A Stanford study just revealed we age according to four distinct “ageotypes”
Here’s what you need to know about the four aging pathways and being that much closer to unlocking individual aging patterns.
Let’s start with aging.
Aging can be characterized by an accumulation of damage leading to breakdown of physiological integrity and increased risk of mortality.
In 2013, a publication by Lopez-Otis et al “The Hallmarks of Aging” was an exciting milestone in the longevity science community as it classified, at the cellular level, the pathways contributing to mammalian aging.
What hasn’t been as clear in longevity science however, are aging pathways at an individual level.
Sure, we are living in an exciting time where consumer genetic tests are available, which can help determine a person’s genetic profile as a sort of blueprint for their bodily functions. But we know that genetics don’t necessarily dictate health outcomes and that human aging is affected by a combination of factors that include one’s environmental and lifestyle.
We can now also quite accurately estimate, using epigenetic testing, a person’s “biological” age (as different from “chronological” age, which is simply a measure of years since birth). This type of testing has shown that a person may be aging faster or slower at a biological level, versus their chronological age. Some scientists have said that the epigenetic clock can more accurately tell you when you’re going to die than relying on your chronological age against average population lifespans.
But epigenetic tests still leave a lot of questions. For example, knowing your biological age won’t reveal “why” you may be aging faster or slower. It also won’t tell you which biological pathways are causing your aging process to speed up or slow down. In other words, knowing your biological age is not the most actionable information. One would need to be quite committed to trialing various health interventions and re-testing their epigenetic clock in order to see whether their lifestyle change or specific medication has made a difference to their rate of aging. It’s inefficient and leaves much to guessing. (However, full disclosure, this is something I’m doing right now for myself, but then again I’m a self-quantification geek).
That’s all to say that defining aging at an individual level, thus far, has been a heterogeneous mystery.
But now, for the first time, distinct pathways associated with age at an individual level have been classified.
In this month’s publication of Nature, a study was published by the Department of Genetics, Stanford University School of Medicine that revealed personal aging markers and ageotypes by looking at the types of molecular pathways that changed over time in an individual. Epigenetics aside, this study found many more different types of molecules, such as transcripts, proteins and metabolites as well as the microbiome, that were associated with age, including 87 significant molecules and 84 significant pathways.
This article was written to distill their findings.
Who was in the study ?
Study participants were 106 pre-diabetic and healthy individuals (age range 29–75 years; median 55.74 years).
35 participants had insulin resistance (IR), 31 participants exhibited insulin sensitivity (IS) and 40 participants had unclassified insulin status.
How were study participants profiled ?
Individual progress was tracked with frequent sampling of quarterly visits for up to 4 years (48 months). Additional samples were collected during periods of physiological stress, such as respiratory viral infections.
Longitudinal and deep multiomics profiling was performed to characterize each individual participant.
“Omics” assays included:
- proteomics using what’s called SWATH-MS, which stands for sequential window acquisition of all theoretical fragment ion spectra mass spectrometry
- metabolomics using LC-MS which stands for untargeted liquid chromatography mass spectrometry
- transcriptomics and microbial profiling using next-generation sequencing
Biomarkers were also tracked to examine correlations with aging.
Finally, study participants were also extensively characterized for various glucose dysregulation parameters, including fasting glucose, HbA1c, oral glucose tolerance tests and insulin resistance (IR) using a steady-state plasma glucose (SSPG) test.
What three types of analyses were performed?
(1) identification of markers and pathways that were positively correlated with aging across all participants
(2) differences between participants who were insulin resistant vs insulin sensitive in their age-associated biomarkers
(3) identification of personal markers and pathways that changed with age for each individual and how these differed between individuals
What were the study results?
Basically, pathway analysis of personal aging molecules in different individuals revealed distinct aging pathways for each individual. And that’s what we’re here to talk about.
1. Several gut microbes correlate with age.
With the increasing popularity in consumer microbiome tests like Viome and UBiome, it’s interesting that this was one of the prominent findings of the study. Fun fact: the human gut microbiome is comprised of 300 to 500 bacterial species, which in turn collectively comprise of ~2 million genes which is greater than the human genome. The number of bacteria within the gut is approximately 10 times greater than the number of cells in the human body. That’s a lot of bacteria. I digress…
The study did confirm a couple of gut bacteria strains that observed changes at cohort level.
It turns out that the gut bacteria Clostridium cluster IV increased in abundance with age along with genus Blautia. The latter is consistent with a recent microbiome study that showed that Blautia hansenii can be used to predict chronological age. The researchers behind that study had actually developed a method of predicting one’s biological age based on their microbiological profiles of gut microbiota. Cool !
However, we’re here to talk about individual aging pathways, and indeed the researchers found that different individuals had distinct trends in their gut microbial changes over time.
One study participant had 56 significant taxonomy level changes in gut microbes while another had only 6 significant changes during the same time, 5 of which were not seen in the aforementioned participant.
2. Significant lifestyle changes such as weight loss can alter an individual’s aging pattern.
To start off, known biomarkers that correlate with aging in large cross-sectional studies were validated.
