Omentin associates with serum metabolite profiles indicating lower diabetes risk: KORA F4 Study.

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.

Clusters of longitudinal risk profile trajectories are associated with cardiometabolic diseases: Results from the population-based KORA cohort.

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.

Association analysis between an epigenetic alcohol risk score and blood pressure.

Background: Epigenome-wide association studies have revealed multiple DNA methylation sites (CpGs) associated with alcohol consumption, an important lifestyle risk factor for cardiovascular diseases. Results: We generated an alcohol consumption epigenetic risk score (ERS) based on previously reported 144 alcohol-associated CpGs and examined the association of the ERS with systolic blood pressure (SBP), diastolic blood pressure (DBP), and hypertension (HTN) in 3,898 Framingham Heart Study (FHS) participants. We found an association of alcohol intake with the ERS in the meta-analysis with 0.09 units higher ERS per drink consumed per day (p < 0.0001). Cross-sectional analyses in FHS revealed that a one-unit increment of the ERS was associated with 1.93 mm Hg higher SBP (p = 4.64E-07), 0.68 mm Hg higher DBP (p = 0.006), and an odds ratio of 1.78 for HTN (p < 2E-16). Meta-analysis of the cross-sectional association of the ERS with BP traits in eight independent external cohorts (n = 11,544) showed similar relationships with blood pressure levels, i.e., a one-unit increase in ERS was associated with 0.74 (p = 0.002) and 0.50 (p = 0.0006) mm Hg higher SBP and DBP, but could not confirm the association with hypertension. Longitudinal analyses in FHS (n = 3,260) and five independent external cohorts (n = 4,021) showed that the baseline ERS was not associated with a change in blood pressure over time or with incident HTN. Conclusions: Our findings provide proof-of-concept that utilizing an ERS is a useful approach to capture the recent health consequences of lifestyle behaviors such as alcohol consumption.

Personality and the use of cancer screenings - Results of the German National Cohort.

Objective: To determine the association between personality characteristics and use of different cancer screenings. Methods: We used data from the German National Cohort (NAKO; mean age was 53.0 years (SD: 9.2 years)) - a population-based cohort study. A total of 132,298 individuals were included in the analyses. As outcome measures, we used (self-reported): stool examination for blood (haemoccult test, early detection of bowel cancer), colonoscopy (screening for colorectal cancer), skin examination for moles (early detection of skin cancer), breast palpation by a doctor (early detection of breast cancer), x-ray examination of the breast ("mammography", early detection of breast cancer), cervical smear test, finger examination of the rectum (early detection of prostate cancer), and blood test for prostate cancer (determination of Prostate-Specific Antigen level). The established Big Five Inventory-SOEP was used to quantify personality factors. It was adjusted for several covariates based on the Andersen model. Unadjusted and adjusted multiple logistic regressions were computed. Results: A higher probability of having a skin examination for moles, for example, was associated with a higher conscientiousness (OR: 1.07, p < 0.001), higher extraversion (OR: 1.03, p < 0.001), higher agreeableness (OR: 1.02, p < 0.001), lower openness to experience (OR: 0.98, p < 0.001) and higher neuroticism (OR: 1.07, p < 0.001) among the total sample. Depending on the outcome used, the associations slightly varied. Conclusions: Particularly higher levels of extraversion, neuroticism and conscientiousness are associated with the use of different cancer screenings. Such knowledge may help to better understand non-participation in cancer screening examinations from a psychological perspective.

Sex-specific trends in incidence of first myocardial infarction among people with and without diabetes between 1985 and 2016 in a German region.

BACKGROUND: The reduction of myocardial infarction (MI) and narrowing the gap between the populations with and without diabetes are important goals of diabetes care. We analyzed time trends for sex-specific incidence rates (IR) of first MI (both non-fatal MI and fatal MI) as well as separately for first non-fatal MI and fatal MI in the population with and without diabetes. METHODS: Using data from the KORA myocardial infarction registry (Augsburg, Germany), we estimated age-adjusted IR in people with and without diabetes, corresponding relative risks (RR), and time trends from 1985 to 2016 using Poisson regression. RESULTS: There were 19,683 people with first MI (34% fatal MI, 71% men, 30% with diabetes) between 1985 and 2016. In the entire study population, the IR of first MI decreased from 359 (95% CI: 345-374) to 236 (226-245) per 100,000 person years. In men with diabetes, IR decreased only in 2013-2016. This was due to first non-fatal MI, where IR in men with diabetes increased until 2009-2012, and slightly decreased in 2013-2016. Overall, fatal MI declined stronger than first non-fatal MI corresponding to IRs. The RR of first MI substantially increased among men from 1.40 (1.22-1.61) in 1985-1988 to 2.60 (2.26-2.99) in 1997-2000 and moderately decreased in 2013-2016: RR: 1.75 (1.47-2.09). Among women no consistent time trend for RR was observed. Time trends for RR were similar regarding first non-fatal MI and fatal MI. CONCLUSIONS: Over the study period, we found a decreased incidence of first MI and fatal MI in the entire study population. The initial increase of first non-fatal MI in men with diabetes needs further research. The gap between populations with and without diabetes remained.

Serum metabolite signatures of cardiac function and morphology in individuals from a population-based cohort.

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.