Why Epidemiology Rules
Previously, I discussed how Hazard Ratios (HR), often for the risk of premature death, are the best metric for measuring the effectiveness of interventions. Now, let's look at epidemiological studies (ES), one of the best sources for HR. ES use observational data to find correlations between actions and health outcomes.
Benefits of Epidemiological Studies
Big Data
Premature death, or all-cause mortality, is the primary outcome of interest for HR. Fortunately, even older adults don't die often. For example, a Norwegian HIIT study, which looked at 70-77-year-olds, despite having an effect size of nearly a 50% hazard ratio and over 8,000 person-years of data, did not reach significance for the HR of premature death1. Reaching significance, even for a strong effect with older participants, generally requires over ten thousand person-years of data.
Ten thousand years of data is challenging for a randomized, controlled trial, but many extensive surveys can be searched for correlations for ES. The USA has the National Health and Nutrition Examination Survey with over 100,000 person-years of data, while the UK Biobank has nearly ten million person-years. These resources are sufficient to detect significant results, even for minor effects.
Ethical Approach to Risky Interventions
Perhaps the most famous use of ES is the association between smoking and lung cancer. Given strong indications that tobacco causes lung cancer, conducting a Randomized Controlled Trial (RCT) where people smoke until death would be unethical. Instead, scientists used ES to show the correlation between smoking and lung cancer.
Ability to Examine Long-Term Effects
Because ES requires large data to find statistically significant correlations, they typically span years, not just weeks or months, like RCTs. This is especially important when dealing with the impacts of diet and exercise. It may take months for the body to adjust to changes in diet or activity. For instance, the immediate response to skipping breakfast is weight loss, as the body gets fewer calories than it is used to. RCTs on skipping breakfast confirm this, with an average loss of half a kilogram (a pound) over nine weeks2.In the long term, though, the body adapts by reducing metabolism or increasing hunger, and ES show that breakfast skippers have a nearly a 50% higher risk of overweight or obesity3.
Similarly, according to the book Burn4, metabolic adaptations to changes in weekly exercise volume take three to five months. Most RCT durations last for weeks and miss these metabolic effects.
Epidemiological Challenges
Reliability of Self-reported Data
A frequent challenge to the findings of epidemiological studies based on surveys is that people may not answer truthfully. It is true that if you estimate how many calories someone eats in a day by asking them what foods they ate and how much of each, you'll end up with much less calories than expected. People don't eat meals with scales, and estimating the weight or amount of servings of food is not a common task. That said, asking someone, "How many servings of fish do you eat weekly?" yields reasonable numbers. Scientists have verified this by comparing food diaries—record the food you ate immediately after eating—with food frequency-type questionnaires (FFQ), and they find some differences. The FFQ responses are close to the food diary responses5.
Trust no other reported foods but me!
Confounding Confounders
Some people question whether epidemiological studies can be trusted because perhaps food, exercise, or habit doesn't affect longevity, but rather, people who do those things are "healthy users" (or "unhealthy users") who distort the data with other habits that do affect longevity. An oft-cited example of health user bias is flu vaccine studies, which can show reduced premature death before flu season starts. Having reviewed those studies, the issue there is not "healthy user" but "financial funding bias"—they're funded by flu vaccine makers. Independent Cochrane studies don't show this issue6. Disregard studies funded by vendors, folks!
Unintended correlations with the study objective are called confounders, and epidemiological studies control for the effects of confounders by adjusting for factors that impact health, such as age, gender, socio-economic status, BMI, or physical activity. These factors are added as a separate variable as the study objective, and their influence is removed from the analysis. Once these factors are controlled, the test results will unlikely change from something correlated with the study control and outcome but not with the confounders.
It's unlikely, but not impossible. It's educational to look at the one factor I have seen affect the results of epidemiological studies, even when the researchers tried to control its impact as a confounder: smoking. Smoking is correlated with coffee drinking and also reduces the effect of caffeine. Although coffee studies controlled for a yes/no smoking confounder, the increased smoking correlation with increased coffee consumption made it appear that drinking too much coffee was unhealthy. It's not, but smoking too much certainly is!
Comparison with Randomized Controlled Trials
A key question in health research is how well epidemiological studies compare to the gold standard of randomized controlled trials (RCTs). A meta-epidemiological study addressed this question by comparing healthcare outcomes assessed in observational studies with those in randomized trials7. The results were encouraging for epidemiological research:
On average, the estimates from observational studies were only about 6% different from those of RCTs. For instance, an observational study might find a correlation of 15%, but the RCT might find a 14% or 16% causality.
This small difference suggests that well-designed observational studies can provide results that are quite close to those of randomized trials.
The study found no systematic tendency for observational studies to overestimate or underestimate treatment effects compared with randomized trials.
This finding reinforces the value of epidemiological studies, especially in situations where RCTs are impractical, unethical, or too short-term to capture important health outcomes.
The Bottom Line
Epidemiological studies offer valuable insights into the relationships between lifestyle factors and health outcomes despite the challenges of self-reported data and potential confounders. These studies have advantages over randomized controlled trials, such as studying long-term effects and utilizing large datasets. By giving access to HR correlations, epidemiological studies provide essential guidance for public health recommendations and future research, helping us make informed decisions about our health and well-being.Moreover, the close alignment between epidemiological studies and RCTs, as demonstrated by the meta-epidemiological study, further validates the importance of observational research in our understanding of health outcomes. While RCTs remain the gold standard for establishing causal relationships, well-designed epidemiological studies can offer comparable insights, especially for long-term health effects and in situations where randomized trials are not feasible.
