Circulating factors affecting human health/longevity

Circulating factors affecting human health/longevity

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Circulating factors present in young mice have been shown to promote rejuvenation of aged mice, suggesting that tissues have inherent capabilities to regenerate, and circulating factors may be promoting/inhibiting this [1].

Specifically; supplementation of GDF11 (growth and differentiation factor 11) can reverse some effects of ageing (muscle tissue rejuvenates, and some degree of cognitive improvements) [2]; conversely circulating CCL11 impairs neurogenesis in aged mice and causes cognitive impairment [3]. GDF11 decreases with age, CCL11 increases with age.

(Image from Bitto et al 2014, [1])

These factors (well, GDF11 at least) are not as highly expressed in humans as in mice [4], so the specific mechanisms may differ.

What other circulating factors been shown to affect health or longevity? Particularly in humans, but other mouse (or 'higher' organisms, e.g. primates) experiments would be interesting too.

  1. Bitto, A., and Kaeberlein, M., “Rejuvenation: it's in our blood.,” Cell Metab., vol. 20, no. 1, pp. 2-4, Jul. 2014.
  2. Sinha, M., et al, “Restoring systemic GDF11 levels reverses age-related dysfunction in mouse skeletal muscle.,” Science, vol. 344, no. 6184, pp. 649-52, May 2014.
  3. Villeda, S., et al, “The ageing systemic milieu negatively regulates neurogenesis and cognitive function.,” Nature, vol. 477, no. 7362, pp. 90-4, Sep. 2011.
  4. Souza, T., et al, “Proteomic identification and functional validation of activins and bone morphogenetic protein 11 as candidate novel muscle mass regulators.,” Mol. Endocrinol., vol. 22, no. 12, pp. 2689-702, Dec. 2008.


It has been shown that oxytocin signaling helps in muscle regeneration by activating MAP Kinase/ERK pathway in skeletal muscles.[1]

Oxytocin is also known to promote adult neurogenesis.[2]


It is a paralog of GDF and like GDF prevents hypertrophy of cardiac muscles.[3]


Though leptin is not really connected to ageing but it regulates appetite and thereby the metabolism [4]. It may contribute indirectly to ageing by reducing food intake and preventing obesity and other metabolic syndromes.

This review summarizes different soluble small (synthetic) molecules that have potential to induce regeneration.

Four preventable risk factors reduce life expectancy in U.S. and lead to health disparities

Boston, MA — A new study led by researchers from the Harvard School of Public Health (HSPH) in collaboration with researchers from the Institute for Health Metrics and Evaluation at the University of Washington estimates that smoking, high blood pressure, elevated blood glucose and overweight and obesity currently reduce life expectancy in the U.S. by 4.9 years in men and 4.1 years in women. It is the first study to look at the effects of those four preventable risk factors on life expectancy in the whole nation.

The researchers also estimated the effects of these risk factors on eight subgroups of the U.S. population, called the “Eight Americas.” The Eight Americas are defined by race, county location and the socioeconomic features of each county. They found that these four risk factors account for a substantial proportion of differentials in life expectancy among these groups. Southern rural blacks had the largest reduction in life expectancy due to these risk factors (6.7 years for men and 5.7 years for women) and Asians the smallest (4.1 years for men and 3.6 years for women).

The study appears in the March 23, 2010 issue of the open-access journal PLoS Medicine and is publicly available here:

“This study demonstrates the potential of disease prevention to not only improve health outcomes in the entire nation but also to reduce the enormous disparities in life expectancy that we see in the U.S.,” said Majid Ezzati, associate professor of international health at HSPH and senior author of the study.

Smoking, high blood pressure, elevated blood glucose and obesity are responsible for hundreds of thousands of deaths from chronic diseases such as cardiovascular diseases, cancers and diabetes, in the U.S. each year. By studying how these risk factors affect mortality and life expectancy, public health officials can better address how to improve the nation’s health and to reduce health disparities.

For their study, the researchers used 2005 data from the National Center for Health Statistics, the National Health and Nutrition Examination Survey, the Behavioral Risk Factor Surveillance System, and an extensive review of epidemiologic studies on the effects of these factors. They estimated the number of deaths that would have been prevented in 2005 if exposure to the four risk factors had been reduced to their optimal levels or commonly used guidelines. They also assessed the benefits for life expectancy, a measure of longevity.

The Eight Americas were defined by the authors in an earlier study as Asians Northland low-income rural whites middle America low-income whites in Appalachia and Mississippi Valley Western Native Americans Black middle America high-risk urban blacks and Southern low-income rural blacks.

The researchers found that a person’s ethnicity and where they live is a predictor of life expectancy and how healthy a person is. Some of the findings include:

  • Asian American men and women had the lowest body mass index (BMI), blood glucose levels and prevalence of smoking
  • Blacks, especially those in the rural South, had the highest blood pressure
  • Whites had the lowest blood pressure
  • Western Native American men and Southern low-income rural black women had the highest BMI
  • Western Native American and low-income whites in the Appalachia and Mississippi Valley had the highest prevalence of smoking

As a result of these patterns, smoking, high blood pressure, elevated blood glucose and overweight and obesity account for almost 20% of disparities in life expectancy across the U.S. These four factors also accounted for three quarters of disparities in cardiovascular mortality and up to half of disparities in cancer mortality.

Below is the number of years that would be gained in life expectancy in the U.S. if each individual risk factor was reduced to its optimal level:

  • Blood pressure: 1.5 years (men), 1.6 years (women)
  • Obesity (measured by body mass index): 1.3 years (men), 1.3 years (women)
  • Blood glucose: 0.5 years (men), 0.3 years (women)
  • Smoking: 2.5 years (men), 1.8 years (women)

“It’s important that public health policy makers understand that these behavioral and metabolic risk factors are not just personal choices or the responsibility of doctors,” said Goodarz Danaei, a postdoctoral research fellow at HSPH and the lead author of the study. “To improve the nation’s overall health and reduce health disparities, both population-based and personal interventions that reduce these preventable risk factors must be identified, implemented, and rigorously evaluated.”

See a press release of Danaei and Ezzati’s 2008 study that identified smoking, high blood pressure and being overweight as the top three preventable causes of death in the U.S.:

See a chart from the Harvard Public Health Review that summarizes the Eight Americas:

This research was supported by a cooperative agreement from the US Centers for Disease Control and Prevention through the Association of Schools of Public Health.

“The Promise of Prevention: The Effects of Four Preventable Risk Factors on National Life Expectancy and Life Expectancy Disparities by Race and County in the United States,” Goodarz Danaei, Eric B. Rimm, Shefali Oza, Sandeep C. Kulkarni, Christopher J. L. Murray, Majid Ezzati, PLoS Medicine, March 2010, vol. 7, issue 3.


Long-distance running has helped our species survive and evolve. Elements of the human physique, like the Achilles tendon and the length of the human body, make our bodies primed for running. Studies show that running can ease depression and anxiety. It can reduce risk of heart disease, diabetes, and obesity. Admittedly, running can cause bone stress injury, bronchospasm, cramps, blisters, and other issues. These physiological and psychological benefits outweigh the health risks that go along with running. Despite the health risk, running’s popularity is still on the rise. Long-distance running has helped people become healthier and happier for millions of years.


Long-distance running has shaped and sharpened the brains of human beings for millions of years. Anthropologists have hypothesized that endurance running allowed humans to capture prey, which ultimately supplied them with the energy for mating (Reynolds)1. Running is a high intensity and low-cost exercise that enables humans to fully utilize their anatomic structure. However, modern humans have failed to take advantage of their athletic capabilities due to time constraints such as long working hours (Lack of exercise)2. This lack of routine physical exercise that humans have adopted has increased the risk for developing health issues such as coronary heart disease (Lee et. al)3. Long-distance running is an exercise that has contributed to the evolution of the human anatomical structure and has influenced human physiological and psychological health over time thus, the exercise possesses significance for human health today.

Since the initial presence of the Homo 2 million years ago, humans have maintained certain anatomic features that have made them well suited for long-distance running. Many of these features help humans save energy. For example, human legs are made up of long spring-like tendons that are connected to short muscle fascicles. This feature enables humans to expend less energy when lifting their feet off of the ground. Specifically, humans possess the Achilles tendon, which connects the heel to plantar flexors in the foot. In fact, these plantar flexors, or longitudinal arches, return approximately 17% of the energy that the foot expends. These spring-like capabilities help contribute to the stride lengths of 2 meters to 3.5 meters that humans are competent of making, allowing them to travel longer distances at a slower speed. These long stride lengths can also be attributed to the long leg length that humans have relative to their body mass (Bramble & Lieberman)4.

