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DOI 10.3390/medicina61061018

Медицинская библиотека · Medicina (Kaunas, Lithuania) · 2025

Machine Learning Analysis of Lipid and Metabolic Profiles in Adults with Adenoid Hyperplasia.

Doğan Mansur, Çiftçi Merve, Yeşil Yusuf

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Аннотация

Background and

Objectives

The nasopharynx, unlike other pharyngeal regions, includes an important part of the immune system, called the adenoid (nasopharyngeal tonsil); its posterior wall contains lymphoid tissue belonging to Waldeyer's ring. Nasopharyngeal posterior wall thickness is often associated with adenoid hyperplasia in adults. The current study aimed to compare the blood lipid and metabolic profiles of adult patients with increased nasopharyngeal posterior wall thickness to those of the healthy population. Materials and

Methods

This study included a cohort of 98 patients, 52 in the control group and 46 diagnosed with increased nasopharyngeal posterior wall thickness due to adenoid hyperplasia. Clinical and biochemical data were collected from medical records at Sivas Cumhuriyet University and Erbaa State Hospital between January 2024 and March 2025. The dataset consisted of the following 11 features: age, sex, total cholesterol, low-density lipoprotein (LDL), high-density lipoprotein (HDL), triglycerides, fasting blood glucose, glycated hemoglobin (HbA1C), C-reactive protein (CRP), alanine aminotransferase (ALT), and aspartate aminotransferase (AST).

Results

HDL was significantly lower in the adenoid hyperplasia group (mean = 48.68, SD = 21.87) compared to the control group (mean = 51.31, SD = 11.80; Kruskal-Wallis H = 4.750, p = 0.029), with a small effect size (Cohen's d = -0.156). ALT was higher in the adenoid hyperplasia group (mean = 26.35, SD = 16.93 vs. 20.88, SD = 11.42; permutation test p = 0.082), suggesting a trend toward significance. HbA1C had a higher mean in the adenoid hyperplasia group (7.88, SD = 9.82 vs. 6.18, SD = 1.18; p = 0.852), with high variability.

Conclusions

In conclusion, this study identified HDL, HbA1C, and ALT as potential biomarkers for nasopharyngeal adenoid hyperplasia, with XGBoost and SHAP providing valuable insights despite dataset constraints.

Для цитирования: Doğan Mansur, Çiftçi Merve, Yeşil Yusuf. Machine Learning Analysis of Lipid and Metabolic Profiles in Adults with Adenoid Hyperplasia.. Medicina (Kaunas, Lithuania). 2025. DOI: 10.3390/medicina61061018.

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