Prioritizing health interventions: what an N of 1 cannot tell you

Bryan Johnson, a technology founder, was reported in 2023 to spend about two million dollars a year testing interventions on himself: timed meals, dozens of supplements, light therapy, hyperbaric oxygen, and frequent measurement of a long list of biomarkers [1]. His published results raise a methodological problem that applies well beyond one person.

The design problem

A self-experiment with one subject and dozens of simultaneous interventions cannot identify which intervention did what. There is no control group, no blinding, and no way to separate the exotic items from the ordinary ones running alongside them: regular sleep, daily training, a controlled diet, and no alcohol. Those habits carry strong evidence in population data on their own. If a composite biological age score improves, the design cannot say which input moved it. Medicine stopped accepting the single-case testimonial as evidence of efficacy for this reason, and the limit applies to a careful, well-funded single case as much as to a careless one.

What the population data say to do first

Prioritization becomes possible when interventions are ranked by measured effect size in large populations.

Social connection. A 2010 meta-analysis of 148 studies covering 308,849 people, followed for an average of 7.5 years, found that people with stronger social relationships had a 50 percent greater likelihood of survival (odds ratio 1.50, 95% CI 1.42 to 1.59). The authors judged the effect comparable to quitting smoking and larger than the effects of obesity or physical inactivity [2]. Isolation carries a measured risk in the same range as the standard cardiovascular risk factors.

Cardiorespiratory fitness and muscle. In a cohort of 122,007 adults who had treadmill testing at the Cleveland Clinic between 1991 and 2014, all-cause mortality fell step by step with fitness; the fittest group had an adjusted hazard ratio of 0.20 compared with the least fit, and the authors reported that low fitness was associated with mortality as strongly as, or more strongly than, smoking, coronary disease or diabetes [3]. In the PURE study of 139,691 adults across high-, middle- and low-income countries, each 5 kg lower grip strength was associated with a 16 percent higher risk of death over a median of four years, and grip strength predicted all-cause and cardiovascular death better than systolic blood pressure did [4]. Both measures are observational; the PURE authors called for trials testing whether improving strength lowers mortality, and those trials have not been done. Both are also trainable, which the exotic items on a protocol list cannot claim with the same evidence.

The standard risk factors. In the INTERHEART case-control study of 15,152 first heart attacks and 14,820 controls in 52 countries, nine modifiable factors (abnormal lipids, smoking, hypertension, diabetes, abdominal obesity, psychosocial stress, low fruit and vegetable intake, alcohol, and lack of regular physical activity) accounted for 90 percent of the population attributable risk of myocardial infarction in men and 94 percent in women [5]. That is a case-control estimate, not proof that correcting each factor prevents its share of events, but each of those factors has intervention trials with outcome data behind it. Sleep was not measured in INTERHEART and belongs on the list on the strength of other evidence.

Interpretation

None of this rules out benefit from the long tail of interventions: hyperbaric oxygen, red light, plasma exchange, and the rest. It sets the order of operations. A protocol built on poor sleep, no training, and untreated blood pressure spends effort on the smallest terms in the equation. The sequence the data support is to fix the factors with the largest measured effects, measure the result, and treat everything after that as optional experimentation held with low confidence. A protocol that a person with a job and a family cannot follow also fails a second test, adherence over years, which is where most of these outcomes are decided.

Sources

1. Fortune. Bryan Johnson is spending $2 million a year to stay young forever. July 14, 2023. https://www.fortune.com/2023/07/14/bryan-johnson-spending-2-million-young-forever
2. Holt-Lunstad J, Smith TB, Layton JB. Social relationships and mortality risk: a meta-analytic review. PLoS Medicine, 2010;7(7):e1000316. https://doi.org/10.1371/journal.pmed.1000316
3. Mandsager K, Harb S, Cremer P, Phelan D, Nissen SE, Jaber W. Association of cardiorespiratory fitness with long-term mortality among adults undergoing exercise treadmill testing. JAMA Network Open, 2018;1(6):e183605. https://doi.org/10.1001/jamanetworkopen.2018.3605
4. Leong DP, Teo KK, Rangarajan S, et al. Prognostic value of grip strength: findings from the Prospective Urban Rural Epidemiology (PURE) study. The Lancet, 2015;386(9990):266-273. https://doi.org/10.1016/S0140-6736(14)62000-6
5. Yusuf S, Hawken S, Ounpuu S, et al. Effect of potentially modifiable risk factors associated with myocardial infarction in 52 countries (the INTERHEART study): case-control study. The Lancet, 2004;364(9438):937-952. https://www.acc.org/Latest-in-Cardiology/Clinical-Trials/2011/01/24/22/24/INTERHEART