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The results of a groundbreaking study examining organ-specific biological age and associations with age-related diseases were published in The Lancet Digital Health. This remarkable study used plasma proteome signatures to determine the biological age of multiple organs, including the lungs, kidneys, liver, pancreas, and brain, as well as the intestines, cardiovascular (heart and arteries) and immune systems. They were able to compare the difference between the biological age and the chronological age of these organs and determine how the “age gap” between these two ages was associated with the risk for 45 age-related diseases, including organ and non-organ-specific associations.
The more than 6,000 participants in this study were part of a 20-year observational and prospective cohort study, known as the Whitehall II study. Initial plasma samples were taken in the late 1990s when the participants, all government officials, were between the ages of 45 to 69. Plasma samples were analyzed using the SomaScan platform, a high-throughput proteomics technology capable of analyzing thousands of proteins in a single sample. Organ-specific age was calculated using techniques developed from previous research, derived by considering the expression pattern of genes in each organ, and comparisons among peers were made to calculate an age gap between biological and chronological age. For example, 14 proteins were used in the estimation of arterial age (compared to 202 for the brain), including lactadherin, collagen alpha-1(VIII) chain, cytokine receptor-like factor 1, etc. Incident age-related diseases were tracked over 20 years, including 45 diseases known to have at least one association with a hallmark of cellular aging, as well as adequate prevalence, and an association with one of the organs included for analysis. Diseases included Parkinson’s disease, diabetes, atrial fibrillation, stroke, several cancers, and renal disease, among others.
Based upon 123,712 person-years of observation, higher organ age gaps were associated with an elevated risk for 32 of the 45 included diseases. Six of these had perfect organ specificity; for example, a hazard ratio of 2.13 per standard deviation increment was observed for liver failure, a risk attributed entirely to the liver age gap. Similarly, lymphatic node metastasis was entirely predicted by the immune system age gap, quite notable given node metastasis represents a secondary malignancy from a primary organ, pointing to immune system aging as critical for cancer progression.
Twelve diseases had partial organ specificity; for example, chronic kidney disease was associated with kidney, heart, immune, and liver gaps, while liver cirrhosis was associated with kidney, arterial, liver, and pancreas age gaps. Some organ-specific diseases were most strongly predicted by that organ’s age gap (e.g., the lung age gap was the largest influence of lung diseases), yet were also influenced by the age gap in other organs. The age gap for some organs was not associated with diseases in the respective organs (i.e., the pancreas, intestines, and nervous system), suggesting a lack of organ specificity. Considering disease co-occurrence, it appeared that participants with an organ-specific disease that is associated with an organ-specific age gap were at greater risk for a second and even a third organ-specific disease (for most, but not all organs).
Overall, this study provides strong evidence for the concept of organ-specific aging. Additionally, using unique proteomic signatures may help identify which organs are aging more rapidly and which diseases are at greater risk. Only “modest correlations” were found between the aging of different organs, highlighting that aging does not occur uniformly throughout each system, and disease development is influenced by the relative differences. However, advanced aging in all organs was linked to a higher risk of multi-organ multimorbidity.
This study not only provides evidence for organ-specific aging, but also indicates that our organ systems are not completely separate. The “partial organ specificity” observed suggests important interactions between organ systems; for example, the risk for stroke was influenced not only by the heart age gap but also by the immune system and liver age gaps. The kidney age gap was associated with disease incidence for 4 different systems (renal, cardiovascular, liver, and pancreas), and kidney-specific diseases, in turn, were predicted by 6 of the 9 organ age gaps, highlighting the close two-way connection between disease and kidney health.
Some of the results were somewhat surprising but plausible. Type 2 diabetes was not associated with the pancreas age gap, for example, but only with the kidney and immune system age gaps. Hypertension and coronary heart disease were not associated with the age gap in any organ system. The immune system age gap was the strongest long-term risk factor for dementia, while it was the intestinal age gap that most strongly predicted the risk of Parkinson's disease, highlighting the distinct pathophysiology that may be driving these chronic “neurological” diseases.
Because this was an observational cohort, none of these associations prove causality, but they strongly frame how age-related disease should be viewed, and highlight the importance of improving the diagnosis and treatment of organ-specific aging.
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