In the emerging world of longevity and healthy living, let’s not forget that science-backed preventive medicine is essential (in addition to optimal diet and exercise).

For example, cardiovascular disease remains the leading cause of morbidity and death worldwide. Premature disease can be crippling and adequate evidence-based preventive care is necessary.

For example, regarding Lp(a):

1. It’s largely genetic! Get screened, and if positive, screen relatives.
2. Lp(a) is an independent risk factor for cardiovascular disease. Yes, even if LDL is normal.
3. Aspirin is primary prevention in those with elevated Lp(a).
4. PCSK-9 inhibitors actually have a moderate effect on Lp(a). They are adjuncts to statins. Oral options coming soon!

So, to summarize: in a world of desired longevity, evidence-based screening (and treatment) is still most important.

We use some cutting age artificial intelligence (AI) in our clinical practice.

I recently had the opportunity to speak with Medscape about the intersection of AI and medical education. One message stands out: good data in, good data out - a classic concept from data science and analytics that is crucial to understand for all that use AI in medicine.

As AI becomes more integrated into healthcare decision-making (soon to be the norm for much of the workforce), all clinicians must consider these important questions:

1. Does the AI's output make sense?
2. What data were used to form the prediction?
3. For which patients does the algorithm perform well?
4. Where is there a lack of data?

These questions remind us that AI is only as good as the data it is trained on. For AI to be truly effective and safe in healthcare, clinicians need to be equipped not just with the ability to trust AI outputs, but with the tools to critically assess them.

“I was in Great Health. So Why Were My Arteries Clogged?”

For those that haven’t read this article in The Wall Street Journal, the author’s cardiac scare is not all too rare. It’s one of the reasons I joined Dr. Arthur Agatston to build a healthy aging preventive care program.

To summarize, the author, Joe Moore, is an athletic 65 year old that established care with a primary care physician. His calcium score was soon discovered to be over 2000, shockingly identifying old plaques in his heart despite his healthy habits. The essay discusses his tumultuous journey thereafter: a cardiac catheterization, a repeat (rising) calcium score, and misdiagnosis of sitosterolemia (hyper-absorbers of cholesterol that worsens plaque). His case, including diagnosis of the plaque later in life, is not all that uncommon.

Conditions such as sitosterolemia are very rare (<1/50,000), but genetic predisposition to subclinical heart disease (whether ABCG5 -sitosterolemia- or variants LIPC, LPA, FH, SRB1, to name a few) is incredibly important. These genetic predispositions, in addition to classic cholesterol abnormalities (LDL, HDL, triglycerides), can increase the risk of vascular disease (heart attack and stroke) at later age. Some people have the inverse - protective genes - despite abnormal cholesterol.

A robust healthy aging program should address cardiometabolic disease prevention inclusive of the spectrum of genetics, advanced new diagnostics, novel technology, and guideline-driven primary care.

Dr. Agatston has an excellent talk on genetic predisposition to cardiovascular disease with GBinsight: “Solving Clinical Dilemmas using Genetic Testing”: