abstract: **Objective** To examine how change in level of physical activity after middle age influences mortality and to compare it with the effect of smoking cessation.
  .
  **Design** Population based cohort study with follow-up over 35 years.
  .
  **Setting** Municipality of Uppsala, Sweden.
  .
  **Participants** 2205 men aged 50 in 1970-3 who were re-examined at ages 60, 70, 77, and 82 years.
  .
  **Main outcome measure** Total (all cause) mortality.
  .
  **Results** The absolute mortality rate was 27.1, 23.6, and 18.4 per 1000 person years in the groups with low, medium, and high physical activity, respectively. The relative rate reduction attributable to high physical activity was 32% for low and 22% for medium physical activity. Men who increased their physical activity level between the ages of 50 and 60 continued to have a higher mortality rate during the first five years of follow-up (adjusted hazard ratio 2.64, 95% confidence interval 1.32 to 5.27, compared with unchanged high physical activity). After 10 years of follow-up their increased physical activity was associated with reduced mortality to the level of men with unchanged high physical activity (1.10, 0.87 to 1.38). The reduction in mortality associated with increased physical activity (0.51, 0.26 to 0.97, compared with unchanged low physical activity) was similar to that associated with smoking cessation (0.64, 0.53 to 0.78, compared with continued smoking).
  .
  **Conclusions** Increased physical activity in middle age is eventually followed by a reduction in mortality to the same level as seen among men with constantly high physical activity. This reduction is comparable with that associated with smoking cessation.
author: Liisa Byberg; Håkan Melhus; Rolf Gedeborg; Johan Sundström; Anders Ahlbom; Björn Zethelius; Lars G Berglund; Alicja Wolk; Karl MichaëlssonCorrespondence to: L Byberg <email@example.com>
date: 2009
references:
title: Total mortality after changes in leisure time physical activity in 50 year old men: 35 year follow-up of population based cohort

# Introduction

Despite the known hazards of physical inactivity, it continues to be a major health problem.1 Physical inactivity is associated with increased incidence rates of obesity, diabetes, cardiovascular diseases, osteoporosis, and cancer.1 2 3 4 Short term randomised controlled trials in young to middle aged adults have shown a healthier risk profile with exercise.4 5 It is therefore recommended that adults engage in at least 30 minutes of moderate physical activity preferably on all days of the week.5 6 7 Adherence to these guidelines is associated with half the risk of mortality seen in sedentary people.8 Nevertheless, about half of all middle aged men in the West do not take part in regular physical activity.1 2 Whereas being physically inactive in younger years seems detrimental, we do not know whether an increase in exercise level later in life reduces mortality rates. If the impact on mortality could be compared with the effects of other changes in lifestyle habits it would be easier to communicate this potential health benefit.

In prospective studies that have examined changes in leisure time physical activity level in relation to mortality,9 10 11 12 13 14 15 few have considered the possibility of additional changes in physical activity behaviours during follow-up.13 14 15 Furthermore, the impact on mortality in relation to age at the time of the activity changes and the time elapsed since the changes occurred have not been established.

We investigated the impact of physical activity on total (all cause) mortality from middle to old age, taking into account changes in activity during follow-up. We studied this in a population based cohort of men aged 50 at baseline who were followed up for more than 35 years and investigated on five occasions. We also compared the effect of an increased level of physical activity on the mortality rate with that achieved by cessation of smoking.

# Methods

The present investigation used data from the population based Uppsala longitudinal study of adult men (ULSAM, [www.pubcare.uu.se/ULSAM](www.pubcare.uu.se/ULSAM)). Figure 1 shows the study population . In 1970 all 2841 men born in 1920-4 and living in the municipality of Uppsala, Sweden, were invited to attend a health survey (survey 1).16 Of those invited, 2322 men (82%) aged 49-51 participated. Participants in survey 1 were invited for reinvestigations at ages 60, 70, and 77 (surveys 2-4). Participants in surveys 3 and 4 were invited for reinvestigation at age 82 (survey 5).

We restricted our study population to the 2205 men who had responded to the questions regarding leisure time physical activity in the self administered questionnaire in survey 1. Identical questions were asked at each survey:

1.  1 Do you spend most of your time reading, watching TV, going to the cinema, or engaging in other, mostly sedentary activities?

2.  2 Do you often go walking or cycling for pleasure?

3.  3 Do you engage in any active recreational sports or heavy gardening at least 3 hours every week?

4.  4 Do you regularly engage in hard physical training or competitive sport?

We considered the two highest activity categories, questions 3 and 4, together as only 5% of the participants reported hard physical training. Participants answering yes to question 1 were categorised as low activity; 2 as medium; and 3 and 4 as high. The questionnaire has been used and validated in other studies and has also been shown to have a high reliability over time.17 18 19 20

Changes in the physical activity level between the first and second survey were considered in four categories: unchanged low (low or medium activity at both surveys), unchanged high (high activity at both surveys), decreased (high in survey 1, low or medium in survey 2), and increased (low or medium in survey 1, high in survey 2).

Based on job titles retrieved from the national censuses from 1970 and 1980, we categorised occupational physical activity as sedentary, light, medium, high, unknown, and none (unemployed).21

Height and weight were measured at each survey and body mass index (BMI, weight (kg)/height (m)^2^) calculated. Obesity was defined as BMI ≥30. Systolic and diastolic blood pressure in the supine position was measured by standard procedures. Information on antihypertensive medication was retrieved by questionnaire, as was information on smoking habits. Smoking status was categorised as current smoker, former smoker, or never smoker. Changes in smoking status between two surveys were categorised as continued smoking, smoking cessation, and never smoking. Total fasting serum cholesterol concentration was measured with enzymatic techniques. The presence or absence of diabetes mellitus was determined at each survey according to the guidelines current at that time. Information on perceived health (classified as not good or good) and alcohol use at surveys 2 and 4 (classified according to the Michigan alcoholism screening test as abstainer, normal, suspected alcohol dependence22) was retrieved from the questionnaires. The census from 1970 provided information on educational level. Socioeconomic group at survey 1 was interview based and categorised as low, medium, and high.23 By linkage to the national hospital discharge register, we collected information on any musculoskeletal, neurological, or psychiatric disorder. The prevalence of each of these disorders and of diabetes was \<1.2% at baseline.

Using the individual 10 digit personal identification number given to all Swedish citizens,24 we traced the entire cohort in the continuously updated Swedish National Population Register until the date of death or 31 December 2006. Our main outcome was total (all cause) mortality. There was no loss to follow-up.

## Statistical analyses

We used Cox's proportional hazards regression with delayed entry to estimate hazard ratios with 95% confidence intervals and survival curves for the association between physical activity and mortality. The values of physical activity, as well as covariate information, were updated by data from each subsequent visit. The last observed value carried forward technique was used for missing data. The non-response frequencies for physical activity were 130, 89, 68, and 0 at surveys 2-5, respectively. Table 1 shows details of the sensitivity analyses.

