Genetic and clinical determinants of abdominal aortic diameter: genome-wide association studies, exome array data and Mendelian randomization study.

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

Association of serum magnesium with metabolic syndrome and the role of chronic kidney disease: A population-based cohort study with Mendelian randomization.

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

Trajectories of glycaemic traits exhibit sex-specific associations with hepatic iron and fat content: Results from the KORA-MRI study.

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

Association of lumbar vertebral bone marrow and paraspinal muscle fat composition with intervertebral disc degeneration: 3T quantitative MRI findings from the population-based KORA study.

OBJECTIVE: To assess the association of lumbar bone marrow adipose tissue fat fraction (BMAT-FF) and paraspinal muscle proton density fat fraction (PDFF) and their interplay with intervertebral disc degeneration (IVDD). METHODS: In this retrospective cross-sectional study based on a prospective population-based cohort, BMAT-FF and PDFF of asymptomatic individuals were calculated based on 3T-MRI dual-echo and multi-echo Dixon VIBE sequences. IVDD was assessed at motion segments L1 to L5 and dichotomized based on Pfirrmann grade ≥ 4 and/or presence of other severe degenerative changes or spinal abnormalities at least at one segment. Pearson's correlation coefficients were calculated for BMAT-FF and PDFF. Univariable and multivariable logistic regression models for IVDD were calculated. RESULTS: Among 335 participants (mean age: 56.2 ± 9.0 years, 43.3% female), the average BMI was 27.7 ± 4.5 kg/m2 and the prevalence of IVDD was high (69.9%). BMAT-FF and PDFF were significantly correlated (r = 0.31-0.34; p < 0.001). The risk for IVDD increased with higher PDFF (OR = 1.45; CI 1.03, 2.04) and BMAT-FF (OR = 1.56; CI 1.16, 2.11). Pairwise combinations of PDFF and BMAT-FF quartiles revealed a lower risk for IVDD in individuals in the lowest BMAT-FF and PDFF quartile (OR = 0.21; CI 0.1, 0.48). Individuals in the highest BMAT-FF and PDFF quartile showed an increased risk for IVDD (OR = 5.12; CI 1.17, 22.34) CONCLUSION: Lumbar BMAT-FF and paraspinal muscle PDFF are correlated and represent both independent and additive risk factors for IVDD. Quantitative MRI measurements of paraspinal myosteatosis and vertebral bone marrow fatty infiltration may serve as imaging biomarkers to assess the individual risk for IVDD. KEY POINTS: • Fat composition of the lumbar vertebral bone marrow is positively correlated with paraspinal skeletal muscle fat. • Higher fat-fractions of lumbar vertebral bone marrow and paraspinal muscle are both independent as well as additive risk factors for intervertebral disc degeneration. • Quantitative magnetic resonance imaging measurements of bone marrow and paraspinal muscle may serve as imaging biomarkers for intervertebral disc degeneration.

From Atherosclerosis to Myocardial Infarction: A Process-Oriented Model Investigating the Role of Risk Factors.

Mathematical models are able to reflect biological processes and to capture epidemiologic data. Thus, they may help elucidate roles of risk factors in disease progression. We propose to account for smoking, hypertension, and dyslipidemia in a previously published process-oriented model that describes the development of atherosclerotic lesions resulting in myocardial infarction (MI). The model is sex-specific and incorporates individual heterogeneity. It was applied to population-based individual risk factors and MI rates (Cooperative Health Research in the Region of Augsburg (KORA) study) together with subclinical atherosclerotic lesion data (Pathobiological Determinants of Atherosclerosis in Youth (PDAY) study). Different model variants were evaluated, testing the association of risk factors with different disease processes. Best fits were obtained for smoking affecting a late-stage disease process, suggesting a thrombogenic role. Hypertension was mainly related to complicated, vulnerable lesions. Dyslipidemia was consistent with increasing the number of initial lesions. By accounting for heterogeneity, individual hazard ratios differ from the population average. The mean individual hazard ratio for smoking was twice the population-based hazard ratio for men and even more for women. Atherosclerotic lesion progression and MI incidence data can be related in a mathematical model to illuminate how risk factors affect different phases of this pathological process.

