Neo
#4
Update Jan 10: More in Nature Aging this week / can anyone access?
https://www.nature.com/articles/s43587-024-00568-5
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They got the cover of Nature last week…
Volume 624 Issue 7990, 7 December 2023
Internal clocks
Animal studies have shown that the ageing process varies not only between individuals but also between organs within an individual. In this week’s issue, Tony Wyss-Coray and his colleagues reveal that this ageing difference holds true for humans, too. The researchers assessed levels of human blood plasma proteins originating from 11 organs to measure organ-specific ageing differences in 5,676 adults. They found that nearly one-fifth of the people showed strongly accelerated ageing in one organ and 1.7% showed ageing in multiple organs. The team suggests that blood proteins offer a ready way to detect the ageing effects in organs, and could help to track health and disease.
This seems to be the key paper
https://www.nature.com/articles/s41586-023-06802-1
@ConquerAging perhaps you should see if the new company above, Teal, and/or Stanford group could sponsor you so you can include this testing in your optimization program
(Thread it short so you may want to skim from the top)
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Neo
#5
It’s possible to
Enroll Now to Request a Spot in our 2024 Launch
I just did - this seems quite exciting to me.
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Neo
#6
Hope they are/will measure GDF12 and Klotho
The researchers found that different organs can age at different rates within the same person. About 20% of the people studied had one organ that was aging much faster than the other organs. They also found that faster aging of certain organs, like the heart and brain, was linked to higher risks of diseases like heart failure and Alzheimer’s disease.
The full paper, open access: Organ aging signatures in the plasma proteome track health and disease
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The post by Eric Topol: is really good - I recommend people check it out.
What I find most interesting, is the info below.
Perhaps someone from the UK can tell me how they define “vegetable dips” (or explain what they are). In North America I tend to think people would define a vegetable dip as salad dressing that you dip vegetables in, but I find it hard to believe that these would be that healthy by themselves.
The Second Study
This work is from Vadim Gladyshev and colleagues from Harvard Medical School and the Broad Institute. Using data from the UK Biobank resource from about 53,000 participants, with measurement of 3,000 plasma proteins, 7 organ-specific aging clocks were confirmed, along with their relationship to clinical sequelae.
They also looked at effect of many foods, medications, and occupations, Below are the data for foods showing which ones were associated with favorable organ-specific effects (top, blue) and unfavorable impact (bottom).
和：
The Third Study
We’re back to Tony Wyss-Corsay and colleagues with a recent preprint of their study of 44,000 UK Biobank participants, 3,000 plasma proteins, for 11 organ-specific clocks. This work fully replicated their earlier publication but again there were a notable extension of findings.
Seen below is the relationship of various interventions including smoking, alcohol, moderate or vigorous exercise, foods on organ-specific age gaps.
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ng0rge
#10
I find this new study from Tony Wyss-Corsay hugely important. I just very carefully pored over the paper here - https://www.biorxiv.org/content/10.1101/2024.06.07.597771v1. I’ll post the PDF. This is a convincing proof of concept for proteomic BioAge Clocks which should be a big step forward from the epigenetic clocks (like DunedinPace). The Eric Topol post only scratches the surface and the graphics are confusing. You need to read the whole paper.
It’s incredible to me that the UK BioBank had data from bloodtests on 44,000 participants that included analysis of 3,000 proteins for each one. Not just that, but longetudinal results from multiple tests over time on the same individual. That’s a huge amount of data that Wyss-Corsay and his team could sift through. Apparently from just one plasma sample you can analyze the proteins to determine unique characteristics that associate the proteins to specific organs. Then by examining the proteins uniquely associated to say the heart or brain, you can see a profile that correlates (unclear if it’s causal) with the age or overall health of that organ.
Proteomics Clock Proof of Concept.pdf (5.6 MB)
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ng0rge
#11
Hopefully Teal Omics can soon make their BioAge Clock test available to the public and keep the price at $300 or less.
Eric Topol also linked to his article in Science about all the advances in using AI in analyzing an individual’s data to better predict their health/longevity outcome.
“The parallel in medicine is forecasting specific, actionable, high risk for individuals to prevent diseases or severe acute events. But we don’t have a gold standard for predicting health outcomes. That is hopefully about to change.”
Take the example of risk for Alzheimer’s disease, where there has been progress for blood biomarkers, but that’s just one layer of data. The risk of an individual could be assessed more accurately with orthogonal genomic data that include the apolipoprotein ε4 (APOε4) allele present in about 20% of the population, and a polygenic risk , which provides complementary, independent predictive value. So can retinal imaging. Add to that electronic health records with both structured and unstructured text, imaging, and lab test results. Ideally, details of sleep history and physical activity would also be included but could otherwise be available from an individual’s wearable biosensor. A recent study using machine learning was able to predict Alzheimer’s disease up to 7 years before diagnosis by integrating electronic health record data for cholesterol, blood pressure, vitamin D, and sex-specific features such as osteoporosis in women or erectile dysfunction and prostatic hypertrophy in men. There’s also the brain organ clock cluster of plasma proteins, indicative of a person’s biological brain age versus their chronological age (known as the CognitionBrain aging model), and which was additive to p-Tau-181 for predicting the diagnosis of Alzheimer’s. Reduced diversity of the gut microbiome and presence of certain proinflammatory microbial species have been identified as a risk factor for Alzheimer’s disease. And don’t forget about linked environmental exposures such as air pollution or high consumption of ultraprocessed foods.