 Sensitivity analysis, showing hazard ratios and 95% confidence intervals for each model

| Physical activity category | Model 1 | Model 2 |
|----|----|----|
| **Last observed value carried forward\*** |  |  |
| High | 1.00† | 1.00† |
| Medium | 1.32 (1.18 to 1.49) | 1.24 (1.10 to 1.39) |
| Low | 1.75 (1.48 to 2.06) | 1.41 (1.19 to 1.67) |
| **Lowest possible level‡** |  |  |
| High | 1.00† | 1.00† |
| Medium | 1.31 (1.16 to 1.48) | 1.22 (1.08 to 1.38) |
| Low | 1.73 (1.50 to 2.00) | 1.50 (1.29 to 1.74) |
| **Highest possible level‡** |  |  |
| High | 1.00† | 1.00† |
| Medium | 1.22 (1.09 to 1.37) | 1.14 (1.02 to 1.29) |
| Low | 1.71 (1.45 to 2.02) | 1.36 (1.15 to 1.62) |
| **Excluding men with missing values§** |  |  |
| High | 1.00† | 1.00† |
| Medium | 1.29 (1.14 to 1.46) | 1.20 (1.06 to 1.36) |
| Low | 1.87 (1.57 to 2.23) | 1.44 (1.20 to 1.72) |
| **Excluding men with missing survey participation¶** |  |  |
| High | 1.00† | 1.00† |
| Medium | 1.59 (1.32 to 1.92) | 1.43 (1.18 to 1.74) |
| Low | 2.04 (1.56 to 2.65) | 1.66 (1.27 to 2.17) |

Model 1 includes physical activity as exposure. Model 2 includes physical activity, smoking status, obesity status, height and weight (both continuous); self perceived health (not good, good); physical activity at work (sedentary, light, medium, high, unknown, unemployed); diabetes mellitus, and any musculoskeletal, neurological, or psychiatric disorders (yes, no for each disorder); and alcohol use (abstainer, normal, suspected alcohol dependence).

\* Missing values imputed by last observed value carried forward method.

†Reference category.

‡Additional error was introduced to imputed values by assigning them lowest or highest possible level.

§Excludes all men with at least one missing observation; 5396 observations remained, representing 1918 participants and 1174 deaths; observation time was 52 593 person years.

¶Includes only men who attended all possible surveys before death; 3188 observations remained, representing 895 participants and 525 deaths; observation time was 22 814 person years.

The proportional hazards assumptions for the Cox models were confirmed formally by Shoenfeld's tests, and graphically by comparing Nelson-Aalen plots. We considered one crude and two multivariable models. The first multivariable model included smoking status (current, former, never); weight and height (both continuous); self perceived health (good, not good); physical activity at work in 1970 and 1980 (sedentary, light, medium, high, unknown; or unemployed); alcohol use at ages 60 and 77 years (abstainer, normal, suspected alcohol dependence); and presence or absence of obesity, diabetes mellitus, and any musculoskeletal, neurological, or psychiatric disorder as covariates. The second multivariable model additionally included systolic and diastolic blood pressure, antihypertensive drugs, total serum cholesterol concentration, educational level, and socioeconomic group.

We assessed the relative excess risk due to interaction, a measure of biological interaction,25 from a Cox's proportional hazards model as described previously.26 27

Remaining life expectancy was calculated as the area under the survival curve using the trapezoidal rule approximation for the integral between the start of the study and the end of follow-up. We calculated differences in remaining life expectancy between exposure levels with 95% confidence intervals for the differences using the bootstrap centile method with 10 000 replications.

In analyses of changes in physical activity level between the ages of 50 and 60, we partitioned follow-up time from date of examination in the second survey into five year intervals to account for non-proportional hazards.

Statistical analyses were performed with Stata/SE 10.1 (StataCorp, College Station, TX), SAS 9.1 (SAS Institute, Cary, NC, USA), and R (R Foundation for Statistical Computing, Vienna, Austria, 2007).

# Results

Table 2 shows baseline characteristics by level of physical activity at age 50. Almost half of the men reported a high level of physical activity, corresponding to at least three hours of recreational sports or heavy gardening a week, 36% reported medium activity, corresponding to walks and cycling; and 15% were sedentary. At the end of follow-up, we had 61 456 person years and 1329 men (60%) had died.

 Characteristics at baseline (survey 1) according to physical activity at age 50. Values are means (SD) or number (percentage) of men

| Variable and category | Physical activity level |  |  |
|----|----|----|----|
|  | Low (n=325) | Moderate (n=802) | High (n=1078) |
| Age (years) | 49.6 (0.6) | 49.6 (0.6) | 49.7 (0.6) |
| Weight (kg) | 78.7 (12.8) | 77.3 (11.1) | 77.8 (10.6) |
| Height (cm) | 175.9 (6.1) | 176.0 (6.0) | 176.5 (5.8) |
| Body mass index (kg/m^2^) | 25.4 (3.7) | 24.9 (3.2) | 25.0 (3.1) |
| No (%) obese\* | 40 (12.3) | 41 (5.1) | 62 (5.8) |
| Supine systolic blood pressure (mm Hg) | 133 (19) | 134 (18) | 132 (17) |
| Supine diastolic blood pressure (mm Hg) | 83 (12) | 84 (11) | 83 (11) |
| No (%) taking antihypertensive drugs | 18 (5.5) | 38 (4.7) | 37 (3.4) |
| Total serum cholesterol (mmol/l) | 6.95 (1.32) | 6.93 (1.33) | 6.83 (1.30) |
| No (%) with self rated good health | 242 (74.5) | 641 (79.9) | 902 (83.7) |
| No (%) by smoking status: |  |  |  |
|  Never smoker | 66 (20.3) | 196 (24.4) | 296 (27.5) |
|  Former smoker | 61 (18.8) | 181 (22.6) | 281 (26.1) |
|  Current smoker | 198 (60.9) | 425 (53.0) | 501 (46.5) |
| No (%) by occupational physical activity in 1970: |  |  |  |
|  Sedentary | 33 (10.2) | 86 (10.7) | 139 (12.9) |
|  Light | 101 (31.1) | 255 (31.8) | 380 (35.3) |
|  Medium | 39 (12.0) | 100 (12.5) | 126 (11.7) |
|  High | 36 (11.1) | 86 (10.7) | 111 (10.3) |
|  Unemployed/unknown | 116 (35.7) | 275 (34.3) | 322 (29.9) |

\*BMI ≥30 kg/m^2^.

The absolute mortality rate was 27.1, 23.6, and 18.4 per 1000 person years for men with low, medium, and high physical activity. The relative rate reduction attributable to high physical activity was 32% compared with low activity and 22% compared with medium activity. The relative mortality rates were highest among sedentary men and lowest among the most active men (table 3, fig 2) . This trend was also apparent in each category of smoking (not shown). The estimated effect of sedentary behaviour on mortality was similar to the effect of smoking and obesity (table 3). Adjustment for covariates attenuated the associations.

 Hazard ratio (HR) and 95% confidence intervals of total mortality for physical activity level in leisure time, smoking, and obesity status using time updated and baseline data