Association of MRI-based adrenal gland volume and impaired glucose metabolism in a population-based cohort study.

OBJECTIVES: The aim of this study was to assess adrenal gland volume by using magnetic resonance imaging (MRI) and to study its role as an indirect marker of impaired glucose metabolism and hypothalamic-pituitary-adrenal (HPA) axis activation in a population-based cohort. METHODS: Asymptomatic participants were enrolled in a nested case-control study and underwent a 3-T MRI, including T1w-VIBE-Dixon sequences. For the assessment of adrenal gland volume, adrenal glands were manually segmented in a blinded fashion. Impaired glucose metabolism was determined using fasting glucose and oral glucose tolerance test. Cardiometabolic risk factors were also obtained. Inter- and intrareader reliability as well as univariate and multivariate associations were derived. RESULTS: Among 375 subjects included in the analysis (58.5% male, 56.1 ± 9.1 years), 25.3% participants had prediabetes and 13.6% had type 2 diabetes (T2DM). Total adrenal gland volume was 11.2 ± 4.2 ml and differed significantly between impaired glucose metabolism and healthy controls with largest total adrenal gland volume in T2DM (healthy controls: 10.0 ± 3.9 ml, prediabetes: 12.5 ± 3.8 ml, T2DM: 13.9 ± 4.6 ml; p < 0.001). In the multivariate analysis, association of T2DM and increased adrenal gland volume was independent of age, sex, hypertension, triglycerides and body mass index (BMI), but was attenuated in subjects with prediabetes after adjustment for BMI. CONCLUSIONS: T2DM is significantly associated with increased adrenal gland volume by MRI in an asymptomatic cohort, independent of age, sex, dyslipidaemia, hypertension and BMI. Adrenal gland volume may represent an indirect marker of impaired glucose metabolism and HPA axis dysfunction.

Validity of fatty liver disease indices in the presence of alcohol consumption.

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

Impact of prenatal and childhood adversity effects around World War II on multimorbidity: results from the KORA-Age study.

BACKGROUND: While risk factors for age-related diseases may increase multimorbidity (MM), early life deprivation may also accelerate the development of chronic diseases and MM. METHODS: This study explores the prevalence and pattern of MM in 65-71 year-old individuals born before, during, and after World War II in Southern Germany based on two large cross-sectional KORA (Cooperative Health Research in the Region of Augsburg) -Age studies in 2008/9 and 2016. MM was defined as having at least two chronic diseases, and birth periods were classified into five phases: pre-war, early war, late war, famine, and after the famine period. Logistic regression models were used to analyze the effect of the birth phases on MM with adjustment for sociodemographic and lifestyle risk factors. Furthermore, we used agglomerative hierarchical clustering to investigate the co-occurrence of diseases. RESULTS: Participants born during the late war phase had the highest prevalence of MM (62.2%) and single chronic diseases compared to participants born during the other phases. Being born in the late war phase was significantly associated with a higher odds of MM (OR = 1.83, 95% CI: 1.15-2.91) after adjustment for sociodemographic and lifestyle factors. In women, the prevalence of joint, gastrointestinal, eye diseases, and anxiety was higher, while heart disease, stroke, and diabetes were more common in men. Moreover, three main chronic disease clusters responsible for the observed associations were identified as: joint and psychosomatic, cardiometabolic and, other internal organ diseases. CONCLUSIONS: Our findings imply that adverse early-life exposure may increase the risk of MM in adults aged 65-71 years. Moreover, identified disease clusters are not coincidental and require more investigation.

Distribution patterns of intramyocellular and extramyocellular fat by magnetic resonance imaging in subjects with diabetes, prediabetes and normoglycaemic controls.

