International Journal of Cardiology 177 (2014) 1036–1041
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Could occupational physical activity mitigate the link between moderate kidney dysfunction and coronary heart disease?☆ Yolande Esquirol a,b,⁎, Mark Tully c, Jean-Bernard Ruidavets a, Damian Fogarty d, Jean Ferrieres a,e, Michael Quinn d, Maria Hughes c, Frank Kee c, on behalf of, the Prime study a
Université Paul Sabatier de Toulouse III, UMR1027, Toulouse F-31073, France CHU Toulouse, SMPE, Toulouse F-31059, France UK Clinical Research Collaboration Centre of Excellence for Public Health, Queen's University Belfast, Belfast, UK d Belfast Health and Social Care Trust, Belfast, UK e CHU Toulouse, Department of Cardiology B, Toulouse F-31059, France b c
a r t i c l e
i n f o
Article history: Received 4 August 2014 Received in revised form 17 September 2014 Accepted 20 September 2014 Available online 30 September 2014 Keywords: Glomerular filtration rate Work Exercise
a b s t r a c t Background: Chronic kidney disease is now regarded as a risk factor for cardiovascular disease. The impact of occupational or non-occupational physical activity (PA) on moderate decreases of renal function is uncertain. Objectives: We aimed to identify the potential association of PA (occupational and leisure-time) on early decline of estimated glomerular filtration rate (eGFR) and to determine the potential mediating effect of PA on the relationship between eGFR and heart disease. Methods: From the PRIME study analyses were conducted in 1058 employed men. Energy expended during leisure, work and commuting was calculated. Linear regression analyses were used to determine the link between types of PA and moderate decrements of eGFR determined with the KDIGO guideline at the baseline assessment. Cox proportional hazards analyses were used to explore the potential effect of PA on the relationship between eGFR and heart disease, ascertained during follow-up over 10 years. Results: For these employed men, and after adjustment for known confounders of GFR change, more time spent sitting at work was associated with increased risk of moderate decline in kidney function, while carrying objects or being active at work was associated with decreased risk. In contrast, no significant link with leisure PA was apparent. No potential mediating effect of occupational PA was found for the relationship between eGFR and coronary heart disease. Conclusion: Occupational PA (potential modifiable factors) could provide a dual role on early impairment of renal function, without influence on the relationship between early decrease of e-GFR and CHD risk. © 2014 Elsevier Ireland Ltd. All rights reserved.
1. Introduction Evidence has emerged on the potential impact of occupational physical activity (PA) on the incidence of cardiovascular diseases (CVD), with negative or positive effects depending on the types of tasks undertaken at work [1]. Chronic kidney disease (CKD) is acknowledged as a risk factor for coronary heart disease [2], and since its prevalence has been increasing, discovering strategies for early prevention is imperative and has been described as a global public health challenge [3–5]. Even though the link between CKD and cardiovascular mortality and morbidity has been well described for advanced stages of CKD [5–9], it now also exists for earlier stages of disease when reductions
☆ These authors take responsibility for all aspects of the reliability and freedom from bias of the data presented and their discussed interpretation. ⁎ Corresponding author at: Faculté de Médecine 37 allées jules guesde 31059 Toulouse, France. Tel.: +33 5 61 77 21 90; fax: +33 5 61 77 75 61. E-mail address: [email protected] (Y. Esquirol).
http://dx.doi.org/10.1016/j.ijcard.2014.09.102 0167-5273/© 2014 Elsevier Ireland Ltd. All rights reserved.
in estimated glomerular filtration rate (eGFR) are comparatively modest [5,10]. A recent Cochrane review has highlighted the potential positive effects of regular leisure exercise on advanced CKD mainly through the improvement of cardiovascular risk (diabetes, high blood pressure) [11–14] and aerobic capacity [11]. However, the effect of physical activity on early stages of CKD is not well-established [12], and few studies have focused specifically on it [15–17]. The eGFR is one of the essential determinants that characterize CKD. According to KDIGO (Kidney Disease: Improving Global Outcomes) in 2012, early impairment of renal function, estimated by eGFR carries a risk of unfavorable prognosis (progress towards end-stage CKD and/or CVD events). However we have little information that distinguishes the different types and contexts for physical activity. It is assumed that occupational physical activity could have an impact on modest impairment of eGFR, and that, this may differ according to the “background” leisure-time physical activity levels. Moreover, the potential effect of each type of physical activity acting directly on any early decrease of eGFR or indirectly through cardiovascular factors is unknown.