| Exposure and category | Time at risk (person years) | No of deaths | Hazard ratio (95% CI) |  |  |
|----|----|----|----|----|----|
|  |  |  | Model 1 | Model 2 | Model 3 |
| **Time updated model\*** |  |  |  |  |  |
| Physical activity: |  |  |  |  |  |
|  High | 28 790 | 531 | 1.00† | 1.00† | 1.00† |
|  Medium | 25 371 | 600 | 1.32 (1.18 to 1.49) | 1.24 (1.10 to 1.39) | 1.22 (1.08 to 1.37) |
|  Low | 7306 | 198 | 1.75 (1.48 to 2.06) | 1.41 (1.19 to 1.67) | 1.40 (1.18 to 1.66) |
| Smoking status: |  |  |  |  |  |
|  Never | 20 657 | 465 | 1.00† | 1.00† | 1.00† |
|  Former | 19 420 | 373 | 1.18 (1.02 to 1.36) | 1.15 (1.00 to 1.32) | 1.10 (0.96 to 1.27) |
|  Current | 21 389 | 491 | 2.09 (1.82 to 2.39) | 1.98 (1.72 to 2.28) | 1.90 (1.64 to 2.19) |
| Obesity status: |  |  |  |  |  |
|  Non-obese | 56 620 | 1157 | 1.00† | 1.00† | 1.00† |
|  Obese | 4847 | 172 | 1.52 (1.29 to 1.78) | 1.49 (1.19 to 1.86) | 1.51 (1.21 to 1.90) |
| **Baseline model‡** |  |  |  |  |  |
| Physical activity: |  |  |  |  |  |
|  High | 30 930 | 617 | 1.00† | 1.00† | 1.00† |
|  Medium | 21 850 | 504 | 1.20 (1.07 to 1.35) | 1.16 (1.03 to 1.31) | 1.14 (1.01 to 1.28) |
|  Low | 8687 | 213 | 1.30 (1.12 to 1.52) | 1.14 (0.98 to 1.34) | 1.13 (0.96 to 1.33) |
| Smoking status: |  |  |  |  |  |
|  Never | 16 626 | 266 | 1.00† | 1.00† | 1.00† |
|  Former | 15 446 | 291 | 1.21 (1.03 to 1.43) | 1.22 (1.03 to 1.44) | 1.20 (1.01 to 1.41) |
|  Current | 29 395 | 777 | 1.87 (1.63 to 2.16) | 1.88 (1.63 to 2.17) | 1.94 (1.67 to 2.24) |
| Obesity status: |  |  |  |  |  |
|  Non-obese | 58 032 | 1220 | 1.00† | 1.00† | 1.00† |
|  Obese | 3434 | 114 | 1.81 (1.50 to 1.20) | 1.30 (1.01 to 1.68) | 1.26 (0.97 to 1.62) |

Model 1 includes one exposure only. Model 2 includes physical activity, smoking status, and obesity status; and as covariates height and weight (both continuous); self perceived health (not good, good); physical activity at work (sedentary, light, medium, high, unknown, unemployed); diabetes mellitus and any musculoskeletal, neurological, or psychiatric disorders (yes, no for each disorder); and alcohol use (abstainer, normal, suspected alcohol dependence). Model 3 as for model 2 but with addition of systolic and diastolic blood pressure, use of antihypertensive drugs, total serum cholesterol concentrations, educational level, and socioeconomic group.

\*Based on time updated data and mortality follow-up.

†Reference category.

‡Based on baseline data and mortality follow-up.

The models in which we used time updated variables showed higher estimates of the effect of physical activity than models with baseline data (table 3).

We converted the differences in death rates into differences in remaining life expectancies from age 50. Men who reported high levels of physical activity were expected to live 3.8 (95% confidence interval 2.7 to 4.8) years longer than sedentary men and 1.8 (1.3 to 2.2) years longer than men who reported medium levels of physical activity. After adjustment for potential confounding variables the corresponding remaining life expectancies were 2.3 (1.3 to 3.3) and 1.1 (0.6 to 1.6) years, respectively.

## Changes in physical activity level

We investigated the influence on mortality of changes in physical activity between the ages of 50 and 60 in the 1759 men who participated in the first and second survey. From the date of examination in survey 2, we studied 34 020 person years and 998 men died. At the age of 60 about 27% (n=474) still reported high physical activity, 11% (n=194) had increased and 24% (n=414) had reduced levels, and 38% (n=677) reported unchanged low activity. Men who changed their physical activity level could not be distinguished from men with unchanged activity based on baseline clinical characteristics (not shown).

Changes in physical activity between the ages of 50 and 60 were associated with mortality (fig 3). After 10 years of follow-up the mortality rate did not differ between men who had increased their physical activity and men who were at an unchanged high level (table 4). This is also illustrated by the parallel failure curves after about age 70 (fig 3). During the first five years of follow-up, however, the mortality rate was 2.6 times higher in men who had increased their level of physical activity compared with men whose physical activity was high but unchanged (table 3). Adjustment for smoking, obesity, and other covariates attenuated the estimates but did not change the interpretation of the results.

  Hazard ratios with 95% confidence intervals for total mortality after different follow-up periods by changes in physical activity between ages of 50 and 60

| Follow-up period and physical activity level | Follow-up (person years) | No of deaths | Hazard ratio (95% CI) |  |
|----|----|----|----|----|
|  |  |  | Model 1 | Model 2 |
| **Unrestricted** |  |  |  |  |
| Unchanged high | 10 508 | 249 | 1.00\* | 1.00\* |
| Increased from low or moderate to high | 4862 | 154 | 1.27 (1.04 to 1.55) | 1.21 (0.99 to 1.48) |
| Reduced from high to low or moderate | 7020 | 233 | 1.53 (1.28 to 1.83) | 1.35 (1.12 to 1.61) |
| Unchanged low or moderate | 11 629 | 362 | 1.47 (1.25 to 1.73) | 1.33 (1.12 to 1.56) |
| **Deaths after 5 years of follow-up** |  |  |  |  |
| Unchanged high | 10 464 | 234 | 1.00\* | 1.00\* |
| Increased from low or moderate to high | 4813 | 136 | 1.18 (0.95 to 1.45) | 1.13 (0.91 to 1.39) |
| Reduced from high to low or moderate | 6952 | 211 | 1.51 (1.25 to 1.82) | 1.33 (1.10 to 1.61) |
| Unchanged low or moderate | 11 542 | 325 | 1.46 (1.23 to 1.73) | 1.31 (1.11 to 1.56) |
| **Deaths after 10 years of follow-up** |  |  |  |  |
| Unchanged high | 10 190 | 198 | 1.00\* | 1.00\* |
| Increased from low or moderate to high | 4649 | 115 | 1.14 (0.90 to 1.43) | 1.10 (0.87 to 1.38) |
| Reduced from high to low or moderate | 6694 | 178 | 1.60 (1.30 to 1.96) | 1.43 (1.17 to 1.76) |
| Unchanged low or moderate | 11 064 | 263 | 1.49 (1.24 to 1.79) | 1.36 (1.13 to 1.64) |
| **Deaths within 5 years of follow-up** |  |  |  |  |
| Unchanged high | 2340 | 15 | 1.00\* | 1.00\* |
| Increased from low or moderate to high | 929 | 18 | 3.05 (1.54 to 6.06) | 2.64 (1.32 to 5.27) |
| Reduced from high to low or moderate | 2028 | 22 | 1.66 (0.86 to 3.20) | 1.50 (0.77 to 2.92) |
| Unchanged low or moderate | 3287 | 37 | 1.75 (0.96 to 3.19) | 1.58 (0.86 to 2.90) |
| **Deaths within 10 years of follow-up** |  |  |  |  |
| Unchanged high | 4589 | 51 | 1.00\* | 1.00\* |
| Increased from low or moderate to high | 1783 | 39 | 1.98 (1.30 to 3.00) | 1.70 (1.12 to 2.60) |
| Reduced from high to low or moderate | 3874 | 55 | 1.25 (0.86 to 1.83) | 1.10 (0.74 to 1.61) |
| Unchanged low or moderate | 6326 | 99 | 1.43 (1.02 to 2.01) | 1.23 (0.87 to 1.74) |

Model 1 includes time updated variable change in physical activity as exposure. Model 2 includes time updated variable change in physical activity as exposure and covariates: smoking status, obesity status, height and weight, self perceived health, physical activity at work, diabetes mellitus, any musculoskeletal, neurological, or psychiatric disorders, and alcohol use.

\*Reference category.

Eighteen of the 154 deaths (12%) among men with increased physical activity occurred within the first five years of follow-up and 21 men died during the subsequent five years. This group with increased mortality during the first 10 years after an increase in physical activity could not be distinguished on the basis of differences in the cardiovascular risk factor profile either at age 50 or 60. Exclusion of men with diagnosis of or echocardiography signs of cardiovascular disease at the age of 50 or 60 did not change any of the observed associations. Neither was there any difference in causes of death between this group and others. Men who maintained a low level of physical activity and men who decreased their level had higher mortality rates than those with unchanged high physical activity, irrespective of the length of follow-up (table 4, fig 3).