Innate skeletal strength and stabilization mechanisms that humans possess also make their bodies adept for long distance running. When the heel of the foot strikes the ground, shock waves move up the body through the spine and into the head, spreading stress throughout the body. The human anatomy is made up of strong skeletal joints and limbs that are able to withstand these high stress levels. The human body is also made up of features that provide stability and balance for long-distance runners. For instance, the trunk and neck of humans incline forward when they run, providing balance for the entirety of the body. Also, when humans run, they swing their arms, which has the potential to cause an imbalance. However, the Homo maintains the derived characteristic of wide shoulders, which counterbalance these vast movements (Bramble & Lieberman)4. These innate features of the body exist to enable humans to excel in long-distance running thus, the human body was designed to continue to utilize these structures.

Although the original structure of the human body possessed features that made people capable long-distance runners, humans have adapted certain traits that have made them more suited for the activity. According to the evolutionary hypothesis, “natural selection drove early humans to become even more athletic.”1 This is because humans that were capable of running longer distances could catch more prey and thus survived to mate and produce viable offspring.1 Because of variation that is present in the human genome, the perhaps less-common traits that benefited the surviving generations became more prevalent. Thus, humans evolved to have longer legs and shorter toes to expend less energy and travel faster for shorter distance runs, less hair and multiplication of eccrine sweat glands to decrease metabolic heat, and inner-ear mechanisms to maintain balance and stability.1, 4 The evolution of these physical traits that have propelled humans to be more skillful runners illustrates the influence that the activity has had on their survival thus, long-distance running must continue to have an impact on the development of the human bodily structure today. Furthermore, for as long as humans pursue long-distance running as a physical activity, bodily structures and mechanisms will continue to adapt to make them more skilled.

Along with contributing to the evolution of anatomical human characteristics, long-distance running has remained a prevalent physical activity because it has had a history of providing psychological benefits. For instance, running causes the brain to release endorphins that are “associated with opioidergic activation in frontolimbic brain regions,” which produces a calming effect that puts one in a euphoric state, or a “runner’s high” (The Runner’s High Roth).5, 6 This euphoria is closely linked with reward, since the sensation is typically felt after experiences involving training.5 Such training regimens may include “accomplishing a previously set goal, such as running 30 minutes without a break or finishing a 5 km run.”6 According to Selma Roth, completing such goals “gives a feeling of empowerment,” along with “increas[ing] self-esteem.”6 Regular running also helps one sustain a healthy weight and creates healthier looking hair and skin through improved blood circulation.6 It is the combination of the increase in internal empowerment induced by the runner’s high and the increased capability of maintaining a healthy external appearance that long-distance running provides that may maximize one’s potential physical attractiveness. It can be concluded that improved physical appearance helps build one’s self-confidence as well. Thus, it is the combination of the increase in confidence that results when one achieves long-distance goals and the maximization of one’s physical appearance that positively contribute to human psychological health.

Running is also a type of physical exercise that can be used to treat and prevent psychological disorders. According to the Archives of Internal Medicine, regular exercise such as long-distance running “lifts depression just as well as prescription antidepressants” (Bauman).7 In one study, 156 men and women with major depressive disorders were split into 3 groups, and the group that exercised aerobically with activities such as running for 40 minutes three times a week had longer lasting effects than the initial response that prescription antidepressants provide.7 This can be attributed partially to the temporary euphoric sensations that one experiences with a “runner’s high.”5 However, if performed regularly, the psychological benefits will endure. Another study of diabetic patients found that when one performs a long-duration aerobic activity such as long-distance running on a long-term basis, it reduced “stress emotions” experienced in response to stressful stimuli and events by the disease. Additionally, smaller increases in physiological reactivity to stressors were found when the participants implemented aerobic exercise (Burish, Sementilli, & Vasterling).8 Another study that measured the effects of aerobic exercise on anxiety sensitivity discussed specifically how high-intensity exercises produced a more rapid reduction in anxiety sensitivity in comparison to low-intensity exercises. Further, only the high-intensity exercise participants had reduced fear of anxiety-related physiological sensations (Rabian et. al).9 Long-distance running is both a high-intensity and long-duration exercise thus the studies regarding stress and exercise correlate with the psychological effects of such activity. In all, physical exercise provides psychological benefits for individuals. Long-distance running is a type of physical exercise, and is particularly advantaged because it is both long-duration and high-intensity. Therefore, long-distance running has provided a positive impact on psychological well-being that continues to impact human health today.

Running has been shown to have an impact on the psychological function of sleep as well. According to one study, healthy adolescents who took part in a daily morning running routine for three weeks had decreased and overall lower insomnia severity scores compared to those of non-runners. Additionally, the runners were found to have higher sleep efficiency, with an increased proportion of deep Stage 3 and REM sleep and a lower proportion of lighter, Stage 1 sleep (Kalak et. al).10 However, partaking in a vigorous exercise like long-distance running approximately four to six hours before one plans to sleep is not recommended because doing so can potentially impair sleep (Youngstedt).11 Thus, one should plan to run in the morning or earlier in the day in order to receive any potential sleep benefits. The improvement of sleep quality that is associated with an exercise such as long-distance running could perhaps reduce the amount of sleep that one needs. This shorter time allocated to sleep could give people more time to fulfill obligations, perhaps making them feel more inclined to continue exercising due to reduced time constraints.

Long-distance running also provides physiological benefits for the human body. Not only does the exercise stimulate the heart, respiratory system, and the brain, but it also reduces cardiovascular mortality.3 According to one study, adult men who ran, regardless of the time and distance, had 30% and 45% lower adjusted risks of all-cause cardiovascular mortality.3 In fact, because of the lowered risk of fatal coronary heart disease associated with marathon running, patients recovering from heart attacks in rehabilitation centers in Toronto and Honolulu implemented marathon training in order to recuperate. Incorporating marathon training by stressing that the “patient” was in fact an “athlete” increased motivation to recover as well (Bassler)12. Additionally, in the 1920s, Eliot Joslin identified exercise as a component of good diabetic therapy. Exercise also “has the potential to reduce plasma insulin” and to “improve metabolic control, insulin sensitivity, glucose tolerance, and the efficiency of the circulatory system.”8 Furthermore, the regimen helps one control his or her weight, burning approximately 374 calories for a 165-pound person who runs six miles per hour for 30 minutes (Burning calories).13 Maintaining a healthy weight is necessary if one wants to lower the risk of developing health issues as well. The plethora of physiological benefits that long-distance running provides should be considered for people who look to engage in the activity regularly today. Long-distance running is beneficial so much so that its physiological health impact is life altering and potentially life-saving.

In fact, several studies have proven that regular running provides the physiological benefit of an increased life expectancy. For instance, the study of adult men who ran and had lower adjusted risks of all-cause cardiovascular mortality showed that the men also had an increased life expectancy of three years.3 Researchers taking part in the Copenhagen City Heart found that Danish men and women joggers had a 44% reduced death rate compared to non-joggers, along with an extension of lifespan of 6.2 years for men and 5.6 years for women. However, it should be noted that the benefit of an increased life expectancy due to running will not be achieved if one participates in it at extreme levels. Dr. Schnohr, the leader of the study, notes, “The relationship appears much like alcohol intakes” (Jogging).14 Moderate alcohol consumption can actually increase one’s lifespan, which Dr. Schnohr compares to the effect moderate running has on people’s health. Moderate running qualifies as participating in the exercise for approximately two to three hours per week according to a number of studies (Running too much).15 The longevity of life that distance running provides should certainly be noted if one wants to lower the possibility of death at a young age.

Although endurance running has provided physiological benefits for humans, medical risks persist, many of which are short-term. In a review of medical problems of marathon runners, the following complications were found. Runners exhibited musculoskeletal problems such as cramps, blisters, and acute ankle and knee injuries. These runners also reported having gastrointestinal issues such as bloating, cramps, nausea, vomiting, diarrhea, and fecal incontinence. These gastrointestinal problems are due to the decrease of blood flow to such areas and increase of blood flow to muscles that are used to run. This redirection of blood flow of working muscles can also decrease renal perfusion, impairing concentrating activities to the kidneys. Runners also have the possibility of an exercise-induced bronchospasm (EIB), a pulmonary complication, which interrupts airflow five to fifteen minutes after the onset of exercise. All of these issues are definite concerns however, exercise-associated collapse (EAC) is the most prevalent short-term problem that long-distance runners have exhibited. In fact, a 12-year study found that 59% of marathon medical tent visits were due to EAC. EAC typically results from heat exhaustion, which is associated with headaches, extreme fatigue, nausea, vomiting, dizziness, and profuse sweating (Sanchez, Corwell, &Berkhoff).16 These short-term problems in relation to distance running most likely exist because engaging in any type of movement can pose risks for the body, especially if it is high intensity. Therefore, one should be aware of such short-terms risks before engaging in distance running so that he or she can gain access to or make preparations for receiving proper medical attention if such problems were to persist.

