6/
This study shows that clinical actions were taken for individuals with a FH pathogenic variant + it leads to lower LDL-C.
It also shows some of the care gaps.

Great foundation to increase use of genetics in healthcare. Need more of these.

👏 authors & 8 health systems 👇

5/
Patients with new/modified therapies had a mean LDL-C reduction of 59mg/dL and 42.0% achieved the hard goal of ≥50% LDL-C reduction or LDL-C ≤70mg/dL.

4/
Overall, 33% received new/modified lipid-lowering therapy within up to 3 years. Rate was 55% for those with a newly documented FH diagnosis code.

See below the new LDL-lowering agents and therapeutic changes stratified by pre-screening treatment status

3/
Aim: to evaluate changes in clinical management and LDL-C levels among patients we identified with FH.

We studied 453 participants who had enough retrospective and prospective EHR data available.

Among those, 85% (n=384) did not have a prior clinical FH diagnosis.

2/
FH is part of the CDC-Tier 1 conditions because it is very high penetrance & actionable

FH is caused by variants in APOB, PCSK9, and LDLR.

Among 211,263 adults participating in The Helix Research Network,

1,020 had FH. That is 1 in 207.
=> more than 1.5 millions in USA with FH.

Check this study on dominant negative mutations in ADA2 that cause ADA2 deficiency -- usually considered a recessive condition.

Beautiful work led by Marjon Wouters and Isabelle Meyts in @medrxivpreprint.bsky.social currently.

www.medrxiv.org/content/10.1...

Great study in @jexpmed.bsky.social.
IFNAR1 or IFNAR2 complete deficiency leads to severe viral disease. Here, authors identify 11 mutations where heterozygosity predisposes to severe viral disease by negative dominance. 1 is common. Paper: rupress.org/jem/article/...

Lastly, a huge thanks to all of the participants in the Helix Research Network and all health systems, PIs, and collaborators involved.

Happy to discuss with anyone interested in this topic.

Many many questions need to be answered. This was one way to start tackling the problem. Because we need to start putting more effort to figure out the 'low' part of the risk scale.

USPSTF now recommends that all women start biennial mammogram screening at age 40.

Taking a 'risk equivalent' approach, women in the 'low risk' group could delay start of mammogram screening by ~5 years.

Based on these results, we decided that for breast cancer, the only group of variants that meet our initial def of variants of potential risk would be the
pLOF vars with AF<0.001% in BARD1, CDH1, MAP3K1, RAD51C,RAD51D and TP53.

We can now risk stratify the entire pop.

Looking at the hazard ratio compared to the avg pop for each group
- Path vars in BRCA1/2 or PALB2,ATM,CHEK2 had high risk (pos contrl)
- Coding vars <0.001% AF (many are VUS) have no impact
- only 1 group had a HR > 2: very rare pLOF in the additional set of genes (ex: rare nonsense in TP53)

We defined 8 exclusive categories of Variants of Potential Risks
(6 after removing the 2 where >90% of the vars are called Pathogenic).
For each, we identified the carriers, and then did a time to event analysis.

So we tried this approach in the Helix Research Network.

It is a fantastic emerging network of health systems in US (& Canada now) with a single research protocol, Exome+ seq for all participants, and nice EHR data.

First experiment was on breast cancer.

But how do you define those variants of potential risks?

Could be VUSs, or VUS high. But in our hands, aggregate effect of these are close to zero. Maybe we can narrow down AND make interpretation automatic. This is key because one goal is to lower overall burden on healthcare enterprise.

This is how we think about risk stratification using both rare variants and common variants.

We added an intermediate level looking at rare variants of potential risk.
Variants that don't meet Pathogenic criteria. But that are suspicious enough to exclude a person from 'low risk' category.