Vasküler Yaşlanmada Bağırsak Mikrobiyotası Kaynaklı Metabolitlerin Entegrasyonu İçin Sistem Biyolojisi Temelli Hesaplamalı Bir Çerçeve: MCAS Modeli

Abstract

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.

Keywords

• Bağırsak Mikrobiyotası
• Vasküler Yaşlanma
• Sistem Biyolojisi
• Trimetilamin N-Oksit
• Kısa Zincirli Yağ Asitleri

A Systems Biology-Based Computational Framework for Integrating Gut Microbiota-Derived Metabolites in Vascular Aging: The MCAS Model

Abstract

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.

Keywords

• Gut microbiota
• Vascular Aging
• Systems Biology
• Trimethylamine N-Oxide
• Short-Chain Fatty Acids

References

1. Galkin, F., Mamoshina, P., Aliper, A., Putin, E., Moskalev, V., Gladyshev, V. N., Zhavoronkov, A. 2020. Human Gut Microbiome Aging Clock Based on Taxonomic Profiling and Deep Learning. iScience, 23(6), 101199.
2. Guedj, A., Volman, Y., Geiger-Maor, A., Bolik, J., Schumacher, N., Künzel, S., Baines, J. F., Nevo, Y., Elgavish, S., Galun, E., Amsalem, H., Schmidt-Arras, D., Rachmilewitz, J. 2020. Gut microbiota shape 'inflamm-ageing' cytokines and account for age-dependent decline in DNA damage repair. Gut, 69(6), 1064-1075.
3. Agnoletti, D., Piani, F., Cicero, A. F. G., Borghi, C. 2022. The Gut Microbiota and Vascular Aging: A State-of-the-Art and Systematic Review of the Literature. Journal of clinical medicine, 11(12), 3557.
4. Behringer E. J. 2023. Impact of aging on vascular ion channels: perspectives and knowledge gaps across major organ systems. American journal of physiology. Heart and circulatory physiology, 325(5), H1012-H1038.
5. Li, T., Chen, Y., Gua, C., Li, X. 2017. Elevated Circulating Trimethylamine N-Oxide Levels Contribute to Endothelial Dysfunction in Aged Rats through Vascular Inflammation and Oxidative Stress. Frontiers in physiology, 8, 350.
6. Claesson, M. J., Jeffery, I. B., Conde, S., Power, S. E., O'Connor, E. M., Cusack, S., Harris, H. M., Coakley, M., Lakshminarayanan, B., O'Sullivan, O., Fitzgerald, G. F., Deane, J., O'Connor, M., Harnedy, N., O'Connor, K., O'Mahony, D., van Sinderen, D., Wallace, M., Brennan, L., Stanton, C., … O'Toole, P. W. 2012. Gut microbiota composition correlates with diet and health in the elderly. Nature, 488(7410), 178-184.
7. Yatsunenko, T., Rey, F. E., Manary, M. J., Trehan, I., Dominguez-Bello, M. G., Contreras, M., Magris, M., Hidalgo, G., Baldassano, R. N., Anokhin, A. P., Heath, A. C., Warner, B., Reeder, J., Kuczynski, J., Caporaso, J. G., Lozupone, C. A., Lauber, C., Clemente, J. C., Knights, D., Knight, R., … Gordon, J. I. 2012. Human gut microbiome viewed across age and geography. Nature, 486(7402), 222-227.
8. Chen, W., Zhang, S., Wu, J., Ye, T., Wang, S., Wang, P., Xing, D. 2020. Butyrate-producing bacteria and the gut-heart axis in atherosclerosis. Clinica chimica acta; international journal of clinical chemistry, 507, 236-241.

