With aging, as we go from youth to middle age and beyond, our bodies undergo a myriad of changes that can impact our health and well-being. This process, known as biological aging, refers to the changes in our bodies at the molecular, cellular, organ, and body level.
One area of particular interest is the calculation of biological age, which refers to the biological changes that occur over time and correlate with disease risk, mortality risk, and overall mental and physical abilities.
Several biomarkers have been proposed by longevity researchers as potential indicators of biological age that can be used by consumers, longevity practitioners, and geriatricians alike, including DNA methylation patterns, transcription factors, signaling proteins, microbiological profiles of the gut microbiome, telomere length, and more. While these biomarkers hold promise for predicting health outcomes and longevity, there is still much debate and ongoing research regarding their accuracy and reliability.
In this article, we will explore these various biomarkers of biological age, their strengths and limitations, and their potential applications for improving our understanding of the aging process and developing interventions to promote healthy aging and improve longevity.
Epigenetic clocks are the first of the biological aging clocks. They look at patterns that emerge in the epigenome, a layer above the genome that determines which genes are turned on and off through a process called methylation. Epigenetic expression is determined by lifestyle and aging, which compose up to 90% of how we age biologically, and epigenetic clocks correlate with age or aging outcomes (morbidity, mortality, and biological abilities, or lack thereof) (R).
First-generation clocks, such as Horvath and Hannum clocks, looked at the correlation between the outputs of the clock to chronological age (R, R). Second-generation clocks, like PhenoAge and GrimAge, took a step forward by integrating environmental variants like smoking and correlated the outputs with morbidity and mortality as opposed to chronological age (R). The third-generation clock – specifically, the DunedinPACE clock – takes into account longitudinal changes over time across 29 different biomarkers to measure how fast the subject is aging (R). The DunedinPACE clock was produced by Columbia and Duke University Professors Moffitt and Caspi and their teams.
The DunedinPACE clock was created using a four-step approach towards developing a simple DNA methylation metric. They refined the pace of aging into a measurement that is obtained from a single blood sample. The measurement is sensitive enough to changes in physiology that occur across multiple organ systems and has a significantly higher ICC or intraclass correlation accuracy level than all other epigenetic clocks to date.
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After consulting with many experts in the field for more than 18 months and evaluating all of the biological clocks, we feel confident that the DunedinPACE clock contained in NOVOS Age is the most powerful, precise, and actionable of all clocks out there.
Measuring Gene Expression with Transcriptomic Clocks
Transcriptomic clocks are a promising new development in the field of aging biomarkers. They are based on measuring changes in gene expression levels over time, specifically changes in messenger RNA (mRNA) levels. mRNA is a molecule that carries genetic information from DNA to the ribosome, where it is used to make proteins.
By measuring changes in mRNA levels over time, researchers can develop a transcriptomic clock that predicts a person’s biological age based on their gene expression profile. The advantage of transcriptomic clocks over other biomarkers is that they are tissue-specific and can provide more detailed information about a person’s health status.
However, developing a transcriptomic clock is not without its challenges. Unlike epigenetic clocks, transcriptomic clocks are more sensitive to environmental factors and may be more tissue-dependent. This means that developing a transcriptomic clock that is accurate across different tissues and populations will require more research and validation.
Proteomic clocks, on the other hand, are based on measuring changes in protein levels over time. As mentioned earlier, proteins have direct effects on our physiology and significantly change their expression levels with age. This makes the proteome an attractive target for researchers who study aging biomarkers.
Like transcriptomic clocks, developing a proteomic clock is challenging due to the complexity of the proteome and the need for accurate and reproducible measurements. Currently, proteomic clocks are only affordable for academic researchers and have not yet been validated in large study cohorts.
A metabolomic clock measures the metabolome, the complete set of small molecule chemicals found within a cell, organ, or organism. The human metabolome includes everything from lipids, carbohydrates, amino acids, and peptides to nucleic acids, organic acids, biogenic amines, all vitamins, minerals, food additives, drugs, cosmetics, pollutants, and contaminants that a person ingests or comes into contact with. The metabolites produced by the body provide a more comprehensive view of biological processes than other omic profiles.
However, metabolomic clocks have not yet shown correlations with chronological age that are as high as those set by epigenetic clocks. Reliable metabolomic aging clocks are yet to be developed as the technology lags due to the challenge posed by metabolomics, including practically anything one comes into contact with, making it difficult for scientists to wrap their heads around.
The Human Microbiome and Microbiomic Clocks
The microbiomic clock, on the other hand, focuses on the human microbiome, which involves many areas of the body, but the colon or gut is the most researched. The gut microbiome is a microbial community that lives in the digestive tract, composed of bacteria, fungi, viruses, and their genes. The diversity and composition of these species vary by diet, lifestyle, genetics, and age. Researchers have developed methods of predicting biological age based on the microbiological profiles of the gut microbiome.
