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المحتوى المقدم من Sano Genetics. يتم تحميل جميع محتويات البودكاست بما في ذلك الحلقات والرسومات وأوصاف البودكاست وتقديمها مباشرة بواسطة Sano Genetics أو شريك منصة البودكاست الخاص بهم. إذا كنت تعتقد أن شخصًا ما يستخدم عملك المحمي بحقوق الطبع والنشر دون إذنك، فيمكنك اتباع العملية الموضحة هنا https://ar.player.fm/legal.
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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
المحتوى المقدم من Sano Genetics. يتم تحميل جميع محتويات البودكاست بما في ذلك الحلقات والرسومات وأوصاف البودكاست وتقديمها مباشرة بواسطة Sano Genetics أو شريك منصة البودكاست الخاص بهم. إذا كنت تعتقد أن شخصًا ما يستخدم عملك المحمي بحقوق الطبع والنشر دون إذنك، فيمكنك اتباع العملية الموضحة هنا https://ar.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

187 حلقات

Artwork
iconمشاركة
 
Manage episode 433032371 series 2631947
المحتوى المقدم من Sano Genetics. يتم تحميل جميع محتويات البودكاست بما في ذلك الحلقات والرسومات وأوصاف البودكاست وتقديمها مباشرة بواسطة Sano Genetics أو شريك منصة البودكاست الخاص بهم. إذا كنت تعتقد أن شخصًا ما يستخدم عملك المحمي بحقوق الطبع والنشر دون إذنك، فيمكنك اتباع العملية الموضحة هنا https://ar.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

187 حلقات

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