Calorie restriction delays disease onset and mortality in monkeys

New study (Colman et al, 2009) in this week's issue of Science on the effect of calorie restriction over a 20 year period in rhesus macaques. Researchers at the Wisconsin National Primate Research Center examined monkeys under 30% CR and controls matched for baseline intake, age, and weight. According to the abstract:

Caloric restriction (CR), without malnutrition, delays aging and extends life span in diverse species; however, its effect on resistance to illness and mortality in primates has not been clearly established. We report findings of a 20-year longitudinal adult-onset CR study in rhesus monkeys aimed at filling this critical gap in aging research. In a population of rhesus macaques maintained at the Wisconsin National Primate Research Center, moderate CR lowered the incidence of aging-related deaths. At the time point reported, 50% of control fed animals survived as compared with 80% of the CR animals. Furthermore, CR delayed the onset of age-associated pathologies. Specifically, CR reduced the incidence of diabetes, cancer, cardiovascular disease, and brain atrophy. These data demonstrate that CR slows aging in a primate species.

Their general findings indicate a clear quantifiable effect of CR in reducing both mortality and age-related morbidities (cancer, cardiovascular disease, metabolic derangements) as well as age-associated loss of gray matter:

Food for thought, though confounding issues such as frequency of eating rather than total amount of caloric intake are not directly addressed.

Monitoring health by counting heartbeats

It's often the case that high-intensity workouts (like Fran or Tabata sprints) cause your heart rate to skyrocket in order to meet the metabolic demands of active muscle. Remarkably, the flow of blood to active muscles may increase to four or five times that of resting cardiac output. Just as remarkable is the fact that your heart rate will typically drop 40% five minutes after exercise completion. These dramatic changes in heart rate are controlled by the sympathetic and parasympathetic components of the autonomic nervous system; sympathetic activation increases cardiac acceleration, contractility and coronary constriction whereas parasympathetic activation promotes cardiac deceleration and coronary dilation. Your heart rate response to exercise is largely determined by the balance of these two systems.

Being a sucker for numbers, I'm always on the lookout for simple, predictive physiological measures. There are a few based on heart rate that are strongly predictive of mortality and turn out (not surprisingly) to be modifiable by training. The first is resting heart rate, which the American Heart Association suggests should be between 60-80 beats per minute (bpm). A fast heart rate is associated with an increased risk of death from cardiovascular as well as noncardiovascular causes (Hjalmarson, 2007; Palatini, 1999). Even within the recommended range (60-80 bpm), a lower resting heart rate is significantly associated with decreased risk of dying from any cause, especially heart attack (Jouven et al., 2005).

The second measure is heart rate reserve, the difference between maximal heart rate and resting heart rate. A smaller dynamic range is associated with increased risk of death from any cause, especially heart attack (Jouven et al., 2005), and a failure to reach predicted peak heart rates during graded exercise is predictive of increased mortality and coronary heart disease incidence (Lauer et al., 1996).

Finally, the last measure is heart rate recovery, the difference between maximal heart rate and heart rate measured some fixed time after cessation of exercise (usually 1 or 2 minutes). The failure to drop at least 30 bpm within 1 minute is associated with increase risk of heart attack. A smaller decrease in heart rate suggests a dysfunction of the parasympathetic system, since the decrease in heart rate immediately following exercise is primarily due to parasympathetic reactivation (Imai et al., 1994; Raymond, 2004).

The above figure bins together all the data below 25 bpm recovery, but if you look more closely at lower ranges, an association with risk of death is even more apparent. For all you stats geeks, below is a conditional trellis plot (click the figure to see a larger version) that illustrates risk of all-cause mortality as a function of age, fitness, peak heart rate and heart rate recovery (Ishwaran et al., 2004). Age is a binary grouping indicated by the orange bars (left column is younger than 45 yo and the right column is older than 45 yo). Fitness is a categorical variable indicated by the green bars (least fit in the top row proceeding to most fit in the bottom row). Peak heart rate and heart rate recovery (measured 1 minute after ceasing exercise) are plotted for each subject for whichever panel they correspond to (age x fitness). That's five freakin variables!

And just for kicks, the figure to the right plots some data from the last time I did 400 meter sprints. There goes (220-age) as a predictor for my maximal heart rate! There are better ways of estimating maximal heart rate (e.g., see Joe Friel's work).

It's worth pointing out that these heart rate measures are not independent (Jouven et al., 2005); in fact they're highly correlated, suggesting that they may be different measures of the same disorder. And while the mechanism(s) underlying the association of these heart rate measures with increased mortality and heart disease remain unknown, the data are consistent with the idea that autonomic system imbalance predisposes people to life-threatening arrythmias (Jouven et al., 2005).