When pursuing health and longevity, the number of possible interventions is staggering. From foods with varying health impacts to diverse physical activities, each promising unique benefits, there are far more options than meals or hours in the day. With the many options, a crucial question arises: How do we evaluate which interventions have enough impact to be worth our time and effort?
Further complicating choosing a health routine, interventions often come with a mix of benefits and harms. For instance, fruits provide healthy polyphenols and fiber but carry pesticides, which can cause cancer. Strength Training increases muscle mass and capability, but excessive Strength Training stresses the body, increasing cardiovascular disease risk1. The many effects of food, exercise, and habits on the body make evaluating the net health effect of interventions challenging.
Hazard Ratios
Enter Hazard Ratios (HR). HR typically refers to the ratio of death from all causes over a given period compared to a control or reference case. However, it can also refer to a particular event, like a stroke or death from cardiovascular disease. An HR of 1.0 or 100% means that the intervention did not affect survival compared to the control. An HR of less than one implies the intervention reduces premature death, and an HR over one means the intervention is associated with earlier death than expected.
Why Hazard Ratio is a Superior Metric
1. Mechanism Agnostic
Many descriptions of interventions will tell you that doing X will benefit biochemical process Y. Therefore, you should do X. Biology is exceptionally complex. It's challenging to see if biochemical process Y is genuinely beneficial in the amount given from X, when X increases it, how many people need more Y, if Y still increases after months, etc. All of these questions can be resolved by looking straight at the HR effect on premature death. No matter how wonderful Y sounds, the conclusive measure is the lifespan impact. And, yes, a longer lifespan is well correlated with a longer span of healthy life2.
2. One Metric to Rule Them All
As mentioned above, many interventions can mix both positive and negative effects, like fruits and vegetables reducing cardiovascular risk, but also possibly carrying pesticides, which can increase cancer risk. Using HR enables direct comparison of these effects by looking at the overall survival rate.
3. Determining the Right Dosage
Not only does HR help in judging the effectiveness of an intervention, but it also aids in determining the appropriate dosage. For instance, while exercise is generally beneficial, extreme levels can stress the body, and HR can show how the benefit or harm changes over different dosages. For fruits, HR reduction in premature death is significant up to the second serving, and there is a little more for the third serving, but there is no significant improvement after that.
4. Comparison Between Health Interventions
Which increases longevity more: drinking 2.5 cups of drip coffee weekly or spending a weekly hour on Strength Training? Strength Training reduces HR by 35% compared with 27% for Drip Coffee.
Which is more harmful, eating one ounce (28g) sausage daily for breakfast or having a daily can of coke? The sausage increases 12% of HR vs. 10% for the coke.
Once familiar with HR impacts, the effect of HR reduction can used to compare different interventions and judge whether they're worth adding to your routine. When I see a study that such-and-such supplement reduces HR by 9%, I compare that with the HR of other interventions. Eating one serving of fruits or vegetables is about the same HR reduction (9%), drinking 2.5 or more cups of drip coffee drops HR by 27%, and walking 12,000 steps daily reduces HR by 60%. I'll stick with drinking coffee and walking and pass on the supplement.
Further Lessons from Hazard Ratios
In cases where different interventions are combined, HR can help understand whether these interventions have a cumulative effect, counteract each other, or have a neutral interaction. This analysis is vital in designing effective health strategies. For example, eating various fruits would give more advantages than just one or two kinds. As people eat more servings of fruit, as you would expect, they eat a greater variety of fruit3. Look again at the fruit HR chart above. Since people who eat more servings of fruit have more variety, if the variety further reduces the HR, the HR should continue to decrease with additional servings. Still, instead, the trend is flat after three servings. The benefit of fruit is fruit, and it is not particular to eating a variety. The same pattern appears for vegetables, as well.
Hypothesis Testing with Hazard Ratios
Comparison of HRs can be used to evaluate possible drivers of different interventions. For example, walking reduces the HR of death by nearly 60% when walking over 12,000 steps daily. Walking that many steps is over an hour daily, so you might wonder, "Can I take those steps fast by running for the same benefits?"
Let's compare them. Here are the HRs of running vs. walking, indexed by the number of steps weekly:
While they both provide a reduction in the HR, it's clear that the benefit is less for running after 50,000 steps weekly, where the running HR benefit begins to decrease. Indeed, studies have quantified that the benefit from walking is different and in addition to the HR benefits of aerobic exercise4.
Common Factors and Hazard Ratios
As another example, two foods give outsized reductions in HR at low doses -- nuts and olive oil. At first glance, they are pretty different. Nuts contain fiber, protein, various vitamins (folate, etc.), and minerals (magnesium, zinc, and potassium). Olive oil has polyphenols and is primarily made of mono-unsaturated fats.
Look at the HR graphs for mixed nuts and olive oil, indexed by one nutrient they share -- mono-unsaturated fat content. Given the similarity in the HR graph, I suspect that mono-unsaturated is the primary driver of their benefits.
As noted before, it's essential to get this small dose of either nuts or olive oil daily; bingeing at the end of the week gives less benefit. Daily intake probably accounts for the apparent smaller benefit of olive oil over nuts at the small dose of one gram daily. It's reasonable to have a couple of grams of olive oil with salad or bread daily, but less than ten grams of nuts may be less common than eating twenty grams every other day.
Conclusion
Hazard Ratios are a beacon of clarity in a world brimming with health advice and interventions. It simplifies the complex decision-making process in personal health by providing a comprehensive, overarching metric that captures the net effect of various health interventions on our overall life expectancy. Look for the impact on HR when evaluating health interventions to make informed decisions for a healthier, longer life.
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