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Lymphedema and lipedema are complex diseases. While the external presentation of swollen legs in lower-extremity lymphedema and lipedema appear similar, current mechanistic understandings of these diseases indicate unique aspects of their underlying pathophysiology. They share certain clinical features, such as fluid (edema), fat (adipose expansion), and fibrosis (extracellular matrix remodeling). Yet, these diverge on their time course and known molecular regulators of pathophysiology and genetics. This divergence likely indicates a unique route leading to interstitial fluid accumulation and subsequent inflammation in lymphedema versus lipedema. Identifying disease mechanisms that are causal and which are merely indicative of the condition is far more explored in lymphedema than in lipedema. In primary lymphedema, discoveries of genetic mutations link molecular markers to mechanisms of lymphatic disease. Much work remains in this area towards better risk assessment of secondary lymphedema and the hopeful discovery of validated genetic diagnostics for lipedema. The purpose of this review is to expose the distinct and shared (i) clinical criteria and symptomatology, (ii) molecular regulators and pathophysiology, and (iii) genetic markers of lymphedema and lipedema to help inform future research in this field.
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PURPOSE: Lipedema is a painful subcutaneous adipose tissue (SAT) disease involving disproportionate SAT accumulation in the lower extremities that is frequently misdiagnosed as obesity. We developed a semiautomatic segmentation pipeline to quantify the unique lower-extremity SAT quantity in lipedema from multislice chemical-shift-encoded (CSE) magnetic resonance imaging (MRI). APPROACH: Patients with lipedema (n=15) and controls (n=13) matched for age and body mass index (BMI) underwent CSE-MRI acquired from the thighs to ankles. Images were segmented to partition SAT and skeletal muscle with a semiautomated algorithm incorporating classical image processing techniques (thresholding, active contours, Boolean operations, and morphological operations). The Dice similarity coefficient (DSC) was computed for SAT and muscle automated versus ground truth segmentations in the calf and thigh. SAT and muscle volumes and the SAT-to-muscle volume ratio were calculated across slices for decades containing 10% of total slices per participant. The effect size was calculated, and Mann-Whitney U test applied to compare metrics in each decade between groups (significance: two-sided P<0.05). RESULTS: Mean DSC for SAT segmentations was 0.96 in the calf and 0.98 in the thigh, and for muscle was 0.97 in the calf and 0.97 in the thigh. In all decades, mean SAT volume was significantly elevated in participants with versus without lipedema (P<0.01), whereas muscle volume did not differ. Mean SAT-to-muscle volume ratio was significantly elevated (P<0.001) in all decades, where the greatest effect size for distinguishing lipedema was in the seventh decade approximately midthigh (r=0.76). CONCLUSIONS: The semiautomated segmentation of lower-extremity SAT and muscle from CSE-MRI could enable fast multislice analysis of SAT deposition throughout the legs relevant to distinguishing patients with lipedema from females with similar BMI but without SAT disease.
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