Phenotype correlations reveal the relationships of physiological systems underlying human ageing

Heterogeneous associations of multiplexed environmental factors and multidimensional aging metrics

Complicated associations between multiplexed environmental factors and aging are poorly understood. We manipulated aging using multidimensional metrics such as phenotypic age, brain age, and brain volumes in the UK Biobank. Weighted quantile sum regression was used to examine the relative individual contributions of multiplexed environmental factors to aging, and self-organizing maps (SOMs) were used to examine joint effects. Air pollution presented a relatively large contribution in most cases. We also found fair heterogeneities in which the same environmental factor contributed inconsistently to different aging metrics. Particulate matter contributed the most to variance in aging, while noise and green space showed considerable contribution to brain volumes. SOM identified five subpopulations with distinct environmental exposure patterns and the air pollution subpopulation had the worst aging status. This study reveals the heterogeneous associations of multiplexed environmental factors with multidimensional aging metrics and serves as a proof of concept when analyzing multifactors and multiple outcomes.

Assessment of Physical Resilience Using Residual Methods and Its Association With Adverse Outcomes in Older Adults

Background and Objectives

Physical resilience (PR) is recognized as the ability to recover from the adverse effects of a stressor. However, there is a lack of consensus on how to optimally measure PR in older adults in general. We aimed to measure PR using residuals from regression analyses and investigated its association with adverse outcomes in older adults.

Research Design and Methods

A total of 6 508 older adults were included from the National Health and Aging Trends Study, which was a population-based prospective cohort study. PR was assessed using residual methods from a linear model regressing the short physical performance battery on clinical diseases, age, sex, race/ethnicity, and health condition. Adverse outcomes included all-cause mortality, falls, and overnight hospitalization.

Results

The mean age was 77.48 (7.84) years. Increased PR was associated with a lower risk of all-cause mortality (hazard ratio [HR] = 0.85, 95% confidence interval [CI]: 0.83-0.87). Compared to participants with reduced PR, those with normal PR had a lower risk for mortality (HR = 0.51, 95% CI: 0.46-0.56). Specifically, restricted cubic spline regression revealed a dose-response relationship between PR and all-cause mortality (p-overall < .0001, p-nonlinear = .011). Additionally, we also found significant associations of increased PR with lower risks of falls (HR = 0.98, 95% CI: 0.96-0.99) and overnight hospitalization (HR = 0.98, 95% CI: 0.97-1.00).

Discussion and Implications

PR, measured by residual methods, was robustly and independently associated with all-cause mortality, falls, and overnight hospitalization. Our findings provide evidence that this approach may be a simple and feasible strategy to assess PR.

Explainable machine learning framework to predict personalized physiological aging

Attaining personalized healthy aging requires accurate monitoring of physiological changes and identifying subclinical markers that predict accelerated or delayed aging. Classic biostatistical methods most rely on supervised variables to estimate physiological aging and do not capture the full complexity of inter-parameter interactions. Machine learning (ML) is promising, but its black box nature eludes direct understanding, substantially limiting physician confidence and clinical usage. Using a broad population dataset from the National Health and Nutrition Examination Survey (NHANES) study including routine biological variables and after selection of XGBoost as the most appropriate algorithm, we created an innovative explainable ML framework to determine a Personalized physiological age (PPA). PPA predicted both chronic disease and mortality independently of chronological age. Twenty-six variables were sufficient to predict PPA. Using SHapley Additive exPlanations (SHAP), we implemented a precise quantitative associated metric for each variable explaining physiological (i.e., accelerated or delayed) deviations from age-specific normative data. Among the variables, glycated hemoglobin (HbA1c) displays a major relative weight in the estimation of PPA. Finally, clustering profiles of identical contextualized explanations reveal different aging trajectories opening opportunities to specific clinical follow-up. These data show that PPA is a robust, quantitative and explainable ML-based metric that monitors personalized health status. Our approach also provides a complete framework applicable to different datasets or variables, allowing precision physiological age estimation.

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.