Biological age instead of the chronological age of different organs and systems can predict the risk of diseases for humans.

The process of aging is known to be a major risk factor for the onset of serious diseases and deaths. Previous studies have highlighted that the rate of aging varies from one person to another. This suggests that the biological age of an individual is different from their chronological age.

The chronological age is the total time that has passed from birth to a specific date. Chronological age is the most common way of defining age [1]. Biological age on the other hand refers to the accumulation of damage at the biological level. Biological age has been observed to predict age-related diseases and overall mortality better than chronological age.

Assessment of biological age can accurately determine an individual’s risk of a certain disease even before the onset of clinical symptoms. Several methods have been developed for the investigation of biological age such as the Klemera and Doubal method (KMD), principal component analysis, and multiple linear regression. Previous methods assumed that a higher correlation between chronological age and biomarkers leads to a better estimation of biological age while later methods treated chronological age as a marker of biological aging.

Longevity.Technology: Biological aging can be analysed by investigating several physiological, molecular, and clinical biomarkers. Among them, physiological biomarkers are most commonly used. These biomarkers can help to indicate personal health levels, select suitable candidates for clinical trials, predict the risk of aging-related diseases, and evaluate healthy-aging intervention programmes [2]. Therefore, they can help to promote a higher lifespan and reduce the burden of age-related diseases throughout the world.

DNA methylation along with proteome and transcriptome signatures have been identified to be useful in the estimation of biological age; moreover, DNA methylation has been identified to be the most important for the determination of aging in humans. However, DNA methylation is organ or tissue-specific and can be obtained only from saliva or blood samples. Therefore, other important aspects of the human body might not be covered by the assessment of only DNA methylation.

A new study used the concept of ‘deep phenotyping’ to obtain detailed information on the health status for the exploration of multiple human systems. The study involved the collection of multi-omics data from a total of 403 features that included 74 metabolomic features, 15 body composition features, 36 immune repertoire features, 210 gut microbiome features, 16 facial skin features, 10 electroencephalography (EGG) features, 34 clinical biochemistry features and 8 physical fitness features from 4066 volunteers [3].

They were then divided into 9 categories for the construction of the biological age of different organs and systems. The biological age of different organs and systems were found to show various correlations; for example, the biological age of the sex hormone and renal systems was observed to be most correlated while that of the renal system and gut microbiome was observed to be negatively correlated.

Moreover, people whose biological age was larger than their chronological age were found to age faster. Some overweight individuals were observed to have a faster aging rate for the nutrition metabolism and physical fitness systems while others have a faster rate for the liver system. The specific biological age of an organ was also found to predict its phenotypes and diseases. The cardiovascular system was found to have the highest predictive power for mortality as compared to the renal and liver systems.

Furthermore, all the different systems were observed to possess distinct genetic architectures as well as signal densities. However, one region that correlated with aging in all systems was identified. This region comprises the major histocompatibility complex on chromosome 6 which is suggestive of the role of the immune system in aging processes [3]. Additionally, several pathways were found to be associated with aging such as nucleotide excision repair (NER) pathway, DNA repair pathways, Hedgehog pathway, insulin signaling pathways, p53 pathway, and Notch signaling pathway. Finally, biological age could also be used in the construction of polygenic risk scores (PRSs) that could predict the longevity of individuals.

Therefore, biological age can be used for accurately predicting the disease status as well as the longevity of humans. They can be integrated into health management along with clinical practices for the elderly. Moreover, since the biomarkers for predicting biological age are easily available from routine physical checkups, their upscaling for health management in larger populations is easier.

[1] https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8640726/
[2] https://www.nature.com/articles/s41598-021-95425-5
[3] https://www.cell.com/cell-reports/fulltext/S2211-1247(22)00186-3