Mechanisms and markers of lung ageing in health and disease

Abstract

Ageing significantly impacts lung function and increases susceptibility to chronic lung diseases. The lung is a complex organ with multiple cell types that undergo cellular age-related perturbations or hallmarks. As knowledge of ageing mechanisms has progressed, we have a better understanding how intracellular adaptations impact cellular crosstalk and integrate to increase the susceptibility to age-related diseases in the lung. Herein, we discuss the prospects of exhaustion of lung progenitor cells, disrupted lung cell plasticity, perturbation in fibroblasts, impaired adaptive immune responses and alterations in lung microenvironment in the promotion of ageing and age-related lung diseases. Additionally, the ageing process trajectory of the lung depends on a combination of biological, genetic, metabolic, biomechanical and sociobehavioural factors that range from protective phenotypes to accelerated ageing phenotypes. We propose the concept of AgEnOmics, which expands the temporal dimension of lung ageing by distinguishing between chronological ageing and accelerated lung ageing phenotypes. Based on this concept, we define biomarkers of biological ageing that will help to define accelerated ageing and early interventions in biological ageing-related lung diseases.

FIGURE 1

Different levels of hallmarks of the ageing lung. A schematic framework categorises lung ageing into primary hallmarks, causes of cellular damage (genomic instability/telomere attrition, deregulated proteostasis/autophagy and mitochondrial dysfunction) affecting immune, epithelial and mesenchymal cells; secondary hallmarks, consequences including senescence, stem cell exhaustion and disrupted cell plasticity; and integrative hallmarks related to tissue homeostasis and function, that encompass inflammageing, dysbiosis, cell population competition leading to cellular imbalance and perturbed fibroblast crosstalk with increased matrix stiffness. ROS: reactive oxygen species. Figure created using BioRender.

FIGURE 2

Interconnection of hallmarks of the ageing lung. A schematic framework highlighting the interconnection between primary hallmarks, secondary hallmarks and integrative hallmarks related to tissue homeostasis and function. These interconnected features underlie the progressive decline in lung function and the development of age-related pulmonary diseases. Figure created using BioRender. AT1/2: alveolar type 1/2; IRS: insulin receptor substrate; mtDNA: mitochondrial DNA; SASP: senescence-associated secretory phenotype.

FIGURE 3

Markers of lung ageing in different sample sources. Biomarkers of ageing can be detected in different bodily fluids as well as lung tissue. Different sources allow for the detection of different hallmarks of ageing that are displayed here. Transparency indicates how well the ageing hallmark can be detected in the sample source. Figure created using BioRender. IPF: idiopathic pulmonary fibrosis; BALF: bronchoalveolar lavage fluid.

FIGURE 4

Multifactorial determinants of biological ageing. Plot of biological age (y-axis) versus chronological age (x-axis) illustrates the variability of biological ageing among individuals. Schematic representation of the ageing continuum: on the left, factors associated with accelerated ageing, including telomere attrition, sun exposure, obesity, smoking and industrial environmental exposures, shift the phenotype upward, while on the right, protective factors such as exercise, maintenance of healthy chromosomes and beneficial supplements promote a protective ageing phenotype. Figure created using BioRender.

FIGURE 5

Strategies for future advances in biology of lung ageing and diseases. A central “AgEnOmics” framework integrates multiomics (genomics, epigenomics, proteomics, clinical omics) with environmental exposure data to distinguish biological from chronological lung ageing, identify biomarkers and inform therapeutic strategies. Surrounding this core concept are layers of novel technological tools, omics approaches, preclinical models and computational biology methods, all convergeing to expand basic knowledge and drive personalised interventions for lung diseases. Figure created using BioRender. BMI: body mass index; SASP: senescence-associated secretory phenotype.