AIM: To evaluate the distribution of intramyocellular lipids (IMCLs) and extramyocellular lipids (EMCLs) as well as total fat content in abdominal skeletal muscle by magnetic resonance imaging (MRI) using a dedicated segmentation algorithm in subjects with type 2 diabetes (T2D), prediabetes and normoglycaemic controls. MATERIALS AND METHODS: Subjects from a population-based cohort were classified with T2D, prediabetes or as normoglycaemic controls. Total myosteatosis, IMCLs and EMCLs were quantified by multiecho Dixon MRI as proton-density fat-fraction (in %) in abdominal skeletal muscle. RESULTS: Among 337 included subjects (median age 56.0 [IQR: 49.0-64.0] years, 56.4% males, median body mass index [BMI]: 27.2 kg/m2 ), 129 (38.3%) were classified with an impaired glucose metabolism (T2D: 49 [14.5%]; prediabetes: 80 [23.7%]). IMCLs were significantly higher than EMCLs in subjects without obesity (5.7% [IQR: 4.8%-7.0%] vs. 4.1% [IQR: 2.7%-5.8%], P < .001), whereas the amounts of IMCLs and EMCLs were shown to be equal and significantly higher in subjects with obesity (both 6.7%, P < .001). Subjects with prediabetes and T2D had significantly higher amounts of IMCLs and EMCLs compared with normoglycaemic controls (P < .001). In univariable analysis, prediabetes and T2D were significantly associated with both IMCLs (prediabetes: ß: 0.76, 95% CI: 0.28-1.24, P = .002; T2D: ß: 1.56, 95% CI: 0.66-2.47, P < .001) and EMCLs (prediabetes: ß: 1.54, 95% CI: 0.56-2.51, P = .002; T2D: ß: 2.15, 95% CI: 1.33-2.96, P < .001). After adjustment for age and gender, the association of IMCLs with prediabetes attenuated (P = 0.06), whereas for T2D, both IMCLs and EMCLs remained significantly and positively associated (P < .02). CONCLUSION: There are significant differences in the amount and distribution ratio of IMCLs and EMCLs between subjects with T2D, prediabetes and normoglycaemic controls. Therefore, these patterns of intramuscular fat distribution by MRI might serve as imaging biomarkers in both normal and impaired glucose metabolism.

Simulating the dynamics of atherosclerosis to the incidence of myocardial infarction, applied to the KORA population.

Analyzing epidemiological data with simplified mathematical models of disease development provides a link between the time-course of incidence and the underlying biological processes. Here we point out that considerable modeling flexibility is gained if the model is solved by simulation only. To this aim, a model of atherosclerosis is proposed: a Markov Chain with continuous state space which represents the coronary artery intimal surface area involved with atherosclerotic lesions of increasing severity. Myocardial infarction rates are assumed to be proportional to the area of most severe lesions. The model can be fitted simultaneously to infarction incidence rates observed in the KORA registry, and to the age-dependent prevalence and extent of atherosclerotic lesions in the PDAY study. Moreover, the simulation approach allows for non-linear transition rates, and to consider at the same time randomness and inter-individual heterogeneity. Interestingly, the fit revealed significant age dependence of parameters in females around the age of menopause, qualitatively reproducing the known vascular effects of female sex hormones. For males, the incidence curve flattens for higher ages. According to the model, frailty explains this flattening only partially, and saturation of the disease process plays also an important role. This study shows the feasibility of simulating subclinical and epidemiological data with the same mathematical model. The approach is very general and may be extended to investigate the effects of risk factors or interventions. Moreover, it offers an interface to integrate quantitative individual health data as assessed, for example, by imaging.

Association of antecedent cardiovascular risk factor levels and trajectories with cardiovascular magnetic resonance-derived cardiac function and structure.

