402 ‒ NMR blood analysis: how mortality risk and more can be assessed from a single blood sample
In a Nutshell
NMR spectroscopy of a single blood sample measures lipoprotein particle concentrations, sizes, and inflammatory markers like GlycA, plus branched-chain amino acids and citrate. These data generate the LPIR insulin-resistance score, the MVX metabolic-vulnerability score, and accurate LDL-P, ApoB, and lipid values. Mortality, cardiovascular events, and diabetes risk track far more closely with particle number and these composite scores than with standard cholesterol numbers, so therapy should be guided by particle counts and MVX, not LDL-C.
These notes were generated by AI and may contain inaccuracies.
Peter Attia welcomes Jim Otvos, creator of the NMR LipoProfile test (LDL-P and HDL-P), noting that he has followed Otvos's work since Tom Daypring introduced it to him in May 2011. Attia explains that many listeners have likely had an LDL-P or HDL-P test without realizing it originated with Otvos.
Otvos earned a PhD in chemistry and biochemistry. He spent 20 years in academia using NMR spectroscopy as a structural tool, first at the University of Wisconsin-Milwaukee and then at North Carolina State University starting in 1990. NMR machines exist in every chemistry department in the country and are used by organic chemists to determine the structure of synthesized molecules.
In 1986, a paper published in the New England Journal of Medicine claimed that a simple NMR test could diagnose cancer (positive or negative) regardless of cancer type. The test measured the width of prominent signals in the NMR spectrum of blood plasma at half-height: narrow signals indicated cancer, broad signals indicated no cancer. The paper lacked mechanistic explanation for why this relationship should exist.
Otvos obtained six leftover plasma samples from healthy individuals at a local hospital. Half showed narrow signals and half showed broad signals. The three individuals with narrow signals were women who had recently given birth, demonstrating that pregnancy was a false positive not mentioned in the original paper.
Sign in to read the full notes
Get access to AI-generated notes, topic timestamps, and more.