May 18, 2020

Diseases

#111 – AMA #14: What lab tests can (and cannot) inform us about our overall objective of longevity

"Metabolic health really matters. It is the common thread that links all of these chronic diseases." — Peter Attia

Read Time 21 minutes

In this “Ask Me Anything” (AMA) episode, Peter explains his framework for understanding what lab tests can (and cannot) inform us as it pertains to overall longevity, with a specific focus on atherosclerosis, cancer, Alzheimer’s disease, and the physical body. Additionally, Peter shares details into two patient case studies around cardiovascular disease, including how the lab results influenced his diagnosis and treatment plan for the patients. Once again, Bob Kaplan, Peter’s head of research, will be asking the questions. If you’re not a subscriber and listening on a podcast player, you’ll only be able to hear a preview of the AMA.

If you’re a subscriber, you can now listen to this full episode on your private RSS feed or on our website at the AMA #14 show notes page.

Subscribe on: APPLE PODCASTS | RSS | GOOGLE | OVERCAST | STITCHER

We discuss:

  • Important lab tests and reference ranges [2:35];
  • How lab testing fits into the overall objective of longevity [4:25];
  • A healthcare system set up to react to a disease rather than prevent it [8:00];
  • The four pillars of chronic disease, and the three components of healthspan [14:30];
  • Atherosclerosis—How much can labs tell us about risk? [18:00];
  • Coronary calcium score (CAC)—interpreting results based on your age [24:15];
  • Cancer—what lab work can tell you, and the future of liquid biopsies [28:00];
  • Alzheimer’s disease—what’s driving Alzheimer’s disease, and what labs can tell you about your risk [33:15];
  • Healthspan and the physical body—where lab testing fits, the endocrine system, and zone 2 testing [39:00];
  • Summarizing the usefulness of lab testing—where it gives great, reasonable, or lousy insight [43:15];
  • Patient case study—elevated Lp(a): Understanding ApoB, and how cholesterol levels get reduced [45:30];
  • Patient case study—familial hypercholesterolemia [59:30];
  • Coming up on a future AMA [1:10:30]; and
  • More.

§

Important lab tests and reference ranges [2:35] 

The following is from AMA #1

Peter’s top five lab tests:

  1. Lp(a)-P (or Lp[a] mass is a reasonable approximation).
  2. APOE genotype.
  3. LDL-P (or ApoB).
  4. OGTT with insulin measurements.
  5. ALT.

Honorable mentions: Hcy, hs-CRP, oxLDL, and oxPL, fibrinogen, Lp-PLA2, ADMA and SDMA are also really helpful to know. Estradiol (E2) as well. Knowing your family history can also tell you something about risk.

Peter’s preferred lab results ranges (which may differ from the “standard” ranges) …

{end of show notes preview}

Would you like access to extensive show notes and references for this podcast (and more)?

Check out this post to see an example of what the substantial show notes look like. Become a member today to get access.

Become a Member


Disclaimer: This blog is for general informational purposes only and does not constitute the practice of medicine, nursing or other professional health care services, including the giving of medical advice, and no doctor/patient relationship is formed. The use of information on this blog or materials linked from this blog is at the user's own risk. The content of this blog is not intended to be a substitute for professional medical advice, diagnosis, or treatment. Users should not disregard, or delay in obtaining, medical advice for any medical condition they may have, and should seek the assistance of their health care professionals for any such conditions.
    • No: when you measure acidity in the urine, you don’t measure any *particular* acidic compound in solution — just the overall balance of acid vs. basic compounds in the solution.

