Id-1 is an important regulator of cellular growth and differentiation and controls malignant progression of breast cancer cells. The aim of our study was to assess the clinical impact of Id-1 expression in breast cancer, i.e., its potential impact on prognosis and prediction of treatment response. Id-1 protein expression was determined immunohistochemically in 191 patients with lymph-node negative breast cancer, and univariate and multivariate survival analysis was carried out. Fifteen (7.9%) specimens showed strong expression, 75 (39.3%) moderate, 55 (28.8%) weak expression and 46 (24.1%) cases no expression of Id-1. Patients with strong or moderate Id-1 expression had a significant shorter overall (p ‫؍‬ 0.003, Cox regression) and disease-free survival (p ‫؍‬ 0.01, Cox regression) compared to those with absent or low expression. Progesterone receptor density was significantly higher in breast cancers with absent/low Id-1 expression compared to those with moderate/strong expression (p < 0.001, t-test). Id-1 expression was significantly stronger in cases positive for p16 INK4a expression compared to those negative for p16 (p ‫؍‬ 0.049, Mann-Whitney test). The influence of Id-1 on clinical outcome seems much stronger in patients with negative estrogen receptor status compared to those with positive status, who received receptor antagonists as adjuvant therapy in most cases. Overexpression of Id-1 protein represents a strong independent prognostic marker in node negative breast cancer, and future therapies inhibiting Id-1 expression might be beneficial for these patients. Our results also suggest that due to the apparent interaction of Id-1 with the steroid-receptor system in breast cancer, hormonal therapies might influence Id-1 expression and its impact on clinical outcome.

HypothesisObesity is one of the main drivers of type 2 diabetes (T2D), but not uniformly associated with the disease. The location of fat accumulation is critical for metabolic health. Specific patterns of body fat distribution such as visceral fat, are closely related to insulin resistance. There might be further, hitherto unknown features of body fat distribution which could additionally contribute to the disease. MethodsWe used machine learning with dense convolutional neural networks (DCNN) to detect diabetes related variables from 2,371 T1-weighted whole-body magnetic resonance imaging (MRI) datasets. MRI was performed in participants undergoing metabolic screening with oral glucose tolerance tests. Models were trained for sex, age, BMI, insulin sensitivity, HbA1c and prediabetes or incident diabetes. The results were compared to conventional models. ResultsThe Area Under the Receiver Operator Characteristic curve was 87% for the T2D discrimination and 68% for prediabetes, both superior to conventional models. Mean absolute regression errors were comparable to conventional models. Heatmaps showed that lower visceral abdominal regions were critical in diabetes classification.Subphenotyping revealed a group with high future diabetes and microalbuminuria risk. InterpretationOur results show that diabetes is detectable from whole-body MRI without additional data. Our technique of heatmap visualization unravels plausible anatomical regions and highlights the leading role of fat accumulation in the lower abdomen in diabetes pathogenesis.

Background Quantification of pancreatic fat (PF) and intrahepatic lipids (IHL) is of increasing interest in subjects at risk for metabolic diseases. There is limited data available on short‐ and medium‐term variability of PF/IHL and on their dependence on nutritional status. Purpose To assess short‐term intraday variations of PF/IHL after a high‐fat meal as well as medium‐term changes after 5 days of high‐caloric diet. Study Type Prospective cohort study. Subjects A total of 12 subjects (six males) for intraday variations study, 15 male subjects for medium‐term high‐caloric diet study and 11 age‐ and body mass index (BMI)‐matched controls. Field Strength/Sequence A 3 T; chemical‐shift encoded multiecho gradient echo sequence. Assessment For the intraday study, subjects were scanned after overnight fasting and after a high fat meal on the same day. For the medium‐term study, 26 subjects were scanned after overnight fasting with 15/11 rescanned after 5 days of high‐calorie diet/isocaloric diet. Proton density fat fraction (PDFF) maps were generated inline on the scanner. Regions of interest were manually drawn in head, body, and tail of pancreas and in the liver by a medical physicist and a doctoral student (26/4 years of experience). PF was calculated as the average of the head, body, and tail measurements. Statistical Tests Repeated measurements ANOVA for assessing changes in PF/IHL, linear correlation analyses for assessing relationships of PF/IHL with BMI. Significance level P < 0.05 for all. Results Nonsignificant changes in PF (2.6 ± 1.0 vs. 2.7 ± 0.9% after high‐fat meal, 1.4 ± 0.8 vs. 1.5 ± 0.6% [high‐caloric diet] and 1.5 ± 0.8 vs. 1.8 ± 1.0% [isocaloric control group]), nonsignificant changes in IHL after high‐fat meal (2.6 ± 1.3 vs. 2.5 ± 0.9%) and in the control group (1.1 ± 0.6 vs. 1.2 ± 1.1%), significantly increased IHL after high‐caloric diet (1.7 ± 2.2% vs. 2.7 ± 3.6%). Nonsignificant changes in PF (2.6 ± 1.0 vs. 2.7 ± 0.9% after high‐fat meal, 1.4 ± 0.8 vs. 1.5 ± 0.6% [high‐caloric diet] and 1.5 ± 0.8 vs. 1.8 ± 1.0% [isocaloric control group]), nonsignificant changes in IHL after high‐fat meal (2.6 ± 1.3 vs. 2.5 ± 0.9%) and in the control group (1.1 ± 0.6 vs. 1.2 ± 1.1%), significantly increased IHL after 5‐days of high‐caloric diet (1.7 ± 2.2% vs. 2.7 ± 3.6%). Data Conclusion Time of day and nutritional status have no significant influence on PF/IHL and are therefore not likely to be major confounders in epidemiologic or clinical studies. Evidence Level 2 Technical Efficacy Stage 1