Y. Esquirol et al. / International Journal of Cardiology 177 (2014) 1036–1041
Thus, the objectives of this study are to assess the potential relationship between different types of physical activity (occupational and leisure) and glomerular filtration rate and also to analyze the potential mediating effect of each type of physical activity on the relationship between the modest declines in eGFR and CHD, over 10 years follow-up.
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Total participants from Belfast centre In PRIME Study N=2745
2. Methods 2.1. Population study From the prospective PRIME study, (Prospective epidemiological study of myocardial infarction), we followed up 2745 men (50–60 years old) from the Belfast center (Northern Ireland) for ten years [18,19]. Serum creatinine was available only in this center and for 1858 subjects. Participants provided signed informed consent and approval from the Belfast ethics committee was obtained.The study protocol conforms to the ethical guidelines of the 1975 Declaration of Helsinki. Because of our interest in the consequences of occupational physical activity on kidney function, unemployed, retired, or men with current disability or sickness were excluded. In addition, men with a history of CVD or history of diabetes mellitus or specific treatment at baseline were also excluded. The analyses were conducted only when serum creatinine and data for confounding factors were available. Because the focus was on the evaluation of factors affecting subtle decreases of kidney function, participants with eGFR b 30 ml/min/1.73 m2 were excluded (Fig. 1). 2.2. Assessment of kidney function Non standardized serum creatinine was measured with the Jaffe method using a kinetic assay, expressed in mg/dl. To obtain standardized measurements, creatinine was adjusted (×0.95) using the calibration proposed by Levey et al. [20] in 2007 and as used in a recent meta-analysis [21]. To be more accurate, eGFR was evaluated by using the recent CKD-EPI formulae [22], taking account standardized creatinine levels referenced in the KDIGO guideline [23]: Standardized serum creatinine ≤ 0.9 mg/dl: GFR = 141 × (Scr/0.9)_0.411 × (0.993)Age; Standardized serum creatinine N 0.9 mg/dl: GFR = 141 × (Scr/0.9)_1.209 × (0.993)Age. The classification of stages proposed by KDIGO was used. 2.3. Assessment of physical activity: leisure, occupational and commuting physical activities From the MONICA Optional Study of physical activity questionnaire [24], levels of all types of physical activity were estimated using the updated (2011) compendium of physical activity and their associated energy expenditures, expressed in METS (Metabolic equivalent of tasks) hour/week [25,26]. Thus, occupational physical activity was evaluated from listed tasks for a typical day at work, expressed in METS hour/week. The responses to the question: ‘how much time do you spend sitting or standing still at work’ were used as variables to define sedentary behavior at work. For participants carrying or lifting light or heavy objects taken separately, a variable (carrying or lifting objects) was created by compiling the energy expenditure of both. Moreover, to describe the overall level of activity of each job, the sum of the energy expenditure during walking at work and carrying or lifting objects was calculated, defined by a new variable ‘active at work’. Leisure time physical activity was calculated according to time spent in different kinds of physical activity regularly carried out per week and for the stated number of months during the previous year. For a typical working day, physical activity undertaken during commuting to work was considered as the time spent walking or biking to or from work. The cumulative total physical activity was also determined as the addition of energy expenditure at work, commuting to and from work and leisure time physical activity, expressed in METS hour/week. 2.4. Assessment of potential confounding factors on kidney function Age and demographics were collected as described before [19]. From a fasting plasma sample, glucose level and lipid parameters were determined. Blood pressure was measured after a 5 minute rest while sitting with an automatic blood pressure analyzer. Body mass index (BMI) (weight/height2) was calculated. In our European study population, each component of metabolic syndrome (Met-S) was evaluated according to an updated consensus definition of metabolic syndrome. The variable metabolic syndrome was dichotomized into two classes if 3 or more components were present [27]. Daily alcohol consumption was categorized into two classes: ≤ 30/N30 g/d. Tobacco consumption was also dichotomised: current smoker or not. Two categories of educational level were provided: b12/≥ 12 years of school. 2.4.1. Follow-up at 10 years Methods of ascertainment of CVD have been described previously [18,28]. The first episode of angina pectoris (without MI or coronary death) and ‘Hard’ CVD (defined by counting only the first MI, or coronary death) were recorded according to the codes of International Classification of Diseases, 9th Revision. In addition, a Medical Committee reviewed the classification of each event.
