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Background: An adequate dietary energy supply is particularly important in patients with lipedema as it promotes weight and fat loss. Accurate estimation of resting metabolic rate (RMR) allows implementing a proper calorie restriction diet in patients with lipedema. Our study aimed to compare actual resting metabolic rate (aRMR) with predicted resting metabolic rate (pRMR) in women with lipedema and to determine the association between individual body composition parameters, body mass index, and aRMR. Methods and Results: A total of 108 women diagnosed with lipedema were enrolled in the study. aRMR was obtained by indirect calorimetry (IC) using FitMate WM metabolic system (Cosmed, Rome, Italy). pRMR was estimated with predictive equations and bioelectric impedance analysis (BIA). All body composition parameters were based on BIA. The mean aRMR in the study group was 1705.2 ± 320.7 kcal/day. This study found the agreement of predictive equations compared to IC is low (<60%). Most methods of predicted RMR measurement used in our study significantly underpredicted aRMR in patients with lipedema. Therefore, the most applied equations remain useless in clinical practice in this specific population due to large individual differences among the studied women. Conclusions: IC is the best tool to evaluate RMR in evaluated patients with lipedema. It is necessary to propose a new equation to RMR determination in clinical practice.
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This study aimed to develop a novel predictive equation for calculating resting metabolic rate (RMR) in women with lipedema. We recruited 119 women diagnosed with lipedema from the Angiology Outpatient Clinic at Wroclaw Medical University, Poland. RMR was assessed using indirect calorimetry, while body composition and anthropometric measurements were conducted using standardized protocols. Due to multicollinearity among predictors, classical multiple regression was deemed inadequate for developing the new equation. Therefore, we employed machine learning techniques, utilizing principal component analysis (PCA) for dimensionality reduction and predictor selection. Regression models, including support vector regression (SVR), random forest regression (RFR), and k-nearest neighbor (kNN) were evaluated in Python's scikit-learn framework, with hyperparameter tuning via GridSearchCV. Model performance was assessed through mean absolute percentage error (MAPE) and cross-validation, complemented by Bland-Altman plots for method comparison. A novel equation incorporating body composition parameters was developed, addressing a gap in accurate RMR prediction methods. By incorporating measurements of body circumference and body composition parameters alongside traditional predictors, the model's accuracy was improved. The segmented regression model outperformed others, achieving an MAPE of 10.78%. The proposed predictive equation for RMR offers a practical tool for personalized treatment planning in patients with lipedema.
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BACKGROUND: Lipedema is characterized by the painful abnormal deposition of adipose tissue in the lower limbs and is often misdiagnosed as obesity. Considering the numerous bothersome physical symptoms of lipedema, women with lipedema may have greater disability and emotional problems than women with lifestyle-induced obesity. OBJECTIVES: Our study aims to assess disability, anxiety and depression symptoms in women with lipedema compared to women with overweight/obesity. MATERIAL AND METHODS: Women with lipedema (n = 45, with a mean age of 41 years) and women who are overweight/obese (n = 43, with a mean age of 44.95 years) were asked to complete the following questionnaires: The World Health Organization Disability Assessment Schedule (WHO-DAS II), Beck's Depression Inventory - II (BDI-II), and The Hospital Anxiety and Depression Scale (HADS). RESULTS: Despite the higher BMI in the overweight/obesity group, the group with lipedema was more disabled in numerous domains of the WHO-DAS II questionnaire, including Life activities - domestic, work and school responsibilities and Participation in society When the influence of BMI was adjusted, a difference in the domain of Mobility was also present. The study groups did not differ in anxiety and depression symptoms. CONCLUSIONS: We showed that behavioral impairment was the main factor affecting functioning in women with lipedema. Emotional symptoms did not differentiate the study groups. Leg volumes and adipose tissue pain intensity were associated with greater disability in women with lipedema, and should be considered in managing women with this condition and in future research estimating the effectiveness of lipedema treatment.
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