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About Long Life Labs

Long Life Labs was founded by longevity researcher Max Peto in 2013. At that time, Max was working with research organizations focused on controlling age-related degeneration in humans, and he was often inspired by how many scientific studies reported health and longevity to be largely under our control when we focus on managing key health biomarkers.

Despite the availability of scientific studies on useful health management advice, the quality of health recommendations in websites, books, and blogs is usually quite poor. Most of these sources confidently assert health recommendations with little or no reference to scientific studies. When they do give scientific references, the writers rarely account for all the available research—preferring to share only what supports their claims and profit motives. They also often reference studies done in yeast, flies, worms, mice, rats, or cells in a Petri dish without warning readers of the irrelevance of these disease models to human health and longevity. Long Life Labs is very concerned that many people are adopting these recommendations without knowing they may be wasting their time and money, or even harming themselves by using interventions that haven’t yet been properly studied in humans.

Long Life Labs is focused on helping everyone–including and especially non-scientists–understand and use scientific research to enhance their health and longevity prospects.

Biomarkers

Biomarkers can be of particular use in both assessing baseline disease risk and judging the value of a proposed strategy for better health. But to our knowledge, no individual or organization focuses on (1) understanding biomarkers of disease risk, (2) understanding how to influence them, (3) teaching others how to use this information for their personal benefit.

This is why “Labs” is in our name: it refers to the lab tests that help estimate risk of disease or the likelihood of living a longer (or shorter) life. Thus, you can then see the relevance of our tagline: Quantifying health for longer life. Rather than give unfounded health recommendations based on incomplete research and author biases and intuitions, health recommendations from Long Life Labs rely strictly on scientific studies of biomarkers–in humans–and their association with disease risk and longevity. Biomarkers can help us put a number value on (that is, they can help quantify) the state of a person’s health, because they have a reliable relationship with the risk of diseases and early death.

Strong associations with disease and early death

Many people have heard of some of the most useful biomarkers but may not know how strongly they’re related to disease risk, nor exactly how they can change them. Blood pressure is a good example. Scientific studies have shown that elevated blood pressure is associated with up to a 6-times higher risk of stroke1. High blood pressure has also been associated with higher risk of heart attack, with one study in Argentina2 reporting between 2.5- and 4-times higher risk of heart attack with elevated blood pressure.

As biomarker associated with sugar and fat metabolism, elevated fasting insulin has been reported in one small study3 to be associated with a 5-times higher risk of developing Alzheimer’s disease—a stronger effect than was reported for the frequently discussed genetic marker ApoE. Reducing blood pressure and fasting insulin is achievable—even without pharmaceuticals—yet most people don’t know how. Teaching this knowledge is one goal of Long Life Labs.

Hundreds of biomarkers

There are hundreds of biomarkers associated with the risk of early death. Long Life Labs founder Max Peto was a lead author on a study published in the journal Aging in 2017 which reviewed over 1,500 scientific papers that reported a combined total of 471 distinct biomarkers associated with early death.4  This study represents a treasure trove of biomarkers we can use to assess the risk of early death. However, choosing the most valuable and practical biomarkers is challenging. Some of these biomarkers are related to one another, such as fasting glucose, fasting insulin, and glycated hemoglobin, all of which are assessments of insulin sensitivity. So which one, or combination of these three, should be monitored, if at all, and why? Care must be taken to avoid duplication of related risk factors like these.

Other biomarkers are associated with the end-stage of a disease process that is likely to kill a person relatively soon, such as the biomarkers albuminuria and creatinine (associated with kidney failure), and coronary artery calcification (associated with heart disease). Because they change near the end of a disease process, these biomarkers aren’t very helpful to healthier people who don’t have the associated conditions.

Long Life Labs studies biomarker research to evaluate which biomarkers are the most useful and controllable (such as by factors like diet, exercise, nutrition, supplementation, and pharmaceuticals). Once we find overwhelming evidence supporting the usefulness of a biomarker for personal health management, we create articles, books, courses, and podcasts to help others understand and manage their own biomarkers of health and longevity.

References

  1. Kario K, Saito I, Kushiro T, Teramukai S, Tomono Y, Okuda Y, Shimada K. Morning Home Blood Pressure Is a Strong Predictor of Coronary Artery Disease: The HONEST Study. J Am Coll Cardiol. 2016 Apr 5;67(13):1519-1527. PMID 27150682.
  2. Ciruzzi M, Pramparo P, Rozlosnik J, Zylberstjn H, Delmonte H, Haquim M, Abecasis B, de La Cruz Ojeda J, Mele E, La Vecchia C, Schargrodsky H. Hypertension and the risk of acute myocardial infarction in Argentina. The Argentine Factores de Riesgo Coronario en America del Sur (FRICAS) Investigators. Prev Cardiol. 2001 Spring;4(2):57-64. PMID 11828201.
  3. Kuusisto J, Koivisto K, Mykkänen L, Helkala EL, Vanhanen M, Hänninen T, Kervinen K, Kesäniemi YA, Riekkinen PJ, Laakso M. Association between features of the insulin resistance syndrome and Alzheimer’s disease independently of apolipoprotein E4 phenotype: cross sectional population based study. BMJ. 1997 Oct 25;315(7115):1045-9. PMID 9366728.
  4. Peto MV, De la Guardia C, Winslow K, Ho A, Fortney K, Morgen E. MortalityPredictors.org: a manually-curated database of published biomarkers of human all-cause mortality. Aging (Albany NY). 2017 Aug 31;9(8):1916-1925. PMID 28858850.

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