Female health is closely intertwined with hormonal fluctuations and aging, both of which profoundly shape immune-metabolic profiles. During the reproductive phase, estrogen and progesterone regulate inflammation, balance cytokine networks, and influence immune cell distribution and lipid metabolism. In contrast, the sharp hormonal decline during the menopausal transition induces a state of low-grade chronic inflammation, marked by elevated cytokines, immune dysregulation, and metabolic imbalance. This shift increases vulnerability to obesity, type 2 diabetes, osteoporosis, and cardiovascular disease. Estrogen loss is recognized as the principal driver of this inflammatory state, and its impact can be quantified through circulating inflammation-related proteins.
This study aims to develop an “Inflammatory Age” model for menopausal women using inflammation-focused proteomic data and machine learning. By integrating key inflammatory biomarkers, the model will provide an objective measure of the biological effects of menopause. We will further examine whether Inflammatory Age acceleration predicts multimorbidity independently of traditional risk factors.
Ultimately, this work seeks to elucidate the inflammatory pathways linking hormonal decline to disease susceptibility and to identify biomarkers and targets for early risk assessment and precision anti-inflammatory interventions in menopausal women.