31 individuals in the study were analyzed with clinical and proteomics assays, which validated the positive age correlation of the following biomarkers:
- HbA1c — a form of hemoglobin in the bloodstream that’s linked to sugar
- apolipoprotein A-IV protein (ApoA4) —a major structural protein component of high-density lipoproteins (HDL)
- PROS1 — a protein important for blood clotting
In total, 99 transcripts that correlated with age in this Stanford study were also identified in larger cross-sectional studies. Yet although molecules can show significant population level trends, at a personal level these trends can be the opposite.
For the six clinical markers significantly associated with age at the population level, researchers observed that these markers did not always associate with aging at an individual level, and often showed associations that were significantly reversed from the population trend.
Now the cases where biomarkers showed a reversal from the population trend, researchers hypothesized were likely due to interventions in the personal life of the individuals, such as lifestyle changes (ie weight loss), suggesting that these markers are actionable and that lifestyle changes can be used to alter an individual’s aging pattern.
The researchers did examine physical activity, food data, medication and BMI changes for the participants. They observed that it is unlikely that the changes we observed were due to lifestyle or medications. However, the data did suggest that in some cases, lifestyle interventions can improve clinical markers.
Four study participants whose HbA1c blood biomarker decreased with age, meaning their glucose metabolism actually improved over the course of the study. Two of these individuals had undergone diet restriction while the other two lost weight, showing improvements in multiple aging-related clinical markers.
Moreover, there were 12 participants whose blood urea nitrogen (BUN) or creatinine saw significantly decreased associations with age . Since both of these clinical markers typically increase with age, this could suggest that kidney function in these 12 individuals actually improved over the course of the study. Looking into the lifestyles of these 12 participants, eight of them were consistently taking statins for the entirety of the study, whereas four were not. What’s more is that the individuals who were on statins where the ones whose creatinine levels deceased significantly.
Notably, one of the two participants who lost weight during the study, improved in all six clinical markers.
3. Individuals with insulin resistance (IR)may experience more rapidly increasing increased inflammation with age than insulin sensitive (IS) individuals.
With the ever-increasing levels of population-level sugar consumption, insulin resistance is a serious population health concern. I personally thought it was great that this study chose to characterize diabetics versus healthy participants to see whether they age differently.
Data analysis did identify ten molecules that significantly correlated with age in the IR group but not in the IS group:
- IgG Fc-binding protein (FCGBP)
- Lumican — an extracellular matrix protein of human articular cartilage responsible for tissue homeostasis
- Non-specific lipid transfer protein (SCP2)
- Xenobiotic 2-aminophenol sulfate
- Monocyte counts were positively correlated and platelet counts were negatively correlated with age in individuals who were IR
Many of the above reside in the inflammation and related immune pathways. It has been shown by previous studies that chronic inflammation, as may be measured by looking at C-reactive protein levels in the blood, is correlated with a number of chronic diseases, so IR individuals may be at risk of aging at a faster rate.
4. Individuals can have 4 distinct aging patterns, or “ageotypes”
And now for the most interesting (in my humble opinion) findings from the study.
In total, data analyses revealed 107 canonical (involving a protein B-catenin) pathways and 147 toxicity pathways. Grouping the pathways that had similar terms revealed four major overlapping pathways — immunity, metabolic, liver dysregulation and kidney dysregulation — which were associated with aging.
Analysis showed that some individuals fall strongly into one or more of these aging pathways, suggesting that they have distinct “ageotypes”. Notably, individuals showed distinct and sometimes opposite patterns of expression in molecules and pathways.
For example, a person may display strong aging in metabolism and kidney dysfunction, but experience no significant changes in immune system or liver function. The opposite may be true in another individual.
For one study participant, the coagulation pathway most strongly correlated with aging. However, for another study subject, the cardiac hypertrophy pathway was the most correlated with aging, suggesting alterations in heart function.
That said, the grim reality is that many individuals displayed strong liver, kidney, metabolic and immune ageotypes, meaning they were aging in all four pathways.
5. No association was observed between ageotypes and BMI, chronological age or insulin resistance/sensitivity status
The study researchers looked out for the possibility that exhibiting one or more of the four ageotypes may be influenced or triggered by or associated with other factors such as chronological age, body mass index (BMI), or the level of one’s insulin sensitivity. Unfortunately, there was no association found between the four ageotypes and the mentioned factors, indicating that differences are either intrinsic or due to more subtle lifestyle changes.
Study researchers hypothesized that an individual’s propensity to exhibit one or more of these aging pathways may be due to individual genetics, personal life habits, medical history or life stresses.
So, how can we apply these learnings?
- Improvements in lifestyle can presumably affect one or more ageotypes.
Two individuals in the study cohort lost weight during the course of the study and showed improvements in multiple aging-related clinical markers.
2. Knowing the main “ageotypes” ”could help get more efficient in targeting interventions at the individual pathway level. For example, to address the metabolic “ageotype” pathway, selective interventions (ie drugs) or lifestyle changes can be targeted at improving metabolic function and biomarkers.
3. The availability of personal time-dependent aging markers potentially enables treatment of aging at an individual level. A step beyond simply knowing one’s genetic blueprint, imagine being able to characterize each person’s canonical and toxicity pathways, and furthermore one’s ageotypes to compile one’s complete aging profile. This type of profiling could help enable highly personalized anti-aging treatment plans by zeroing in on one’s individual aging pathways.
Thanks for reading! I write on Medium about lifespan extension and health performance optimization related topics, so you can follow me to get notified when I write more articles like this one.
