Artigos

Trends Endocrinol Metab. 2024 Apr 29:S1043-2760(24)00087-0. doi: 10.1016/j.tem.2024.04.004. Online ahead of print.ABSTRACTLipedema is a poorly understood disorder of adipose tissue characterized by abnormal but symmetrical deposition of subcutaneous white adipose tissue (WAT) in proximal extremities. Here, we propose that the underlying cause for lipedema could be triggered by a selective accumulation of bacterial lipopolysaccharides (LPS; also known as endotoxin) in gluteofemoral WAT. Together with a malfunctioning complement system, this induces low-grade inflammation in the depot and raises its uncontrollable expansion. Correspondingly, more attention should be paid in future research to the endotoxemia prevalent in patients with lipedema. We would like to propose that proper management of endotoxemia can reduce the progression and even improve the state of disease in patients with lipedema.PMID:38688780 | DOI:10.1016/j.tem.2024.04.004

Trends Endocrinol Metab. 2024 Apr 29:S1043-2760(24)00087-0. doi: 10.1016/j.tem.2024.04.004. Online ahead of print.ABSTRACTLipedema is a poorly understood disorder of adipose tissue characterized by abnormal but symmetrical deposition of subcutaneous white adipose tissue (WAT) in proximal extremities. Here, we propose that the underlying cause for lipedema could be triggered by a selective accumulation of bacterial lipopolysaccharides (LPS; also known as endotoxin) in gluteofemoral WAT. Together with a malfunctioning complement system, this induces low-grade inflammation in the depot and raises its uncontrollable expansion. Correspondingly, more attention should be paid in future research to the endotoxemia prevalent in patients with lipedema. We would like to propose that proper management of endotoxemia can reduce the progression and even improve the state of disease in patients with lipedema.PMID:38688780 | DOI:10.1016/j.tem.2024.04.004

Trends Endocrinol Metab. 2024 Apr 29:S1043-2760(24)00087-0. doi: 10.1016/j.tem.2024.04.004. Online ahead of print.ABSTRACTLipedema is a poorly understood disorder of adipose tissue characterized by abnormal but symmetrical deposition of subcutaneous white adipose tissue (WAT) in proximal extremities. Here, we propose that the underlying cause for lipedema could be triggered by a selective accumulation of bacterial lipopolysaccharides (LPS; also known as endotoxin) in gluteofemoral WAT. Together with a malfunctioning complement system, this induces low-grade inflammation in the depot and raises its uncontrollable expansion. Correspondingly, more attention should be paid in future research to the endotoxemia prevalent in patients with lipedema. We would like to propose that proper management of endotoxemia can reduce the progression and even improve the state of disease in patients with lipedema.PMID:38688780 | DOI:10.1016/j.tem.2024.04.004

Trends Endocrinol Metab. 2024 Apr 29:S1043-2760(24)00087-0. doi: 10.1016/j.tem.2024.04.004. Online ahead of print.ABSTRACTLipedema is a poorly understood disorder of adipose tissue characterized by abnormal but symmetrical deposition of subcutaneous white adipose tissue (WAT) in proximal extremities. Here, we propose that the underlying cause for lipedema could be triggered by a selective accumulation of bacterial lipopolysaccharides (LPS; also known as endotoxin) in gluteofemoral WAT. Together with a malfunctioning complement system, this induces low-grade inflammation in the depot and raises its uncontrollable expansion. Correspondingly, more attention should be paid in future research to the endotoxemia prevalent in patients with lipedema. We would like to propose that proper management of endotoxemia can reduce the progression and even improve the state of disease in patients with lipedema.PMID:38688780 | DOI:10.1016/j.tem.2024.04.004

