Accelerating research on biological aging and mental health: Current challenges and future directions

Abstract

Aging is associated with complex biological changes that can be accelerated, slowed, or even temporarily reversed by biological and non-biological factors. This article focuses on the link between biological aging, psychological stressors, and mental illness. Rather than comprehensively reviewing this rapidly expanding field, we highlight challenges in this area of research and propose potential strategies to accelerate progress in this field. This effort requires the interaction of scientists across disciplines - including biology, psychiatry, psychology, and epidemiology; and across levels of analysis that emphasize different outcome measures - functional capacity, physiological, cellular, and molecular. Dialogues across disciplines and levels of analysis naturally lead to new opportunities for discovery but also to stimulating challenges. Some important challenges consist of 1) establishing the best objective and predictive biological age indicators or combinations of indicators, 2) identifying the basis for inter-individual differences in the rate of biological aging, and 3) examining to what extent interventions can delay, halt or temporarily reverse aging trajectories. Discovering how psychological states influence biological aging, and vice versa, has the potential to create novel and exciting opportunities for healthcare and possibly yield insights into the fundamental mechanisms that drive human aging.

Keywords: Biological age; Brain; DNA methylation; Mitochondria; Psychopathology; Telomere length.

Figure 1.. Integrative model for the transduction of mental health into biological aging and downstream disease manifestations.

(Left) Two main domains of mental health are considered: 1. Acute and chronic psychosocial stressors, which include distress and other subjective experiences; 2. Mental illness and clinical psychopathology (e.g., depression, anxiety, schizophrenia, bipolar disorder, etc). (Middle) These factors are transduced into biological age indicators, which span functional and physiological, brain structure and function, cellular, and molecular levels of analysis. In turn, the reverse association may transduce increased biological age into increased vulnerability and resilience to life stressors. The mechanisms responsible for the bi-directional flow of information between psychological states, psychopathology, and biological age indicators largely remain to be defined. (Right) Increased biological aging reflected in individual or combinations of biological age indicators manifest in symptoms across multiple interconnected systems, represented here as a functional network. Mental health domains can also directly contribute to disease manifestations (bottom arrow). Molecular indicators refer to components that are inert when in isolation (e.g. DNA, proteins) whereas cellular indicators refer to animated “living” components (e.g. breathing mitochondria, dividing/secreting cells).

Figure 2.. Computing age acceleration using cross-sectional and longitudinal data.

(A) From cross-sectional data, accelerated aging is established when biological age is over-predicted relative to the chronological age reference (group regression line). (B) In longitudinal data, the rate of aging is directly determined from multiple measurements in the same person (also same tissue and cell type). The slope for each individual can be compared to the theoretical slope of 1 to ascertain true aging acceleration or deceleration.

Figure 3.. Major limitations of biological age indicators.

(Left) Biological age indicators are measured in samples from different tissues and organs of the body. (Middle) Individual tissues such as blood and the cortical regions of the brain also exhibit substantial heterogeneity marked by relative abundance of different cell-types. (Right) Three hypothetical kinetics for biological age indicators are shown: The first shows a stable decline, which is typically assumed, but not necessarily accurate for most biological age indicators. The second illustrates an indicator subject to either circadian regulation or monthly estrous cycle, thus exhibiting regular oscillations. One such example is cortisol. Knowledge of this oscillatory pattern can be used to adequately schedule time of sampling (e.g., morning or evening) and derive useful parameters (e.g., cortisol awakening response). The third indicator shows irregular fluctuations, which could arise from sensitivity to acute stress mediators or to behavior (exercise, sleep, or other). Note the two hypothetical pairs of assessments on each trajectory. Mis-timing of measurement ① for the regularly oscillating measure leads to pseudo-reversal of the biological aging indicator. Assessment ② shows an exaggerated decline in the irregular fluctuation.

Figure 4.. Overview of the biological age indicator prediction process and recommended minimum reporting guidelines.

(Left) Example workflow for calculating a epigenetic age indicator and age acceleration. (Right) Recommended workflow that can inform study design, execution, analysis, and preparation of methods section in the resulting reports. Adherence to such standards for reporting results would facilitate harmonization of datasets across laboratories and cohort studies.

Figure 5.. Topology of biological age indicators, upstream measures, and downstream integrative indices and panels.

Biologically-informed and functionally relevant composite indices integrating two or more indicators can be derived from individual indicators. Their added predictive power should be validated by either in-sample cross-validation or preferably out-of-sample validation. Integrative biological age panels may eventually outperform single biological age indicators and indices due to the strength of their association, their construct stability, and/or their greater generalizability across individuals and independent samples.