Aside from their utility for predicting death, these measures are also interesting because they can be modified by training. Following training, heart rate recovery is accelerated (Darr et al., 1988; Imai et al., 1994; Sugawara et al., 2001) and resting heart rate is decreased (Wilmore et al., 2008). Changes to maximal heart rate are less clear, with some evidence for a slight decrease following endurance training (Darr et al., 1988; Wilmore et al., 2008). So if you're bored, or looking for another way to track progress, break out the stopwatch or heart rate monitor and start logging! Indeed, Levine (1997) showed that the total number of heartbeats in a lifetime is remarkably constant across a wide range of variation in mammals.If we take seriously the idea that a human heart is physiologically predetermined to beat ~3 billion times in a lifetime, perhaps it wouldn't hurt to make reducing your resting heart rate an objective.

Intermittent fasting or caloric restriction?

Intermittent fasting and caloric restriction are associated with many of the same benefits (Mattson, 2005). In trying to sort out the differences between these protocols, people will often treat IF and CR as if they are completely different things. It's not clear to me that they are entirely dissociable. I've previously mentioned that it is common for intermittently fasted rodents to eat slightly less than ad libitum controls. This varies from study to study and with the duration of dietary restriction (as does the degree of bodyweight difference between IF and control animals). The data in the figure below are from rats (Leveille, 1972) and mice (Anson et al., 2003), respectively.

It's worth noting that the C57BL/6 strain of mice (right panel) are intermediate (relative to other mouse strains) in terms of how much less IF mice eat than controls (Goodrick et al., 1990). And while Anson et al. (2003) claim a dissociation between IF and CR, the truth is that their animals were calorically restricted for much of the study. The question in my mind is whether both dietary protocols tap into the same underlying mechanism? Or can IF and CR interact to produce better than predicted results? The problem is that IF involves some CR and CR involves some IF. The following quote is from Leveille's paper, and is worth reproducing. Note that the term "meal-fed" refers to an intermittent diet where access to food is provided for only 2 hours each day and "nibbling" refers to ad libitum food access.

The results of the longevity study (experiment A) are difficult to interpret. Although the meal-fed animals had a significantly longer life-span than the nibbling rats the difference cannot be ascribed to meal-eating, per se. In addition to the metabolic differences between the meal-fed and nibbling rats there existed a significant difference in body weight and presumably in body fat. Thus these results can be interpreted as supportive of the classic experiments of McCay et al. (21, 22) showing that rats whose food intake was restricted had a significantly longer life-span. However, it is important to note that in McCay's studies as well as other similar studies (23,24) the restricted animals were very likely "meal-fed." It has recently been noted that restricting rats to 80% of ad libitum intake quickly results in a shift in eating pattern in which the animals ingest their daily food allowance within a 2-hour period (25). Thus one cannot determine unequivocally whether the increase in life span is due to the reduced food intake or to the meal pattern.

Masoro (2004) makes a similar point, and while his earlier study (Masoro et al., 1995) attempts to tease apart IF and CR, even he admits that the evidence does not conclusively eliminate the role of meal patterning. It seems that studying the meal patterning of individual animals within days may help to clarify this issue. This may also bear on the confusing results from human studies, since meal patterning is often not reported and maybe not even recorded.

Turning back the clock

Figure 1 from a recent paper by Melov et al. (2007) studying gene expression profiles in older adults following resistance training. This figure is not particularly useful, and I included because it looks cool (hmmmm). Don't worry though, the results are actually pretty interesting. Melov et al. were interested in how resistance training alters gene expression profiles in skeletal muscle, and whether any of these changes were consistent with a reversal of age-related muscular impairments. They used microarray technology to map the transcription profiles in skeletal muscle of older adults (~70yo) before and after 6 months of full-body resistance training. Perhaps unsurprisingly, a bunch of genes are differentially expressed when older untrained adults are compared to sedentary younger adults, including some associated with mitochondrial function (see also Zahn et al., 2006). However, after 6 months of resistance training (which resulted in significant strength gains), the gene expression profile for older adults was markedly different. A subset of the genes that showed an age-related difference also showed a change in expression due to exercise. Notably, those genes that were expressed at a level lower than young adults were upregulated while those genes that were expressed at a level higher than young adults were downregulated. As the authors put it, the transcriptional signature of aging was reversed back to that of younger levels. Now, I don't have a good sense of how to interpret expression levels, and the authors tended to plot their data on relative scales, so I can't assess the magnitude of these results, but it's an interesting application of gene profiling technology. Unfortunately, there was no individual level correlation of strength gains with changes in gene expression profile (in those genes significantly associated with age and exercise), although this may be a power issue (I guess people aren't jumping for the chance to get repeated muscle biopsies with a 5mm diameter needle!?).

Melov S, Tarnopolsky MA, Beckman K, Felkey K, Hubbard A (2007) Resistance Exercise Reverses Aging in Human Skeletal Muscle. PLoS ONE 2(5): e465 doi:10.1371/journal.pone.0000465

Zahn JM, Sonu R, Vogel H, Crane E, Mazan-Mamczarz K, et al. (2006) Transcriptional Profiling of Aging in Human Muscle Reveals a Common Aging Signature. PLoS Genet 2(7): e115 doi:10.1371/journal.pgen.0020115