9. Zhang D, Jian YP, Zhang YN, et al. Short-chain fatty acids in diseases. Cell Commun Signal. 2023;21(1):212.
10. Mariat, D., Firmesse, O., Levenez, F., Guimarăes, V., Sokol, H., Doré, J., Corthier, G., Furet, J. P. 2009. The Firmicutes/Bacteroidetes ratio of the human microbiota changes with age. BMC microbiology, 9, 123
11. Lu, Y., Zhang, Y., Zhao, X., Shang, C., Xiang, M., Li, L., Cui, X. 2022. Microbiota-derived short-chain fatty acids: Implications for cardiovascular and metabolic disease. Frontiers in cardiovascular medicine, 9, 900381.
12. Wang, Z., Klipfell, E., Bennett, B. J., Koeth, R., Levison, B. S., Dugar, B., Feldstein, A. E., Britt, E. B., Fu, X., Chung, Y. M., Wu, Y., Schauer, P., Smith, J. D., Allayee, H., Tang, W. H., DiDonato, J. A., Lusis, A. J., Hazen, S. L. 2011. Gut flora metabolism of phosphatidylcholine promotes cardiovascular disease. Nature, 472(7341), 57-63.
13. Sun, X., Jiao, X., Ma, Y., Liu, Y., Zhang, L., He, Y., Chen, Y. 2016. Trimethylamine N-oxide induces inflammation and endothelial dysfunction in human umbilical vein endothelial cells via activating ROS-TXNIP-NLRP3 inflammasome. Biochemical and biophysical research communications, 481(1-2), 63-70.
14. Yao, Y., Xu, Y., Wang, W., Zhang, J., Li, Q. 2017. Glucagon-like peptide-1 improves β-cell dysfunction by suppressing the miR-27a-induced downregulation of ATP-binding cassette transporter A1. Biomedicine & pharmacotherapy = Biomedecine & pharmacotherapie, 96, 497-502.
15. Alsulami, M., Alamri, H., Barhoumi, T., Munawar, N., Alghanem, B. 2025. The effect of TMAO on aging-associated cardiovascular and metabolic pathways and emerging therapies. Molecular and cellular biochemistry, 480(11), 5659-5669.
16. Du, Y., Li, X., Su, C., Xi, M., Zhang, X., Jiang, Z., Wang, L., Hong, B. 2020. Butyrate protects against high-fat diet-induced atherosclerosis via up-regulating ABCA1 expression in apolipoprotein E-deficiency mice. British journal of pharmacology, 177(8), 1754-1772.
17. Fellows, R., Denizot, J., Stellato, C., Cuomo, A., Jain, P., Stoyanova, E., Balázsi, S., Hajnády, Z., Liebert, A., Kazakevych, J., Blackburn, H., Corrêa, R. O., Fachi, J. L., Sato, F. T., Ribeiro, W. R., Ferreira, C. M., Perée, H., Spagnuolo, M., Mattiuz, R., Matolcsi, C., … Varga-Weisz, P. 2018. Microbiota derived short chain fatty acids promote histone crotonylation in the colon through histone deacetylases. Nature communications, 9(1), 105.
18. Shapiro, H., Thaiss, C. A., Levy, M., Elinav, E. 2014. The cross talk between microbiota and the immune system: metabolites take center stage. Current opinion in immunology, 30, 54-62.
19. Robles-Vera, I., Toral, M., de la Visitación, N., Aguilera-Sánchez, N., Redondo, J. M., Duarte, J. 2020. Protective Effects of Short-Chain Fatty Acids on Endothelial Dysfunction Induced by Angiotensin II. Frontiers in physiology, 11, 277.
20. Davie J. R. 2003. Inhibition of histone deacetylase activity by butyrate. The Journal of nutrition, 133(7 Suppl), 2485S-2493S.
21. Saeedi Saravi, S. S., Pugin, B., Constancias, F., Shabanian, K., Spalinger, M., Thomas, A., Le Gludic, S., Shabanian, T., Karsai, G., Colucci, M., Menni, C., Attaye, I., Zhang, X., Allemann, M. S., Lee, P., Visconti, A., Falchi, M., Alimonti, A., Ruschitzka, F., Paneni, F., … Beer, J. H. 2025. Gut microbiota-dependent increase in phenylacetic acid induces endothelial cell senescence during aging. Nature aging, 5(6), 1025-1045.
22. Zhang, M., Wu, J. F., Chen, W. J., Tang, S. L., Mo, Z. C., Tang, Y. Y., Li, Y., Wang, J. L., Liu, X. Y., Peng, J., Chen, K., He, P. P., Lv, Y. C., Ouyang, X. P., Yao, F., Tang, D. P., Cayabyab, F. S., Zhang, D. W., Zheng, X. L., Tian, G. P., … Tang, C. K. 2014. MicroRNA-27a/b regulates cellular cholesterol efflux, influx and esterification/hydrolysis in THP-1 macrophages. Atherosclerosis, 234(1), 54-64.
23. Yue, R., Dutta, A. 2022. Computational systems biology in disease modeling and control, review and perspectives. NPJ systems biology and applications, 8(1), 37.
24. Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Duchesnay, É. 2011. Scikit-learn: Machine learning in Python. the Journal of machine Learning research, 12, 2825-2830.
25. DeGroat, W., Abdelhalim, H., Peker, E., Sheth, N., Narayanan, R., Zeeshan, S., Liang, B. T., Ahmed, Z. 2024. Multimodal AI/ML for discovering novel biomarkers and predicting disease using multi-omics profiles of patients with cardiovascular diseases. Scientific reports, 14(1), 26503.