We studied men who increased their physical activity level in more detail. The mortality rate after more than 10 years of follow-up was halved in men who had increased their physical activity to a high level both from a medium (adjusted hazard ratio 0.58, 0.39 to 0.87) and a low (0.51, 0.26 to 0.97) level. There was no evident reduction in mortality rate among men who increased their physical activity from low to medium (0.81, 0.51 to 1.31). As a comparison, smoking cessation was associated with a 40% lower mortality rate after more than 10 years of follow-up compared with current smoking (0.64, 0.53 to 0.78). Men who had never smoked had a 60% lower mortality rate than current smokers (0.42, 0.33 to 0.52).

There was no evidence of biological interaction between increased physical activity and smoking cessation in their effects on mortality; the relative excess risk due to interaction was −0.40 (−1.30 to 0.50).

# Discussion

## Principal findings

Increased levels of physical activity in middle age have an effect on mortality. After a 10 year period of increased physical activity the excess mortality seen in inactive men was reduced to the same levels of mortality as seen in physically active men. There is, however, a period of at least 5 years before this risk reduction during which the risk is higher. The halved mortality rate after 10 years of follow-up after increased physical activity between the ages of 50 and 60 (compared with continued inactivity) was similar to that seen after smoking cessation (compared with continued smoking).

## Strengths and weaknesses of the study

The main strengths of our study include the population based design, the long follow-up from a time before the men reached ages with a high mortality rate, the repeated measurements, and the objective and complete information on mortality. Our analyses also took into account changes over time in potential confounders including smoking, obesity, self perceived health, and morbidity and in classic risk factors for mortality including hypertension and total cholesterol concentration. We also adjusted our estimates for socioeconomic group and educational level.

The main limitation of our study is its restriction to men and the crude assessment of physical activity by questionnaire, with risk of misclassification possibly leading to underestimation of the results.28 There is, however, no ideal test to measure physical activity,29 either by questionnaire or by more objective assessments. We used a questionnaire created in collaboration with the Swedish National Institute of Public Health that estimates physical activity over the life cycle.30 Although our categories indexing physical activity should be regarded as reflecting common perceptions or patterns of physical activity rather than precise measures of levels, the levels estimated from these four simple questions are positively associated with physiological measurements of fitness,16 17 skeletal muscle capillary density,31 and pedometer results,32 indicating that these levels do represent physical activity. Simple questionnaires are well suited for the study of changed behaviour in large epidemiological studies,28 and the repetition of identical questions at each survey is one of the main strengths of our study.

The last observed value carried forward technique could introduce bias, despite being the preferred imputation method.33 As results from sensitivity analysis were consistent with the main results, it seems unlikely that this imputation method introduced bias. In our multivariable models, the adjustment for variables that can be regarded not only as confounders but also as intermediates on the causal pathway—such as perceived health, body weight, and diabetes—might also have conservatively biased our estimates.

## Strengths and weaknesses in relation to other studies

Many previous studies have used baseline data and changes in activity to examine whether physical activity influences mortality, without applying a time dependent approach in their analyses.9 10 11 12 34 35 Our associations between baseline data on physical activity and mortality rates were generally weaker than when we took into account changes within individuals in the level of physical activity during follow-up, further emphasising the importance of using time updated variables when behaviour such as physical activity is studied in relation to clinical outcomes.36 Time dependent life table analysis for estimation of remaining life expectancy among 50 year old men in the Framingham Heart Study showed 3.9-4.1 years longer life expectancy for men reporting high physical activity and 1.6-1.8 years longer life expectancy for men reporting medium physical activity, compared with the sedentary group.13 14 We obtained estimates of similar magnitude.

Total mortality was substantially influenced by the length of time since the change in physical activity took place. One recent study that used time dependent analysis to investigate change in physical activity in relation to mortality in men and women aged 19 to over 90 years found an association between change in physical activity and mortality in men under the age of 70.15 In that study, however, the direction of change, the age when the change in physical activity occurred, and the time that had elapsed since the change remained unclear.

A change in physical activity in the lower activity range might reflect a change in sedentary behaviour rather than in actual physical activity. Recent research showed that sedentary behaviour and physical activity were independently associated with obesity.37 Future research should aim to separate these two effects.

As measurement of physical activity is crude and difficult to compare between studies, we wanted to compare the effect of physical activity on mortality with the effect of smoking. Self reported smoking has a high validity,38 and smoking is probably the most well established modifiable behavioural risk factor for premature death.39 The graded inverse association between physical activity level and mortality was independent of and present in each category of smoking status. The impact of increased physical activity on mortality was on par with the effect of smoking cessation. A recent study in women found a comparable reduction in mortality after smoking cessation.40

Our observational results might differ from those from meta-analyses of clinical trials because such trials tend to be conducted in high risk populations and are designed so that the exposure has a low measurement error and that the probability of confounding is reduced. The effect we observed after increased physical activity, however, is larger than the effect of a 1 mmol/l reduction in cholesterol concentration (18% reduction of total mortality)41 and the effect of treatment with any antihypertensive drug (10% reduction of total mortality).42

## Possible explanations

We previously showed that an increase in the level of physical activity in the present cohort was associated with beneficial changes in the metabolic profile, including a decrease in body weight and in blood concentrations of insulin, proinsulin, glucose, and lipids.43 An improved metabolic profile reduces the prevalence of metabolic syndrome, obesity, and cardiovascular disease. This is one suggested pathway whereby physical activity is thought to reduce mortality.13 34 The effect of hypertension and high total cholesterol concentration on mortality is well established, but in our analysis the association between physical activity and mortality was independent of these. Other suggested mechanisms for the effect of physical activity on mortality include changes in inflammatory and hormonal responses and gut mobility44 and effects on the nervous systems, which influence neuromuscular and brain function.45 All these mechanisms might be important for the progression of many diseases and general wellbeing. In older people physical activity is also important for maintaining muscle strength, postural balance,46 47 functional independence, and health related quality of life.48 The effect of physical activity on the risk of premature death might be modulated by genetic differences.49 Recent results in Swedish twins, however, argue against genetics as a cause of the association between physical activity and mortality.50 In addition, inactive twins have been reported to have shorter telomere lengths corresponding to higher biological age by up to 10 years compared with their more active co-twin.51

## Implications, unanswered questions, and future research

Within the first five years after the investigation at the age of 60 we observed an increased risk associated with increased physical activity compared with men with unchanged high activity. Given the small numbers of deaths we are reluctant to place a strong emphasis on this increased risk, especially as mortality was not higher than mortality in men who continued to be sedentary. Although there might not be a true increase in mortality soon after increased physical activity, there is nonetheless no immediate benefit. This has also been observed in a community based cluster randomised trial in older people in whom participation in an exercise programme had no effect on mortality during the two year follow-up.52 There was, however, a much larger than expected improvement in health related quality of life after the intervention, suggesting that increased activity might have early health benefits that are collectively too small to have an impact on total mortality.

Our results could have important implications in the planning of clinical trials as these would need to be long enough to extend beyond the period of no benefit and to take into consideration the ethical implications of a possible increase in short term risk. Different follow-up times might also yield different results in observational studies.

It is difficult to quantify the least amount of physical activity needed for a beneficial effect on mortality in our study. Our dose-response relations suggest that the more activity the better, but on the basis of our data it is not possible to determine whether the recommended minimum amount of physical activity was achieved or not.5 6 7

We could not identify any characteristics distinguishing those who died shortly after taking up physical activity. A healthy survivor effect cannot be excluded as survival until age 60 was necessary for this analysis. Further research should investigate whether and to what extent increased physical activity affects mortality in the period soon after the change. In addition, the effects in other age groups and in women need to be studied. Mechanisms and reasons for an active choice to change physical activity are not fully understood.