These short-term problems may not be avoidable, but certain factors can explain why one may be more at risk of developing such issues. In the review of medical problems at marathons, the number of miles a runner trained per week inversely correlated with the incidence of an injury.16 In other words, further training reduces the risk of short-term issues and those who train less carry a greater risk. Environmental factors can determine one’s chance of encountering post-marathon problems as well. If one were planning to run a spring marathon, he or she would have to train in the winter. The increase in heat between the seasons could make it difficult for the runner to adjust to the conditions. For instance, runners might not ingest the proper amount of fluids needed for their bodies to run in the warmer weather, and could become dehydrated as a result. Dehydration in turn can worsen or increase the risk for gastrointestinal issues and EAC. Fortunately, these problems are short-term and can be fixed within a few days. They also tend to resolve themselves with rest alone.16 These factors are important to consider when one plans on engaging in long-distance races such as marathons however, the severity of such short-term abnormalities is low enough that long-distance runners should not be tremendously concerned about them.

Risks of long-term physical injuries for distance runners exist as well. Specifically, long-distance runners are prone to developing bone stress injury (BSI) in long bones such as the tibia, fibula, and femur because of the rearfoot strike pattern that they use. Additionally, runners may develop BSI in the pelvis and lumbar spine. This condition makes the bone unable to withstand repetitive mechanical loading, resulting in structural fatigue and localized bone pain and tenderness (Warden, Davis, & Fredericson).17 Between one third and two thirds of competitive cross-country and long-distance runners have had this BSI condition.17 One can be classified as either low or high-risk, with the high-risk BSI patients being more prone to complete bone fracture. In addition, management of the condition depends on classification however, both approaches involve temporary discontinuation of running, modification of workout regiments, and a gradual return to the exercise.17 Although BSI can be a treatable condition, long-distance running may present negative outcomes for participants of the activity thus, the effects should be taken into account when considering engaging in the exercise.

However, studies indicate that long-distance runners can avoid such long-term physiological injuries and risks. For instance, recent research has indicated that frequent long-distance runners, such as marathon and cross-country runners, are less prone to these injuries than novice, recreational, or even competitive runners.17 In other words, the more that an athlete trains his or her body, the lesser the risk he or she has of developing musculoskeletal injuries when running a long race.16 Furthermore, Roth reports that regular runners “will have stronger bones as they age compared to those who do not run” as well as stronger muscles which protect these bones.6 In fact, clinical studies have proven that muscle size and strength are directly related to BSI susceptibility.17 Other factors associated with training regimens such as shoe type, inserts, and surface type affect risk as well.17 Individual considerations such as age, body mass index (BMI), diet and nutrition, endocrine status and hormones, physical activity history, bone diseases, and medications influencing the bones modify the ability of the bones as well.17 The combination of factors that modify the load applied to bones and factors that modify the ability of bones determines one’s susceptibility.17 Yet, other risk factors remain unproven that might have to do with the role that genetics or specific inherited traits have on certain runners’ abilities (Incidence, risk factors and prevention).18 Nonetheless, it is still known that humans have adapted certain hereditary traits that have shaped them into a more athletically capable species.1 Ways to combat long-term physiological risks associated with long-distance running must be taken into account if one plans to take part in the exercise in the future in order to prevent conditions such as BSI. Furthermore, if people practice proper training regimens and considers their individual history and capabilities, then they can receive the maximum benefits from long-distance running.

Modern humans have inherited traits such as longer limbs that make them more efficient runners however, slower, long-distance running has remained widespread. According to one source, jogging “became popular in the 1970s when middle-aged men started running to reduce their risk of heart attacks and strokes.” However, some of these men who participated in the exercise died, and this prompted questions about whether such running was too strenuous on the body.13 Despite this ongoing debate about whether running is beneficial or harmful, popularity of distance running continues to be on the rise. Running USA reported that participation in marathons increased by 2.2% between 2010 and 2011 (Has the Marathon Boom Peaked?).19 The company also stated that there were a total of 425,000 marathon runners in the United States in 2008, increasing by 20 percent from the beginning of the decade (Parker-Pope).20 In total it is estimated that there are 30 million runners and 1,800 running clubs in the United States (Plack).21 Both the physiological and psychological impacts that the exercise has had could explain its ever-present and growing popularity. Perhaps it is the advantages that this physical activity has provided that have helped maintain its prevalence not only since the 1970s, but also for thousands of years prior. Thus, communal long-distance races such as 5ks, half-marathons, and marathons will continue to take place and follow this upward trend in popularity.

Long-distance running has been practiced by humans for millennia, shaping and evolving their anatomical structure. Running has been proven to have both physiological and psychological effects, many of which have been shown to improve human health. Human health is a concern that remains ever-present, and physical exercise is necessary in order to maintain an active lifestyle. Thus, long-distance running is a popular sport with significant health benefits that addresses a variety of health issues such as obesity and depression. Incorporating long-distance running into one’s daily routine may prevent and treat such conditions and could even prove to be life-altering.

1. Reynolds G. 2013. Humans Have a History of Running, Big Brains. Pittsburgh Post – Gazette.

2. Lack of exercise. 2013. Derby Evening Telegraph4.

3. Lee DC, Pate RR, Lavie CJ, Sui X, Church TS, Blair SN. 2014. Leisure-time running reduces all-cause and cardiovascular mortality risk. J Am Coll Cardiol64(5):472-81.

4. Bramble DM and Lieberman DE. 2004. Endurance running and the evolution of homo. Nature432(7015):345-52.

5. 2008. The Runner’s High: Opioidergic Mechanisms in the Human Brain. Cerebral Cortex18(11): 2523-2531.

6. Roth S. 2011. The benefits of running. McClatchy - Tribune Business News.

7. Bauman A. 2000. Running lifts depression. Runner's World: 19.

8. Burish TG, Sementilli ME, Vasterling JJ. 1988. The Role of Aerobic Exercise in Reducing Stress in Diabetic Patients. The Diabetes Educator 12(3): 197-201.

9. Rabian BA, Berman ME, Broman-Fulks JJ, Webster MJ. 2004. Effects of Aerobic Exercise on Anxiety Sensitivity. Behaviour Research and Therapy 42(2): 125-126

10. Kalak N, Gerber M, Roumen K, Mikoteit T, Yordanova J, Puhse Uwe, Holsboer-Trachsler E, Brand S. 2012. Daily Morning Running for 3 Weeks Improved Sleep and Psychological Functioning in Healthy Adolescents Compared With Controls. Journal of Adolescent Health 51(6): 615-622

11. Youngstedt SD, Kline CE. 2006. Epidemiology of exercise and sleep. Sleep and Biological Rhythms 4(3): 215-221

12. Bassler TJ. 1975. Life expectancy and marathon running. The American Journal of Cardiology 36(3): 410-411

13. Burning calories. 2009. Air Force Times :32.

14. Jogging 'increases life expectancy'. 2012. BreakingNews.Ie.

15. Running too much could shorten your lifespan. Kashmir Monitor. 2014 Apr 04.

16. Sanchez LD, Corwell B, Berkoff D. 2006. Problems of marathon runners. American Journal of Emergency Medicine 24: 608-615

17. Warden SJ, Davis IS, Fredericson M. 2014. Management and Prevention of Bone Stress Injuries in Long-Distance Runners. Journal of Orthopaedic & Sports Physical Therapy 44(1): 749-765.

18. Tonoli DC, Cumps E, Aerts I, Verhagen E, Meeusen R. 2010. Incidence, risk factors and prevention of running related injuries in long-distance running: A systematic review injury, location and type. Sport & Geneeskunde 43(5): 12-18.

19. Has the Marathon Boom Peaked? 26 February 2013. Runner’s World.

20. Parker-Pope T. 2009. The human body is built for distance. New York Times.

21. Plack L. 2015. Can Running Cause Osteoarthritis?. ACSM’s Health & Fitness Journal 19(1) 23-28

Aging can be tackled at different levels. First, there is a loss in molecular fidelity, so that some talk about molecular aging. Second, there is the aging of each cell, known as cell senescence. Cell senescence is a crucial mechanism for development but becomes deleterious when it affects stem and immune cells function, challenging tissue homeostasis. Aging is thus characterized by a changing phenotype at tissue and organ level. Recently, the hypothesis that aging could be driven by systemic factors has triggered intense scientific investigation.

This review examines the biological main causes of aging targeted by experimental anti-aging therapies. For complementary information, you can read The Hallmarks of Aging from Cell [12].



Americans have a shorter life expectancy compared with residents of almost all other high-income countries. We aim to estimate the impact of lifestyle factors on premature mortality and life expectancy in the US population.


Using data from the Nurses’ Health Study (1980–2014 n=78 865) and the Health Professionals Follow-up Study (1986–2014, n=44 354), we defined 5 low-risk lifestyle factors as never smoking, body mass index of 18.5 to 24.9 kg/m 2 , ≥30 min/d of moderate to vigorous physical activity, moderate alcohol intake, and a high diet quality score (upper 40%), and estimated hazard ratios for the association of total lifestyle score (0–5 scale) with mortality. We used data from the NHANES (National Health and Nutrition Examination Surveys 2013–2014) to estimate the distribution of the lifestyle score and the US Centers for Disease Control and Prevention WONDER database to derive the age-specific death rates of Americans. We applied the life table method to estimate life expectancy by levels of the lifestyle score.


