This study systematically identified blood biomarkers associated with multimorbidity in older adults through large-scale cohort analysis, revealing metabolic dysregulation as a core driving factor, and validated the predictive model's accuracy in an independent cohort.
Literature Overview
The study, titled 'Shared and specific blood biomarkers for multimorbidity,' published in Nature Medicine, reviews and summarizes the analysis of 54 blood biomarkers in individuals aged 60 and above, systematically exploring their associations with different dimensions of multimorbidity—including number of diseases, disease clustering patterns, and 15-year disease accumulation rate. Using methods such as LASSO regression and principal component analysis, the study identified multiple biomarkers significantly associated with multimorbidity and externally validated longitudinal outcomes in the BLSA cohort. It highlights the central role of metabolic dysfunction in multimorbidity development, suggesting its potential as a therapeutic target. The paragraph is coherent and logically structured, ending with a Chinese period.Background Knowledge
Multimorbidity—defined as the co-occurrence of two or more chronic conditions in an individual—has become a major health challenge in aging societies, affecting over 90% of people aged 60 and above. It significantly reduces quality of life and increases risks of disability, dementia, hospitalization, and mortality. Although modern medicine has extended lifespan, the burden of chronic diseases has concurrently risen, and traditional single-disease treatment models are inadequate for managing the complex interactions of multiple conditions. Multimorbidity is now believed to arise not randomly, but from shared biological mechanisms such as mitochondrial dysfunction, cellular senescence, dysregulated nutrient sensing, and chronic inflammation—collectively described as the 'nine hallmarks of aging.' These mechanisms reduce physiological resilience, increasing susceptibility to multiple diseases. However, previous studies have largely focused on limited biological processes or simple disease counts, lacking in-depth analysis of multimorbidity heterogeneity (e.g., different disease clustering patterns) and dynamic disease accumulation. This study integrates multiple biomarkers with multidimensional multimorbidity phenotypes to uncover shared and specific biological underpinnings, offering novel targets for interventions aimed at delaying multimorbidity progression.
Research Methods and Experiments
The study was based on the Swedish National Study on Aging and Care in Kungsholmen (SNAC-K) cohort, including 2,247 individuals aged 60 and above, analyzing 54 baseline blood biomarkers reflecting inflammation, metabolism, vascular health, neurodegeneration, and organ damage. Multimorbidity was assessed in three ways: total number of baseline chronic diseases, multimorbidity patterns identified via latent class analysis (LCA), and disease accumulation rate over a 15-year follow-up. LASSO regression was used to analyze associations between biomarkers and each multimorbidity indicator, and principal component analysis (PCA) was applied to identify biomarker combinations. External validation was conducted in the Baltimore Longitudinal Study of Aging (BLSA) with 522 participants to evaluate the predictive accuracy of the models.Key Conclusions and Perspectives
Research Significance and Prospects
This study is the first to systematically uncover the shared and specific blood biomarker profiles of multimorbidity, emphasizing metabolic dysregulation as a core pathological mechanism. It provides new insights into the biological basis of multimorbidity and supports the 'geroscience' hypothesis—that targeting aging mechanisms can prevent multiple chronic diseases simultaneously. The identified biomarker panels hold promise for clinical risk stratification and early identification of high-risk individuals, enabling timely interventions.
Future research should further explore whether these biomarkers can serve as intervention targets—for instance, by modulating metabolic pathways through lifestyle or pharmacological interventions to slow multimorbidity progression. Additionally, as findings are primarily derived from Swedish populations, their generalizability needs to be confirmed in other racial and cultural groups. Integrating multi-omics data (e.g., transcriptomics, epigenomics) could provide a more comprehensive understanding of the molecular networks underlying multimorbidity.
Conclusion
This study systematically analyzed blood biomarkers in older adults, revealing shared and specific biological mechanisms underlying multimorbidity. It found that metabolic-related markers such as GDF15, HbA1c, cystatin C, leptin, and insulin were significantly associated across multiple dimensions of multimorbidity, suggesting that metabolic dysregulation is a core pathway driving disease accumulation. Moreover, distinct disease clustering patterns exhibited specific biomarker signatures, reflecting the heterogeneity of multimorbidity. The disease accumulation rate was associated with GGT and albumin, underscoring the role of liver function and nutritional status in dynamic multimorbidity progression. Principal component analysis further confirmed GDF15 and cystatin C as major drivers. These findings were validated in an independent cohort, demonstrating strong predictive performance. The results not only deepen our understanding of the biological basis of multimorbidity but also offer potential targets for interventions aimed at delaying multimorbidity progression. Future strategies targeting metabolic pathways may enable early prediction and prevention of multimorbidity, ultimately improving health in aging populations.