Participants with serum creatinine data available N=1858
Employed participants (Excluded Unemployed, retired, or men with current disability or sickness) N=1538
Participants without CVD history and diabetes or treatment N=1388
Participants with eGFR≥30 mL/min/1.73m2 N=1376
Final population (excluded data missing from confounding factors) N= 1058
Fig. 1. study design.
2.5. Statistical methods Basic descriptive statistics on the qualitative and quantitative variables using frequency counts or means and standard deviation were performed. Comparisons of groups were undertaken using chi-squared tests for contingency tables or one-way analysis of variance. Homogeneity of variance and normality of distributions were tested and appropriate analyses applied. Physical activity parameters, both occupational and leisure-time, were dealt with as continuous variables. Multivariate linear regression was used to determine the relationship between potential explanatory factors and the increase of eGFR (continuous variable), expressed by β coefficients standardized per one standard deviation change and 95% CI. Finally, interactions were also tested between predictors of interest and potential confounding factors. Two models were constructed depending on confounding factors that were integrated in the model: model A adjusted on age and model B adjusted on age, metabolic syndrome, treatment for hypertension, behavioral habits (tobacco and alcohol consumption), and social factors (educational level). To analyze the relationship between eGFR and occupational physical activity according to the accompanying levels of leisure-time physical activity, stratified analyses were conducted by different levels of leisure-time physical activity (divided into tertiles to compare the distribution). The hazard ratios (HR) and 95% CI for each predictor variable and their effects on incidence of ‘hard’ CVD or angina pectoris were calculated in a proportional hazards Cox analysis, with the proportionality assumption tested by examining the Schoenfeld residuals. To be compared with the relevant literature and according to usual medical
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Table 1 Clinical, biological and lifestyle characteristics according to eGFR classes defined by CKD creatinine equation. N = 1058
All
Stage 1: eGFR ≥90 ml/min/1.73 m2 Stage 2: 60 b eGFR b 90 ml/min/1.73 m2 Stage 3: 30 b eGFR b 60 ml/min/1.73 m2 P value (N = 590) (N = 413) (N = 55)
Age (years), mean/SD Current smoker, % Alcohol intake N 30 g/day, % Education level N 12 years, % Metabolic syndrome, yes, % Creatinine (mg/dL), mean (SD) eGFR, mean (SD)
54.5 (2.9) 26.4 60.6 39.5 26.5 0.96 (0.2) 88.8 (15.5)
53.8 (2.7) 31.2 62.5 36.3 23.9 0.85 (0.1) 98.5 (10.1)
55.4 (2.7) 20.3 57.4 45.5 29.3 1.05 (0.1) 79.8 (7.9)
b0.001 b0.001 0.23 0.003 0.09 b0.001 b0.001
55.4 (2.9) 20.00 63.6 29.1 32.7 1.51 (0.2) 51.9 (6.4)
eGFR, estimated glomerular filtration rate. Results expressed by mean and standard deviation or percentage. Chi-squared tests for contingency tables and one-way analysis of variance were used to compare means for variables that had a normal distribution.
practice, for these analyses eGFR was categorized into three classes according to stages defined by KDIGO: ≥90, 89–60, 30–59 ml/min/1.73 m2. To analyze the potential mediating effect of the different types of physical activity on CVD, a method already described elsewhere was used [29]. The proportion of the effect (PE) on eGFR stages explained by the potential mediators (different types of physical activity) was calculated as PE = (Total effect − direct effect)/total effect * 100, and expressed by mean and 95% IC. We used bootstrap sampling (500 bootstrap samples) to obtain 95% confidence intervals (CIs), adjusted on model B. Interactions between classes of eGFR and the type of physical activity were checked.