## Conclusion

In conclusion, we have shown a graded reduction in total mortality risk with increasing physical activity level in men. Increased physical activity in middle age increases longevity after an induction period of up to 10 years of no benefit. An increase in physical activity has the same impact on lowering mortality rate in the long term as smoking cessation. Thus, efforts for promotion of physical activity, even among middle aged and older men, are important.

### What is already known on this topic

1.  Physical activity is beneficial for health and prolongs life among younger individuals

2.  Inactivity and resulting disease remains a major health problem

### What this study adds

1.  There is a dose dependent association between level of physical activity and total mortality in middle aged men

2.  Increased physical activity prolongs life among middle aged and older men, though there might be a period of 5-10 years before an effect is seen on total mortality

3.  The effect on mortality after increased physical activity is on par with that of smoking cessation

**Cite this as:** *BMJ* 2009;338:b688


abstract: Data recently published in *BMC Biology* provide insights into the normal physiological function of c-*myc* in the development and regeneration of the mammary gland and indicate a key role in epithelial cell proliferation, elaboration of ductal alveoli, and the biosynthetic capacity and milk production of the mature organ.
  .
  See related research article <http://www.biomedcentral.com/1741-7007/7/63>
author: Nicole M Sodir; Gerard I Evan
date: 2009
institute: 1Department of Pathology and Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, CA 94143-0502, USA
references:
title: Nursing some sense out of Myc

The three Myc proteins, c-Myc, L-Myc and N-Myc, are all basic helix-loop-helix (bHLH)-Zip transcription factors that coordinate cell proliferation, cell cycle progression, cell growth, metabolism, differentiation and tissue remodeling, as well as a variety of protective checkpoint mechanisms such as growth arrest and apoptosis. Myc proteins form heterodimers with their bHLH-Zip partner Max, trans-activating genes by binding at canonical E-Box CAC^G^/~A~TG recognition elements in target gene promoters and repressing other genes through interactions with the zinc finger protein Miz-1 and/or recruitment of the DNA methyl-transferase corepressor Dnmt3a (Figure 1) \[1\]. Expression array, SAGE, chromatin IP, promoter scanning and whole cell proteomic approaches identify thousands of Myc target genes with diverse roles in virtually every aspect of cell and tissue behavior, including growth, metabolism, cell cycle, differentiation, telomere maintenance, DNA damage and repair, intracellular membrane transport, cytoskeleton, cytokine production, angiogenesis, invasion and apoptosis \[2\] (see also <http://www.myc-cancer-gene.org/site/mycTargetDB.asp>).

In normal cells expression of Myc proteins is tightly regulated, either by developmental cues or, in the case of c-Myc expression in adult regenerative tissues, by mitogenic stimulation. In the absence of such proactive induction, short-lived Myc mRNAs and proteins are rapidly cleared from cells, which then default back to their non-proliferative state. This tight control of Myc expression is defective in almost all cancers, either because of deregulating mutations in the Myc genes themselves, or through the relentless induction of Myc expression by upstream oncogenic signals such as the Wnt/β-catenin, Notch or RTK/Ras pathways. Moreover, ectopic activation of Myc is sufficient in many adult tissues to engage, co ordinate and maintain the diverse intracellular (cell growth, cell cycle progression, biosynthetic metabolism, ribogenesis and translation) and extracellular (release of cytokines and chemokines, recruitment of inflammatory cells, extensive stromal remodeling, invasion and angiogenesis) processes that somatic cells require for their orderly expansion (Figure 1). Myc's highly pleiotropic effects are mirrored by its wide range of gene targets, estimated to encompass some 20% of all vertebrate genes \[2\].

It is notable that the role of Myc as a pivotal coordinator of cell proliferation is common to almost all adult tissues. Since different tissues vary widely in their architecture, regenerative capacity and the risks of infection and oncogenesis, many of Myc's potential target genes are likely to be regulated in a context-dependent fashion; that is, the extent of their control by Myc (and, hence, the actual execution of their cognate biological programs) is dependent on other factors such as cell type and history, local environment and circumstance.

# Effects of Myc inhibition/knockout on normal tissues and cells

A pressing question in Myc biology is whether or not Myc function is essential for somatic cell proliferation. Myc proteins exert very broad but relatively subtle effects on cells. Their impact on expression of individual genes is generally modest and there is no known gene whose expres sion is dependent solely on Myc: rather, Myc appears to modify the efficiency with which more bespoke transcription factors regulate their targets, in part through its general impact on chromatin architecture and accessibility \[3\]. Myc is therefore unlikely to be essential for any single transcriptional program but may nonetheless act as the essential coordinator that integrates all the disparate transcriptional growth programs into a coherent strategic whole. Unfortunately, the experimental data remain unclear on the requirement for Myc function in cell prolifera tion, one complication being functional complementation between different members of the Myc family, principally c- and N-Myc since L-Myc has much weaker intrinsic transcriptional activity \[4\]. These may be constitutively co-expressed or induced to compensate for any deficit in their siblings. c-*myc*-deficient mice die by embryonic day 10.5 (E10.5) with overt hematopoietic, vascular and placental defects \[5\]. While this indicates a critical and non-redundant role for c-*myc* in developmental angiogenesis and erythropoiesis, the consequent embryonic lethality obscures any obligate role c-*myc* might have in tissues beyond this developmental stage. However, it is certain that c-*myc* is also required for proliferation and elaboration of many other somatic tissues (see below). Germ-line knockouts of N-*myc* in mice also die *in utero* around E11.5 from profound defects in multiple tissues, including the central and peripheral nervous systems and multiple epithelial tissues \[6\]. L-Myc-deficient mice, by contrast, show no overt pheno type \[7\]. Rat1 fibroblast cells do not detectably express L-Myc and N-Myc and, when somatically deleted for c-*myc*, remain viable but exhibit dramatically slower proliferative rates and severe cell cycle defects. Loss of c-*myc* compro mises multiple phases of the cell cycle, slowing progression through both G1 and G2 and depressing and delaying activities of all cyclin Cdk complexes. A similar failure to proliferate is also evident in primary mouse embryo fibroblasts derived from mice in which c-myc has been con dition ally deleted.

More recently, both conditional deletion and dominant negative inhibition have been used to investigate *in vivo* the roles of Myc proteins in adult tissues. Surprisingly, initial studies in skin \[8\] and gut \[9\] suggested that deletion of c-*myc* had little impact on maintenance of either regenerative tissue or hematopoietic stem cells \[10\]. In the latter case at least, such independence from c-Myc is due to co-expression of N-Myc, consistent with known degeneracy in c-Myc and N-Myc function. More recent conditional deletion studies in the gastrointestinal (GI) tract, however, suggest that the apparent lack of requirement for c-Myc in tissue homeostasis may be an artifact of relatively low penetrance of expression of the Cre recombinase that excises the c-*myc* gene, since driving the Cre expression from a more powerful and pervasive promoter induces profound and lethal attrition of intes tinal crypts \[11\]. In addition, systemic expression *in vivo* of a dominant interfering Myc bHLHL-Zip dimerization domain mutant (Omomyc) that efficiently blocks c-Myc, N-Myc and L-Myc-dependent transcriptional activation triggers a marked reduction of proliferation in tissues with rapid turnover (for example intestinal epithelium, skin, bone marrow, testis) but has no discernible acute effect on adult organs with low proliferative indices (for example pancreas, kidney, liver, heart or lung). Strikingly, this massive reduction in proliferation rate of regenerative tissues in such Omomyc-expressing animals is not accompanied by any cell death or loss of tissue integrity: consequently, such systemic Myc inhibition has no pathological negative impact on the animals, even over extended periods (4 to 12 weeks). More over, the phenotypic effects of such Myc inhibition are completely and rapidly reversible upon subsequent Omomyc downregulation \[12\].