During up to 34 years of follow-up, we documented 42 167 deaths. The multivariable-adjusted hazard ratios for mortality in adults with 5 compared with zero low-risk factors were 0.26 (95% confidence interval [CI], 0.22–0.31) for all-cause mortality, 0.35 (95% CI, 0.27–0.45) for cancer mortality, and 0.18 (95% CI, 0.12–0.26) for cardiovascular disease mortality. The population-attributable risk of nonadherence to 5 low-risk factors was 60.7% (95% CI, 53.6–66.7) for all-cause mortality, 51.7% (95% CI, 37.1–62.9) for cancer mortality, and 71.7% (95% CI, 58.1–81.0) for cardiovascular disease mortality. We estimated that the life expectancy at age 50 years was 29.0 years (95% CI, 28.3–29.8) for women and 25.5 years (95% CI, 24.7–26.2) for men who adopted zero low-risk lifestyle factors. In contrast, for those who adopted all 5 low-risk factors, we projected a life expectancy at age 50 years of 43.1 years (95% CI, 41.3–44.9) for women and 37.6 years (95% CI, 35.8–39.4) for men. The projected life expectancy at age 50 years was on average 14.0 years (95% CI, 11.8–16.2) longer among female Americans with 5 low-risk factors compared with those with zero low-risk factors for men, the difference was 12.2 years (95% CI, 10.1–14.2).


Adopting a healthy lifestyle could substantially reduce premature mortality and prolong life expectancy in US adults.


Clinical Perspective

What Is New?

A comprehensive analysis of the impact of adopting low-risk lifestyle factors on life expectancy in the US population is lacking.

Adherence to 5 low-risk lifestyle-related factors (never smoking, a healthy weight, regular physical activity, a healthy diet, and moderate alcohol consumption) could prolong life expectancy at age 50 years by 14.0 and 12.2 years for female and male US adults compared with individuals who adopted zero low-risk lifestyle factors.

What Are the Clinical Implications?

Americans could narrow the life-expectancy gap between the United States and other industrialized countries by adopting a healthier lifestyle.

Prevention should be a top priority for national health policy, and preventive care should be an indispensable part of the US healthcare system.

The United States is one of the wealthiest nations worldwide, but Americans have a shorter life expectancy compared with residents of almost all other high-income countries, 1,2 ranking 31st in the world for life expectancy at birth in 2015. 3 In 2014, with a total health expenditure per capita of $9402, 4 the United States was ranked first in the world for health expenditure as a percent of gross domestic product (17.1%). 4 However, the US healthcare system has focused primarily on drug discoveries and disease treatment rather than prevention. Chronic diseases such as cardiovascular disease (CVD) and cancer are the most common and costly of all health problems but are largely preventable. 5 It has been widely acknowledged that unhealthy lifestyles are major risk factors for various chronic diseases and premature death. 6

More than 2 decades ago, McGinnis and Foege 7 and McGinnis and colleagues 8 suggested that the nation’s major health policies should move to emphasize reducing unhealthy lifestyles. A meta-analysis 9 of 15 studies including 531 804 participants from 17 countries with a mean follow-up of 13.24 years suggested that ≈60% of premature deaths could be attributed to unhealthy lifestyle factors, including smoking, excessive alcohol consumption, physical inactivity, poor diet, and obesity. A healthy lifestyle was associated with an estimated increase of 7.4 to 17.9 years in life expectancy in Japan, 10 the United Kingdom, 11 Canada, 12 Denmark, 13 Norway, 13 and Germany. 13,14 However, a comprehensive analysis of the impact of adopting low-risk lifestyle factors on life expectancy in the US population is lacking. Therefore, our aim was to evaluate the potential impact of individual and combined lifestyle factors on premature death and life expectancy in the US population.


The data, analytical methods, and study materials will be made available to other researchers from the corresponding authors on reasonable request for purposes of reproducing the results or replicating the procedure.

Overall Design

We first quantified the association between lifestyle-related low-risk factors and mortality on the basis of cohort data from the NHS (Nurses’ Health Study) 15,16 and the HPFS (Health Professionals Follow-Up Study). 17 Then, we used data from the NHANES (National Health and Nutrition Examination Surveys 2013–2014) to estimate the distribution of the lifestyle-related factors among the US population. 18 Furthermore, we derived the death rates of Americans from the CDC WONDER (Centers for Disease Control and Prevention Wide-Ranging Online Data for Epidemiologic Research) database. 19 Finally, we combined the results from those 3 sources to estimate the extended life expectancy associated with different categories of each individual lifestyle factor and a combination of low-risk lifestyle factors.

Study Population

The NHS began in 1976, when 121 700 female nurses 30 to 55 years of age responded to a questionnaire gathering medical, lifestyle, and other health-related information. In 1980, 92 468 nurses also responded to a validated food frequency questionnaire. 15,16 The HPFS 17 was established in 1986, when 51 529 male US health professionals (dentists, optometrists, osteopaths, podiatrists, pharmacists, and veterinarians) 40 to 75 years of age completed a mailed questionnaire about their medical history and lifestyle, including a food frequency questionnaire. We excluded participants with implausible energy intakes (women: <500 or >3500 kcal/d men: <800 or >4200 kcal/d), with a body mass index (BMI) <18.5 kg/m 2 at baseline, or with a missing value for BMI, physical activity, alcohol, or smoking. After these exclusions, 78 865 female and 44 354 male participants remained in the analysis at baseline. The NHS and HPFS were approved by the institutional review board of Brigham and Women’s Hospital in Boston completion of the self-administered questionnaire was considered to imply informed consent.

We used the NHANES (2013–2014) 18 to estimate the population distribution of lifestyle-related factors among American adults. The analytical population consisted of 2128 adults 50 to 80 years of age with complete information on diet, BMI, physical activity, alcohol use, and smoking status. We also excluded participants with BMIs of <18.5 kg/m 2 . The NHANES 18 included a nationally representative sample of the US population. It was approved by the National Center for Health Statistics research ethics review board. Signed consents were obtained from all participants.

Data Collection

Diet in the NHS and HPFS was assessed every 4 years with a validated food frequency questionnaire asking the frequency, on average, a participant had consumed a particular amount of a specific type of food during the previous year. 15,16 Physical activity levels were investigated with a validated questionnaire and updated every 2 years. 20 Body weight and smoking habits were self-reported and updated every 2 years. Alcohol consumption was also collected by the food frequency questionnaire. Biennial questionnaires were used to collect information on potential confounders such as age, ethnicity, multivitamin use, regular aspirin use, postmenopausal hormone use (NHS only), and the presence or absence of a family history of diabetes mellitus, cancer, or myocardial infarction.

Dietary data in the NHANES 18 were collected by an interviewer-administered, computer-assisted, 24-hour dietary recall, which was an in-depth interview conducted by a trained interviewer who solicited detailed information about everything that the participant ate and drank in the prior 24 hours. Body weight and height were measured in a mobile examination center with standardized techniques and equipment. Smoking status was self-reported and included questions about numbers of cigarettes, pipes, or cigars smoked per day and whether the participant had smoked at least 100 cigarettes in his or her lifetime. Participants also reported duration of moderate and vigorous physical activity during leisure time and at work. Usual alcohol intakes were recorded by two 24-hour dietary recalls. 18

Low-Risk Lifestyle Score

We included 5 lifestyle-related factors: diet, smoking, physical activity, alcohol consumption, and BMI. Because this study was focused on modifiable lifestyle factors, we did not include clinical risk factors such as hypertension, hypercholesterolemia, or medication use in the score.

Diet quality in the NHS, HPFS, and NHANES was assessed with the Alternate Healthy Eating Index score (Methods in the online-only Data Supplement), which is strongly associated with the onset of cardiometabolic disease in the general population. 21–23 We defined a healthy diet as a diet score in the top 40% of each cohort distribution. For smoking, we defined low risk as never smoking. For physical activity, we classified low risk as >30 min/d of moderate or vigorous activities (including brisk walking) that require the expenditure of at least 3 metabolic equivalents per hour. We defined low-risk alcohol consumption as moderate alcohol consumption, for example, 5 to 15 g/d for women and 5 to 30 g/d for men. BMI was calculated as self-reported weight (kilograms) divided by height (meters squared). Low-risk body weight was defined as BMI in the range of 18.5 to 24.9 kg/m 2 .

For each low-risk factor, the participant received a score of 1 if he or she met the criterion for low risk. If the participant did not meet the criterion, he or she was classified as high risk for that factor and received a score of 0. The sum of these 5 scores provided a total number of low-risk factors of 0, 1, 2, 3, 4, or 5, with higher scores indicating a healthier lifestyle.

Ascertainment of Deaths

In the NHS and HPFS, deaths were identified from state vital statistics records, the National Death Index, reports by the families, and the postal system. 24 The follow-up for death in both cohorts was at least 98% complete. A physician reviewed death certificates or medical records to classify the cause of death according to International Classification of Diseases, Eighth Revision in the NHS (International Classification of Diseases, Ninth Revision in the HPFS).