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 ß = - 4.91 ml/m2, 95% CI - 7.89; - 1.94, p < 0.01 and high risk cluster ß = - 7.00 ml/m2, 95% CI - 10.73; - 3.28, p < 0.001 compared to the low risk cluster). The multivariable longitudinal trajectory clusters added significantly to explain variation in CMR traits beyond the multivariable risk profile obtained at Exam 3. CONCLUSIONS: Cardiovascular disease risk factor levels, measured over a time period of 14 years, were associated with CMR-derived measures of cardiac structure and function. Longitudinal multivariable trajectory clusters explained a greater proportion of the inter-individual variation in cardiac traits than multiple risk factor assessed contemporaneous with the CMR exam.

Cardiac Troponin I and Incident Stroke in European Cohorts: Insights From the BiomarCaRE Project.

BACKGROUND AND PURPOSE: Stroke is a common cause of death and a leading cause of disability and morbidity. Stroke risk assessment remains a challenge, but circulating biomarkers may improve risk prediction. Controversial evidence is available on the predictive ability of troponin concentrations and the risk of stroke in the community. Furthermore, reports on the predictive value of troponin concentrations for different stroke subtypes are scarce. METHODS: High-sensitivity cardiac troponin I (hsTnI) concentrations were assessed in 82 881 individuals (median age, 50.7 years; 49.7% men) free of stroke or myocardial infarction at baseline from 9 prospective European community cohorts. We used Cox proportional hazards regression to determine relative risks, followed by measures of discrimination and reclassification using 10-fold cross-validation to control for overoptimism. Follow-up was based upon linkage with national hospitalization registries and causes of death registries. RESULTS: Over a median follow-up of 12.7 years, 3033 individuals were diagnosed with incident nonfatal or fatal stroke (n=1654 ischemic strokes, n=612 hemorrhagic strokes, and n=767 indeterminate strokes). In multivariable regression models, hsTnI concentrations were associated with overall stroke (hazard ratio per 1-SD increase, 1.15 [95% CI, 1.10-1.21]), ischemic stroke (hazard ratio, 1.14 [95% CI, 1.09-1.21]), and hemorrhagic stroke (hazard ratio, 1.10 [95% CI, 1.01-1.20]). Adding hsTnI concentrations to classical cardiovascular risk factors (C indices, 0.809, 0.840, and 0.736 for overall, ischemic, and hemorrhagic stroke, respectively) increased the C index significantly but modestly. In individuals with an intermediate 10-year risk (5%-20%), the net reclassification improvement for overall stroke was 0.038 (P=0.021). CONCLUSIONS: Elevated hsTnI concentrations are associated with an increased risk of incident stroke in the community, irrespective of stroke subtype. Adding hsTnI concentrations to classical risk factors only modestly improved estimation of 10-year risk of stroke in the overall cohort but might be of some value in individuals at an intermediate risk.

Glucose and insulin levels are associated with arterial stiffness and concentric remodeling of the heart.

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 < 0.001) lower left ventricular end-diastolic volume index, a 0.042 mm/m2.7 (0.014 to 0.070) higher left ventricular wall-thickness index, a 0.12 mmHg m2.7/ml (0.06 to 0.17; p < 0.001) greater arterial stiffness index and a 0.037 g/ml (0.018 to 0.056; p < 0.001) higher left ventricular concentricity. CONCLUSIONS: Our findings suggest that higher glucose levels in the prediabetic range and insulin resistance might lead to higher arterial stiffness and concentric remodeling of the heart.

Characteristics and associated risk factors of diverticular disease assessed by magnetic resonance imaging in subjects from a Western general population.