BACKGROUND Quantification of pancreatic fat (PF) and intrahepatic lipids (IHL) is of increasing interest in cross-sectional epidemiological and interventional studies in subjects at risk for metabolic diseases. Up to now, short- and medium-term variations as well as their dependence on actual nutritional status are almost unknown. PURPOSE or HYPOTHESIS To assess short-term intraday variations of PF/IHL after a high-fat meal as well as medium-term changes after 5 days of high-caloric diet with a 1500 kcal surplus on individual daily energy requirement. STUDY TYPE Prospective cohort study. SUBJECTS Twelve healthy subjects (6m/6f) for intraday variations, 15 healthy male subjects for medium-term high-caloric diet and 11 age- and BMI-matched controls. FIELDSTRENGTH/SEQUENCE 3 T whole-body imager (Magnetom Vida, Siemens Healthineers, Erlangen, Germany), assessment of proton density fat fraction by chemical-shift encoded MRI (multi-echo gradient echo sequence, qDixon). ASSESSMENT Manually drawn regions of interest in head, body and tail of pancreas as well as in liver by an experienced medical physicist carefully avoiding inclusion of surrounding visceral fat (pancreas) or blood-vessels (liver). STATISTICAL TESTS Repeated measurements Anova for variabilities of PF and IHL, linear correlation analyses for relation of PF, IHL and BMI. Significance level p < 0.05 for all. RESULTS (must have numerical data and statistical testing for each phrase) Non-significant changes in PF in both studies (2.5±0.9 vs. 2.5±1.0% after high-fat meal, 1.4±0.8 vs. 1.6±0.6% after high-caloric diet and 1.6±0.7 vs. 1.8±1.0% in the isocaloric control group), unchanged IHL after high-fat meal (2.5±0.9 vs. 2.4±1.0 %) and in the control group (1.1±0.6 vs. 1.2±1.1%), but significantly increased IHL after 5-day high-caloric diet (1.6±2.2% vs. 2.6±3.6%, p < 0.05). DATA CONCLUSION Daytime and nutritional status have no significant influence on ectopic fat depots in pancreas and liver and will therefore represent no major confounders in epidemiologic or clinical studies.

Obesity is one of the main drivers of the globally rising prevalence of type 2 diabetes (T2D). Yet, obesity is not uniformly associated with metabolic consequences. The location of fat accumulation is critical for metabolic health. Specific patterns of body fat distribution, such as an increased ratio of visceral to subcutaneous fat, are closely related to insulin resistance which is crucial in the pathogenesis of T2D. There might be further, hitherto unknown features of body fat distribution which could additionally contribute to the disease. We used a machine learning approach with dense convolutional neural networks (DCNN) to detect diabetes related variables from 2371 T1-weighted whole-body magnetic resonance image (MRI) data sets. Each single measurement was labelled by sex, age, BMI, insulin sensitivity, HbA1c and prediabetes or incident diabetes. The result was compared to conventional models using segmented body fat compartment volumes. Anatomical labels were assigned to locations in the DCNN gradient heatmaps that are critical for discrimination. The AUC-ROC was 0.87 for the discrimination of diabetes and 0.68 for prediabetes. Classification performance was superior to conventional models. Mean absolute regression errors were comparable to those of the conventional models. Heatmaps clearly showed that lower visceral abdominal regions were most critical in diabetes classification, while other significant areas comprised upper legs, arms and the neck region.Our results show that diabetes is detectable from whole-body MRI without any blood glucose measurement. Our technique of heatmap visualization unravels plausible anatomical regions and highlights the leading role of fat accumulation in the lower abdomen in the pathogenesis of T2D. Disclosure R. Wagner: Advisory Panel; Self; Novo Nordisk A/S. Speaker’s Bureau; Self; Novo Nordisk A/S. Other Relationship; Self; Eli Lilly and Company. B. Dietz: None. J. Machann: None. P. Schwab: Employee; Self; Roche Pharma. J.K. Dienes: Advisory Panel; Spouse/Partner; Novo Nordisk A/S. Speaker’s Bureau; Spouse/Partner; Novo Nordisk A/S. Other Relationship; Spouse/Partner; Eli Lilly and Company. S. Reichert: Other Relationship; Self; Lilly Diabetes. A.L. Birkenfeld: None. H. Haering: None. F. Schick: None. N. Stefan: None. M. Heni: Research Support; Self; Boehringer Ingelheim Pharmaceuticals, Inc., Sanofi. Speaker’s Bureau; Self; Novo Nordisk A/S. H. Preissl: None. B. Schölkopf: None. S. Bauer: None. A. Fritsche: None. Funding German Federal Ministry of Education and Research (01GI0925)