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Addiction and Health، جلد ۱۵، شماره ۴، صفحات ۲۳۰-۲۳۹
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عنوان انگلیسی Comparative and Predictive Analysis of Clinical and Metabolic Features of Anorexia Nervosa and Bulimia Nervosa
چکیده انگلیسی مقاله Background: Eating disorders have become increasingly prevalent over the years; the age at which they appear has decreased, 
and they can lead to serious illness or death. Therefore, the number of studies on the matter has increased. Eating disorders like 
anorexia nervosa (AN) and bulimia nervosa (BN) are affected by many factors including mental illnesses that can have serious 
physical and psychological consequences. Accordingly, the present study aimed to compare the clinical and metabolic features 
of patients with AN and BN and identify potential biomarkers for distinguishing between the two disorders.
Methods: Clinical data of 41 participants who sought treatment for eating disorders between 2012 and 2022, including 29 AN 
patients and 12 BN patients, were obtained from NPIstanbul Brain Hospital in Istanbul, Turkey. The study included the clinical 
variables of both outpatient and inpatient treatments. Principal component analysis (PCA) was utilized to gain insights into 
differentiating AN and BN patients based on clinical characteristics, while machine learning techniques were applied to identify 
eating disorders.
Findings: The study found that thyroid hormone levels in patients with AN and BN were influenced by non-thyroidal illness 
syndrome (NTIS), which could be attributed to various factors, including psychiatric disorders, substance abuse, and medication
use. Lipid profile comparisons revealed higher triglyceride levels in the BN group (P<0.05), indicating increased triglyceride 
synthesis and storage as an energy source. Liver function tests showed lower levels of aspartate aminotransferase (AST) and alanine 
aminotransferase (ALT) in BN patients (P<0.05), while higher prolactin levels (P<0.05) suggested an altered hypothalamicpituitary-gonadal axis. Imbalances in minerals such as calcium and magnesium (P<0.05) were observed in individuals with 
eating disorders. PCA effectively differentiated AN and BN patients based on clinical features, and the Naïve Bayes (NB) model 
showed promising results in identifying eating disorders. 
Conclusion: The findings of the study provide important insights into AN and BN patients’ clinical features and may help guide 
future research and treatment strategies for these conditions.
کلیدواژه‌های انگلیسی مقاله Eating disorder, Anorexia, Bulimia, Laboratory diagnosis, Machine learning
نویسندگان مقاله Reyhan Betül Dönmez |
Faculty of Medicine, Üsküdar University, Istanbul, Turkey

Tuğba Nur Demirel |
Faculty of Medicine, Üsküdar University, Istanbul, Turkey

Cem Bilgin |
Faculty of Medicine, Üsküdar University, Istanbul, Turkey

Nevzat Tarhan |
Department of Physiatry, Üsküdar University, NP Hospital,Istanbul, Turkey

Özden Örkçü |
Vocational School of Food Technology, Üsküdar University, Istanbul, Turkey

Zeynep Ceylan |
Department of Industrial Engineering, Faculty of Engineering, Samsun University, Samsun, Turkey

Zozan Guleken |
1.Department of Physiology, Faculty of Medicine, Gaziantep Islam Science and Technology University, Gaziantep, Turkey 2. Medical College of Rzeszów University, Rzeszów, Poland

نشانی اینترنتی https://ahj.kmu.ac.ir/article_92380_53f48ad45d13f61d5e715d0876dd9d04.pdf
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