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

Normalized indices derived from visceral adipose mass assessed by MRI and their correlation with markers for insulin resistance and prediabetes

Visceral adipose tissue (VAT) plays an important role in the pathogenesis of insulin resistance (IR), prediabetes and type 2 diabetes. However, VAT volume alone might not be the best marker for insulin resistance and prediabetes or diabetes, as a given VAT volume may differently impact on these metabolic traits based on body height, gender, age and ethnicity. In a cohort of 1295 subjects from the Tübingen Diabetes Family Study (TDFS) and in 9978 subjects from the UK Biobank (UKBB), undergoing magnetic resonance imaging for quantification of VAT volume, total adipose tissue (TAT, in the TDFS), total abdominal adipose tissue (TAAT) in the UKBB, and total lean tissue (TLT), VAT volume and several VAT-indices were investigated for their relationships with insulin resistance and glycemic traits. VAT-related indices were calculated by correcting for body height (VAT/m: VAT/body height; VAT/m²: VAT/(body height)², and VAT/m³: VAT/(body height)³), TAT (%VAT), TLT (VAT/TLT) and weight (VAT/WEI), with closest equivalents used within the UKBB dataset. Prognostic values of VAT and VAT-related indices for insulin sensitivity, HbA1c levels and prediabetes/diabetes were analyzed for males and females. Males had higher VAT volume and VAT-related indices than females in both cohorts (p < 0.0001) and VAT volume has shown to be a stronger determinant for insulin sensitivity than anthropometric variables. Among the parameters uncorrected VAT and derived indices, VAT/m³ most strongly correlated negatively with insulin sensitivity and positively with HbA1c levels and prediabetes/diabetes in the TDFS (R² = 0.375/0.305 for females/males for insulin sensitivity, 0.178/0.148 for HbA1c levels vs. – e.g. – 0.355/0.293 and 0.144/0.133 for VAT, respectively) and positively with HbA1c (R² = 0.046/0.042) in the UKBB for females and males. Furthermore, VAT/m³ was found to be a significantly better determinant of insulin resistance or prediabetes than uncorrected VAT volume (p < 0.001/0.019 for females/males regarding insulin sensitivity, p < 0.001/< 0.001 for females/males regarding HbA1c). Evaluation of several indices derived from VAT volume identified VAT/m³ to most strongly correlate with insulin sensitivity and glucose metabolism. Thus, VAT/m³ appears to provide better indications of metabolic characteristics (insulin sensitivity and pre-diabetes/diabetes) than VAT volume alone

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

Novel desmoplakin mutation: juvenile biventricular cardiomyopathy with left ventricular non-compaction and acantholytic palmoplantar keratoderma

Two sons of a consanguineous marriage developed biventricular cardiomyopathy. One boy died of severe heart failure at the age of 6 years, the other was transplanted because of severe heart failure at the age of 10 years. In addition, focal palmoplantar keratoderma and woolly hair were apparent in both boys. As similar phenotypes have been described in Naxos disease and Carvajal syndrome, respectively, the genes for plakoglobin (JUP) and desmoplakin (DSP) were screened for mutations using direct genomic sequencing. A novel homozygous 2 bp deletion was identified in an alternatively spliced region of DSP. The deletion 5208_5209delAG led to a frameshift downstream of amino acid 1,736 with a premature truncation of the predominant cardiac isoform DSP-1. This novel homozygous truncating mutation in the isoform-1 specific region of the DSP C-terminus caused Carvajal syndrome comprising severe early-onset heart failure with features of non-compaction cardiomyopathy, woolly hair and an acantholytic form of palmoplantar keratoderma in our patient. Congenital hair abnormality and manifestation of the cutaneous phenotype in toddler age can help to identify children at risk for cardiac death

Quantitative and qualitative differences in subcutaneous adipose tissue stores across lipodystrophy types shown by magnetic resonance imaging

BACKGROUND: Lipodystrophies are characterized by redistributed subcutaneous fat stores. We previously quantified subcutaneous fat by magnetic resonance imaging (MRI) in the legs of two patients with familial partial lipodystrophy subtypes 2 and 3 (FPLD2 and FPLD3, respectively). We now extend the MRI analysis across the whole body of patients with different forms of lipodystrophy. METHODS: We studied five subcutaneous fat stores (supraclavicular, abdominal, gluteal, thigh and calf) and the abdominal visceral fat stores in 10, 2, 1, 1 and 2 female subjects with, respectively, FPLD2, FPLD3, HIV-related partial lipodystrophy (HIVPL), acquired partial lipodystrophy (APL), congenital generalized lipodystrophy (CGL) and in six normal control subjects. RESULTS: Compared with normal controls, FPLD2 subjects had significantly increased supraclavicular fat, with decreased abdominal, gluteal, thigh and calf subcutaneous fat. FPLD3 subjects had increased supraclavicular and abdominal subcutaneous fat, with less severe reductions in gluteal, thigh and calf fat compared to FPLD2 subjects. The repartitioning of fat in the HIVPL subject closely resembled that of FPLD3 subjects. APL and CGL subjects had reduced upper body, gluteal and thigh subcutaneous fat; the APL subject had increased, while CGL subjects had decreased subcutaneous calf fat. Visceral fat was markedly increased in FPLD2 and APL subjects. CONCLUSION: Semi-automated MRI-based adipose tissue quantification indicates differences between various lipodystrophy types in these studied clinical cases and is a potentially useful tool for extended quantitative phenomic analysis of genetic metabolic disorders. Further studies with a larger sample size are essential for confirming these preliminary findings

Feasibility of MR-Based Body Composition Analysis in Large Scale Population Studies

Introduction Quantitative and accurate measurements of fat and muscle in the body are important for prevention and diagnosis of diseases related to obesity and muscle degeneration. Manually segmenting muscle and fat compartments in MR body-images is laborious and time-consuming, hindering implementation in large cohorts. In the present study, the feasibility and success-rate of a Dixon-based MR scan followed by an intensity-normalised, non-rigid, multi-atlas based segmentation was investigated in a cohort of 3,000 subjects. Materials and Methods 3,000 participants in the in-depth phenotyping arm of the UK Biobank imaging study underwent a comprehensive MR examination. All subjects were scanned using a 1.5 T MR-scanner with the dual-echo Dixon Vibe protocol, covering neck to knees. Subjects were scanned with six slabs in supine position, without localizer. Automated body composition analysis was performed using the AMRA Profiler™ system, to segment and quantify visceral adipose tissue (VAT), abdominal subcutaneous adipose tissue (ASAT) and thigh muscles. Technical quality assurance was performed and a standard set of acceptance/rejection criteria was established. Descriptive statistics were calculated for all volume measurements and quality assurance metrics. Results Of the 3,000 subjects, 2,995 (99.83%) were analysable for body fat, 2,828 (94.27%) were analysable when body fat and one thigh was included, and 2,775 (92.50%) were fully analysable for body fat and both thigh muscles. Reasons for not being able to analyse datasets were mainly due to missing slabs in the acquisition, or patient positioned so that large parts of the volume was outside of the field-of-view. Discussion and Conclusions In conclusion, this study showed that the rapid UK Biobank MR-protocol was well tolerated by most subjects and sufficiently robust to achieve very high success-rate for body composition analysis. This research has been conducted using the UK Biobank Resource