Erciyes Üniversitesi Fen Bilimleri Enstitüsü Fen Bilimleri Dergisi 1012-2354 Erciyes Üniversitesi Systems Biology Sistem Biyolojisi Vasküler Yaşlanmada Bağırsak Mikrobiyotası Kaynaklı Metabolitlerin Entegrasyonu İçin Sistem Biyolojisi Temelli Hesaplamalı Bir Çerçeve: MCAS Modeli A Systems Biology-Based Computational Framework for Integrating Gut Microbiota-Derived Metabolites in Vascular Aging: The MCAS Model https://orcid.org/0000-0002-8781-3877 Özbay Erkan Karamanoğlu Mehmetbey Üniversitesi https://orcid.org/0000-0001-9905-2908 Örücü Serkan Karamanoğlu Mehmetbey Üniversitesi https://orcid.org/0000-0003-4602-1061 Karagöz Sami Karamanoğlu Mehmetbey Üniversitesi https://orcid.org/0000-0001-6231-886X Gülbay Sait Ramazan Karaman Halk Sağlığı Laboratuvarı https://orcid.org/0000-0001-8953-8302 Işık Bülent Karamanoğlu Mehmetbey Üniversitesi 03 23 2026 42 1 01 26 2026 03 18 2026 Copyright © 1985, Erciyes Üniversitesi Fen Bilimleri Enstitüsü Fen Bilimleri Dergisi 1985 Erciyes Üniversitesi Fen Bilimleri Enstitüsü Fen Bilimleri Dergisi

Yaşlanma, enerji metabolizmasındaki, epigenomik düzenlemedeki ve kronik düşük dereceli inflamasyondaki birbiriyle bağlantılı değişiklikler tarafından şekillendirilen çok faktörlü bir süreçtir. Burada, mikrobiyom kaynaklı metabolitleri vasküler yaşlanma ile ilişkili biyolojik özelliklerle sistem biyolojisi yaklaşımını kullanarak bütünleştirmek için çok katmanlı bir hesaplamalı model tanımlanmaktadır. Mikrobiyal, metabolik ve epigenomik bileşenleri birleştiren bileşik bir indeks oluşturulmuş ve herkese açık veri kümelerine dayanarak analiz edilmiştir. İç içe geçmiş çapraz doğrulama şeması içinde uygulanan L2-düzenlileştirilmiş bir lojistik regresyon modeli, genel model davranışını değerlendirmek için kullanılmıştır. Entegre model, ortalama AUC değeri 0.893 (95% GA: 0.787-0.959), PR-AUC değeri 0.913 ve Brier skoru 0.147 olmak üzere tutarlı biçimde kararlı bir performans göstermiştir. Ablasyon analizleri ayrıca mikrobiyal özelliklerin model ayrım gücüne en güçlü katkıyı sağladığını, metabolik ve epigenomik bileşenlerin ise esas olarak model kararlılığına katkıda bulunduğunu göstermiştir. Tüm bunlar birlikte ele alındığında, bu model mikrobiyota ile ilişkili biyolojik örüntülerin vasküler yaşlanma ile ilişkili olarak yorumlanması için bütünleştirici bir hesaplamalı yaklaşım sunmaktadır.

Aging is a multifactorial process that is shaped by interconnected alterations in energy metabolism, epigenomic regulation, and chronic low-grade inflammation. Here, a multilayered computational model is described to integrate microbiome-derived metabolites with vascular aging-related biological features using a systems biology approach. A composite index that combines microbial, metabolic, and epigenomic components was constructed and analyzed based on publicly available datasets. An L2-regularized logistic regression model implemented with in a nested cross-validation scheme was employed to evaluate overall model behavior. The integrated model demonstrated consistently stable performance, with a mean AUC of 0.893 (95% CI: 0.787-0.959), a PR-AUC of 0.913, and a Brier score of 0.147. Ablation analyses further showed that microbial features contributed most strongly to model discrimination, while metabolic and epigenomic components mainly contributed to model stability. Taken together, this model provides an integrative computational approach for interpreting microbiota-associated biological patterns in relation to vascular aging.

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