Association between abdominal adiposity and subclinical measures of left-ventricular remodeling in diabetics, prediabetics and normal controls without history of cardiovascular disease as measured by magnetic resonance imaging: results from the KORA-FF4 Study

Objectives: Local, abdominal fat depots may be related to alterations in cardiac function and morphology due to a metabolic linkage. Thus, we aimed to determine their association with subtle cardiac changes and the potential interaction with hyperglycemic metabolic states. Methods: Subjects from the general population and without history of cardiovascular disease were drawn from the Cooperative Health Research in the Region of Augsburg FF4 cohort and underwent 3 T cardiac and body MRI. Measures of abdominal adiposity such as hepatic proton-density fat fraction [PDFFhepatic], subcutaneous (SAT) and visceral abdominal fat (VAT) as well as established cardiac left-ventricular (LV) measures including LV remodeling index (LVCI) were derived. Associations were determined using linear regression analysis based on standard deviation normalized predictors. Results: Among a total of 374 subjects (56.2 ± 9.1 years, 58% males), 49 subjects had diabetes, 99 subjects had prediabetes and 226 represented normal controls. Only subtle cardiac alterations were observed (e.g. LVCI: 1.13 ± 0.30). While SAT was not associated, increasing VAT and increasing PDFFhepatic were independently associated with increasing LVCI (β = 0.11 and 0.06, respectively), decreasing LV end-diastolic volume (β = − 6.70 and 3.23, respectively), and decreasing LV stroke volume (β = − 3.91 and − 2.20, respectively). Hyperglycemic state did not modify the associations between VAT or PDFF and LV measures (interaction term: all p ≥ 0.29). Conclusion: In a healthy population, VAT but also PDFFhepatic were associated with subclinical measures of LV remodeling without evidence for a modifying effect of hyperglycemic state

No association between variation in the NR4A1 gene locus and metabolic traits in white subjects at increased risk for type 2 diabetes

<p>Abstract</p> <p>Background</p> <p>The nuclear receptor NR4A1 is implicated in metabolic regulation in insulin-sensitive tissues, such as liver, adipose tissue, and skeletal muscle. Functional loss of NR4A1 results in insulin resistance and enhanced intramuscular and hepatic lipid content. Therefore, we investigated in a cohort of white European subjects at increased risk for type 2 diabetes whether genetic variation within the <it>NR4A1 </it>gene locus contributes to prediabetic phenotypes, such as insulin resistance, ectopic fat distribution, or β-cell dysfunction.</p> <p>Methods</p> <p>We genotyped 1495 subjects (989 women, 506 men) for five single nucleotide polymorphisms (SNPs) tagging 100% of common variants (MAF = 0.05) within the <it>NR4A1 </it>gene locus with an r²= 0.8. All subjects underwent an oral glucose tolerance test (OGTT), a subset additionally had a hyperinsulinemic-euglycemic clamp (n = 506). Ectopic hepatic (n = 296) and intramyocellular (n = 264) lipids were determined by magnetic resonance spectroscopy. Peak aerobic capacity, a surrogate parameter for oxidative capacity of skeletal muscle, was measured by an incremental exercise test on a motorized treadmill (n = 270).</p> <p>Results</p> <p>After appropriate adjustment and Bonferroni correction for multiple comparisons, none of the five SNPs was reliably associated with insulin sensitivity, ectopic fat distribution, peak aerobic capacity, or indices of insulin secretion (all p ≥ 0.05).</p> <p>Conclusions</p> <p>Our data suggest that common genetic variation within the <it>NR4A1 </it>gene locus may not play a major role in the development of prediabetic phenotypes in our white European population.</p

GENTEL : GENerating Training data Efficiently for Learning to segment medical images