In a recent study published in *BMC Biology*, Stoelzle *et al.* \[13\] investigate the specific physiological role of c-*myc* in the mammary gland by conditional gene deletion using Cre-loxP recombinase technology. Cre (Cyclization Recombination) is a sequence-specific DNA recombinase that is used to excise stretches of DNA flanked either side by loxP (Locus of X-over P1) DNA sequences. Expression of Cre under the control of a tissue-specific promoter can then be used to delete specific gene sequences in a tissue-specific manner. Whey acidic protein *(WAP)iCre* transgenic mice were used to delete the *loxP*-flanked c-*myc* locus specifically in luminal alveolar cells, starting at mid-pregnancy and continuing throughout lactation. Affected *c-myc*^*fl*/*fl*^*;WAPiCre*^+^ mothers exhibited a marked deficit in milk production and capacity to nurse their young, associated with a failure of terminal end buds to elaborate alveoli within the lactating mammary gland. Since the *WAP* promoter is triggered during first pregnancy, parous *c-myc*^*fl*/*fl*^*;WAPiCre*^+^ start subsequent pregnancies with c-myc already deleted. During such pregnancies, mice show dramatic deficits in mammary proliferation and maturation, although the resulting hypoplastic tissues differentiate normally. This proliferative deficit is mirrored by the markedly reduced efficiency of mammary tissue regeneration when parous *c-myc*^*fl*/*fl*^*;WAPiCre*^+^ mammary tissue was transplanted into cleared fat pads of immuno-compromised recipient mice.

While such data suggest that proliferation of mammary epithelial progenitor cells is still possible in the absence of c-*myc*, albeit at reduced efficiency, the usual uncertainties of Cre-loxP technology obtain. The progressive appearance of the signature restriction fragment induced by Cre deletion of c-*myc* clearly indicates accumulation of cells with deleted c-Myc, but it is less clear what proportion of cells retain either one or both functional c-*myc* allele(s). No surrogate marker of recombination was used to estimate deletion efficiency and, as the authors point out, c-Myc immunohistochemistry remains a capricious and insensitive arbiter of recombination efficiency. Any remaining c-*myc*-competent cells would be expected to contribute variably to initial growth and subsequent regeneration of mammary tissues and their uneven representation might explain the partial capacity of parous *c-myc*^*fl*/*fl*^*;WAPiCre*^+^ mammary tissue to reconstitute the gland in naïve recipients, albeit at reduced efficacy.

It is notable that c-*myc*-deleted mutant glands exhibit alveolar cells with condensed ER and secretory capacity and reduced levels of ribosomal RNA and milk proteins. They are also far less efficient at producing the principal milk proteins α-lactalbumin and β-casein. Taken together, this indicates an important role for c-*myc* in establishing, and perhaps maintaining, biosynthetic output of the mammary alveolar cells. Such data recall c-myc deletion in intestinal epithelium, which generates c-Myc-deficient crypt progenitor cells that are not only smaller, cycle more slowly and enter mitosis with decreased cell size, but also exhibit reduced biosynthetic activity relative to their wild-type counterparts. Thus, studies in both mammary and intestinal epithelium concur that c-Myc is important for cells to establish the necessary biosynthetic capacity to progress expeditiously through the cell cycle. However, whether this indicates that c-Myc also plays a role in *maintaining* the specialized protein synthetic capacity of differentiated mammary epithelial cells is unclear: it is equally plausible that absence of c-Myc during the ontogeny of these secretory cells left them with irremediable structural deficits in intracellular architecture that preclude efficient production of milk proteins. Indeed, this latter notion would be most consistent with the selective deficiency such cells show in production of milk proteins, which presumably requires the unusual secretory architecture of mature mammary alveolar cells but not other 'housekeeping' polypeptides like β-actin and GAPDH, which do not. Distinguishing between the two possibilities awaits studies in which deleted Myc function is subsequently restored in established mammary epithelium to ascertain whether or not this reinstates biosynthetic capacity, either immediately or during subsequent pregnancy-induced re-growth.

# Myc as a target in cancer therapy

The pivotal, and largely non-redundant, role Myc appears to play in coordinating proliferation of tumor cells has reignited interest in it as a possible target for cancer therapy. This notion is strengthened by the widespread deregulation of Myc observed in human cancers and by emerging evidence that inactivation of Myc function can suppress both initiation and evolution of cancers in multiple tissue types and trigger the rapid and complete regression of established tumors, even in cancers where Myc itself is not the 'driving' oncogene \[12\]. However, since Myc is also required for the proliferation of many normal tissues, there have always been concerns that Myc inhibition might be accompanied by catastrophic side effects in normal tissues. Recently, however, such concerns have been largely allayed by data indicating that the impact of systemic Myc inhibition, at least over the relatively short term, is only mild and completely reversible. The studies by Stoelzle *et al.* \[13\] offer additional reassurance by demon stra ting that inhibition of Myc is not toxic *per se* for established, adult mammary tissue, although clearly care would need to be taken in administering any Myc inhibitor during periods of mammary tissue regeneration since this might lead to transient, and perhaps permanent, mammary gland dysfunction. For the moment, pharmacological inhibitors remain a pipe dream. However, advances in drug design, strategy and delivery may some day allow us to target the non-redundant processes that really make tumors tick. Of these, Myc is an obvious and provocative candidate.


date: 2013-04
title: In This Issue of *Diabetes*

Edited by Helaine E. Resnick, PhD, MPH

# A Case for the Development of DPP-4 Inhibitors for Stroke Prevention in Diabetes?

The study by Darsalia et al. shows that linagliptin, a drug currently in use against type 2 diabetes, may also protect against stroke. In this issue of *Diabetes* (**p. 1289**), the investigators studied the antistroke efficacy of linagliptin, a dipeptidyl peptidase-4 (DPP-4) inhibitor, in type 2 diabetic mice. Type 2 diabetes is a risk factor for stroke, and stroke patients with diabetes have a greater risk of stroke recurrence and mortality than nondiabetic stroke patients. Glucagon-like peptide-1 (GLP-1) receptor (GLP-1R) agonists are in clinical use against type 2 diabetes. GLP-1R activation can also occur by prolonging the half-life of endogenous GLP-1 via inhibition of DPP-4. In a first set of experiments, 25-week-old obese diabetic mice were treated with either 10 mg/kg/body weight linagliptin per day or 2 mg/kg/body weight sulfonylurea glimepiride per day or vehicle for 7 weeks. Glimepiride was chosen because it does not affect the incretin system. At 4 weeks into the treatment, stroke was induced by transient middle cerebral artery occlusion. In a second set of experiments, 10-week-old control mice were treated with linagliptin, glimepiride, or vehicle for 7 weeks, similar to the first experiment. Again, all mice were subjected to stroke at 4 weeks. Linagliptin was efficacious against stroke in both type 2 diabetic mice and control mice. However, glimepiride showed antistroke efficacy only in control mice. The investigators propose that glimepiride's neuroprotective effects are mediated by increased insulin secretion, whereas the efficacy of linagliptin is glucose independent and likely involves GLP-1. These results suggest that the development of DPP-4 inhibitors for stroke prevention and treatment in patients with diabetes, as well as in nondiabetic high-risk patients, may be warranted. — *Laura Gehl, PhD*

Darsalia et al. The DPP-4 inhibitor linagliptin counteracts stroke in the normal and diabetic mouse brain: a comparison with glimepiride. Diabetes 2013;62:1289–1296