OBJECTIVES: Diverticular disease represents an increasing pathology and healthcare burden worldwide. Our aim was to study the prevalence, extent and distribution of asymptomatic diverticular disease assessed by magnetic resonance imaging (MRI) in a sample of a Western population. METHODS: Subjects from a population-based cohort study who underwent 3-T MRI were analyzed for the prevalence and extent of diverticula of the colon using an isotropic VIBE-Dixon gradient-echo sequence. The extent of diverticular disease was categorized according to the number of diverticula in each colonic segment. Univariate and adjusted analyses were performed to assess associated characteristics and risk factors. RESULTS: Among 393 subjects included in the analysis (56.4 ± 9.2 years, 57.5% males), 164 (42%) had diverticular disease, with the highest prevalence in the left-sided colonic segments (93% diverticular disease in the descending and sigmoid segment). Subjects with advanced diverticular disease were older (62.1 vs. 54.4 years) and had a higher body mass index (BMI), LDL cholesterol levels and systolic blood pressure (30.2 ± 5.1 vs. 27.8 ± 4.9 kg/m2, 149.8 ± 29.3 vs. 135.2 ± 32.9 mg/dl and 128.2 ± 14.1 vs. 118.4 ± 16.1 mmHg, respectively; all p > 0.003) compared with subjects without diverticular disease. In contrast, no significant correlation could be found for gender, physical activity, smoking status and alcohol consumption (all p > 0.31). Intra-rater reliability was excellent for all colonic segments (intra-class correlation [ICC] = 0.99-1.00), and inter-rater reliability was excellent for left- and right-sided colonic segments (ICC = 0.84-0.97). CONCLUSIONS: These findings provide insights into the disease mechanism of asymptomatic diverticular disease and may help to improve prevention of diverticulosis and its associated complications. KEY POINTS: • Overall prevalence of asymptomatic diverticular disease assessed by MRI was 42%, affecting predominantly the left-sided colon. • Asymptomatic diverticular disease was associated with age and cardiometabolic risk factors. • Magnetic resonance imaging reveals insights into the pathophysiologic mechanism of asymptomatic diverticular disease.

Association of glycemic status and segmental left ventricular wall thickness in subjects without prior cardiovascular disease: a cross-sectional study.

BACKGROUND: Left ventricular (LV) hypertrophy and changes in LV geometry are associated with increased cardiovascular mortality. Subjects with type 2 diabetes have an increased risk of such alterations in cardiac morphology. We sought to assess the association of glycemic status and LV wall thickness measured by cardiac magnetic resonance (CMR), and potential interactions of hypertension and diabetes. METHODS: CMR was performed on 359 participants from a cross-sectional study nested in a population-based cohort (KORA FF4) free of overt cardiovascular disease. Participants were classified according to their glycemic status as either control (normal glucose metabolism), prediabetes or type 2 diabetes. Segmentation of the left ventricle was defined according to the American Heart Association (AHA) 16-segment model. Measurements of wall thickness were obtained at end-diastole and analyzed by linear regression models adjusted for traditional cardiovascular risk factors. RESULTS: LV wall thickness gradually increased from normoglycemic controls to subjects with prediabetes and subjects with diabetes (8.8 ± 1.4 vs 9.9 ± 1.4 vs 10.5 ± 1.6 mm, respectively). The association was independent of hypertension and traditional cardiovascular risk factors (ß-coefficient: 0.44 mm for prediabetes and 0.70 mm for diabetes, p-values compared to controls: p = 0.007 and p = 0.004, respectively). Whereas the association of glycemic status was strongest for the mid-cavity segments, the association of hypertension was strongest for the basal segments. CONCLUSION: Abnormal glucose metabolism, including pre-diabetes, is associated with increased LV wall thickness independent of hypertension.

Estimation of Trait-Model Parameters in a MOD Score Linkage Analysis.