Trends Endocrinol Metab. 2024 Apr 29:S1043-2760(24)00087-0. doi: 10.1016/j.tem.2024.04.004. Online ahead of print.ABSTRACTLipedema is a poorly understood disorder of adipose tissue characterized by abnormal but symmetrical deposition of subcutaneous white adipose tissue (WAT) in proximal extremities. Here, we propose that the underlying cause for lipedema could be triggered by a selective accumulation of bacterial lipopolysaccharides (LPS; also known as endotoxin) in gluteofemoral WAT. Together with a malfunctioning complement system, this induces low-grade inflammation in the depot and raises its uncontrollable expansion. Correspondingly, more attention should be paid in future research to the endotoxemia prevalent in patients with lipedema. We would like to propose that proper management of endotoxemia can reduce the progression and even improve the state of disease in patients with lipedema.PMID:38688780 | DOI:10.1016/j.tem.2024.04.004

Trends Endocrinol Metab. 2024 Apr 29:S1043-2760(24)00087-0. doi: 10.1016/j.tem.2024.04.004. Online ahead of print.ABSTRACTLipedema is a poorly understood disorder of adipose tissue characterized by abnormal but symmetrical deposition of subcutaneous white adipose tissue (WAT) in proximal extremities. Here, we propose that the underlying cause for lipedema could be triggered by a selective accumulation of bacterial lipopolysaccharides (LPS; also known as endotoxin) in gluteofemoral WAT. Together with a malfunctioning complement system, this induces low-grade inflammation in the depot and raises its uncontrollable expansion. Correspondingly, more attention should be paid in future research to the endotoxemia prevalent in patients with lipedema. We would like to propose that proper management of endotoxemia can reduce the progression and even improve the state of disease in patients with lipedema.PMID:38688780 | DOI:10.1016/j.tem.2024.04.004

Trends Endocrinol Metab. 2024 Apr 29:S1043-2760(24)00087-0. doi: 10.1016/j.tem.2024.04.004. Online ahead of print.ABSTRACTLipedema is a poorly understood disorder of adipose tissue characterized by abnormal but symmetrical deposition of subcutaneous white adipose tissue (WAT) in proximal extremities. Here, we propose that the underlying cause for lipedema could be triggered by a selective accumulation of bacterial lipopolysaccharides (LPS; also known as endotoxin) in gluteofemoral WAT. Together with a malfunctioning complement system, this induces low-grade inflammation in the depot and raises its uncontrollable expansion. Correspondingly, more attention should be paid in future research to the endotoxemia prevalent in patients with lipedema. We would like to propose that proper management of endotoxemia can reduce the progression and even improve the state of disease in patients with lipedema.PMID:38688780 | DOI:10.1016/j.tem.2024.04.004

Trends Endocrinol Metab. 2024 Apr 29:S1043-2760(24)00087-0. doi: 10.1016/j.tem.2024.04.004. Online ahead of print.ABSTRACTLipedema is a poorly understood disorder of adipose tissue characterized by abnormal but symmetrical deposition of subcutaneous white adipose tissue (WAT) in proximal extremities. Here, we propose that the underlying cause for lipedema could be triggered by a selective accumulation of bacterial lipopolysaccharides (LPS; also known as endotoxin) in gluteofemoral WAT. Together with a malfunctioning complement system, this induces low-grade inflammation in the depot and raises its uncontrollable expansion. Correspondingly, more attention should be paid in future research to the endotoxemia prevalent in patients with lipedema. We would like to propose that proper management of endotoxemia can reduce the progression and even improve the state of disease in patients with lipedema.PMID:38688780 | DOI:10.1016/j.tem.2024.04.004

Trends Endocrinol Metab. 2024 Apr 29:S1043-2760(24)00087-0. doi: 10.1016/j.tem.2024.04.004. Online ahead of print.ABSTRACTLipedema is a poorly understood disorder of adipose tissue characterized by abnormal but symmetrical deposition of subcutaneous white adipose tissue (WAT) in proximal extremities. Here, we propose that the underlying cause for lipedema could be triggered by a selective accumulation of bacterial lipopolysaccharides (LPS; also known as endotoxin) in gluteofemoral WAT. Together with a malfunctioning complement system, this induces low-grade inflammation in the depot and raises its uncontrollable expansion. Correspondingly, more attention should be paid in future research to the endotoxemia prevalent in patients with lipedema. We would like to propose that proper management of endotoxemia can reduce the progression and even improve the state of disease in patients with lipedema.PMID:38688780 | DOI:10.1016/j.tem.2024.04.004