# Questioning the Use of Resveratrol for Managing Obesity-Related Metabolic Complications

Data in this issue of *Diabetes* (**p. 1186**) raise questions about the utility of resveratrol as a nutritional supplement in metabolic disorders. The study by Poulsen et al. explored the use of resveratrol in obese human subjects. The polyphenolic compound resveratrol, which is prevalent in nature, has been seen for years as a potential tool in managing obesity and obesity-related complications. In rodent models, resveratrol has been shown to protect against diet-induced metabolic abnormalities, and resveratrol is currently sold as an over-the-counter human nutritional supplement. Yet very few data are available to support the use of resveratrol in humans. Poulsen et al. conducted a randomized, placebo-controlled, double-blinded, parallel-group trial, in which 24 obese men with no other health problems were randomly assigned to resveratrol or placebo treatment for 4 weeks. Subjects in the resveratrol group received 1,500 mg *trans*-resveratrol daily for the 4-week period. Insulin sensitivity was assessed by hyperinsulinemic-euglycemic clamp. Subjects were clamped at a blood glucose level of ∼5 mmol/L with an insulin infusion of 0.5 mU/kg/min. Insulin levels increased from ∼75 pmol/L in the basal period to ∼315 pmol/L in the clamp period, and no significant differences were observed between the two groups for either period. Similarly, resveratrol failed to have a significant effect on fasting glucose, fasting insulin, blood pressure, resting energy expenditure, or inflammatory and metabolic biomarkers. Although the high incidence of obesity and type 2 diabetes calls for novel approaches to obesity management, these results do not support the use of resveratrol as a nutritional supplement for obese humans without a defined metabolic defect. — *Laura Gehl, PhD*

Poulsen et al. High-dose resveratrol supplementation in obese men: an investigator-initiated, randomized, placebo-controlled clinical trial of substrate metabolism, insulin sensitivity, and body composition. Diabetes 2013;62:1186–1195

# Blocking AβImproves Insulin Sensitivity in AD Mouse Model

In this issue of *Diabetes* (**p. 1159**), Zhang et al. found that not only does amyloid-β (Aβ) induce hepatic insulin resistance in vivo but that neutralizing Aβ also improves insulin sensitivity in an Alzheimer disease (AD) mouse model. In previous research, this group reported that in vitro Aβ induces insulin resistance via JAK/STAT3/SOCS-1 signaling. The new series of experiments confirmed that those results hold in vivo. Treatment with Aβ42 in C57BL/6J mice showed both increased fasting blood glucose levels and markedly reduced insulin sensitivity, without significant change in body weight. Further demonstrating the effects of Aβ, the researchers treated APPswe/PSEN1dE9 (APP/PS1) mice with control IgG or 6C8 or 1H3 anti-Aβ neutralizing antibodies. After 4 months of intraperitoneal injections, mice treated with either 6C8 or 1H3 anti-Aβ antibodies showed significantly decreased fasting blood glucose levels. After 9 months, mice treated with 6C8 anti-Aβ antibody showed both decreased fasting blood glucose levels and improved insulin sensitivity. Further experiments in APP/PS1 mice explored the mechanism of insulin resistance and showed that treatment with Aβ42 caused upregulation of SOCS-1 and activation of JAK2/STAT3 signaling in the liver. Neutralization of Aβ with 1H3 anti-Aβ antibody treatment inhibited the JAK2/STAT3/SOCS-1 signaling pathway. Expanding on this, the researchers used RNAi to knockdown JAK2 in APP/PS1 mice; consequently, JAK2/STAT3/SOCS1 signaling was inhibited, and both insulin sensitivity and glucose metabolism improved. This new report adds to the body of knowledge about the relationship between type 2 diabetes and AD and suggests that reducing Aβ levels or inhibiting JAK2 activity has potential as therapeutic interventions for insulin resistance and type 2 diabetes. — *Deborah Elbaum, MD*

Zhang et al. Amyloid-β induces hepatic insulin resistance in vivo via JAK2. Diabetes 2013;62:1159–1166

# Rac1 Regulates Contraction-Induced Glucose Uptake in Skeletal Muscle

In this issue of *Diabetes* (**p. 1139**), Sylow et al. establish that Ras-related C3 botulinum toxin substrate 1 (Rac1) is not only activated by muscle contraction but that it is also necessary for contraction-induced skeletal muscle glucose uptake. By quantifying the GTP-bound Rac1 immediately after exercise, the investigators demonstrated that Rac1 is activated by exercise in both mice and humans. Confirmatory experiments in mice showed Rac1 to be essential in contraction-induced glucose uptake. Soleus and extensor digitorum longus (EDL) muscles were incubated with one of two Rac1 inhibitors, NSC23766 or Rac1 inhibitor II. Treatment with either inhibitor decreased contraction-induced glucose uptake in the muscle. Treatment with NSC23766 reduced glucose uptake by ∼55% in both soleus and EDL muscle, and the corresponding decreases after treatment with Rac1 inhibitor II were 58% in soleus muscle and 22% in EDL. Transgenic mice with a muscle-specific inducible Rac1 knockout also showed decreased contraction-induced glucose uptake. Further experiments showed Rac1 activation to be independent of AMPK; neither targeted AMPK kinase activity deletion nor AICAR-induced AMPK activation altered normal Rac1 activation. This established that Rac1 is not activated downstream of AMPK. Rac1 is already known to be important for insulin-induced GLUT4 translocation; this series of experiments introduces and establishes its importance in exercise-induced skeletal muscle glucose uptake. As Rac1 activation appears to be either independent or upstream of AMPK activity (or both), further studies are needed to help elucidate this signal transduction pathway. In the future, pharmacological activators of Rac1 might prove to be novel therapies for the control of blood glucose levels. — *Deborah Elbaum, MD*

Sylow et al. Rac1 is a novel regulator of contraction-stimulated glucose uptake in skeletal muscle. Diabetes 2013;62:1139–1151


abstract: I believed that genomics was like a tsunami: a force that, when it crested, would change everything.
author: Gregory A Petsko
date: 2005
institute: 1Rosenstiel Basic Medical Sciences Research Center, Brandeis University, Waltham, MA 02454-9110, USA
title: Tsunami

It began at the bottom of the ocean, off the west coast of Indonesia. From there it spread outward, silently, invisible under the surface of the water. When it came ashore, in forty countries, some as far as 4,000 miles from where it started, it killed upwards of 150,000 people. There have been deadlier catastrophes - the earthquake in China in the mid-1970s killed a quarter of a million in that country, and the cyclone that devastated Bangladesh in the decade before that is thought to have caused half a million deaths - but none that involved so many different nations scattered across so much of the earth's surface. The tsunami spawned by the magnitude 9.0 earthquake of 26 December 2004 was perhaps the first truly global natural disaster in modern history: a third of the world's countries were directly affected. The worldwide scope of the destruction reminds us of something that genomics is also starting to make clear: that we are all truly one people, that national and racial differences are artificial and insignificant compared to the common bond of our humanity. Despite all our technological prowess and environmental hubris, we also have yet another grim reminder that Nature, not Man, is still boss of this planet.

The word 'tsunami' comes from the Japanese words for harbor (tsu) and wave (nami). It refers to a series of giant undersea waves that travel at high velocity for very long distances, and that crest when they hit a shoreline in the form of a devastating surge, sometimes as much as 30 meters high. Tsunamis are often called 'tidal waves' but that's a misnomer: the phenomenon has nothing to do with the tides. It has its origins, like everything else that involves the earth's surface, in plate tectonics.

