Background Physical activity (PA) is considered a cornerstone of diabetes mellitus management to prevent complications, but conclusive evidence is lacking.
Methods This prospective cohort study and meta-analysis of existing studies investigated the association between PA and mortality in individuals with diabetes. In the EPIC study (European Prospective Investigation Into Cancer and Nutrition), a cohort was defined of 5859 individuals with diabetes at baseline. Associations of leisure-time and total PA and walking with cardiovascular disease (CVD) and total mortality were studied using multivariable Cox proportional hazards regression models. Fixed- and random-effects meta-analyses of prospective studies published up to December 2010 were pooled with inverse variance weighting.
Results In the prospective analysis, total PA was associated with lower risk of CVD and total mortality. Compared with physically inactive persons, the lowest mortality risk was observed in moderately active persons: hazard ratios were 0.62 (95% CI, 0.49-0.78) for total mortality and 0.51 (95% CI, 0.32-0.81) for CVD mortality. Leisure-time PA was associated with lower total mortality risk, and walking was associated with lower CVD mortality risk. In the meta-analysis, the pooled random-effects hazard ratio from 5 studies for high vs low total PA and all-cause mortality was 0.60 (95% CI, 0.49-0.73).
Conclusions Higher levels of PA were associated with lower mortality risk in individuals with diabetes. Even those undertaking moderate amounts of activity were at appreciably lower risk for early death compared with inactive persons. These findings provide empirical evidence supporting the widely shared view that persons with diabetes should engage in regular PA.
Diabetes mellitus is a major cause of illness and premature death in most countries.1 Efforts to reduce the impact of diabetes complications have been predominantly aimed at controlling hyperglycemia, hypertension, and dyslipidemia by using medication strategies, despite the lack of evidence of long-term benefits.2,3 However, diabetes management should extend to an overall intervention strategy that includes lifestyle modification to reduce the risk of complications.4
Lifestyle measures, including physical activity (PA), are key factors for self-management in patients with diabetes to prevent macrovascular complications and premature mortality.5 Increased PA has long been considered a cornerstone of diabetes management. Persons with diabetes are recommended to engage in at least 150 minutes per week of moderate-intensity aerobic PA.5,6 Walking has been of particular interest because it requires no specific facilities, can be easily implemented in the daily routine, and is relatively safe.7
In the general population, being physically active has been associated with a lower risk of overall and cardiovascular disease (CVD) mortality compared with being inactive.8 Because persons with diabetes are at higher risk for CVD and premature death, it is important to determine whether PA can produce similar beneficial effects in this high-risk population. Indeed, a meta-analysis9 of 14 controlled trials in diabetic persons showed that exercise programs had beneficial effects on glycemic control. Several prospective cohort studies10-21 have found that higher PA levels were associated with reduced CVD and total mortality rates, but conclusive high-level evidence is lacking.
The objective was to investigate whether PA—total, leisure time, and walking—was associated with CVD and total mortality in a large cohort of individuals with diabetes. A meta-analysis summarizing evidence from prospective studies was performed to put the present findings in context and to provide a higher level of evidence.
Study design and population
The EPIC study (European Prospective Investigation Into Cancer and Nutrition) is an ongoing prospective study of 519 978 men and women aged 35 to 70 years from 23 study centers in 10 European countries.22 Within the EPIC study, a cohort was established of participants with a confirmed diagnosis of diabetes mellitus at baseline between 1992 and 2000. As described previously,23,24 15 study centers from 6 countries provided additional data on diabetes diagnosis and medications. No information was available to distinguish type 1 and 2 diabetes mellitus. To be considered diabetic, a self-reported diagnosis at baseline had to be confirmed by at least 1 additional information source. Depending on the available options in the study centers, these sources included contact with a physician, self-reported use of diabetes medication, confirmation of self-reported diabetes status during follow-up, linkage to diabetes registries, and a baseline glycated hemoglobin (HbA1c) level greater than 6%. The cohort comprised 6412 individuals with confirmed diabetes at study enrollment. After the exclusion of participants without follow-up information on vital status (n = 27), participants with extreme or implausible energy intakes (n = 177), and participants with missing PA data (n = 349, including the cohort in Umeå, Sweden), the analytical sample included 5859 individuals.
At baseline, participants received a lifestyle questionnaire by mail, which they completed at home. This questionnaire asked about occupational activity and duration and frequency of walking, cycling, gardening (average values of summer and winter), household work, do-it-yourself activities, and sports during the past year.
Total PA was investigated using the Cambridge Physical Activity Index,25 which combines self-reported occupational activity with time participating in cycling and sports. Occupational activity was categorized as sedentary, standing, manual, or heavy manual. The sum of hours per week spent on cycling and sports was categorized into 4 levels. Based on a 4 × 4 matrix, participants were divided into 4 categories, that is, inactive (sedentary job and no recreational activity), moderately inactive, moderately active, and active (sedentary job with >1 hour of recreational activity per day, standing or physical job with some recreational activity, or a heavy manual job). The index has been shown to have acceptable repeatability, and it was positively associated with objective measures of the ratio of daytime expenditure to resting metabolic rate and cardiorespiratory fitness.25
Leisure-time PA included walking, cycling, gardening, sports, household work, and do-it-yourself activities. Duration and frequency were directly assessed, and intensity, that is, energy expenditure, was estimated by assigning metabolic equivalents (METs), ranging from 3 for walking and household activities to 6 for sports.26 A MET is defined as the ratio of work metabolic rate to a standard metabolic rate of 1.0 kcal (4.184 kJ) × kg − 1 × h − 1. One MET is the energy expended by a person while sitting quietly.
Diabetes duration was calculated by subtracting the self-reported year of diagnosis or, when available, the exact date of diagnosis supplied by the physician from the year of baseline examination. Insulin therapy or use of oral hypoglycemic agents was either self-reported during the visit at the study center or was obtained through medical verification; this information was not collected in Spain and Denmark. Moreover, the lifestyle questionnaire included a question about insulin therapy. When a participant did not report the use of diabetes medication during the visit or reported insulin therapy in the questionnaire, we assumed that the participant did not take medication.
Dietary intake, including alcohol consumption during the past year, was assessed using country-specific instruments.22 Weight and height were measured with participants not wearing shoes. Systolic and diastolic blood pressures were measured by trained personnel at baseline except in Navarra, Spain. The HbA1c level was measured in erythrocytes of blood collected at baseline except in Denmark. Smoking behavior, educational level, and prevalence of myocardial infarction, stroke, and cancer were assessed using questionnaires.
Sixty-one participants (1%) were lost to follow-up. For those completely followed up, causes and dates of deaths were ascertained using record linkages with local, regional, or central cancer registries, boards of health, or death indices. An exception was Germany, where deceased participants were identified with follow-up mailings and subsequent inquiries to municipality registries, regional health departments, physicians, or hospitals. Mortality data were coded according to the International Classification of Diseases, Injuries, and Causes of Death, Tenth Revision, using the codes I00-I99 for CVD mortality.
Hazard ratios (HRs) and 95% CIs of total and CVD mortality were calculated using Cox proportional hazards regression and a commercially available software program (SAS, version 9.2; SAS Institute, Inc).27 Total PA was analyzed in 4 categories, and leisure-time PA (MET-hours per week) and walking (hours per week) were analyzed in quartiles. The lowest category or quartile was the reference. Center and age at recruitment in 1-year categories were entered as stratum variables.28 Age was used as the underlying time scale, with entry time defined as the participant's age (in years) at recruitment and exit time defined as the participant's age (in years) at death or censoring.
The HRs were adjusted for sex (model 1); disease duration (years); use of diabetes-related medication (none, insulin, oral hypoglycemic agents, or both); self-reported myocardial infarction, stroke, or cancer; alcohol consumption (grams per day); smoking status (never, former [quit ≤10, 11-20, or >20 years ago], or current), smoking duration (≤10, 11-20, 21-30, 31-40, or ≥41 years), and number of cigarettes currently smoked (<15, 15-24, or ≥25 per day); education (5 categories); energy intake (kilocalories per day); and factor scores for the first 3 patterns derived from factor analysis on 16 food groups (model 2).