In a 2020 paper, researchers used a form of artificial intelligence, a deep neural network, to achieve results within 3.94 years of mean absolute error of chronological age, which is close to the standards set by epigenetic clocks. Certain bacteria have been identified as pro-aging, while others are anti-aging. For example, akkermansia is a pro-longevity bacterial species.
Although microbiome clocks have a lot of potential, they are all corporate black box IP, and none of them have been subject to validation in large cohorts of thousands of people or scientific scrutiny, as seen with the DunedinPACE clock. Therefore, it is not recommended to use any of them at this point without scientific evidence and validation.
As mentioned earlier, the phenotypic clock is based on observable characteristics resulting from the interaction of genes and the environment. The PhenoAge clock is a well-known example of a phenotypic clock, which combines epigenetic and phenotypic analyses.
The PhenoAge clock uses an algorithm that requires blood biomarkers and chronological age to output a biological age and predictions of physical function. Researchers used a combination of epigenetic analysis, blood test analysis, and phenotypic analysis to create this clock. They looked at factors like lymphocyte percentage, albumin, and glucose levels to create an algorithm that could predict biological age based on blood biomarker numbers.
A later-generation phenomic clock, which is yet to be published as of the date of this article, uses 50 times more data than the PhenoAge clock and considers organ systems like the immune system, cardiovascular system, hepatic system, brain, pulmonary system, musculoskeletal system, renal system, and metabolic system. This study utilized UK Biobank data and longitudinal organ imaging to reveal a multi-organ aging network, which suggests that an organ’s biological age influences the aging of other organ systems.
Researchers found that accelerated body aging is associated with lifestyle and environmental factors, telomere length, and predicts survival time when diagnosed, as well as the likelihood of suffering from premature death. They also found that specific organ ages can predict 16 different chronic diseases associated with aging.
In summary, phenomic clocks are a promising approach to predicting biological age based on multiple factors, including epigenetic and phenotypic analyses. While the PhenoAge clock is a well-known example of this approach, a new phenomic clock that uses much more data and considers multiple organ systems has shown great potential in predicting age-related diseases and outcomes. However, more research and scientific scrutiny are necessary to understand and validate these clocks fully.
Telomeres are the protective end caps of chromosomes that get shorter with each cell division and thus with age. While they were once considered by the field of gerontology to be a promising biomarker for biological age, tests based on telomere length have not been found to be accurate enough due to the wide range of results depending on age. However, telomere length is more predictive of morbidity and mortality once they become too short.
For example, researchers have found that a telomere length of five kilobases is the telomeric brink, which denotes a high risk of disease or mortality. Shortening of telomeres can be slowed by DNA and cellular protection from lifestyle factors, while lengthening can be achieved via an enzyme called telomerase that can be produced through specific foods and supplements. The correlation between chronological age and telomere length is negative 0.3, indicating a weak correlation, but it is still a correlation and a worthwhile biomarker to consider as part of your Longevity Journey.
There is also a mention of glycomics, which have given rise to glycan clocks. Results from glycan tests have not shown a strong correlation to chronological or biological age and have had extreme discrepancies in some cases. Therefore, at present, the latest generation of epigenetic tests are favored for their accuracy, precision, and the amount of research devoted to the field. However, we at NOVOS remain open-minded about any potential evolution in the research behind glycan tests.
Epigenetic Clock; The Most Promising Aging Test
In conclusion, biological age testing is a field of research that has the potential to revolutionize how we approach health and aging. While chronological age can give us a general idea of where we are in life, it fails to take into account the various factors that can influence how well we are aging. Biological age testing, on the other hand, provides a more accurate picture of our overall health and can help us identify areas where we need to focus our efforts to stay healthy and age gracefully.
There are various methods for biological age testing, including epigenetic testing, gene expression analysis, and telomere length testing. Of these methods, epigenetic testing has emerged as the most promising due to its accuracy, precision, and the amount of research devoted to the field. Gene expression analysis is also a promising area of research that can help us better understand how our genes influence our health.
While telomere length testing was once thought to be a promising biomarker for biological age, it has since been found to have a wide range of results depending on age and is most predictive of morbidity and mortality once they become too short. However, it can still be a useful biomarker for cellular health and longevity.
In summary, while biological age testing is not yet perfect, it has the potential to improve our understanding of health and aging greatly. As research in this field continues to evolve, we can expect to see more accurate and precise methods of testing emerge, providing us with a better understanding of how to stay healthy and possibly reverse aging to live longer.
Learn more about NOVOS Age’s best-in-class, 3-in-1 biological age testing kit here.