A roadmap to build a phenotypic metric of ageing: insights from the Baltimore Longitudinal Study of Aging.

Over the past three decades, considerable effort has been dedicated to quantifying the pace of ageing yet identifying the most essential metrics of ageing remains challenging due to lack of comprehensive measurements and heterogeneity of the ageing processes. Most of the previously proposed metrics of ageing have been emerged from cross-sectional associations with chronological age and predictive accuracy of mortality, thus lacking a conceptual model of functional or phenotypic domains. Further, such models may be biased by selective attrition and are unable to address underlying biological constructs contributing to functional markers of age-related decline. Using longitudinal data from the Baltimore Longitudinal Study of Aging (BLSA), we propose a conceptual framework to identify metrics of ageing that may capture the hierarchical and temporal relationships between functional ageing, phenotypic ageing and biological ageing based on four hypothesized domains: body composition, energy regulation, homeostatic mechanisms and neurodegeneration/neuroplasticity. We explored the longitudinal trajectories of key variables within these phenotypes using linear mixed-effects models and more than 10 years of data. Understanding the longitudinal trajectories across these domains in the BLSA provides a reference for researchers, informs future refinement of the phenotypic ageing framework and establishes a solid foundation for future models of biological ageing.

Associations of polycyclic aromatic hydrocarbons mixtures with cardiovascular diseases mortality and all-cause mortality and the mediation role of phenotypic ageing: A time-to-event analysis.

The associations of polycyclic aromatic hydrocarbons (PAHs) with cardiovascular diseases (CVDs) and all-cause mortality are unclear, especially the joint effects of PAHs exposure. Meanwhile, no studies have examined the effect of phenotypic ageing on the relationship between PAHs and mortality. Therefore, this study aimed to investigate the independent and joint associations between PAHs and CVDs, all-cause mortality, and assess whether phenotypic age acceleration (PhenoAgeAccel) mediate this relationship. We retrospectively collected data of 11,983 adults from the National Health and Nutrition Examination Survey database. Firstly, Cox proportional hazards regression and restricted cubic splines were applied to evaluate the independent association of single PAH on mortality. Further, time-dependent Probit extension of Bayesian Kernel Machine Regression and quantile-based g-computation models were conducted to test the joint effect of PAHs on mortality. Then, difference method was used to calculate the mediation proportion of PhenoAgeAccel in the association between PAHs and mortality. Our results revealed that joint exposure to PAHs showed positive association with CVDs and all-cause mortality. By controlling potential confounders, 1-Hydroxynapthalene (1-NAP) (HR = 1.24, P = 0.035) and 2-Hydroxyfluorene (2-FLU) (HR = 1.25, P < 0.001) showed positive association with CVDs mortality, and they were the top 2 predictors (weight: 0.82 for 1-NAP, 0.14 for 2-FLU) of CVDs mortality. 1-NAP (HR = 1.15, P < 0.001) and 2-FLU (HR = 1.13, P < 0.001) also showed positive association with all-cause mortality, and they were also the top 2 predictors of all-cause mortality (weight: 0.66 for 1-NAP, 0.34 for 2-FLU). PhenoAgeAccel mediated the relationship between 1-NAP, 2-FLU and CVDs, all-cause mortality, with a mediation proportion of 10.00 % to 24.90 % (P < 0.05). Specifically, the components of PhenoAgeAccel including C-reactive protein, lymphocyte percent, white blood cell count, red cell distribution width, and mean cell volume were the main contributors of mediation effects. Our study highlights the hazards of joint exposure of PAHs and the importance of phenotypic ageing on the relationship between PAHs and mortality.

Phenotype correlations reveal the relationships of physiological systems underlying human ageing.

Ageing is characterized by degeneration and loss of function across multiple physiological systems. To study the mechanisms and consequences of ageing, several metrics have been proposed in a hierarchical model, including biological, phenotypic and functional ageing. In particular, phenotypic ageing and interconnected changes in multiple physiological systems occur in all ageing individuals over time. Recently, phenotypic age, a new ageing measure, was proposed to capture morbidity and mortality risk across diverse subpopulations in US cohort studies. Although phenotypic age has been widely used, it may overlook the complex relationships among phenotypic biomarkers. Considering the correlation structure of these phenotypic biomarkers, we proposed a composite phenotype analysis (CPA) strategy to analyse 71 biomarkers from 2074 individuals in the Rugao Longitudinal Ageing Study. CPA grouped these biomarkers into 18 composite phenotypes according to their internal correlation, and these composite phenotypes were mostly consistent with prior findings. In addition, compared with prior findings, this strategy exhibited some different yet important implications. For example, the indicators of kidney and cardiovascular functions were tightly connected, implying internal interactions. The composite phenotypes were further verified through associations with functional metrics of ageing, including disability, depression, cognitive function and frailty. Compared to age alone, these composite phenotypes had better predictive performances for functional metrics of ageing. In summary, CPA could reveal the hidden relationships of physiological systems and identify the links between physiological systems and functional ageing metrics, thereby providing novel insights into potential mechanisms underlying human ageing.