International audienceAccurately segmenting MRI images is crucial for many clinical applications. However, manually segmenting images with accurate pixel precision is a tedious and time consuming task. In this paper we present a simple, yet effective method to improve the efficiency of the image segmentation process. We propose to transform the image annotation task into a binary choice task. We start by using classical image processing algorithms with different parameter values to generate multiple, different segmentation masks for each input MRI image. Then, the user, instead of segmenting the pixels of the images, she/he only needs to decide if a segmentation is acceptable or not. This method allows us to efficiently obtain high quality segmentations with minor human intervention. With the selected segmentations we train a state-of-the-art neural network model. For the evaluation, we use a second MRI dataset (1.5T Dataset), acquired with a different protocol and containing annotations. We show that the trained network i) is capable to automatically segment cases where none of the classical methods obtained a high quality result ii) generalizes to the second MRI dataset, which was acquired with a different protocol and never seen at training time ; and iii) allows to detect miss-annotations in this second dataset. Quantitatively, the trained network obtains very good results : DICE score - mean 0.98, median 0.99- and Hausdorff distance (in pixels) - mean 4.7, median 2.0-.La segmentation précise d'images à résonnance magnétiques (IRM) est cruciale pour de nombreuses applications cliniques. Cependant, une segmentation manuelle visant une précision au niveau du pixel est une tâche longue et fastidieuse. Dans cet article, nous proposons une méthode simple pour améliorer l'efficacité de la segmentation d'images. Nous proposons de transformer la tâche d'annotation d'une image en une tâche de choix binaire. D'abord, nous utilisons plusieurs algorithmes classiques de traitement d'image pour générer plusieurs candidats de masques de segmentation. Ensuite, l'utilisat.eur.rice, au lieu de segmenter les pixels des images, décide si une segmentation est acceptable ou non. Cette méthode nous permet d'obtenir efficacement un grand nombre de segmentations de haute qualité avec une intervention humaine li-mitée. Avec les images et leurs segmentations sélectionnées, nous entrainons un réseau de neurones de l'état de l'art qui prédit les segmentations à partir des images d'entrée. Nous le validons sur un autre jeu de données IRM, acquis avec un protocole différent, et qui contient des segmentations. Nous montrons que le réseau entrainé 1) est capable de segmenter automatiquement des cas où aucune des méthodes classiques n'a obtenu un résultat de haute qualité, 2) est capable de segmenter un autre jeu de don-nées IRM, acquis avec un protocole différent et jamais vu lors de l'entrainement, et 3) permet de détecter des annotations erronées dans ce jeu de données. Quantitativement, le réseau entrainé obtient de très bons résultats : Score DICE-moyenne 0,98 et médiane 0,99-et distance de Hausdorff (en pixels)-moyenne 4,7, médiane 2,0

High Protein Diets Improve Liver Fat and Insulin Sensitivity by Prandial but Not Fasting Glucagon Secretion in Type 2 Diabetes

Glucagon (GCGN) plays a key role in glucose and amino acid (AA) metabolism by increasing hepatic glucose output. AA strongly stimulate GCGN secretion which regulates hepatic AA degradation by ureagenesis. Although increased fasting GCGN levels cause hyperglycemia GCGN has beneficial actions by stimulating hepatic lipolysis and improving insulin sensitivity through alanine induced activation of AMPK. Indeed, stimulating prandial GCGN secretion by isocaloric high protein diets (HPDs) strongly reduces intrahepatic lipids (IHLs) and improves glucose metabolism in type 2 diabetes mellitus (T2DM). Therefore, the role of GCGN and circulating AAs in metabolic improvements in 31 patients with T2DM consuming HPD was investigated. Six weeks HPD strongly coordinated GCGN and AA levels with IHL and insulin sensitivity as shown by significant correlations compared to baseline. Reduction of IHL during the intervention by 42% significantly improved insulin sensitivity [homeostatic model assessment for insulin resistance (HOMA-IR) or hyperinsulinemic euglycemic clamps] but not fasting GCGN or AA levels. By contrast, GCGN secretion in mixed meal tolerance tests (MMTTs) decreased depending on IHL reduction together with a selective reduction of GCGN-regulated alanine levels indicating greater GCGN sensitivity. HPD aligned glucose metabolism with GCGN actions. Meal stimulated, but not fasting GCGN, was related to reduced liver fat and improved insulin sensitivity. This supports the concept of GCGN-induced hepatic lipolysis and alanine- and ureagenesis-induced activation of AMPK by HPD

Magnetic resonance imaging of obesity and metabolic disorders: Summary from the 2019 ISMRM Workshop

More than 100 attendees from Australia, Austria, Belgium, Canada, China, Germany, Hong Kong, Indonesia, Japan, Malaysia, the Netherlands, the Philippines, Republic of Korea, Singapore, Sweden, Switzerland, the United Kingdom, and the United States convened in Singapore for the 2019 ISMRM-sponsored workshop on MRI of Obesity and Metabolic Disorders. The scientific program brought together a multidisciplinary group of researchers, trainees, and clinicians and included sessions in diabetes and insulin resistance; an update on recent advances in water–fat MRI acquisition and reconstruction methods; with applications in skeletal muscle, bone marrow, and adipose tissue quantification; a summary of recent findings in brown adipose tissue; new developments in imaging fat in the fetus, placenta, and neonates; the utility of liver elastography in obesity studies; and the emerging role of radiomics in population-based “big data” studies. The workshop featured keynote presentations on nutrition, epidemiology, genetics, and exercise physiology. Forty-four proffered scientific abstracts were also presented, covering the topics of brown adipose tissue, quantitative liver analysis from multiparametric data, disease prevalence and population health, technical and methodological developments in data acquisition and reconstruction, newfound applications of machine learning and neural networks, standardization of proton density fat fraction measurements, and X-nuclei applications. The purpose of this article is to summarize the scientific highlights from the workshop and identify future directions of work

