Sarah Urbut

Cardiologist and scientist @MGHHearthealth @harvardmed fascinated by statistics, genomics @broadinstitute, and exploring the world on two wheels.

[Sarah Urbut @tigerstatdoc]

@ShuangSong6 @HarvardBiostats Excited to present this work under the incredible mentorship of @pnatarajanmd @SashaGusevPosts @g_parmigiani !

[Shuang Song @ShuangSong6]

Kick off the PQG Working Group Seminar Series with Dr. Urbut's fascinating research! Join us next Tuesday (Mar 24)!

[Pradeep Natarajan @pnatarajanmd]

@drjohnm Many thanks @drjohnm. Fully agree with you. For that reason, I’m excited by this work led by @tigerstatdoc & with @SashaGusevPosts G. Parmigiani modeling numerous health trajectories simultaneously in interpretable fashion to better predict & prioritize https://t.co/S4fbe8me21

[Harriette Van Spall, MD MPH 🇨🇦 @hvanspall]

Our most comprehensive global #statistics #epidemiology update with 700 pages on #CV, #metabolic, #kidney, #brain dz @Circ_AHA

Most disorders have shared #riskfactors that are preventable and treatable!

Want to know the stats on each??

https://t.co/3cS5wDkoSy

[Sarah Urbut @tigerstatdoc]

@aklfahed @MGHHeartHealth Awesome work with @g_parmigiani, @SashaGusevPosts and @pnatarajanmd, all star team!

[Sarah Urbut @tigerstatdoc]

@aklfahed @MGHHeartHealth Thank you @aklfahed !

[Akl Fahed @aklfahed]

When Bayesian statistics meets cardiology
@tigerstatdoc @MGHHeartHealth
#AHA25

[Sumeet Khetarpal @SumeetKhetarpal]

The Khetarpal lab at UVA is seeking a research associate (postdoc)! Please see the posting and reach out with any questions! https://t.co/GNNQldP6uo

[Sarah Urbut @tigerstatdoc]

@pnatarajanmd @NEJMEvidence @MGBResearchNews @harvardmed @broadinstitute @MGHMedicine @MassGeneralMDs @CGM_MGH @mghcvrc @MGHHeartHealth thank you @pnatarajanmd! such an important question and an honor to be featured in @NEJMEvidence - excited about the use of Bayesian hierarchical models to disentangle sources of variation within and between ancestries, cohorts, and sampling...

[Pradeep Natarajan @pnatarajanmd]

Delighted to share our study led by @tigerstatdoc demonstrating the genetic generalizability of LDL-cholesterol's association with coronary artery disease across 1M individuals globally https://t.co/kw5uh7RRZd @NEJMEvidence

While clinical trials of LDL-C-lowering medicines have shown improvement in CAD risk, they generally don't reflect the breadth of patients we evaluate for such medicines. Subgroup analyses in trials to assess for differences are generally very underpowered as they are not the main intention of trials.

The world's human genetic data is (1) larger and covers many more communities and geographies than clinical trials, and (2) can enable causal inference methodologies across these groups.

We assembled data across the US, Europe, Middle East, and South Asia to examine this relationship across individuals of African, admixed American, East Asian, European, Middle Eastern, and South Asian ancestry.

Adjusting for variability in allele frequencies and performance, harmonized the same LDL-C effect, we observed (1) a significant positive association with CAD and (2) heterogeneity in this association.

While this is not a substitute for clinical trials reflecting the breadth of patients we see, the results (1) provide reassurance regarding current clinical practice extrapolating existing trials of LDL-C-lowering therapies, (2) the relationship between LDL-C and CAD appears generally robust across communities & solidifies this as a core pathway for CAD risk reduction, and (3) indicates that investment in adequately powered LDL-C-lowering therapy trials in other communities is likely to yield favorable returns.

Many thanks to the team!
@skoyamamd @HornsbyWhitney @gina_peloso @aklfahed @wallacemwang & others

[Joe Pickrell @joe_pickrell]

Enjoyed reading this paper on how to jointly model genetics and longitudinal health records

https://t.co/YW0b5Uxqk5

[Sarah Urbut @tigerstatdoc]

@joe_pickrell Thank you so much @joe_pickrell !!

[Sarah Urbut @tigerstatdoc]

@BellomoTiffany Thank you @BellomoTiffany :)

[Sarah Urbut @tigerstatdoc]

Announcing our much-awaited updated aladynoulli, a fully interpretable Bayesian model that considers predictive and explanatory disease trajectories across the life course for individuals, anchored on underlying PGS. https://t.co/Q8kqRC9GqU

[Sasha Gusev @SashaGusevPosts]

Amazing work from Sarah on Bayesian inference of longitudinal EHR trajectories and latent disease signatures. Check out the app!

[Sarah Urbut @tigerstatdoc]

@SashaGusevPosts Thank you @SashaGusevPosts !

[Sarah Urbut @tigerstatdoc]

@teapoow thanks - as described in the prePrint, the GitHub repo is private to collaborators only now, but please see an example of visualizing model at work at https://t.co/RXMxxX9mk9

[Sarah Urbut @tigerstatdoc]

@pnatarajanmd @SashaGusevPosts @broadinstitute @MGBResearchNews @harvardmed @MGHHeartHealth @CGM_MGH @mghcvrc an amazing team, thank you @pnatarajanmd !

[Sarah Urbut @tigerstatdoc]

@BennyStrobes thank you!

[Pradeep Natarajan @pnatarajanmd]

Excellent work by @tigerstatdoc constructing a generalized lifelong interpretable disease risk prediction model using genetics and clinical factors

[Sarah Urbut @tigerstatdoc]

Follow the math, follow the pt with associated app at https://t.co/RXMxxX9mk9. Our model is truly generative, explicitly modeling the risk captured by EHR histories through Bayesian the *magic* 🪄 of mixture modeling. @SashaGusevPosts @pnatarajanmd @g_parmigiani .