Among the covariates, proportions of missing data were 31% for diabetes medication, 8% for disease duration, 5% for prevalence of cancer, 1% for prevalence of myocardial infarction or stroke, and less than 1% for diet and education. These missing values were imputed using the multiple imputation technique.29 All the variables included in the multivariable adjustment model were included in the imputation procedure, and 20 duplicate data sets were sampled.30
In sensitivity analyses, it was checked whether additional adjustment for HbA1c level, BMI, and systolic blood pressure affected the risk estimates. Prevalent cases of myocardial infarction, stroke, and cancer and participants with follow-up of less than 2 years (n = 5039) were excluded. Statistical interaction by sex, body mass index (BMI), and insulin therapy was tested by adding a product term to the multivariable adjustment model. Finally, results were compared with analyses without multiple imputation (n = 5376), in which participants with missing values for continuous variables were excluded, and missing values for categorical variables were modeled as a separate indicator variable.
A systematic literature search in MEDLINE and ISI Web of Knowledge for prospective studies on PA published up to December 2010 yielded 4344 publications, of which 12 were included in the meta-analysis (eFigure 1). Study quality scores were assigned using the Newcastle-Ottawa Scale eTable quality criteria included representativeness, exposure and outcome ascertainment, adjustment, follow-up, and attrition.31 The most complete adjusted HRs and 95% CIs of the highest vs the lowest activity category were extracted. Study selection, quality assessment, and data extraction was performed by 2 of us (D.S. and B.B.) independently; any discrepancies between the 2 were resolved by discussion. Fixed- and random-effects meta-analyses with inverse variance weighting were performed using R package “meta” (version 2.12.2). Heterogeneity was assessed by the Q statistic and the I2 index.
Individuals who were more physically active were younger, were more likely to be male, had a lower BMI and a lower HbA1c level, and had a shorter diabetes duration than did those who were inactive (Table 1). They were also more likely to use insulin and to report fewer comorbidities.
After median follow-up of 9.4 years, 755 participants had died (13%). Death due to CVD accounted for 28% of all deaths (n = 212). Total PA was inversely associated with total and CVD mortality (Table 2). The lowest HR was observed in persons categorized as moderately active: the HR in the multivariable adjustment model was 0.62 (95% CI, 0.49-0.78) for total mortality. When excluding heavy manual workers and nonworkers from the analyses, the lowest risk was still observed in the moderately active group. Leisure-time PA was also associated with a lower risk of CVD and total mortality: the HR in the highest category was 0.73 (95% CI, 0.57-0.93) for total mortality. The association with CVD mortality was weaker in magnitude and nonsignificant but showed the same trend. Participants who walked more than 2 hours per week had lower CVD mortality risk compared with those in the lowest activity group: the HR in the category of 2 to 4.5 hours per week was 0.54 (95% CI, 0.36-0.82). The relationship of walking with total mortality was less pronounced. Additional adjustment for vigorous PA did not alter the association.
Adjustment for intermediate factors did not affect the risk estimates. For total PA, the HR in moderately active persons was 0.62 (95% CI, 0.50-0.79) after additional adjustment for HbA1c and 0.62 (95% CI, 0.49-0.78) after additional adjustment for BMI or systolic blood pressure. Excluding participants with comorbidities at baseline led to lower HRs: for total PA, the HR in moderately active persons was 0.58 (95% CI, 0.43-0.77) for total mortality and 0.31 (95% CI, 0.15-0.60) for CVD mortality. Sex seemed to modify the association between total PA and total mortality (P for interaction = .04, multivariable model). Women had a lower HR across quartiles than did men, but the trend showed the same direction (the HR in the highest category was 0.79 [95% CI, 0.59-1.05] in men and 0.59 [95% CI, 0.36-0.96] in women). The analyses without multiple imputation showed similar results (the HR for total mortality in the highest category of total PA was 0.70 [95% CI, 0.54-0.90]), indicating that missing observations did not influence the effect estimates.