26. Barabási, A. L., Oltvai, Z. N. 2004. Network biology: understanding the cell's functional organization. Nature reviews. Genetics, 5(2), 101-113.
27. Chen, Y., Wang, H., Lu, W., Wu, T., Yuan, W., Zhu, J., Lee, Y. K., Zhao, J., Zhang, H., Chen, W. 2022. Human gut microbiome aging clocks based on taxonomic and functional signatures through multi-view learning. Gut microbes, 14(1), 2025016.
28. Stebegg, M., Silva-Cayetano, A., Innocentin, S., Jenkins, T. P., Cantacessi, C., Gilbert, C., Linterman, M. A. 2019. Heterochronic faecal transplantation boosts gut germinal centres in aged mice. Nature communications, 10(1), 2443.
29. Ren, J., Li, H., Zeng, G., Pang, B., Wang, Q., Wei, J. 2023. Gut microbiome-mediated mechanisms in aging-related diseases: are probiotics ready for prime time?. Frontiers in pharmacology, 14, 1178596.
30. Turkish Statistical Institute. Causes of Death Statistics, 2023 - Table 2: Classification by cause of death (ICD-10) Dataset. Ankara: Turkish Statistical Institute. https://data.tuik.gov.tr/Bulten/Index?p=Olum-ve-Olum-Nedeni-İstatistikleri-2023-53709. (Access Date, 21.10.2025).
31. Efron, B., Tibshirani, R. J. 1994. An introduction to the bootstrap. pp 168-195. New York, NY: Chapman & Hall/CRC, 456p
32. Guyon I, Elisseeff, A. 2003. An Introduction to Variable and Feature Selection. Journal of Machine Learning Research, 3, 1157-1182.
33. Hoyles, L., Fernández-Real, J. M., Federici, M., Serino, M., Abbott, J., Charpentier, J., Heymes, C., Luque, J. L., Anthony, E., Barton, R. H., Chilloux, J., Myridakis, A., Martinez-Gili, L., Moreno-Navarrete, J. M., Benhamed, F., Azalbert, V., Blasco-Baque, V., Puig, J., Xifra, G., Ricart, W., … Dumas, M. E. 2018. Molecular phenomics and metagenomics of hepatic steatosis in non-diabetic obese women. Nature medicine, 24(7), 1070-1080.
34. Yang, R., Pang, J., Zhong, X., Pang, S., Hu, X., Wei, C., Yan, W., Chen, X., Zhao, R., Xu, B., Cao, Z. 2025. Molecular mechanisms of aberrant fatty acids metabolism in driving cardiovascular diseases: key regulatory targets and dietary interventions. Food & function, 16(15), 5961-5993.
35. Budoff, M. J., de Oliveira Otto, M. C., Li, X. S., Lee, Y., Wang, M., Lai, H. T. M., Lemaitre, R. N., Pratt, A., Tang, W. H. W., Psaty, B. M., Siscovick, D. S., Hazen, S. L., Mozaffarian, D. 2025. Trimethylamine-N-oxide (TMAO) and risk of incident cardiovascular events in the multi ethnic study of Atherosclerosis. Scientific reports, 15(1), 23362.
36. Steyerberg, E. W., Vergouwe, Y. 2014. Towards better clinical prediction models: seven steps for development and an ABCD for validation. European heart journal, 35(29), 1925-1931.
37. Pencina, M. J., D'Agostino, R. B., Sr, Steyerberg, E. W. 2011. Extensions of net reclassification improvement calculations to measure usefulness of new biomarkers. Statistics in medicine, 30(1), 11-21.
38. Chen, K., Zheng, X., Feng, M., Li, D., Zhang, H. 2017. Gut Microbiota-Dependent Metabolite Trimethylamine N-Oxide Contributes to Cardiac Dysfunction in Western Diet-Induced Obese Mice. Frontiers in physiology, 8, 139.
39. Montero-Melendez, T., Dalli, J., Perretti, M. 2013. Gene expression signature-based approach identifies a pro-resolving mechanism of action for histone deacetylase inhibitors. Cell death and differentiation, 20(4), 567-575.
40. Tang, W. H., Kitai, T., Hazen, S. L. 2017. Gut Microbiota in Cardiovascular Health and Disease. Circulation research, 120(7), 1183-1196.
41. Canfora, E. E., Meex, R. C. R., Venema, K., Blaak, E. E. 2019. Gut microbial metabolites in obesity, NAFLD and T2DM. Nature reviews. Endocrinology, 15(5), 261-273.
42. Zhu, W., Gregory, J. C., Org, E., Buffa, J. A., Gupta, N., Wang, Z., Li, L., Fu, X., Wu, Y., Mehrabian, M., Sartor, R. B., McIntyre, T. M., Silverstein, R. L., Tang, W. H. W., DiDonato, J. A., Brown, J. M., Lusis, A. J., Hazen, S. L. (2016). Gut Microbial Metabolite TMAO Enhances Platelet Hyperreactivity and Thrombosis Risk. Cell, 165(1), 111-124.
43. Kurilshikov, A., van den Munckhof, I. C. L., Chen, L., Bonder, M. J., Schraa, K., Rutten, J. H. W., Riksen, N. P., de Graaf, J., Oosting, M., Sanna, S., Joosten, L. A. B., van der Graaf, M., Brand, T., Koonen, D. P. Y., van Faassen, M., LifeLines DEEP Cohort Study, BBMRI Metabolomics Consortium, Slagboom, P. E., Xavier, R. J., Kuipers, F., Hofker, M. H., … Fu, J. 2019. Gut Microbial Associations to Plasma Metabolites Linked to Cardiovascular Phenotypes and Risk. Circulation research, 124(12), 1808-1820.