We also derived the population all-cause, cardiovascular (I00–I99), and cancer mortality (C00–D48) rates for 2014 by sex and single-year ages ranging from 50 to 84 years from the CDC WONDER database of the US population. 19 Because the database provides mortality rates only up to age of 84, we estimated the all-cause and cause-specific mortality rates in single years of age from 85 to 105 years by extrapolation based on a Poisson regression model with both linear and quadratic terms for the midpoints of single-year age groups minus age of 49.5 years (Methods and Figure I in the online-only Data Supplement).

Statistical Analysis

Participants contributed person-time from the return of the baseline questionnaire (NHS, 1980 HPFS, 1986) until the date of death or the end of the follow-up period (June 30, 2014, for NHS and January 30, 2014, for HPFS), whichever came first. We used Cox proportional hazard models to calculate the adjusted hazard ratios (HRs) of all-cause, cancer, and cardiovascular mortality with their 95% confidence intervals (CIs) across categories of each individual factor and joint classification of number of low-risk factors (0, 1, 2, 3, 4, or 5).

Because lifestyle factors may affect mortality risk over an extended period of time, to best represent long-term effects, we calculated cumulative average levels of lifestyle factors using the latest 2 repeated measurements for our primary analysis of diet, physical activity, and alcohol consumption. For example, in the NHS, mortality cases that occurred between 1980 and 1982 were examined in relation to physical activity on the basis of data collected on the 1980 questionnaire, the average of the 1980 and 1982 physical activity measurements was used to assess risk of mortality in the 1982 to 1984 follow-up period, the average of the 1982 and 1984 physical activity measurements was used to assess risk of mortality in the 1984 to 1986 follow-up period, and so forth. For dietary Alternate Healthy Eating Index score and alcohol use, the average was calculated on the basis of 4-year repeated measurements. Smoking status was estimated from both smoking history and most recent status updated every other year and classified into 5 categories: never, past, and current smoking of 1 to 14, 15 to 24, and ≥25 cigarettes per day. To minimize the reverse causality bias resulting from weight loss caused by preexisting illness, we applied the lifelong maximum BMI. 25 For example, we applied the maximum value of BMI at age 18 years and BMI in 1980 to predict mortality between 1980 and 1982 and the maximum value of BMI at age 18 years, BMI in 1980, and BMI in 1982 to predict mortality between 1982 and 1984, and so forth. The same analytical strategy was applied to the HPFS. If data on low-risk factors were missing at a given time point, the last observation was carried forward. The following covariates were included in the multivariable model: age, ethnicity, current multivitamin use, current aspirin use, menopausal status and hormone use (women only), and family history of diabetes mellitus, myocardial infarction, or cancer. We applied a competing-risk regression model for cause-specific mortality by including lifestyle factors as exposure and other risk factors as unconstrained covariates, allowing the effects of the covariates to vary across cause-specific mortality. 26

We calculated the hypothetical population-attributable risk, an estimation of the percentage of premature mortality in the study population that theoretically would not have occurred if all people had been in the low-risk category, assuming that the observed associations represent causal effects. For these analyses, we used a single binary categorical variable (with all 5 low-risk factors) and compared participants in the low-risk category with the rest of the population (without all 5 low-risk factors or with any high-risk factor) to calculate the HRs. We combined these HRs with the prevalence of the low-risk category among American adults based on NHANES data to estimate the population-attributable risk. 27

To calculate the life expectancy of participants following different levels of healthy lifestyles, we used life tables. We built the life table starting at age 50 years and ending at age 105 years with the following 3 estimates to calculate the cumulative survival from 50 years onward: (1) sex- and age-specific HRs of mortality associated with numbers of low-risk lifestyles derived from the NHS and HPFS (2) sex- and age-specific population mortality rate of all causes, cardiovascular mortality (I00–I99), and cancer mortality (C00–D48) from the US CDC WONDER database 19 and (3) age- and sex-specific population prevalence of the number of low-risk lifestyles derived from the NHANES. 18 We fitted multivariable-adjusted Cox regression models for each sex separately to calculate the age-specific HRs for mortality by the number of low-risk factors compared with zero low-risk factors. The model specification included linear and quadratic terms for the age variable (every 5 years up to 85 years) and the interactions between the number of low-risk factors and linear and quadratic terms of the age variable. The age-specific HRs for mortality were obtained as linear combinations of the relevant estimated coefficients, with age fixed at values corresponding to midpoints of 5-year age groups from age 50 to 85 years. The HR of age >85 years was assumed to be the same as that in the 85-year age group. Then we applied the age- and sex-specific HRs to estimate the life expectancy at different ages by the number of low-risk lifestyle factors (online-only Data Supplement).

In the sensitivity analysis, we applied the sex-specific HRs (adjusted for age only) for all-cause and cause-specific mortality to test the robustness of our findings. To address the potential aging effect on the association between lifestyle and mortality, we conducted a sensitivity analysis limited to NHS and HPFS participants <75 years of age. We conducted 3 stratified analyses: 1 analysis stratified by smoking status, another stratified by BMI status to estimate the joint effect of other 4 lifestyle factors, and the third stratified by baseline disease status (with or without elevated cholesterol, hypertension, or diabetes mellitus). To address the concern about the potential adverse effects of moderate alcohol intake, we created a healthy lifestyle score that was based on the other 4 low-risk factors without alcohol.

Because the binary variables could not account for the gradient in mortality risk with more extreme levels of these lifestyle factors, we conducted a third sensitivity analysis in which we calculated an expanded low-risk score on the basis of the associations between each lifestyle factor and mortality in the cohorts. We assigned scores of 1 (least healthy) to 5 (most healthy) to the categories of the lifestyle factors and summed the points across all 5 factors (score range, 5–25 points). For this analysis, the healthiest group was defined as never smoking, BMI between 18.5 and 22.9 kg/m 2 , moderate alcohol intake (5–14.9 g/d), moderate or vigorous activity duration of ≥6 h/wk, and the highest quintile of the Alternate Healthy Eating Index diet score.

We used SAS version 9.3 (SAS Institute Inc, Cary, NC) to analyze the data. Statistical significance was set at a 2-tailed value of P<0.05. We used Monte Carlo simulation (parametric bootstrapping) with 10 000 runs to calculate the CIs of the life expectancy estimation with @RISK 7.5 (Palisade Corp, Ithaca, NY).


At baseline, participants with a higher number of low-risk lifestyle factors were slightly younger, more likely to use aspirin, and less likely to use multivitamin supplements (Table 1). During a median of 33.9 years of follow-up of women and 27.2 years of follow-up of men, 42 167 deaths were recorded (13 953 deaths resulting from cancer and 10 689 deaths caused by CVD).

Table 1. Participant Characteristics* at Baseline According to the Number of Low-Risk Lifestyle Factors

BMI indicates body mass index HPFS, Health Professionals’ Follow-up Study and NHS, Nurses’ Health Study.

*Values are means (SD) or percentages and are standardized to age distribution of the study population except age itself.

†Low-risk lifestyle factors included cigarette smoking (never smoking), physically active (≥3.5 h/wk of moderate to vigorous intensity activity), high diet quality (upper 40% of Alternate Healthy Eating Index), moderate alcohol intake of 5 to 15 g/d (women) or 5 to 30 g/d (men), and normal weight (BMI, 18.5–24.9 kg/m 2 ).

Each individual component of a healthy lifestyle showed a significant association with risk of total mortality, cancer mortality, and CVD mortality (Table 2). A combination of 5 low-risk lifestyle factors was associated with an HR of 0.26 (95% CI, 0.22–0.31) for all-cause mortality, 0.35 (95% CI, 0.27–0.45) for cancer mortality, and 0.18 (95% CI, 0.12–0.26) for CVD mortality compared with participants with zero low-risk factors. The population-attributable risk of nonadherence to 5 low-risk lifestyle factors was 60.7% (95% CI, 53.6–66.7) for all-cause mortality, 51.7% (95% CI, 37.1–62.9%) for cancer mortality, and 71.7% (95% CI, 58.1–81.0) for cardiovascular mortality. We observed a similar association between the low-risk lifestyle factors and mortality before 75 years of age (Table I in the online-only Data Supplement). The low-risk lifestyle factors were associated with lower risk of cause-specific mortality in women and men similarly (Figure II in the online-only Data Supplement).

Table 2. HRs (95% CIs) of Total and Cause-Specific Mortality According to Individual Lifestyle Risk Factors*

CI indicates confidence interval CVD, cardiovascular disease HR, hazard ratio and PAR, population-attributable risk.

*Multivariable-adjusted HR adjusted for age sex ethnicity current multivitamin use current aspirin use family history of diabetes mellitus, myocardial infarction, or cancer and menopausal status and hormone use (women only).

†Low-risk lifestyle factors included cigarette smoking (never smoking), physically active (≥3.5 h/wk of moderate to vigorous intensity activity), high diet quality (upper 40% of Alternate Healthy Eating Index), moderate alcohol intake of 5 to 15 g/d (women) or 5 to 30 g/d (men), and normal weight (body mass index, 18.5–24.9 kg/m 2 ).