In the 12 cohort studies10,13-15,17-21,32-34 included in the meta-analyses, verification criteria of diabetes status ranged from self-report to an oral glucose tolerance test; 4 studies used official diagnostic criteria (Table 3). Sample sizes ranged from 29221 to 5859 (the present study), and mean follow-up was 12.5 years. Physical activity was assessed by questionnaire in 8 studies10,14,17,19,20,32-34 and by interview in 4.13,15,18,21 Participants were divided into PA categories ranging from inactive to active. All but 1 study13 adjusted for a wide range of multiple risk factors. Study quality ranged from 613,14,19,34 to 918 stars of a maximum of 9. Three studies19,20,33 reported on total PA but measured only leisure-time activities and were, therefore, classified as such.
The meta-analyses of prospective studies showed that the highest levels of total and leisure-time PA and walking were associated with a lower risk of total and CVD mortality compared with a low activity level (Figures 1, 2, and 3). Magnitudes of associations were similar for total and CVD mortality.
Significant heterogeneity was found for total PA and total mortality (I2 = 69%, P = .01) and for walking and total mortality (I2 = 71%; P = .01). For walking, an influential meta-analysis revealed that the present study contributed most heterogeneity, although excluding the present study from the meta-analyses did not substantially influence the pooled HR. Visual inspection of funnel plots did not indicate publication bias (eFigure 2).
In this prospective analysis and meta-analysis of individuals with diabetes, higher levels of total PA, leisure-time PA, and walking were associated with a lower risk of total and CVD mortality. In the prospective analysis, people who reported being moderately physically active had lower mortality risk compared with those who reported being physically inactive.
In persons with diabetes, an increase in PA has been shown to reduce HbA1c levels9,35 and improve insulin sensitivity.36 Moreover, PA has been shown to have beneficial effects on inflammation, hypertension, dyslipidemia, endothelial function, and abdominal adiposity in persons with and without diabetes.37-40
The association between total PA and mortality was slightly J-shaped. This could have been due to misclassification of activity levels, which may be higher in the most physically active group owing to labeling bias. This result is in contrast to the other studies15,19,21,32,34 included in the meta-analysis, which showed linear inverse associations, with the lowest observed HR in the highest quartile. In the present study, participants with heavy manual work occupations were automatically assigned to the “active” category. Because such people are more frequently exposed to occupational risk factors and more often have a lower socioeconomic status, they may have a more unfavorable risk profile.41 However, excluding heavy manual workers and nonworkers from the analyses did not change the findings. Thus, diabetic individuals who are physically inactive seem to have a higher risk of early death, and already being moderately active may improve survival.
Walking may reduce the risk of CVD in people with diabetes by improving glycemic control and other risk factors.7 In the present study and meta-analysis, persons in the highest quartiles of walking duration had a lower risk of CVD mortality compared with those in the lowest quartile. In contrast, walking was not related to significantly lower total mortality risk in this study, whereas other studies10,15,18,19 in the meta-analysis reported strong inverse relationships. In the present study, persons in the highest category reported walking more than 9 hours per week. Walking levels were lower in the other studies included: in comparison, persons in the active category in the study by Tanasescu et al19 walked more than 16 MET-hours per week. It is known that Europeans are more active than North American populations,42 and it has been observed in a Dutch population that activities of at least moderate intensity, but not lower intensity, such as walking, were related to reduced CVD incidence.43 This seems to be in contrast to our findings on walking and CVD mortality. However, because no information on walking pace was available, we cannot draw conclusions about walking intensity. In conclusion, although these results did not reach statistical significance, from the meta-analysis the potential benefits of walking on mortality are well established.
Reverse causality could have overestimated the mortality risks if diabetes or comorbidities at baseline led to inactivity. Excluding participants with comorbidities at baseline or the first 2 years of follow-up strengthened the risk estimates, indicating that reverse causality is unlikely to explain the results. However, residual confounding or misclassification cannot be excluded because measures of disease severity and comorbidities were self-reported.
Adjustment for factors on the causal pathway may underestimate the magnitude of the true association between PA and mortality.11,12 However, risk estimates were not affected by additional adjustment for the intermediate factors HbA1c level, BMI, and systolic blood pressure.