  1. I have not listened to the pod yet — just read the notes and listened to the last couple of minutes to see what was teased for the followup — but based on that alone, this is great stuff, which I’ll be looking forward to listening to: thank you very much, Dr. Attia.
    You mention Lp(a)-P as being one of “Peter’s top five lab tests,” but don’t give a range in the table — please update (for both mass and particle number, since only the former is actually readily available). You might want to discuss rough conversions in light of:
    https://www.lipidjournal.com/article/S1933-2874(14)00307-9/abstract

    Also, one thing you don’t appear to have mentioned is 24 hour urinary C-peptide, which Dr. Attia has recommended as a possible readout of integrated insulin production on the pod and even suggested as a top test on his recent IHMC interview. Giving a recommended value in the Table for now would be great, and then discussion of this on a followup pod

    Finally: Peter, I was worried about your long silent period on social media, and it’s clear from your surfacing video on IG that you underwent some kind of personal crisis. I’m very sorry for the pain you and maybe your family have suffered, and despite the great value I get from your pods and other material, I want you to know that we all want you to take the time and put in whatever kind of personal investments you need to become whole (again) on all levels. Pillar #3, you know?

    • Agree 100% on the last point, Mikal. Peter, thanks for what you do — I know you will but do take much deserved and needed time for yourself and your family.

  2. When you say a person’s lab results are in the xth percentile, where is this coming from? What exactly do you mean? Also, I can’t find the ApoB app for iPhone. Is it still around?

    • Amy: the percentiles come from the population distribution of the patients being tested by the lab. So it’s the distribution of results for a given test in every patient that has been tested by LabCorp, or Quest, or whoever.

      apoB isn’t an app: it’s a lab value. apoB is the essential protein of an LDL particle, and since each LDL-P has one apoB in it, a given number of apoBs tells you essentially the same thing as LDL particle count.

      • Peter talked about an app called “ApoB” that was developed to determine which possible genetic mutations one has causing their hyperlipidemia.

      • @Mikkal R, thank for the reply. That’s what I was thinking but I’m an nurse practitioner and have been reading lab results for almost 25 years. The lab results never display the results as percentiles. I bet he calculates them on his own. I’ve seen some very specialized lab results reported as MoM- “Multiples of the Mean.”
        Regarding ApoB, yeah, I know it’s an LDL particle. During the podcast, he pulled up an app that he called ApoB on his phone to plug in results and get possible genetic reasons behind those results. Check the show notes and comments from others.
        A google search turned up this mention but I can’t find the app in the iPhone app store. “This clinical diagnosis is consistent with the findings from application of the de Graff/Sniderman ApoB algorithm (now available for Android and iPhones as the ApoB app).” found here- https://www.lipid.org/node/2182

  3. Thank you, Dr. Attia, for this very valuable and informative podcast. Your teaching on lipids and CVD has benefited many of my patients (and family) who are still referred to me, a dietitian, for a low cholesterol diet because of routine lipid screening. I wish your podcast were listened to by more doctors. Please keep up this important service.

  4. What and where is the App that Peter talked about in the podcast – that can calculate risk based on lipid #’s?

  5. Re: Liquid biopsies

    You said they’d be available in an investigative capacity. What is that?

    If you could order a liquid biopsy test today and my wife tested positive for a breast cancer undetectable by imaging could you move forward with the same treatment plan as if it were visible via imaging?

    • @Joshua- Per cardiologist Dr. Joe Khan’s 10-11-19 podcast (called Heart Doc VIP), normal is below 70 nmol/L. Over 125 nmol/L is high risk (per AHA/ACC).
      Canadian guidelines (2016) say above 75 nmol/L is abnormal.

  6. Great AMA! I especially liked the case studies. Helps a non-medical person like me follow the conversation better. Also, you seemed to skip what the intended use of the drugs were and the results you were hoping to see. I think a one-line summary at the end of each case would be great. For example, “So in conclusion, s/he had high ldl, …, we tried [drug name] because [drug effects or reason other drugs were not a good fit], hoping to see the [lab tests go below/increase/etc].” Just a suggestion, but regardless, thanks much for this AMA!!

Leave a Reply

Facebook icon Twitter icon Instagram icon Pinterest icon Google+ icon YouTube icon LinkedIn icon Contact icon