243 replies to this topic

[albedo]

Two interesting studies from the same Swedish team using a similar data set using Levine's PhenotypicAge and PhenoAge and comparison with different BA measurement methodologies.

 

Mak JKL, McMurran CE, Kuja-Halkola R, et al. Clinical biomarker-based biological aging and risk of cancer in the UK Biobank. Br J Cancer. 2023;129(1):94-103.

 

Mak JKL, McMurran CE, Hägg S. Clinical biomarker-based biological ageing and future risk of neurological disorders in the UK Biobank. J Neurol Neurosurg Psychiatry. Published online October 26, 2023.

[albedo]

(Nature Medicine 2024)

Looks like we are really progressing to clinical validation ... (see also https://www.agingconsortium.org/)

 

Moqri M, Herzog C, Poganik JR, et al. Validation of biomarkers of aging. Nat Med. Published online February 14, 2024.

https://pubmed.ncbi....h.gov/38355974/

 

"The search for biomarkers that quantify biological aging (particularly 'omic'-based biomarkers) has intensified in recent years. Such biomarkers could predict aging-related outcomes and could serve as surrogate endpoints for the evaluation of interventions promoting healthy aging and longevity. However, no consensus exists on how biomarkers of aging should be validated before their translation to the clinic. Here, we review current efforts to evaluate the predictive validity of omic biomarkers of aging in population studies, discuss challenges in comparability and generalizability and provide recommendations to facilitate future validation of biomarkers of aging. Finally, we discuss how systematic validation can accelerate clinical translation of biomarkers of aging and their use in gerotherapeutic clinical trials."

 

"...We anticipate that the ideal biomarkers of aging shall have moderate
to strong associations with chronological age and predict multiple
aging-related outcomes beyond mortality, such as functional decline,
frailty, chronic diseases and disability and (multi)morbidity. They
should be sensitive to upstream factors thought to influence aging
such as stress, adverse events, environment, genetics and lifestyle, and
they should mediate the relationship between these factors and aging
outcomes. They should do so in many diverse populations and should
do so relatively similarly across populations. Biomarkers of aging that
meet these requirements should be prioritized for validation as screening
and diagnostic biomarkers and eventually as surrogate endpoints
in clinical trials..."

 

 Screenshot 2024-02-17 235503.png   261.24KB   0 downloads

 

See also the prior work published in Cell, sort of preparation to the work for Nat. Med. above:

 

Moqri M, Herzog C, Poganik JR, et al. Biomarkers of aging for the identification and evaluation of longevity interventions. Cell. 2023;186(18):3758-3775.

https://pubmed.ncbi....h.gov/37657418/

 

"With the rapid expansion of aging biology research, the identification and evaluation of longevity interventions in humans have become key goals of this field. Biomarkers of aging are critically important tools in achieving these objectives over realistic time frames. However, the current lack of standards and consensus on the properties of a reliable aging biomarker hinders their further development and validation for clinical applications. Here, we advance a framework for the terminology and characterization of biomarkers of aging, including classification and potential clinical use cases. We discuss validation steps and highlight ongoing challenges as potential areas in need of future research. This framework sets the stage for the development of valid biomarkers of aging and their ultimate utilization in clinical trials and practice."

[albedo]

(Nature Medicine 2024)

Looks like we are really progressing to clinical validation ... (see also https://www.agingconsortium.org/)

 

Moqri M, Herzog C, Poganik JR, et al. Validation of biomarkers of aging. Nat Med. Published online February 14, 2024.

https://pubmed.ncbi....h.gov/38355974/

 

"The search for biomarkers that quantify biological aging (particularly 'omic'-based biomarkers) has intensified in recent years. Such biomarkers could predict aging-related outcomes and could serve as surrogate endpoints for the evaluation of interventions promoting healthy aging and longevity. However, no consensus exists on how biomarkers of aging should be validated before their translation to the clinic. Here, we review current efforts to evaluate the predictive validity of omic biomarkers of aging in population studies, discuss challenges in comparability and generalizability and provide recommendations to facilitate future validation of biomarkers of aging. Finally, we discuss how systematic validation can accelerate clinical translation of biomarkers of aging and their use in gerotherapeutic clinical trials."