Trends Endocrinol Metab. 2024 Apr 29:S1043-2760(24)00087-0. doi: 10.1016/j.tem.2024.04.004. Online ahead of print.ABSTRACTLipedema is a poorly understood disorder of adipose tissue characterized by abnormal but symmetrical deposition of subcutaneous white adipose tissue (WAT) in proximal extremities. Here, we propose that the underlying cause for lipedema could be triggered by a selective accumulation of bacterial lipopolysaccharides (LPS; also known as endotoxin) in gluteofemoral WAT. Together with a malfunctioning complement system, this induces low-grade inflammation in the depot and raises its uncontrollable expansion. Correspondingly, more attention should be paid in future research to the endotoxemia prevalent in patients with lipedema. We would like to propose that proper management of endotoxemia can reduce the progression and even improve the state of disease in patients with lipedema.PMID:38688780 | DOI:10.1016/j.tem.2024.04.004

Trends Endocrinol Metab. 2024 Apr 29:S1043-2760(24)00087-0. doi: 10.1016/j.tem.2024.04.004. Online ahead of print.ABSTRACTLipedema is a poorly understood disorder of adipose tissue characterized by abnormal but symmetrical deposition of subcutaneous white adipose tissue (WAT) in proximal extremities. Here, we propose that the underlying cause for lipedema could be triggered by a selective accumulation of bacterial lipopolysaccharides (LPS; also known as endotoxin) in gluteofemoral WAT. Together with a malfunctioning complement system, this induces low-grade inflammation in the depot and raises its uncontrollable expansion. Correspondingly, more attention should be paid in future research to the endotoxemia prevalent in patients with lipedema. We would like to propose that proper management of endotoxemia can reduce the progression and even improve the state of disease in patients with lipedema.PMID:38688780 | DOI:10.1016/j.tem.2024.04.004

Trends Endocrinol Metab. 2024 Apr 29:S1043-2760(24)00087-0. doi: 10.1016/j.tem.2024.04.004. Online ahead of print.ABSTRACTLipedema is a poorly understood disorder of adipose tissue characterized by abnormal but symmetrical deposition of subcutaneous white adipose tissue (WAT) in proximal extremities. Here, we propose that the underlying cause for lipedema could be triggered by a selective accumulation of bacterial lipopolysaccharides (LPS; also known as endotoxin) in gluteofemoral WAT. Together with a malfunctioning complement system, this induces low-grade inflammation in the depot and raises its uncontrollable expansion. Correspondingly, more attention should be paid in future research to the endotoxemia prevalent in patients with lipedema. We would like to propose that proper management of endotoxemia can reduce the progression and even improve the state of disease in patients with lipedema.PMID:38688780 | DOI:10.1016/j.tem.2024.04.004

Trends Endocrinol Metab. 2024 Apr 29:S1043-2760(24)00087-0. doi: 10.1016/j.tem.2024.04.004. Online ahead of print.ABSTRACTLipedema is a poorly understood disorder of adipose tissue characterized by abnormal but symmetrical deposition of subcutaneous white adipose tissue (WAT) in proximal extremities. Here, we propose that the underlying cause for lipedema could be triggered by a selective accumulation of bacterial lipopolysaccharides (LPS; also known as endotoxin) in gluteofemoral WAT. Together with a malfunctioning complement system, this induces low-grade inflammation in the depot and raises its uncontrollable expansion. Correspondingly, more attention should be paid in future research to the endotoxemia prevalent in patients with lipedema. We would like to propose that proper management of endotoxemia can reduce the progression and even improve the state of disease in patients with lipedema.PMID:38688780 | DOI:10.1016/j.tem.2024.04.004