It is important to emphasize that our ability to integrate all these layers of data into multimodal AI models is still at an early stage, and until now has been restricted to electronic health records and genomics, or electronic health records and medical images. High frequency or continuous data from wearable sensors have yet to be incorporated, in addition to many other layers. It’s an analytical challenge that is currently being tackled, and, if and once successful, can be widely applied to other promising forecasting technologies, such as the use of digital twins with nearest-neighbor analysis of vast population-level medical datasets."
https://www.science.org/doi/10.1126/science.adp7977
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ng0rge
#12
Here is more on proteomics and the UK Biobank Pharma Proteomics Project (UKB-PPP) which I find fascinating. Does anyone know if NHANES ( National Health and Nutrition Examination Survey) has any protein data like this?
“Identifying individuals who are at a high risk of age-related morbidities may aid in personalized medicine. Circulating proteins can discriminate disease cases from controls and delineate the risk of incident diagnoses. While singular protein markers offer insight into the mediators of disease, simultaneously harnessing multiple proteins may improve clinical utility. Here, we demonstrate how large-scale proteomic sampling can identify candidate protein targets and facilitate the prediction of leading age-related incident outcomes in mid to later life.”
“We modeled ProteinScores alongside clinical biomarkers, polygenic risk scores (PRS) and metabolomics measures to investigate how these markers may be used to augment risk stratification.”
“We identified differential plasma protein levels that were associated with the onset of 23 diseases (including leading causes of disability and reductions in healthy life expectancy) and all-cause mortality. The maximal follow-up period was 15 years across the 24 outcomes.”
“Fifty-four proteins had significant associations with eight or more incident morbidities.
Of the 54 proteins, growth differentiation factor 15 (GDF15) had the largest number of associations (11 incident outcomes), followed by interleukin-6 (IL-6) and plasminogen activator urokinase receptor (PLAUR) (10 incident outcomes).”
“This study quantified circulating proteome signatures that are reflective of multiple incident diseases in mid to later life. These data suggest that augmenting traditional risk factors with proteomic, metabolomic and genetic data types may further hone risk stratification.
We demonstrated that relatively few circulating proteins can add value to risk stratification up to a decade before formal diagnoses. ProteinScores for incident type 2 diabetes, COPD, ischemic heart disease, Alzheimer’s dementia, Parkinson’s disease and death demonstrated value beyond a comprehensive set of 26 covariates. The scores minimize the need for the extensive recording of lifestyle, physical and biomarker measures, offering a streamlined set of metrics to proxy for an individual’s health status.”
“Our ProteinScore for type 2 diabetes outperformed the PRS (polygenic risk scores), likely due to proteins representing an interface that captures genetic, environmental and lifestyle contributions to disease risk.”
“Of the 720 proteins that were identified as indicators of multimorbidity status, 716 were associated with age in a previous analysis of the same dataset. Future studies could explore their possible causal contributions to disease and whether they have differential effects across the life course.”
** Blood protein assessment of leading incident diseases and mortality in the UK Biobank**
https://www.nature.com/articles/s43587-024-00655-7
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One interesting note on the Biomarker / aging clock market more generally. Talking with the Venture Capitalists lately about the investment opportunities in the biomarker space, the general belief seems to be its very unattractive one for investment because there is a lot of competition (many different clocks), it takes a long time and a lot of money to do proper validation of the clocks, its a relatively small market (at the high prices that provide the margins of interest to Venture Capitalists).
Long term it seems most of this market will need to be developed with support from non-profit groups like Hevolution fund to drive down the prices and do the validation testing that will be required to get truly valuable aging clocks of all sorts.
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ng0rge
#15
I check the news regularly for anything on Teal Omics but it’s quiet. Other than the recent Wyss-Coray paper and some news about new funding for Teal Omics, I haven’t heard anything. I thought by now they would be closer to a launch of their clock test. Especially with the other proteomics clock project that you posted also planning a consumer launch.
Not sure why you would say that it’s a relatively small market. It seems to me that there is a large demand/unmet market for any BioAge Clock test that is markedly more accurate, reliable and repeatable than DunedinPace (the current standard). Certainly could be widely used in research and volume to the consumer would be based on price. My assumption is that DunedinPace is profitable for the companies that offer it - particularly TruDiagnostic.
Its a small market at the current prices … e.g. $150 to $300 or so. To be a larger market the prices needs to come down dramatically, but then the margins will not be very good (unless extreme scale efficiencies).
I’m just relaying the Venture Capitalist’s review of the market. It can still be a “nice” business opportunity, its just not the type of business that VCs will invest in as its not going to $100 million + in a few years with high gross margins.
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ng0rge
#17
@Neo Have you heard any news or update about the “2024 Launch”?
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Thanks. This is very interesting. It makes sense to look for and improve the weak link
“Unlike interventions in the mouse and other animal models, we are learning how difficult it is to slow body-wide aging in people. A far more realistic objective would be to slow the biological aging process in a particular organ in people who are extreme agers.”