‡Estimation of PAR of having any high-risk factors was based on the prevalence of not having all 5 low-risk factors among American adults from NHANES (National Health and Nutrition Examination Surveys) data.

We observed a modest difference in HRs across age groups (Figure 1A). Using these age- and sex-specific HRs, we estimated that the life expectancy at age 50 years was 29.0 years (95% CI, 28.3–29.8) for women and 25.5 years (95% CI, 24.7–26.2) for men who adopted zero low-risk lifestyle factors. In contrast, for those who adopted all 5 low-risk factors, we projected a life expectancy at age 50 years of 43.1 years (95% CI, 41.3–44.9) for women and 37.6 years (95% CI, 35.8–39.4) for men (Figure 1B). Equivalently, women with 5 low-risk lifestyle factors could gain 14.0 years (95% CI, 11.8–16.8) of life expectancy on average, and men could gain 12.2 years (95% CI, 10.1–14.2) of life expectancy compared with those with zero low-risk lifestyle factors (Figure 1C). The preceding inferences were similar in sensitivity analyses using sex-specific HRs adjusted for age (Figure IIIA and IIIB in the online-only Data Supplement). Among women, on average, ≈30.8% of the gained life expectancy at age 50 years from adopting 5 versus zero low-risk lifestyle factors was attributable to reduced CVD death and the remainder to lower cancer (21.2%) or other causes (48.0%) of mortality. For men, the corresponding percentage was 34.1%, 22.8%, and 43.1%, respectively (Figure IIIC in the online-only Data Supplement). We observed a consistent dose-response relationship between the increasing number of low-risk factors and gained life expectancy among both smokers and nonsmokers (Figure IV in the online-only Data Supplement), among both normal-weight and overweight adults (Figure V in the online-only Data Supplement), and among individuals with and without chronic conditions at baseline (Figure VI in the online-only Data Supplement).

Figure 1. Life expectancy estimated from the overall mortality rate of Americans (Centers for Disease Control and Prevention [CDC] report), the prevalence of lifestyle factors using NHANES (National Health and Nutrition Examination Surveys) data 2013 to 2014, and age- and sex-specific hazard ratios. A, Hazard ratio B, life expectancy at age 50 years C, life expectancy by age. Low-risk lifestyle factors included cigarette smoking (never smoking), physically active (≥3.5 h/wk of moderate to vigorous intensity activity), high diet quality (upper 40% of Alternate Healthy Eating Index), moderate alcohol intake of 5 to 15 g/d (female) or 5 to 30 g/d (male), and normal weight (body mass index <25 kg/m 2 ). Estimates of cumulative survival from 50 years of age onward among the 5 lifestyle risk factor groups were calculated by applying the following: (1) all-cause and cause-specific mortality rates were obtained from the US CDC WONDER database (2) distribution of different numbers of low-risk lifestyles was based on the US NHANES 2013 to 2014 and (3) multivariate-adjusted hazard ratios (sex- and age-specific) for all-cause mortality associated with the 5 low-risk lifestyles compared with those without any low-risk lifestyle factors, adjusted for ethnicity, current multivitamin use, current aspirin use, family history of diabetes mellitus, myocardial infarction, or cancer, and menopausal status and hormone use (women only), were based on data from the NHS (Nurses’ Health Study) and HPFS (Health Professionals Follow-up Study). CDC WONDER indicates Centers for Disease Control and Prevention Wide-Ranging Online Data for Epidemiologic Research and Ref, reference.

In a sensitivity analysis using a low-risk score without moderate alcohol intake, the projected life expectancy at age 50 years was on average 11.4 years (95% CI, 9.5–13.3) longer among female Americans with 4 low-risk factors compared with those with zero low-risk factors for men, the difference was 10.0 years (95% CI, 9.2–10.9 Figure VII in the online-only Data Supplement).

We also estimated the gained life expectancy related to each of the lifestyle factors. As expected, increased exercise, not smoking or a reduced amount of smoking if a smoker, a healthy dietary pattern, moderate alcohol intake, and optimal body weight were all associated with longer life expectancy (Figure 2). The estimate based on the expanded low-risk score indicated a 20.5-year difference in life expectancy at age 50 years in women (19.6 years among men) who adhered to the highest expanded lifestyle score compared with the lowest expanded score (Figure VIII in the online-only Data Supplement).

Figure 2. Projected gained or lost life expectancy according to individual low-risk lifestyle factors. A, Physical activity B, smoking C, diet D, alcohol E, body mass index. Estimates of cumulative survival from 50 years of age onward among different levels of each lifestyle factor were calculated by applying the following: (1) all-cause and cause-specific mortality rates were obtained from the US CDC WONDER database (2) distributions of different groups of each lifestyle factor were based on the US NHANES (National Health and Nutrition Examination Surveys) 2013 to 2014 (3) multivariate-adjusted hazard ratios (sex-specific) for all-cause and cause-specific mortality associated with each lifestyle factor adjusted for ethnicity current multivitamin use current aspirin use family history of diabetes mellitus, myocardial infarction, or cancer and menopausal status and hormone use (women only) were based on data from the NHS (Nurses’ Health Study) and HPFS (Health Professionals Follow-up Study). AHEI indicates Alternate Healthy Eating Index BMI, body mass index CDC WONDER, Centers for Disease Control and Prevention Wide-Ranging Online Data for Epidemiologic Research cigs, cigarettes Q, quartile and Ref, referent.


We estimated that adherence to 5 low-risk lifestyle-related factors could prolong life expectancy at age 50 years by 14.0 and 12.2 years for female and male US adults, respectively, compared with individuals who adopted zero low-risk lifestyle factors. These estimates suggest that Americans could narrow the life-expectancy gap between the United States and other industrialized countries by adopting a healthier lifestyle. In 2014, the life expectancy for American adults at age 50 years was 33.3 years for women and 29.8 years for men. 28 We estimated that the life expectancies were 29.0 years for women and 25.5 years for men if they had zero low-risk factors but could be extended to 43.1 years for women and 37.6 years for men if they adopted all 5 low-risk factors. However, in US adults, adherence to a low-risk lifestyle pattern has decreased during the last 3 decades, from 15% in 1988 to 1992 to 8% in 2001 to 2006, 29 driven primarily by the increasing prevalence of obesity.

The life expectancy of Americans increased from 62.9 years in 1940 to 76.8 years in 2000 and 78.8 years in 2014. 28 This increase could be the result of a number of factors such as improvements in living standards, improved medical treatment, substantial reduction in smoking, 30 and a modest improvement in diet quality. 23 However, some unhealthy lifestyle factors may have counterbalanced the gain in life expectancy, particularly the increasing obesity epidemic 30,31 and decreasing physical activity levels. 32 In our study, three fourths of premature CVD deaths and half of premature cancer deaths in the United States could be attributed to lack of adherence to a low-risk lifestyle. There is still much potential for improvement in health and life expectancy, which depends not only on an individual’s efforts but also on the food, physical, and policy environments. 33,34 A recent study found that low-income residents in relatively wealthy areas such as New York and San Francisco had significantly longer life expectancies than those in poorer regions such as Gary, IN, and Detroit. 35 This phenomenon suggests that the living environment contributes to life expectancy beyond socioeconomic status. For instance, residents in affluent cities have more access to public health services and less exposure to smoking because of the more restricted policies on smoking in public. 35 Studies 36 have linked healthy eating and exercise habits with built, social, and socioeconomic environment assets (access to parks, social ties, affluence) and unhealthy behaviors with built environment inhibitors (access to fast food outlets), suggesting that supporting environments for health lifestyle should be 1 part of the promotion of longevity for the US population. Prevention should be a top priority for national health policy, and preventive care should be an indispensable part of the healthcare system.

Our estimation of gained life expectancy by adopting a low-risk lifestyle was broadly consistent with previous studies. A healthy lifestyle was associated with an estimated greater life expectancy of 8.3 years (women) and 10.3 years (men) in Japan, 10 17.9 years in Canada, 12 and 13.9 years (women) and 17.0 years (men) in Germany, 14 as well as 14 years’ difference in chronological age in the United Kingdom. 11 Data from 3 European cohorts from Denmark, Germany, and Norway 13 suggested that men and women 50 years of age who had a favorable lifestyle would live 7.4 to 15.7 years longer than those with an unfavorable lifestyle. These estimates were somewhat different because of different definitions of a low-risk lifestyle and study population characteristics. 10,12–14

We observed that a healthy diet pattern, moderate alcohol consumption, nonsmoking status, a normal weight, and regular physical activity were each associated with a low risk of premature mortality. Smoking is a strong independent risk factor of cancer, diabetes mellitus, CVDs, and mortality potentially through inducing oxidative stress and chronic inflammation, and smoking cessation has been associated with a reduction of these excess risks. 37–39 A healthy dietary pattern and its major food components have been associated with lower risk of morbidities and mortality of diabetes mellitus, CVD, cancer, and neurodegenerative disease, 40 and its potential health benefits have been replicated in clinical trials. 41 Physical activity and weight control significantly reduced the risk of diabetes mellitus, cardiovascular risk factors, and breast cancer. 42–44 Although no long-term trial of alcohol consumption on chronic disease risk has been conducted, cardiovascular benefits of moderate alcohol consumption have been consistently observed in large cohort studies. 45 Results of our sensitivity analysis further indicated that combinations of the healthy lifestyle factors were particularly powerful: the larger the number of low-risk lifestyle factors, the longer the potential prolonged life expectancy, regardless of the combined factors. 5

A major strength of this study is the long follow-up of 2 large cohorts with detailed and repeated measurements of diet and lifestyle and low rates of loss to follow-up. Another important strength is the combination of the cohort estimates with a nationally representative study, the NHANES, which improved the generalizability of our findings. Although the HRs between lifestyle factors and mortality were estimated from only our cohort data, they were similar to those published in other populations. 9–14 Because our cohorts included mostly white health professionals, we could not specifically examine the overall impact of lifestyle adherence among different ethnic subgroups further studies are warranted to examine the impact of lifestyle factors in other ethnic and racial groups.

