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EP 146: The biology of aging with Austin Argentieri, Research Fellow at Harvard Medical School, Affiliate Member of the Broad Institute, and Research Fellow at Massachusetts General Hospital
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Manage episode 433032371 series 2631947
Conteúdo fornecido por Sano Genetics. Todo o conteúdo do podcast, incluindo episódios, gráficos e descrições de podcast, é carregado e fornecido diretamente por Sano Genetics ou por seu parceiro de plataforma de podcast. Se você acredita que alguém está usando seu trabalho protegido por direitos autorais sem sua permissão, siga o processo descrito aqui https://pt.player.fm/legal.
0:00 Intro to The Genetics Podcast
01:00 Welcome to Austin
01:42 What is aging and how should we think about it?
03:50 Discussion of Austin’s recent breakthrough paper on aging, including the questions he set out to answer, and the outcomes of the research
06:32 How Austin’s work focuses on using large-scale population proteomics data to create accurate estimates of biological age across diverse populations
08:10 Understanding aging in people whose protein-predicted age and chronological age diverge significantly
09:40 How a single biological estimate of proteomic age is highly predictive of all major non-cancer causes of death (within a dataset)
11:46 Validating the significance of proteomic signature in populations that are genetically and geographically distinct from the cohort on which the statistical models were trained (UK Biobank)
14:48 How not all model types are equal for estimating biological age and making generalizations from biological data across diverse populations
17:38 How far fewer than 3,000 proteins are necessary to make a prediction of biological age and how a select few are particularly significant
20:04 What is it about the 20 proteins identified by Austin’s team that make them highly predictive of biological age?
23:18 Why infamous studies searching for “fountain of youth” genes have never found any definitive answers
27:24 Why conditions associated with increased age often have high heritability, even though heritability of aging is very low
29:34 Decoding proteomic signatures for age to identify risk of developing age-related conditions
32:29 Translating this research into therapeutic development
36:51 Could protein levels associated with “decelerated” aging be replicated in someone experiencing “accelerated” aging?
39:32 How Austin became involved with the biology of aging and proteomics
42:42 What Austin and his team will be working on next
44:38 Closing remarks
Please consider rating and reviewing us on your chosen podcast listening platform!
Find out more:
Find Austin on Twitter (X)
179 episódios
MP3•Home de episódios
Manage episode 433032371 series 2631947
Conteúdo fornecido por Sano Genetics. Todo o conteúdo do podcast, incluindo episódios, gráficos e descrições de podcast, é carregado e fornecido diretamente por Sano Genetics ou por seu parceiro de plataforma de podcast. Se você acredita que alguém está usando seu trabalho protegido por direitos autorais sem sua permissão, siga o processo descrito aqui https://pt.player.fm/legal.
0:00 Intro to The Genetics Podcast
01:00 Welcome to Austin
01:42 What is aging and how should we think about it?
03:50 Discussion of Austin’s recent breakthrough paper on aging, including the questions he set out to answer, and the outcomes of the research
06:32 How Austin’s work focuses on using large-scale population proteomics data to create accurate estimates of biological age across diverse populations
08:10 Understanding aging in people whose protein-predicted age and chronological age diverge significantly
09:40 How a single biological estimate of proteomic age is highly predictive of all major non-cancer causes of death (within a dataset)
11:46 Validating the significance of proteomic signature in populations that are genetically and geographically distinct from the cohort on which the statistical models were trained (UK Biobank)
14:48 How not all model types are equal for estimating biological age and making generalizations from biological data across diverse populations
17:38 How far fewer than 3,000 proteins are necessary to make a prediction of biological age and how a select few are particularly significant
20:04 What is it about the 20 proteins identified by Austin’s team that make them highly predictive of biological age?
23:18 Why infamous studies searching for “fountain of youth” genes have never found any definitive answers
27:24 Why conditions associated with increased age often have high heritability, even though heritability of aging is very low
29:34 Decoding proteomic signatures for age to identify risk of developing age-related conditions
32:29 Translating this research into therapeutic development
36:51 Could protein levels associated with “decelerated” aging be replicated in someone experiencing “accelerated” aging?
39:32 How Austin became involved with the biology of aging and proteomics
42:42 What Austin and his team will be working on next
44:38 Closing remarks
Please consider rating and reviewing us on your chosen podcast listening platform!
Find out more:
Find Austin on Twitter (X)
179 episódios
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