Physical activity was associated with a lower total mortality risk in diabetic individuals. These associations are in line with those found in the general population, where PA relates to a 33% lower risk of overall mortality and a 35% lower risk of CVD mortality compared with inactivity.8 The present meta-analysis was a “high vs low” comparison. This is a common practice for meta-analyses of observational studies, but results can be difficult to interpret because absolute levels of PA will vary between studies and are unknown.44 However, this was the best option based on the available data.
Statistically significant heterogeneity was found for the associations between total PA and walking and total mortality. Because statistical heterogeneity is based only on the effect estimates and their precision, it is important to consider clinical heterogeneity. All the studies included in the meta-analysis were comparable in terms of study design, diabetes population, and outcome. However, an important issue when performing meta-analyses of PA is comparability of the exposure assessment, which was heterogeneous across the included studies. Physical activity was assessed by questionnaire or interview, with varying questions, categories, and classifications. Questionnaires, including interviews, are the most common tools for PA assessment in large epidemiologic studies because they are inexpensive and feasible. In general, PA questionnaires have a low reliability and low validity but can be adequately used to rank individuals.45 It was considered appropriate to combine the studies by meta-analyses because all measured common perceptions of PA levels.
In conclusion, evidence from the present study and from previous studies summarized by meta-analyses supports the widely held view that PA is beneficially associated with lower mortality in people with diabetes. Although these findings highlight that persons with diabetes should engage in regular PA,5 they need to be confirmed in a long-term randomized controlled trial. Also, because not many patients with diabetes adhere to this advice,46 future research should elucidate the determinants of physical inactivity and design successful strategies to promote active lifestyles.
Correspondence: Diewertje Sluik, MSc, German Institute of Human Nutrition Potsdam-Rehbrücke, Epidemiology Arthur-Scheunert-Allee, 114-116 Nuthetal 14558, Germany (Diewertje.Sluik@dife.de).
Accepted for Publication: May 12, 2012.
Published Online: August 6, 2012. doi:10.1001/archinternmed.2012.3130
Author Contributions: Ms Sluik had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analyses. Study concept and design: Sluik, Amiano, Ardanaz, Tumino, Boeing, and Nöthlings. Acquisition of data: Sluik, Kaaks, Teucher, Tjønneland, Overvad, Amiano, Ardanaz, Bendinelli, Pala, Tumino, Ricceri, Mattiello, Nilsson, Wennberg, Rolandsson, Boutron-Ruault, Clavel-Chapelon, Boeing, and Nöthlings. Analysis and interpretation of data: Sluik, Buijsse, Muckelbauer, Overvad, Østergaard, Pala, Spijkerman, Monninkhof, May, Franks, Nilsson, Rolandsson, Fagherazzi, Huerta Castaño, Gallo, and Nöthlings. Drafting of the manuscript: Sluik. Critical revision of the manuscript for important intellectual content: Buijsse, Muckelbauer, Kaaks, Teucher, Johnsen, Tjønneland, Overvad, Østergaard, Amiano, Ardanaz, Bendinelli, Pala, Tumino, Ricceri, Mattiello, Spijkerman, Monninkhof, May, Franks, Nilsson, Wennberg, Rolandsson, Fagherazzi, Boutron-Ruault, Clavel-Chapelon, Huerta Castaño, Gallo, Boeing, and Nöthlings. Statistical analysis: Sluik and Buijsse. Obtained funding: Overvad, Tumino, and Nöthlings. Administrative, technical, and material support: Teucher, Tjønneland, Østergaard, Ardanaz, Nilsson, Rolandsson, Gallo, and Boeing. Study supervision: Buijsse, Muckelbauer, Kaaks, Overvad, Amiano, Ardanaz, Boeing, and Nöthlings.
Financial Disclosure: None reported.
Funding/Support: This study was supported by a European Foundation for the Study of Diabetes/sanofi-aventis grant (Dr Nöthlings).
Role of the Sponsor: The European Foundation for the Study of Diabetes and sanofi-aventis had no role in the design or conduct of the study, collection or analysis of the data, or preparation or approval of the manuscript and did not have any influence on the contents.
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