The current study has several limitations. First, diet and lifestyle factors were self-reported thus, measurement errors are inevitable. However, the use of repeated measures of these variables could reduce measurement errors and represent long-term diet and lifestyle. Second, we counted the number of lifestyle factors on the basis of the dichotomized value of each lifestyle factor, although the lifestyle factors were differentially associated with mortality. However, our analysis based on an expanded score considered different levels of each risk factor and yielded similar results. Third, we did not fully consider the baseline comorbid conditions and background medical therapies. Although our stratification analysis by baseline chronic conditions of diabetes mellitus, hypertension, and elevated cholesterol provided some support for the hypothesis that adopting a healthy lifestyle is important for both healthy individuals and those with existing chronic conditions, further studies among individuals with diagnosed cancer and CVDs are warranted.


We estimate that adherence to a low-risk lifestyle could prolong life expectancy at age 50 years by 14.0 and 12.2 years in female and male US adults compared with individuals without any of the low-risk lifestyle factors. Our findings suggest that the gap in life expectancy between the United States and other developed countries could be narrowed by improving lifestyle factors.


The authors thank the participants and staff of the NHS and the HPFS who contributed data for their valuable contributions, as well as the following state cancer registries for their help: Alabama, Arizona, Arkansas, California, Colorado, Connecticut, Delaware, Florida, Georgia, Idaho, Illinois, Indiana, Iowa, Kentucky, Louisiana, Maine, Maryland, Massachusetts, Michigan, Nebraska, New Hampshire, New Jersey, New York, North Carolina, North Dakota, Ohio, Oklahoma, Oregon, Pennsylvania, Rhode Island, South Carolina, Tennessee, Texas, Virginia, Washington, and Wyoming. The authors assume full responsibility for analyses and interpretation of these data.

Sources of Funding

The cohorts were supported by grants UM1 CA186107, R01 HL034594, R01 HL60712, R01 HL088521, P01 CA87969, UM1 CA167552, and R01 HL35464 from the National Institutes of Health. Drs Kaptoge and Di Angelantonio acknowledge grant support from the British Heart Foundation (SP/09/002) and UK Medical Research Council (G0800270). Dr Pan acknowledged grant support from the National Key Research and Development Program of China (2017YFC0907504). Dr Wang was supported by a postdoctoral fellowship granted by the American Heart Association (16POST31100031).



*Drs Li and Pan contributed equally.

Sources of Funding, see page 354

The online-only Data Supplement, podcast, and transcript are available with this article at

Healthy food for thought: Think about what you eat

Food has been shown to be an important part of how people age. In one study, scientists investigated how dietary patterns influenced changes in BMI and waist circumference, which are risk factors for many diseases. Scientists grouped participants into clusters based on which foods contributed to the greatest proportion of calories they consumed. Participants who had a "meat and potatoes" eating pattern had a greater annual increase in BMI, and participants in the "white-bread" pattern had a greater increase in waist circumference compared with those in the "healthy" cluster. "Healthy" eaters had the highest intake of foods like high-fiber cereal, low-fat dairy, fruit, nonwhite bread, whole grains, beans and legumes, and vegetables, and low intake of red and processed meat, fast food, and soda. This same group had the smallest gains in BMI and waist circumference.

Scientists think there are likely many factors that contribute to the relationship between diet and changes in BMI and waist circumference. One factor may involve the glycemic index value (sometimes called glycemic load) of food. Foods with a low glycemic index value (such as most vegetables and fruits and high-fiber, grainy breads) decrease hunger but have little effect on blood sugar and therefore are healthier. Foods like white bread have a high glycemic index value and tend to cause the highest rise in blood sugar.

Another focus of research is the relationship between physical problems and micronutrient or vitamin deficiency. Low concentrations of micronutrients or vitamins in the blood are often caused by poor nutrition. Not eating enough fruits and vegetables can lead to a low carotenoid concentration, which is associated with a heightened risk of skeletal muscle decline among older adults. Low concentrations of vitamin E in older adults, especially in older women, is correlated with a decline in physical function. Compared with other older adults, those with low vitamin D levels had poorer results on two physical performance tests. Women with a low vitamin D concentration were more likely to experience back pain. These studies support the takeaway message: the nutrients you get from eating well can help keep muscles, bones, organs, and other parts of the body strong throughout life.

So, eating well is not just about your weight. It can also help protect you from certain health problems that occur more frequently among older adults. And, eating unhealthy foods can increase your risk for some diseases. If you are concerned about what you eat, talk with your doctor about ways you can make better food choices.


We approached the investigation of the mechanisms shaping the lifespan changes of human RBCs, a subject inaccessible to direct experimentation, by applying a dedicated extension of the core red cell model RCM [1], the Lifespan model (Fig 1). We started by questioning the nature and range of RBC responses to be expected from deformation-induced PIEZO1 activation during single capillary transits [1], and followed this up in this paper with a systematic exploration of the dynamic combinations of homeostatic processes that could deliver the documented patterns of change throughout the

2•10 5 transits RBCs experience during their long circulatory lifespan.

A first result was the demonstration of the inadequacy of repeated PIEZO1 channel activations during capillary transits to generate long-term progressive RBC densification on their own (Fig 2), their cumulative power fading rapidly within days to minimal densification levels, thus ruling out the quantal hypothesis [10,47]. The mechanism that finally emerged involved a complex interplay among a quartet of membrane transporters (PIEZO1, Gardos channels, PMCA and Na/K pumps) involving a tightly defined decay pattern for the pumps (Figs 3–5) and additional modulating influences by all other membrane and homeostatic components of the RBC (Figs 6–8), reported and analysed in detail in Results and Analysis.

Looking back at the variety of conditions which failed on compliance with the established densification-late-reversal pattern (Figs 4 and 5), there are clear indications that RBC volume stability and hence adequate rheological performance throughout extended lifespans could also be attained by other, apparently simpler alternatives. Playing with the model unconstrained by facts, one alternative emerged which, surprisingly, was also tightly constrained, that of a RBC without PIEZO1 channels (PzX = 0) and without Gardos channels (PKGardosMax = 0 in RS), but with a well balanced pattern of pump decays (in min -1 , kCaP = 8*10 −5 kNaP = 1*10 −6) ), a simple pump-controlled duet mechanism in which the opposite swelling-shrinking effects elicited by decaying Na/K and PMCA pumps, respectively, remain well balanced throughout. A RBC like this, free from sudden permeability changes during capillary transits by the absence of PIEZO1 channels, and exempt from hyperdense collapse threats (Fig 8) by the absence of Gardos channels could sustain excellent volume stability and optimal rheology throughout extended lifespans at slightly lower metabolic cost that a quartet cell exposed to periodic PMCA stimulation. This prompted the question of what favoured or determined the evolution of the quartet mechanism in human RBCs.

There is no strong argument for a selective preference of quartet over duet mechanisms or other alternatives. In different species the universals of optimal economy and rheology providing extended lifespans are realized with very different strategies, typical of adaptive solutions on the go operating on pre-existing conditions [48]. There are well documented instances of species whose RBCs lack Gardos channels [49], have kinetically diverse and even absent Na/K pumps [50,51] and varied Na/K concentration ratios [52], have different constellations of membrane transporters controlling RBC volume and homeostasis [53], differ substantially between foetal and mature RBCs [54], have completely different cytoskeletal structures, with and without vestigial organelle retention [55,56]. There is no information available yet on how widespread the presence of PIEZO1 channels is in RBCs from different species, and therefore on how central its role may be in the dynamics of capillary circulation. So far, the only documented constant in all species appears to be the calcium pump around which all the different lifespan strategies of RBCs evolved.