It is hard to imagine that the theory of plate tectonics, which is at the heart of all modern geological science, is only a hundred years old and was not widely accepted until the 1970s. Schoolchildren had noticed for hundreds of years that the facing shapes of South America and Africa could be fitted neatly together like pieces of a jigsaw puzzle to make a single entity (Francis Bacon had noticed it in 1620 but drew no conclusion), but it wasn't until 1908 that the amateur American geologist Frank Bursley Taylor proposed that the continents had once slid around and that this motion might have thrust up the world's mountain chains. His theory was taken up by the German planetary astronomer-turned-meteorologist Alfred Wegener, who in 1912 proposed that all the world's continents had once been part of a single giant landmass he called Pangaea, which had split apart in a process of lateral motion that was still continuing. Traditional geologists attacked both Wegener and his ideas viciously, and it wasn't until the decade after his death (he froze to death on a scientific expedition in Greenland in 1930) that the great English geologist Arthur Holmes provided an explanation for how Wegener's motion could occur. In a textbook published in 1944 he speculated that heat caused by the decay of radioactive elements in the earth's crust could produce powerful convection currents that could slide the continents around on the earth's surface. He has probably as good a claim as anyone to be the father of the modern view of continental drift, although, curiously, he often expressed skepticism about his own theory. Harry Hess, a Princeton University geologist, figured out in the 1950s that there were two large plates of land under the floor of the Atlantic Ocean and that their relative motion was responsible for the topography and geology of the seafloor. Finally, in 1963, Cambridge University geophysicist Drummond Matthews and his graduate student Fred Vine used magnetic readings to prove that the seafloor and the continents were in motion. (Canadian geologist Lawrence Morley came up with the same result at the same time but his paper was rejected by the *Journal of Geophysical Research*.) J. Tuzo Wilson of Toronto showed at about the same time how plate tectonics could explain the behavior of the ocean floor at mid-ocean ridges. Still, even in the 1970s, many textbooks of geology continued to dismiss plate tectonics as physically impossible.

Today we know that the surface of the earth is composed of about a dozen large plates and almost two dozen smaller ones, all moving in different directions. Where they grind against one another (regions geologists call 'subduction zones'), the tremendous force can be released either slowly and steadily, giving rise to thermally active regions like Iceland, or sporadically and violently, giving rise to earthquakes and volcanic eruptions. That is what happened on 27 August 1883, when subduction along the Java Trench, where the Indo-Australian plate is moving under the Indonesian Island chain, caused the explosive eruption of the volcano on the Indonesian island of Krakatoa that in turn generated waves that reached 41 meters in height, destroying 165 coastal towns and villages along the Sunda Strait between the islands of Java and Sumatra and killing 36,417 people. (Hollywood made a movie about this great disaster in 1969: *Krakatoa*, *East of Java*. In case you are ever tempted to equate Hollywood productions with history, let me point out that Krakatoa is west of Java.) That is probably what happened in 1648 B.C., when the entire Minoan civilization on the island of Crete was wiped out, in a single day, as the result of a tsunami created by the explosive eruption of the volcano on Santorini.

And that is what happened on 26 December 2004, when the Indian plate slid underneath the Burma plate (a subduction zone), driving a 6oo-mile long piece of the earth's crust 20 to 50 feet upwards on the floor of the Indian Ocean. This sudden rise in the seafloor displaced an enormous volume of water - exactly as if the ocean were a swimming pool and someone had just dropped a large block of concrete into it. The displacement spread outward in all directions, like the ripples that would spread from that block. But because the event occurred underwater, the displacement traveled underwater until it encountered the sharply rising seafloor on the edge of an island or continent. When the undersea waves hit this obstacle, they were pushed straight up, compressed into walls of water that surged over the landmass.

The speed of a tsunami depends on the square of the depth of the ocean: the deeper the water, the faster the displacement travels. The Indonesian tsunami formed in deep water, which meant that the wave velocity reached upwards of 800 km/h, the speed of a commercial jet aircraft. When they encounter the shallow depths of a coastline the speed of tsunami waves slows to perhaps 45 km/h, still fast enough to do tremendous damage. At top speed it took the tsunami less than 7 hours to cross the Indian Ocean and reach the east coast of Africa, where the waves came ashore in Somalia and killed 150 people who cannot possibly have understood that the power that was destroying them had been spawned more than 3,000 miles away.

Like the ripples from that dropped block of concrete, a tsunami is actually a series of waves, usually spaced about 15-20 minutes apart, with troughs in between them. To the observer on shore, the approach of a tsunami often begins with a rapid receding of the shoreline, much further out than normal. This is followed, 8-10 minutes later, by the first wave, which surges onto the shore, often traveling a half a mile or more inland. As the cycle repeats, the first wave recedes, carrying anything loose back out to sea. Then the next wave hits. In Thailand, Sri Lanka and Indonesia, where the worst damage occurred, many people who survived the impact of the first wave were swept out to sea as it receded, or were killed by one of the surges that followed. But what fascinates me the most about tsunamis is that, until they reach land, they are practically unnoticeable on the surface of the ocean. Their amplitude in deep water is often only a meter or less. An ocean liner or a fishing vessel would pass right over them, completely unaware that underneath it a force was racing onward that, when it surfaced, could obliterate an entire country.

I've been thinking a lot about that sort of thing recently, because it seems to me that it's a pretty good metaphor for what is going on in science. I started writing this column because I believed that genomics was like a tsunami: a force that, when it crested, would change everything, and I wanted to have an excuse to think about what that would mean. The true impact of the genomics revolution is only starting to be apparent now, and it's very different from what it was predicted to be when the Human Genome Project began in the late 1980s. It has not had a significant impact on human health yet - the disease genes that have been discovered have generally come from specific individual research programs, and pharmacogenomics has initially focused on polymorphisms in genes that were already identified before the human genome was sequenced. Genomics has produced technology, such as cDNA microarrays and mass spectroscopy-based proteomics, that is likely to play a major role in diagnostics in the near future, but not necessarily in treatment. No, the major effects, which are now rolling across biology like a series of waves, are cultural.

Because of genomics, data gathering and analysis is now valued highly - in some instances above hypothesis-driven research. Because of genomics, targeted big science projects, such as structural genomics, that aim to produce easily appreciated results (usually in the form of large amounts of data), are consuming a large chunk of funding that would otherwise go to individual investigator-initiated basic research. Because of genomics, there is a perception in some quarters that when you have analyzed something you have understood it; mathematical modeling of biological processes is beginning to become a substitute for experimental probing. Because of genomics, a kind of mysticism is creeping back into biology: we use terms, like 'systems biology' and 'emergent properties', that have echoes of vitalism in them -almost as though we are starting to believe that we cannot explain the behavior of living systems in terms of the physics and chemistry of their component parts. We can argue about whether this is good or bad for our field. We can argue about how we should react to it. But we cannot ignore it. None of this could have been appreciated in 1990. It was all moving beneath the surface, moving rapidly and inexorably and now it is upon us. Until the next tsunami comes along, genomics, like molecular biology before it, will change our scientific world whether we like it or not.

So one of the lessons I take from what happened on 26 December is the folly of believing that things will remain as they are today. We go about our lives and our careers unaware of the great forces that move, unseen, beneath the usual tide of events. Until they crest they are almost undetectable, so we don't talk about them or plan for them. And once they do crest, they can change our careers and our lives in a very short time. It would seem that, of all the qualities we need to survive and thrive in an unpredictable world, flexibility - adaptability if you will - might just be the most valuable. The other lesson is a practical one. If you're standing on shore, looking out at what seems to be a perfectly calm ocean, and you suddenly see the shoreline receding rapidly, exposing the sea bottom much farther out than usual, turn around and run like hell away from the water. You need to get a half mile inland; a mile would be better. You have, if you're lucky, maybe 10 minutes.

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