Cardiometabolic multimorbidity (the presence of two or more cardiovascular diseases) is becoming a growing public health problem, especially among aging populations. Insulin resistance, measured by the TyG index, reflects metabolic dysfunction, while biological age reflects cumulative physiological aging. These two dimensions of cardiometabolic vulnerability are biologically interconnected, but their joint significance for multimorbidity risk remained unclear. This work stands somewhat apart: nearby in the archive are studies of aging as loss of meaning, cellular methylation, and physical activity against dementia, but no one had combined metabolic and age-related burden into a single index for predicting multiple cardiovascular events.
Data from the prospective CHARLS cohort (China, 2011-2020) were used. The analysis included 8767 participants free of cardiometabolic multimorbidity at baseline. The TyG index was calculated using the formula ln(triglycerides × glucose / 2). Biological age was assessed by two methods: Light-BA (creatinine, glucose, C-reactive protein) and KDM-BA (seven biomarkers). Biological age acceleration (BAA) was defined as the residual from regressing biological age on chronological age. Composite indices TyG-Light-BA and TyG-KDM-BA were constructed as the product of TyG and biological age. Cox models, multistate illness-death models, cumulative exposure analysis, and trajectory analysis (K-means) based on repeated measurements from 2011 and 2015 were applied.
Over a median follow-up of 108 months, 873 participants (10%) developed cardiometabolic multimorbidity. A joint elevation of TyG and accelerated biological aging was associated with the highest risk: HR 2.37 (95% CI 1.92-2.92) for Light-BA and 2.26 (95% CI 1.84-2.78) for KDM-BA. Each one standard deviation increase in the composite TyG-Light-BA index raised the risk by 62% (HR 1.62), and TyG-KDM-BA by 49% (HR 1.49). A nonlinear dose-dependent relationship with threshold values was observed. In trajectory analysis for 2011-2015, a high composite level yielded an HR up to 5.17 versus low. The multistate model showed an association of the composite with all transitions: from health to first disease, to multimorbidity, and to death. However, the composite did not improve discrimination compared with models where TyG and biological age entered separately.
This work is the first to combine metabolic and age-related burden into a single index for predicting cardiometabolic multimorbidity, whereas neighboring studies focused on a single dimension. The difference from archive neighbors lies in scale: they examined individual aspects of aging (methylation, physical fitness), while this study demonstrates the synergy of metabolic dysfunction and biological age in a large cohort. However, the composite index does not outperform separate models, which honestly limits its clinical value: it is a screening tool, not a new biomarker.
The topic could move toward external validation of the composite index in independent populations and with more frequent repeated measurements. Studying the mechanisms linking metabolic stress and accelerated aging, including oxidative stress and chronic inflammation, is promising. The index might be adapted for personalized prevention, taking into account age and phenotypic subgroups.
The results could influence primary prevention of cardiovascular diseases by helping to identify people at high risk of multimorbidity at early stages. They also stimulate research in geroscience and metabolic medicine.
Conduct external validation of the TyG-BA composite in cohorts from other countries and ethnic groups. Organize studies with more frequent biomarker measurements to accurately assess cumulative exposure.
For cardiovascular epidemiology, the challenge of integrating multiple biomarkers into simple and interpretable risk indices remains unresolved. This work shows that simply multiplying metabolic and age-related measures does not improve prediction compared with separate accounting, which raises the question of finding more adequate mathematical forms for combining heterogeneous factors.
Biological age by the Light-BA method uses just three biomarkers that are routinely measured in an ordinary clinic: creatinine, glucose, and C-reactive protein. This makes aging assessment accessible in virtually any laboratory.
The idea of measuring biological age and its link to disease echoes the film 'In Time' (2011), where people have built-in clocks counting down their lifespan. In reality, biological age can be estimated from a blood test, and it predicts the risk of multiple diseases.
Not published on arXiv. Here are its page, the PDF and the formalization: you can check it yourself, not only read our account.