Machine learning allows robust classification of visceral fat in women with obesity using common laboratory metrics
Por:
Palmieri F, Akhtar NF, Pané A, Jiménez A, Olbeyra RP, Viaplana J, Vidal J, de Hollanda A, Gama-Perez P, Jimenez-Chillaron JC and Garcia-Roves PM
Publicada:
27 jul 2024
Ahead of Print:
27 jul 2024
Resumen:
The excessive accumulation and malfunctioning of visceral adipose tissue (VAT) is a major determinant of increased risk of obesity-related comorbidities. Thus, risk stratification of people living with obesity according to their amount of VAT is of clinical interest. Currently, the most common VAT measurement methods include mathematical formulae based on anthropometric dimensions, often biased by human measurement errors, bio-impedance, and image techniques such as X-ray absorptiometry (DXA) analysis, which requires specialized equipment. However, previous studies showed the possibility of classifying people living with obesity according to their VAT through blood chemical concentrations by applying machine learning techniques. In addition, most of the efforts were spent on men living with obesity while little was done for women. Therefore, this study aims to compare the performance of the multilinear regression model (MLR) in estimating VAT and six different supervised machine learning classifiers, including logistic regression (LR), support vector machine and decision tree-based models, to categorize 149 women living with obesity. For clustering, the study population was categorized into classes 0, 1, and 2 according to their VAT and the accuracy of each MLR and classification model was evaluated using DXA-data (DXAdata), blood chemical concentrations (BLDdata), and both DXAdata and BLDdata together (ALLdata). Estimation error and R2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\hbox {R}<^>{2}$$\end{document} were computed for MLR, while receiver operating characteristic (ROC) and precision-recall curves (PR) area under the curve (AUC) were used to assess the performance of every classification model. MLR models showed a poor ability to estimate VAT with mean absolute error >= 401.40\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\ge 401.40$$\end{document} and R2 <= 0.62\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\hbox {R}<^>{2} \le 0.62$$\end{document} in all the datasets. The highest accuracy was found for LR with values of 0.57, 0.63, and 0.53 for ALLdata, DXAdata, and BLDdata, respectively. The ROC AUC showed a poor ability of both ALLdata and DXAdata to distinguish class 1 from classes 0 and 2 (AUC = 0.31, 0.71, and 0.85, respectively) as also confirmed by PR (AUC = 0.24, 0.57, and 0.73, respectively). However, improved performances were obtained when applying LR model to BLDdata (ROC AUC >=\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\ge $$\end{document} 0.61 and PR AUC >=\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\ge $$\end{document} 0.42), especially for class 1.
These results seem to suggest that, while a direct and reliable estimation of VAT was not possible in our cohort, blood sample-derived information can robustly classify women living with obesity by machine learning-based classifiers, a fact that could benefit the clinical practice, especially in those health centres where medical imaging devices are not available. Nonetheless, these promising findings should be further validated over a larger population.
Filiaciones:
Palmieri F:
Biophysics unit, Department of Physiological Sciences, Faculty of Medicine and Health, Universitat de Barcelona, Bellvitge campus, 08907, Barcelona, Spain
Nutrition, Metabolism and Gene Therapy Group
Diabetes and Metabolism Program
Bellvitge Biomedical Research Institute (IDIBELL), 08908, Barcelona, Spain
Akhtar NF:
Escola d'Enginyeria de Barcelona Est (EEBE) Universitat Politècnica De Catalunya. Barcelona Tech-UPC, 08019, Barcelona, Spain
Pané A:
Obesity Unit, Endocrinology and Nutrition Department, Hospital Clínic de Barcelona, 08036, Barcelona, Spain
Centro de Investigación Biomédica en Red de la Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III (ISCIII), 28029, Madrid, Spain
Jiménez A:
Obesity Unit, Endocrinology and Nutrition Department, Hospital Clínic de Barcelona, 08036, Barcelona, Spain
Centro de Investigación Biomédica en Red de la Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III (ISCIII), 28029, Madrid, Spain
Fundació Clínic per a la Recerca Biomèdica (FCRB)-Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), 08036, Barcelona, Spain
Olbeyra RP:
Fundació Clínic per a la Recerca Biomèdica (FCRB)-Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), 08036, Barcelona, Spain
Viaplana J:
Fundació Clínic per a la Recerca Biomèdica (FCRB)-Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), 08036, Barcelona, Spain
Vidal J:
Obesity Unit, Endocrinology and Nutrition Department, Hospital Clínic de Barcelona, 08036, Barcelona, Spain
Fundació Clínic per a la Recerca Biomèdica (FCRB)-Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), 08036, Barcelona, Spain
Centro de Investigación Biomédica en Red de Diabetes y Enfermedades Metabólicas Asociadas (CIBERDEM), Instituto de Salud Carlos III (ISCIII), 28029, Madrid, Spain
de Hollanda A:
Obesity Unit, Endocrinology and Nutrition Department, Hospital Clínic de Barcelona, 08036, Barcelona, Spain
Centro de Investigación Biomédica en Red de la Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III (ISCIII), 28029, Madrid, Spain
Fundació Clínic per a la Recerca Biomèdica (FCRB)-Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), 08036, Barcelona, Spain
Gama-Perez P:
Biophysics unit, Department of Physiological Sciences, Faculty of Medicine and Health, Universitat de Barcelona, Bellvitge campus, 08907, Barcelona, Spain
Jimenez-Chillaron JC:
Biophysics unit, Department of Physiological Sciences, Faculty of Medicine and Health, Universitat de Barcelona, Bellvitge campus, 08907, Barcelona, Spain
Metabolic diseases of pediatric origin unit, Institut de Recerca Sant Joan de Déu - Barcelona Children's Hospital, 08950, Esplugues del Llobregat, Spain
Garcia-Roves PM:
Biophysics unit, Department of Physiological Sciences, Faculty of Medicine and Health, Universitat de Barcelona, Bellvitge campus, 08907, Barcelona, Spain
Nutrition, Metabolism and Gene Therapy Group
Diabetes and Metabolism Program
Bellvitge Biomedical Research Institute (IDIBELL), 08908, Barcelona, Spain
Centro de Investigación Biomédica en Red de la Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III (ISCIII), 28029, Madrid, Spain
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