Experimental gerontology · 2026
Proteomic signatures of systemic inflammation in aging, multimorbidity, and mortality.
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Jiang Zhou, Ling Danyang, Chen Ziwei, Zhou Haolong, Cui Zhangbo, Ma Xiaowen, Zheng Shuo, Wang Hongxin, Wang Qi
Аннотация
BACKGROUND: Chronic low-grade inflammation is central to biological aging, but routine inflammatory biomarkers capture limited molecular heterogeneity. We aimed to develop a proteomic inflammaging score (PIS) and evaluate its practical utility.
METHODS: In 40,471 England participants from UK biobank, we used LASSO to identify proteins associated with six inflammatory biomarkers (CRP, SII, SIRI, MLR, NLR, PLR), and validated them in 5250 non-England cohort. Deep neural networks generated biomarker-specific scores, integrated into PIS via elastic-net Cox regression. We evaluated associations with mortality, age-related diseases, and aging biomarkers, and compared predictive utility (C-index, NRI, IDI) against conventional markers. DE-SWAN analysis was used to characterize nonlinear age-related proteomic changes, and genetics analyses were performed to investigate genetic architecture.
RESULTS: A total of 1112 proteins were identified to associated with all six inflammation biomarkers and 93 proteins retained in simplified versions. Both full (HR = 1.88, 95%CI: 1.79-1.98) and simplified (HR = 1.88, 95%CI: 1.79-1.97) PIS were positively associated with all-cause mortality and multiple aging-related phenotypes. PISs outperformed conventional inflammatory and aging biomarkers, significantly improving mortality prediction: for all-cause mortality, clinical indices plus simplified PIS achieved a C-index of 0.758 (95% CI, 0.752-0.765). External validation in the non-England cohort showed comparable predictive performance. Proteomic inflammation crests were near ages 50, 62-63 and 67 years. 25 lead SNPs were associated with PIS, linking PIS to inflammatory traits and aging biomarkers.
CONCLUSION: PIS provides a compact, interpretable proteomic measure of inflammaging and captures mortality, multisystem disease burden, and aging-related biology in population-scale cohorts.
METHODS: In 40,471 England participants from UK biobank, we used LASSO to identify proteins associated with six inflammatory biomarkers (CRP, SII, SIRI, MLR, NLR, PLR), and validated them in 5250 non-England cohort. Deep neural networks generated biomarker-specific scores, integrated into PIS via elastic-net Cox regression. We evaluated associations with mortality, age-related diseases, and aging biomarkers, and compared predictive utility (C-index, NRI, IDI) against conventional markers. DE-SWAN analysis was used to characterize nonlinear age-related proteomic changes, and genetics analyses were performed to investigate genetic architecture.
RESULTS: A total of 1112 proteins were identified to associated with all six inflammation biomarkers and 93 proteins retained in simplified versions. Both full (HR = 1.88, 95%CI: 1.79-1.98) and simplified (HR = 1.88, 95%CI: 1.79-1.97) PIS were positively associated with all-cause mortality and multiple aging-related phenotypes. PISs outperformed conventional inflammatory and aging biomarkers, significantly improving mortality prediction: for all-cause mortality, clinical indices plus simplified PIS achieved a C-index of 0.758 (95% CI, 0.752-0.765). External validation in the non-England cohort showed comparable predictive performance. Proteomic inflammation crests were near ages 50, 62-63 and 67 years. 25 lead SNPs were associated with PIS, linking PIS to inflammatory traits and aging biomarkers.
CONCLUSION: PIS provides a compact, interpretable proteomic measure of inflammaging and captures mortality, multisystem disease burden, and aging-related biology in population-scale cohorts.