Trends Endocrinol Metab. 2024 Apr 29:S1043-2760(24)00087-0. doi: 10.1016/j.tem.2024.04.004. Online ahead of print.ABSTRACTLipedema is a poorly understood disorder of adipose tissue characterized by abnormal but symmetrical deposition of subcutaneous white adipose tissue (WAT) in proximal extremities. Here, we propose that the underlying cause for lipedema could be triggered by a selective accumulation of bacterial lipopolysaccharides (LPS; also known as endotoxin) in gluteofemoral WAT. Together with a malfunctioning complement system, this induces low-grade inflammation in the depot and raises its uncontrollable expansion. Correspondingly, more attention should be paid in future research to the endotoxemia prevalent in patients with lipedema. We would like to propose that proper management of endotoxemia can reduce the progression and even improve the state of disease in patients with lipedema.PMID:38688780 | DOI:10.1016/j.tem.2024.04.004

Trends Endocrinol Metab. 2024 Apr 29:S1043-2760(24)00087-0. doi: 10.1016/j.tem.2024.04.004. Online ahead of print.ABSTRACTLipedema is a poorly understood disorder of adipose tissue characterized by abnormal but symmetrical deposition of subcutaneous white adipose tissue (WAT) in proximal extremities. Here, we propose that the underlying cause for lipedema could be triggered by a selective accumulation of bacterial lipopolysaccharides (LPS; also known as endotoxin) in gluteofemoral WAT. Together with a malfunctioning complement system, this induces low-grade inflammation in the depot and raises its uncontrollable expansion. Correspondingly, more attention should be paid in future research to the endotoxemia prevalent in patients with lipedema. We would like to propose that proper management of endotoxemia can reduce the progression and even improve the state of disease in patients with lipedema.PMID:38688780 | DOI:10.1016/j.tem.2024.04.004

Metabolites. 2024 Apr 19;14(4):235. doi: 10.3390/metabo14040235.ABSTRACTThis 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.PMID:38668363 | PMC:PMC11052101 | DOI:10.3390/metabo14040235

Metabolites. 2024 Apr 19;14(4):235. doi: 10.3390/metabo14040235.ABSTRACTThis 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.PMID:38668363 | PMC:PMC11052101 | DOI:10.3390/metabo14040235

Metabolites. 2024 Apr 19;14(4):235. doi: 10.3390/metabo14040235.ABSTRACTThis 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.PMID:38668363 | PMC:PMC11052101 | DOI:10.3390/metabo14040235

Metabolites. 2024 Apr 19;14(4):235. doi: 10.3390/metabo14040235.ABSTRACTThis 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.PMID:38668363 | PMC:PMC11052101 | DOI:10.3390/metabo14040235

Metabolites. 2024 Apr 19;14(4):235. doi: 10.3390/metabo14040235.ABSTRACTThis 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.PMID:38668363 | PMC:PMC11052101 | DOI:10.3390/metabo14040235

Metabolites. 2024 Apr 19;14(4):235. doi: 10.3390/metabo14040235.ABSTRACTThis 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.PMID:38668363 | PMC:PMC11052101 | DOI:10.3390/metabo14040235

Metabolites. 2024 Apr 19;14(4):235. doi: 10.3390/metabo14040235.ABSTRACTThis 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.PMID:38668363 | PMC:PMC11052101 | DOI:10.3390/metabo14040235

Metabolites. 2024 Apr 19;14(4):235. doi: 10.3390/metabo14040235.ABSTRACTThis 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.PMID:38668363 | PMC:PMC11052101 | DOI:10.3390/metabo14040235

Metabolites. 2024 Apr 19;14(4):235. doi: 10.3390/metabo14040235.ABSTRACTThis 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.PMID:38668363 | PMC:PMC11052101 | DOI:10.3390/metabo14040235

Metabolites. 2024 Apr 19;14(4):235. doi: 10.3390/metabo14040235.ABSTRACTThis 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.PMID:38668363 | PMC:PMC11052101 | DOI:10.3390/metabo14040235