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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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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)
  continue reading

179 episódios

Artwork
iconCompartilhar
 
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)
  continue reading

179 episódios

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