Detailed pathophysiological manifestations of early disease in the context of prediabetes are poorly understood. This study aimed to evaluate the extent of early signs of metabolic and cardio-cerebrovascular complications affecting multiple organs in individuals with prediabetes. Subjects without a history of stroke, coronary artery disease, or peripheral artery disease were enrolled in a case-control study nested within the Cooperative Health Research in the Region of Augsburg (KORA) FF4 cohort and underwent comprehensive MRI assessment to characterize cerebral parameters (white matter lesions, microbleeds), cardiovascular parameters (carotid plaque, left ventricular function, and myocardial late gadolinium enhancement [LGE]), and metabolic parameters (hepatic proton-density fat fraction [PDFF] and subcutaneous and visceral abdominal fat). Among 400 subjects who underwent MRI, 103 subjects had prediabetes and 54 had established diabetes. Subjects with prediabetes had an increased risk for carotid plaque and adverse functional cardiac parameters, including reduced early diastolic filling rates as well as a higher prevalence of LGE compared with healthy control subjects. In addition, people with prediabetes had significantly elevated levels of PDFF and total and visceral fat. Thus, subjects with prediabetes show early signs of subclinical disease that include vascular, cardiac, and metabolic changes, as measured by whole-body MRI after adjusting for cardiometabolic risk factors.

Screening programs are recommended for individuals at risk (IAR) from families with familial pancreatic cancer (FPC). However, reliable imaging methods or biomarkers for early diagnosis of pancreatic ductal adenocarcinoma (PC) or its precursor lesions are still lacking. The ability of circulating microRNAs (miRNAs) to discriminate multifocal high-grade precursor lesions or PC from normal was examined. The presence of miRNA-21, -155, -196a, -196b and -210 was analyzed in the serum of transgenic KPC mice to test their ability to distinguish mice with different grades of pancreatic intraepithelial neoplasia (mPanIN1-3) or PC from control mice. Serum levels of miR-196a and -196b were significantly higher in mice with PanIN2/3 lesions (n = 10) or PC (n = 8) as compared to control mice (n = 10) or mice with PanIN1 lesions (n = 10; P = .01). In humans, miR-196a and -196b were also diagnostic. Patients with PC, sporadic (n = 9) or hereditary (n = 10), and IAR with multifocal PanIN2/3 lesions (n = 5) had significantly higher serum levels than patients with neuroendocrine pancreatic tumors (n = 10) or chronic pancreatitis (n = 10), IAR with PanIN1 or no PanIN lesions (n = 5), and healthy controls (n = 10). The combination of both miR-196a and -196b reached a sensitivity of 1 and specificity of 0.9 (area under the curve = 0.99) to diagnose PC or high-grade PanIN lesions. In addition, preoperative elevated serum levels of miR-196a and -196b in patients with PC or multifocal PanIN2/3 lesions dropped to normal after potential curative resection. The combination of miR-196a and -196b may be a promising biomarker test for the screening of IAR for FPC.

Objectives: We assessed whether liver fat content, as determined by MRI, correlates with blood pressure (BP), a major vascular risk factor, in individuals from the general population without history of stroke and coronary or peripheral artery disease. Methods: Cross-sectional data from 384 participants (161 women; aged 39–73 years) of a MRI substudy of the KORA FF4 survey were used. Hepatic fat fraction (HFF) was measured in the left and right lobe of the liver using single voxel multiecho ¹H-spectroscopy and at the level of the portal vein using a multiecho Dixon-sequence. Associations of HFF with SBP and DBP as well as hypertension were assessed by right censored normal regression (accounting for antihypertensive treatment) and by logistic regression, respectively. Results: High levels of HFF measured on the level of the portal vein (90th percentile, 21.8%), compared with low HFF levels (10th percentile, 1.7%), were associated with higher SBP (131 vs. 122 mmHg; overall P = 0.001), higher DBP (82 vs. 76 mmHg, P < 0.001) and with higher odds of hypertension [odds ratio (OR) = 2.16, P = 0.025]. A level of 5.13% (54th percentile) was identified as optimal HFF cut-off for the prediction of hypertension (OR = 2.00, P = 0.015). Alcohol consumption emerged as an effect modifier for the association between HFF and hypertension (nonalcohol drinker: OR = 3.76, P = 0.025; alcohol drinker: OR = 1.59, P = 0.165). Conclusion: MRI-derived subclinical HFF is associated with SBP and DBP as well as with hypertension in participants from the general population without history of cardiovascular disease.

