Caloric restriction is the gold standard life extension strategy, validated over thousands of experiments in many animal species. How can we reconcile this with consistent findings that people who are slightly overweight live longer than normal or underweight folks?
The one fact that everyone in the field of aging agrees on is that animals fed less live longer. This is the result that got me interested in the field 25 years ago, and it is still the most robust finding in the field, verified in dozens of species from yeast cells to Rhesus monkeys.
Are humans different from all other animals?
Last month, a study came out of Ohio State U based on the famous Framingham database, including medical and demographic information on 5,000 people and their offspring, tracked over 74 years. The take-home message was that the people who lived longest were average weight when young and gained weight during their middle years. There were not enough people who had actually lost weight to constitute a subgroup, but the group identified as “low-normal weight” all through their lives showed up with 40% higher all-cause mortality than those that gained weight.
“For any given individual, it’s probably true that
the less you eat the longer you live.”
The argument went thus: Weight is mostly fixed by genetics, and the genetic component of weight does not affect longevity. It is relative calorie intake that affects longevity, relative to genetics, body type, and metabolism. For example, a study of genetically obese mice found that they had shortened lifespans if they were fed ad libitem, however, if the obese mice were calorically restricted, they actually lived longer than genetically normal mice, and even longer than CR normal mice, despite the fact that they still appeared plump.
This line of reasoning led me to hypothesize that the reason overweight people tend to live longer is that they are motivated to restrict calories, whereas people (like me) who don’t get fat no matter how much we eat feel no social pressure to restrain our gluttony.
I thought at the time that we ought to see this effect much more in women than in men, because overweight women are ostracized in our culture, whereas men are not. What I found, contrary to my prediction, was that the BMI with lowest mortality (in Japan) is 23-25 for men, compared to 21-23 for women [Matsuo, 2012].
So, is it time to consider the possibility that caloric restriction doesn’t extend human life expectancy?
New Ohio State Study
The new study is based on the 74-year-old Framingham cohort, people whose health and daily habits have been followed over time. Also followed was a Framingham Offspring cohort, the children of the original Framingham cohort. Almost all the original cohort have now died (so we have extensive mortality data), but many of the offspring cohort is still alive. The authors treat the two cohorts separately, and get somewhat different results for the two cohorts. Dr Zheng was kind enough to send me the full preprint with supplemental tables, and since it’s not yet available online, I’ve made it available for you to read here on GDrive.
The study looks not just at BMI but also at the change in BMI over mid- to late-life years. They classify the trajectories in seven groups, and analyze them using a Cox model. They find that the group that has lowest mortality had an average trajectory beginning at BMI=22 at age 30, increasing gradually to BMI=27 at age 80. The group was broadly defined, so that initial BMI could be anywhere from 18.5 at the low end to 25 at the high end.
|Cox Proportional Hazard Model
This statistical method is standard for studies like this evaluating effect on mortality. It is designed to take into account the steep rise in mortality with age, and weight different deaths according to when they occur. The standard assumption is that the mortality curve with age is changed by a multiplicative factor associated with each variable. The mortality curve retains the same shape across ages, but it slides up or down (on a log scale) according to which factors apply to a given subgroup. For example, having a graduate degree may multiply your risk of dying by 0.9 across the board, and eating red meat may multiply your risk by 1.2, so the model actually derives these numbers by assuming that meat-eaters with a graduate degree will have a relative probability of death 1.08 times the control group, and this applies at every age. (where 1.08 = 0.9 * 1.2)Is this quantitatively realistic? Everyone knows it is not, but it yields a single number which is a good benchmark for different longevity factors, and it allows different studies to report their results in a common format for comparison.
Division of subjects into seven groups was somewhat arbitrary, and was done to facilitate statistical analysis. The red railroad tracks represents midline of the trajectory associated with “longest lifespan”, defined above as the minimum Cox factor. The lowest weight group was associated with a Cox factor of 1.4, meaning 40% more likely to die (at a given age) than the red railroad track trajectory.
On the other side
CR extends lifespan in almost every animal model in which it has been tried. I won’t dwell on this, because it’s so well known, but I’ll note that CR works better in short-lived animals, as a percentage of lifespan and the enthusiastic projections of Roy Walford now seem overstated. I have said that I think CR in humans is good for 3 to 5 years. Do I still think so? There is good evidence for CR in humans.
- Food shortages during World War II in some European countries were associated with a sharp decrease in coronary heart disease mortality, which increased again after the war ended.[Fontana, 2007]
- Fontana performed in-depth metabolic profiles of people identified from the Caloric Restriction Society who were disciplining themselves to eat less. Relative to people at a comparable age, he found “a very low level of inflammation as evidenced by low circulating levels of C-reactive protein and TNFα, serum triiodothyronine levels at the low end of the normal range, and a more elastic ‘younger’ left ventricle, as evaluated by echo-doppler measures of LV stiffness.” 
- There is at least preliminary evidence that weight loss tends to set back the aging clock, as measured by several methylation algorithms 
- Higher BMI is associated with older methylation age 
- C-reactive protein in the blood, the most common measure of inflammation, increases with increasing BMI 
- Loss of insulin sensitivity is a hallmark of aging, driving many age-related diseases. There is a strong correlation between BMI and diabetes 
- BMI is linked to most common cancers, the #2 source of mortality. Here’s a good review by Wolin .
- BMI is also a factor in cardiovascular disease, the #1 killer. This study from Malaysia  found a trend of increasing CVD at every BMI level, but — like other studies — also found that all-cause mortality was lowest for BMI 25-30, which has traditionally been called “overweight”.
So, why doesn’t weight gain show up as a risk factor for faster aging?
I will continue this discussion in Part 2, and try to resolve this paradox in part, but (spoiler alert) I remain puzzled, after a month of reading on the subject.