 

"...We anticipate that the ideal biomarkers of aging shall have moderate
to strong associations with chronological age and predict multiple
aging-related outcomes beyond mortality, such as functional decline,
frailty, chronic diseases and disability and (multi)morbidity. They
should be sensitive to upstream factors thought to influence aging
such as stress, adverse events, environment, genetics and lifestyle, and
they should mediate the relationship between these factors and aging
outcomes. They should do so in many diverse populations and should
do so relatively similarly across populations. Biomarkers of aging that
meet these requirements should be prioritized for validation as screening
and diagnostic biomarkers and eventually as surrogate endpoints
in clinical trials..."

 

Screenshot 2024-02-17 235503.png

 

See also the prior work published in Cell, sort of preparation to the work for Nat. Med. above:

 

Moqri M, Herzog C, Poganik JR, et al. Biomarkers of aging for the identification and evaluation of longevity interventions. Cell. 2023;186(18):3758-3775.

https://pubmed.ncbi....h.gov/37657418/

 

"With the rapid expansion of aging biology research, the identification and evaluation of longevity interventions in humans have become key goals of this field. Biomarkers of aging are critically important tools in achieving these objectives over realistic time frames. However, the current lack of standards and consensus on the properties of a reliable aging biomarker hinders their further development and validation for clinical applications. Here, we advance a framework for the terminology and characterization of biomarkers of aging, including classification and potential clinical use cases. We discuss validation steps and highlight ongoing challenges as potential areas in need of future research. This framework sets the stage for the development of valid biomarkers of aging and their ultimate utilization in clinical trials and practice."

 

Nice presentation by two of the main authors of the studies, hosted at NUS by one the stars of aging research (Prof. Brian Kennedy):

 

 

Edited by albedo, 08 April 2024 - 05:10 PM.

[albedo]

Comparing clocks. PhenoAgeV2 seem leading with others too ....

 

"...The most rigorous of the four, AA2 task demonstrates that second-generation aging clocks (PhenoAgeV2
[98], GrimAgeV1 [17], GrimAgeV2 [99], and PhenoAgeV1 [16]) appear on top, particularly
at predicting aging acceleration for the ISD class (Fig. 3E, Supplementary Materials Fig.
A5). Nevertheless, all clocks failed to detect aging acceleration in patients with cardiovascular and
metabolic diseases, at least at the statistically significant level (see Figs. A3 and A4 for results
without FDR correction)..." (red mine)

 

ComputAgeBench: Epigenetic Aging Clocks Benchmark

Dmitrii Kriukov, Evgeniy Efimov, Ekaterina A Kuzmina, Ekaterina E Khrameeva, Dmitry V Dylov

bioRxiv 2024.06.06.597715; doi: https://doi.org/10.1...24.06.06.597715

https://www.biorxiv.....06.06.597715v1

 

 

"The success of clinical trials of longevity drugs relies heavily on identifying integrative health and aging biomarkers, such as biological age. Epigenetic aging clocks predict the biological age of an individual using their DNA methylation profiles, commonly retrieved from blood samples. However, there is no standardized methodology to validate and compare epigenetic clock models as yet. We propose ComputAgeBench, a unifying framework that comprises such a methodology and a dataset for comprehensive benchmarking of different clinically relevant aging clocks. Our methodology exploits the core idea that reliable aging clocks must be able to distinguish between healthy individuals and those with aging-accelerating conditions. Specifically, we collected and harmonized 66 public datasets of blood DNA methylation, covering 19 such conditions across different ages and tested 13 published clock models. We believe our work will bring the fields of aging biology and machine learning closer together for the research on reliable biomarkers of health and aging."