BACKGROUND/AIMS: Theoretically, the trait-model parameters (disease allele frequency and penetrance function) can be estimated without bias in a MOD score linkage analysis. We aimed to practically evaluate the MOD score approach regarding its ability to provide unbiased trait-model parameters for various pedigree-type and trait-model scenarios. We further investigated the ability of the MOD score approach to detect imprinting using affected sib pairs (ASPs) and affected half-sib pairs (AHSPs) when all parental genotypes are missing. METHODS: Simulated pedigree data were analyzed using the GENEHUNTER-MODSCORE software package. Parameter estimation performance in terms of bias and variability was evaluated with regard to trait-model type and pedigree complexity. RESULTS: Generally, parameters were estimated with lower bias and variability with increasing pedigree complexity, especially for recessive and overdominant models. However, dominant and additive models could hardly be distinguished even when using 3-generation pedigrees. Imprinting could clearly be detected for mixtures of mainly ASPs and only few AHSPs with the common parent of the imprinted sex, even though no parental genotypes were available. CONCLUSION: Our results provide guidance to researchers regarding the possibility to estimate trait-model parameters by a MOD score analysis, including the degree of imprinting, with certain types of pedigrees.

Categorical variables with many categories are preferentially selected in bootstrap-based model selection procedures for multivariable regression models.

Automated variable selection procedures, such as backward elimination, are commonly employed to perform model selection in the context of multivariable regression. The stability of such procedures can be investigated using a bootstrap-based approach. The idea is to apply the variable selection procedure on a large number of bootstrap samples successively and to examine the obtained models, for instance, in terms of the inclusion of specific predictor variables. In this paper, we aim to investigate a particular important problem affecting this method in the case of categorical predictor variables with different numbers of categories and to give recommendations on how to avoid it. For this purpose, we systematically assess the behavior of automated variable selection based on the likelihood ratio test using either bootstrap samples drawn with replacement or subsamples drawn without replacement from the original dataset. Our study consists of extensive simulations and a real data example from the NHANES study. Our main result is that if automated variable selection is conducted on bootstrap samples, variables with more categories are substantially favored over variables with fewer categories and over metric variables even if none of them have any effect. Importantly, variables with no effect and many categories may be (wrongly) preferred to variables with an effect but few categories. We suggest the use of subsamples instead of bootstrap samples to bypass these drawbacks.

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.

Deep learning to estimate impaired glucose metabolism from Magnetic Resonance Imaging of the liver: An opportunistic population screening approach.

AIM: Diabetes is a global health challenge, and many individuals are undiagnosed and not aware of their increased risk of morbidity/mortality although dedicated tests are available, which indicates the need for novel population-wide screening approaches. Here, we developed a deep learning pipeline for opportunistic screening of impaired glucose metabolism using routine magnetic resonance imaging (MRI) of the liver and tested its prognostic value in a general population setting. METHODS: In this retrospective study a fully automatic deep learning pipeline was developed to quantify liver shape features on routine MR imaging using data from a prospective population study. Subsequently, the association between liver shape features and impaired glucose metabolism was investigated in individuals with prediabetes, type 2 diabetes and healthy controls without prior cardiovascular diseases. K-medoids clustering (3 clusters) with a dissimilarity matrix based on Euclidean distance and ordinal regression was used to assess the association between liver shape features and glycaemic status. RESULTS: The deep learning pipeline showed a high performance for liver shape analysis with a mean Dice score of 97.0±0.01. Out of 339 included individuals (mean age 56.3±9.1 years; males 58.1%), 79 (23.3%) and 46 (13.6%) were classified as having prediabetes and type 2 diabetes, respectively. Individuals in the high risk cluster using all liver shape features (n = 14) had a 2.4 fold increased risk of impaired glucose metabolism after adjustment for cardiometabolic risk factors (age, sex, BMI, total cholesterol, alcohol consumption, hypertension, smoking and hepatic steatosis; OR 2.44 [95% CI 1.12-5.38]; p = 0.03). Based on individual shape features, the strongest association was found between liver volume and impaired glucose metabolism after adjustment for the same risk factors (OR 1.97 [1.38-2.85]; p<0.001). CONCLUSIONS: Deep learning can estimate impaired glucose metabolism on routine liver MRI independent of cardiometabolic risk factors and hepatic steatosis.