Elevated serum uric acid (SUA) is associated with a variety of medical conditions, such as hypertension, diabetes and obesity. Analyses investigating uric acid and obesity were primarily conducted using anthropometric measures like BMI and waist circumference. However, different adipose tissue depots might be differentially affected in uric acid metabolism. We analyzed the relation of SUA with visceral, subcutaneous and hepatic fat as quantified by Magnetic Resonance Imaging in N = 371 individuals from a cross-sectional sample of a population-based cohort. Associations of SUA and fat depots were calculated by regressions adjusted for potential confounders. We found that SUA was correlated with all fat measures (e.g. Pearson’s r between SUA and hepatic fat: 0.50, 95%-CI: 0.42, 0.57). Associations with visceral and hepatic fat, but not with subcutaneous fat, remained evident after adjustment for anthropometric measures (e.g. visceral fat: β = 0.51 l, 95%-CI: 0.30 l, 0.72 l). In conclusion, these results show how different adipose tissue compartments are affected by SUA to varying degrees, thus emphasizing the different physiological roles of these adipose tissues in uric acid metabolism.

Objective:To study the relationship of area- and volumetric-based visceral and subcutaneous adipose tissue (VAT and SAT) by MRI and their ratio in subjects with impaired glucose metabolism from the...

Manually extracted imaging-based body composition measures from a single-slice area (A) have shown associations with clinical outcomes in patients with cardiometabolic disease and cancer. With advances in artificial intelligence, fully automated volumetric (V) segmentation approaches are now possible, but it is unknown whether these measures carry prognostic value to predict mortality in the general population. Here, we developed and tested a deep learning framework to automatically quantify volumetric body composition measures from whole-body magnetic resonance imaging (MRI) and investigated their prognostic value to predict mortality in a large Western population. The framework was developed using data from two large Western European population-based cohort studies, the UK Biobank (UKBB) and the German National Cohort (NAKO). Body composition was defined as (i) subcutaneous adipose tissue (SAT), (ii) visceral adipose tissue (VAT), (iii) skeletal muscle (SM), SM fat fraction (SMFF), and (iv) intramuscular adipose tissue (IMAT). The prognostic value of the body composition measures was assessed in the UKBB using Cox regression analysis. Additionally, we extracted body composition areas for every level of the thoracic and lumbar spine (i) to compare the proposed volumetric whole-body approach to the currently established single-slice area approach on the height of the L3 vertebra and (ii) to investigate the correlation between volumetric and single slice area body composition measures on the level of each vertebral body. In 36,317 UKBB participants (mean age 65.1 ± 7.8 years, age range 45-84 years; 51.7% female; 1.7% [634/36,471] all-cause deaths; median follow-up 4.8 years), Cox regression revealed an independent association between V_SM (adjusted hazard ratio [aHR]: 0.88, 95% confidence interval [CI] [0.81-0.91], p = 0.00023), V_SMFF (aHR: 1.06, 95% CI [1.02-1.10], p = 0.0043), and V_IMAT (aHR: 1.19, 95% CI [1.05-1.35], p = 0.0056) and mortality after adjustment for demographics (age, sex, BMI, race) and cardiometabolic risk factors (alcohol consumption, smoking status, hypertension, diabetes, history of cancer, blood serum markers). This association was attenuated when using traditional single-slice area measures. Highest correlation coefficients (R) between volumetric and single-slice area body composition measures were located at vertebra L5 for SAT (R = 0.820) and SMFF (R = 0.947), at L3 for VAT (R = 0.892), SM (R = 0.944), and at L4 for IMAT (R = 0.546) (all p < 0.0001). A similar pattern was found in 23,725 NAKO participants (mean age 53.9 ± 8.3 years, age range 40-75; 44.9% female). Automated volumetric body composition assessment from whole-body MRI predicted mortality in a large Western population beyond traditional clinical risk factors. Single slice areas were highly correlated with volumetric body composition measures but their association with mortality attenuated after multivariable adjustment. As volumetric body composition measures are increasingly accessible using automated techniques, identifying high-risk individuals may help to improve personalised prevention and lifestyle interventions. This project was conducted using data from the German National Cohort (NAKO) (www.nako.de). The NAKO is funded by the Federal Ministry of Education and Research (BMBF) [project funding reference numbers: 01ER1301A/B/C, 01ER1511D, and 01ER1801A/B/C/D], federal states of Germany and the Helmholtz Association, the participating universities and the institutes of the Leibniz Association. This research has been conducted using the UK Biobank Resource under Application Number 80337. MJ was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)-518480401. VKR was funded by American Heart Association Career Development Award 935176 and National Heart, Lung, and Blood Institute-K01HL168231.