Mutant PIEZO1 channels in hereditary xerocytosis (HX) were found to exhibit a number of kinetic abnormalities the most prominent of which was a marginally reduced inactivation kinetics following brief stretch-activation pulses [57]. Simulations with the Lifespan model show how relatively small inactivation delays can lead, after myriad capillary transits, to profound RBC dehydration approaching hyperdense collapse (Fig 8) with similarities to observed alterations in HX RBCs [58]. Protection against severe falciparum malaria, the reason for the persistence of many genetic mutations affecting RBC hydration in human populations, is contributed by two common conditions: anaemia and the presence of subpopulations of dense RBCs which falciparum merozoites fail to infect [59], thus preventing the build up of the high parasitaemias required to cause cerebral malaria, the main malaria killer [60,61].

The lifespan model opens the way for further in depth studies on the changes in RBC homeostasis during circulatory senescence. With growing information databases on the genetics and pathologies associated with the transport systems that control the lifespan of RBCs, model versions encoding known or hypothesized abnormalities of those transporters may become useful tools in furthering the understanding of the pathophysiology and clinical manifestations of the diseased conditions. Within the mathematical-computational framework of the red cell and lifespan models applied here for human RBCs, components can easily be modified and adapted to explore RBC homeostasis, circulatory dynamics and lifespan strategies across species paving the way for future studies on the comparative circulatory biology and pathology of RBCs, and on the effects of alternating oxy-deoxy capillary transits.

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Longevity Consortium

The Purpose of the Longevity Consortium (LC) is to integrate analyses of the genomic, proteomic, and metabolomic bases of human longevity and the lifespans of animal species into models of the molecular pathways that contribute to human longevity. An important goal is to identify pathways that are amenable to pharmacologic intervention.

Multiple studies of model organisms and humans have suggested that genetic variants, proteins, metabolites, as well as other biomolecules and molecular-physiologic processes, could play roles in mediating longevity, healthspan, and age-related disease in humans. Unfortunately, the direct relevance of many of these factors to human longevity, healthspan and age-related disease is uncertain, as is the amenability of these factors to pharmacological modulation. Therefore, well-integrated and sophisticated research strategies are needed to determine the degree to which various factors both influence human longevity and are amenable to pharmacological manipulation. The LC has a history of precedent-setting studies seeking to identify factors that influence human longevity and healthspan, and has defined goals and research strategies to elucidate additional factors affecting longevity, as well as their interactions and translatability into targets for pharmacotherapeutic manipulation . To enable appropriate integration in human longevity research, the LC will leverages a multiple investigator structure with 5 interlinked research projects and 3 integrative cores. These projects and cores include: A cross-species project focusing on cellular and organismal phenotypes led by Richard Miller a metabolomics project led by Oliver Fiehn a Centenarians project co-led by Thomas Perls and Paola Sebastiani a proteomics project led by Eric Orwoll and a disease context project led by Nicholas Schork. The cores include a Systems Biology core led by Nathan Price, a Chemoinformatics core led by Thomas Girke, and an administrative core with an overall administrative component led by Steve Cummings.

To learn more about specific projects, please click the blue funnel icons of the following flowchart or visit the image cards below.

How Does a Heat Wave Affect the Human Body?

Climate change promises to bring with it longer, hotter summers to many places on the planet. This June turned out to be the fourth-hottest month ever recorded&mdashglobally&mdashscientists are reporting. With more heat waves on the horizon, and a big one currently sweeping much of the U.S., the risk of heat-related health problems has also been on the rise.

Heat exhaustion is a relatively common reaction to severe heat and can include symptoms such as dizziness, headache and fainting. It can usually be treated with rest, a cool environment and hydration (including refueling of electrolytes, which are necessary for muscle and other body functions). Heat stroke is more severe and requires medical attention&mdashit is often accompanied by dry skin, a body temperature above 103 degrees Fahrenheit, confusion and sometimes unconsciousness.

Extreme heat is only blamed for an average of 688 deaths each year in the U.S., according to the Centers for Disease Control and Prevention (CDC). But when sustained heat waves hit a region, the other health ramifications can be serious, including sunstroke and even major organ damage due to heat.

The Chicago heat wave in the summer of 1995 killed an estimated 692 people and sent at least 3,300 people to the emergency room. An observational study of some of those patients revealed that 28 percent who were diagnosed at the time with severe heat stroke had died within a year of being admitted to the hospital, and most who initially survived the high temperatures had "permanent loss of independent function," according to a 1998 study of the heat wave, published in Archives of Internal Medicine.

As temperatures linger above our bodies' own healthy internal temperature for longer periods of time, will we humans be able to take the heat? We spoke with Mike McGeehin, director of the CDC's Environmental Hazards and Health Effects Program, to find out just why&mdashand how&mdasha warm, sunny summer day can do us in.

[An edited transcript of the interview follows.]

How do humans cope with hot, hot weather?
The two ways we cope with heat are by perspiring and breathing.

So is it the heat or humidity that is the real killer?
The humidity is a huge factor. If you have tremendously high temperatures and high humidity, a person will be sweating but the sweat won't be drying on the skin. That&rsquos why it's not just heat but the combination of heat and humidity that matters. That combination results in a number called the apparent temperature or "how it feels".

Obviously there are thresholds for both temperature and humidity above which we see an increase in death, and it's going to be a different temperature in Phoenix than it's going to be in Chicago.

The other major factor in terms of temperature that causes both mortality and morbidity is the temperature that it falls to in the evening. If the temperature remains elevated overnight, that's when we see the increase in deaths. The body becomes overwhelmed because it doesn't get the respite that it needs.

What kind of impact does extreme, sustained heat have on the human body?
The systems in the human body that enable it to adapt to heat become overwhelmed. When a person is exposed to heat for a very long time, the first thing that shuts down is the ability to sweat. We know that when perspiration is dried by the air there is a cooling effect on the body. Once a person stops perspiring, in very short order a person can move from heat exhaustion to heat stroke.

What happens in the transition from heat exhaustion to heat stroke?
It begins with perspiring profusely, and when that shuts down, the body becomes very hot. Eventually that begins to affect the brain, and that's when people begin to get confused and can lose consciousness.

The analogy we use is if you're driving a car and you notice that the temperature light comes on, what's happening is the cooling system of the car is becoming overwhelmed. If you turn off the car and let it cool eventually you can start driving again. But if you continue to drive the car, the problem goes beyond the cooling system to affect the engine, and eventually the car will stop.

What other areas of the body does this extreme overheating affect?
As the body temperature increases very rapidly, the central nervous system and circulatory system are impacted.

In places where there have been prolonged heat exposures, there is probably a broad impact on many organ systems. From heat waves that have been studied, like in Chicago, there are increases in emergency department visits and hospital stays for medical crises that are not normally associated with heat, such as kidney problems.

But it really hasn't been studied very much. One of the reasons for that is the main focus of the studies has been on mortality from heat waves, and there hasn't been that much focus on morbidity. That would take looking at people who are hospitalized from heat exhaustion or heat stroke and following them into the future.

Before someone gets full-blow heat stroke, what are the body's early reactions to excessive heat?
Heat rash and muscle cramps are early signs of people being overwhelmed by heat. If those aren't dealt with, it can lead to more severe symptoms.

Cramping of muscles can be for a number of different issues, including electrolytes not getting to the muscles.

People should be aware that their skin turning red and dry are indicators that heat is impacting them.

Who is the most vulnerable to extended high temperatures?
We know the risk factors for dying from heat are urban dwellers who are elderly, isolated and don't have access to air conditioning. Obese people are at increased risk as are people on certain medications. And people who are exercising or working in the heat, who don't meet those criteria, can be at risk.

What medications can make the body more susceptible to extreme heat?
In the study from the 1995 Chicago heat wave, we found that diuretics for high blood pressure were some that did, and beta blockers&mdasha number of studies showed that people taking them could be at increased risk.

There are some studies that have shown that certain mental health medications may impact a person's ability to deal with the heat. But that's a difficult one to get at. When you look at the number of people who die in a heat wave and the number of people who are taking those medications, the numbers can get pretty small pretty quickly.

What's the hottest temperature a healthy human can tolerate?
We don't know that&mdashno one knows that. There are different humans, different humidities, different types of temperature.

Have we not evolved to cope with super hot weather?
Certainly society has evolved in dealing with the heat&mdashand that has been in the development of air conditioners. The number-one factor that ameliorates death from heat is access to air conditioning.

And I've read that fans don't work to prevent overheating in really hot temperatures&hellip
Not only does it not work, it actually makes it worse. We compare it to a convection oven. By blowing hot air on a person, it heats them up rather than cools them down.

Are modern humans neglecting to do something our ancestors did to survive the heat?
I think it's always been a problem. There's history over hundreds of years of people dying of heat. Philadelphia in 1776 had a major heat wave that caused deaths.

We're also living to older ages, and we're more urban now than we have been in the history of the human species. That intense crowding can combine with the heat island effect in big cities. Our elderly people are also more isolated than they have been in the past, so those factors can play a part, too.

The IPCC, the Intergovernmental Panel on Climate Change, the thing that they are most comfortable in predicting, that the science is most solid for, is the increase in many parts of the world in the duration and intensity of heat waves.