Common Genetic Variation in the SERPINF1 Locus Determines Overall Adiposity, Obesity-Related Insulin Resistance, and Circulating Leptin Levels

OBJECTIVE: Pigment epithelium-derived factor (PEDF) belongs to the serpin family of peptidase inhibitors (serpin F1) and is among the most abundant glycoproteins secreted by adipocytes. In vitro and mouse in vivo data revealed PEDF as a candidate mediator of obesity-induced insulin resistance. Therefore, we assessed whether common genetic variation within the SERPINF1 locus contributes to adipose tissue-related prediabetic phenotypes in humans. SUBJECTS/METHODS: A population of 1,974 White European individuals at increased risk for type 2 diabetes was characterized by an oral glucose tolerance test with glucose and insulin measurements (1,409 leptin measurements) and genotyped for five tagging SNPs covering 100% of common genetic variation (minor allele frequency ≥ 0.05) in the SERPINF1 locus. In addition, a subgroup of 486 subjects underwent a hyperinsulinaemic-euglycaemic clamp and a subgroup of 340 magnetic resonance imaging (MRI) and spectroscopy (MRS). RESULTS: After adjustment for gender and age and Bonferroni correction for the number of SNPs tested, SNP rs12603825 revealed significant association with MRI-derived total adipose tissue mass (p = 0.0094) and fasting leptin concentrations (p = 0.0035) as well as nominal associations with bioelectrical impedance-derived percentage of body fat (p = 0.0182) and clamp-derived insulin sensitivity (p = 0.0251). The association with insulin sensitivity was completely abolished by additional adjustment for body fat (p = 0.8). Moreover, the fat mass-increasing allele of SNP rs12603825 was significantly associated with elevated fasting PEDF concentrations (p = 0.0436), and the PEDF levels were robustly and positively associated with all body fat parameters measured and with fasting leptin concentrations (p<0.0001, all). CONCLUSION: In humans at increased risk for type 2 diabetes, a functional common genetic variant in the gene locus encoding PEDF contributes to overall body adiposity, obesity-related insulin resistance, and circulating leptin levels

Proton magnetic resonance spectroscopy in skeletal muscle: Experts' consensus recommendations

H-1-MR spectroscopy of skeletal muscle provides insight into metabolism that is not available noninvasively by other methods. The recommendations given in this article are intended to guide those who have basic experience in general MRS to the special application of H-1-MRS in skeletal muscle. The highly organized structure of skeletal muscle leads to effects that change spectral features far beyond simple peak heights, depending on the type and orientation of the muscle. Specific recommendations are given for the acquisition of three particular metabolites (intramyocellular lipids, carnosine and acetylcarnitine) and for preconditioning of experiments and instructions to study volunteers.Peer reviewe

Quantifying the improvement of surrogate indices of hepatic insulin resistance using complex measurement techniques

We evaluated the ability of simple and complex surrogate-indices to identify individuals from an overweight/obese cohort with hepatic insulin-resistance (HEP-IR). Five indices, one previously defined and four newly generated through step-wise linear regression, were created against a single-cohort sample of 77 extensively characterised participants with the metabolic syndrome (age 55.6±1.0 years, BMI 31.5±0.4 kg/m2; 30 males). HEP-IR was defined by measuring endogenous-glucose-production (EGP) with [6–62H2] glucose during fasting and euglycemic-hyperinsulinemic clamps and expressed as EGP*fasting plasma insulin. Complex measures were incorporated into the model, including various non-standard biomarkers and the measurement of body-fat distribution and liver-fat, to further improve the predictive capability of the index. Validation was performed against a data set of the same subjects after an isoenergetic dietary intervention (4 arms, diets varying in protein and fiber content versus control). All five indices produced comparable prediction of HEP-IR, explaining 39–56% of the variance, depending on regression variable combination. The validation of the regression equations showed little variation between the different proposed indices (r2 = 27–32%) on a matched dataset. New complex indices encompassing advanced measurement techniques offered an improved correlation (r = 0.75, P<0.001). However, when validated against the alternative dataset all indices performed comparably with the standard homeostasis model assessment for insulin resistance (HOMA-IR) (r = 0.54, P<0.001). Thus, simple estimates of HEP-IR performed comparable to more complex indices and could be an efficient and cost effective approach in large epidemiological investigations