3. Results Serum creatinine levels were available in 1858 men. Populations excluded and included were comparable in age (54.6/55.1 years old respectively), in cardiovascular history (P = 0.11), and in frequency of ‘hard’ cardiovascular and angina events, with P value at 0.4 and 0.1 respectively. In this selected population, 1538 participants were working men, 132 men had a previous history of CVD. After excluding men with missing data for potential confounders, and any history of mellitus diabetes or treatment and participants with an estimated GFR b 30 mL/min/1.73 m2, the remaining population was composed of 1058 active participants (Fig. 1). From the CKD-EPI creatinine equation [22,23], 55.8% of this population had normal renal function (with GFR ≥ 90 ml/min/1.73 m2); 39.1% had stage 2 with a modest reduction in eGFR to levels between 60 and 89 ml/min/1.73 m2; and 5.2% had stage 3 with a moderate decease of eGFR to levels between 30 and 59 ml/min/1.73 m2. Just over 26% of men satisfied the definition of metabolic syndrome, but this proportion was higher among men with impaired renal function (Table 1). Consistently, men classified in stages 2 or 3 spent more energy sitting or standing still at work and expended less energy carrying or lifting objects compared to men classified in stage 1. Levels of leisure
physical activity did not differ significantly between those with normal and mild or moderately reduced renal function. Energy expenditure while commuting to work decreased inversely with the eGFR stage of CKD. Total energy expenditure from physical activity (all types combined) decreased in the groups of reduced renal function (Table 2). Table 3 shows the results of the multivariate linear regression, adjusting for several confounding factors (known to interact with renal function), calculated to explain the relation between the increase of eGFR and the different types of physical activity. They confirmed an inverse effect between time spent sitting or standing still at work and the increase in eGFR and a positive effect of time spent carrying or lifting objects and being active at work. Energy expenditure outside work for commuting at work had a positive and significant effect on renal function in this analysis, while leisure physical activity had not. To untangle the role of occupational activity and the levels of leisure physical activity, stratified analyses were conducted (Table 4). The effects of occupational physical activity were more pronounced when the level of leisure-time physical activity was low. Being sedentary had a negative effect, while being active had a positive effect on the levels of eGFR. These effects tended to disappear as levels of leisuretime physical activity increased Table 4. To analyze the potential mediating effect of the different types of physical activity on the relationship between CVD and eGFR, Cox proportional hazards analyses were conducted adjusted for several confounding factors. During 10 years follow-up, 45 first angina events and 44 ‘hard’ events (MI or coronary death) were recorded. The incidence rate/1000 people's years and 95% CI were respectively 4.6% (3.4–6.1) and 4.4% (3.2–5.8). According to stage of eGFR, for angina events and ‘Hard’ events, the incidences were respectively: stage 1: 3.2%/4.2%; stage 2: 6.1%/4.3%; and stage 3: 8.1%/5.8%. After adjustment on confounding factors, the results confirmed that the risk of incident angina rose with increasing stage of eGFR, while no impact was
Table 2 Energy expenditure of different tasks at work, commuting, leisure-time physical activity according to eGFR classes defined by CKD creatinine equation. N = 1058
Stage 1: eGFR ≥90 ml/min/1.73 m2 (N = 590)
Stage 2: 60 b eGFR b 90 ml/min/1.73 m2 (N = 413)
Stage 3: 30 b eGFR b 60 ml/min/1.73 m2 (N = 55)
P value
SD
Mean
SD
Mean
SD
Mean
SD
P value
6.7 23.3 31.8 38.8
10.5 8.3 47.6 55.9
6.5 23.3 31.7 39.1
11.9 6.1 42.7 48.8
6.7 24.4 30.3 37.9
11.1 3.4 53.8 57.1
7.8 11.6 39.8 40.9
0.01a 0.01a 0.01a 0.007a
18.5 6.1 38.4
18.1 4.1 88.7
18.2 6.9 38.4
19.2 2.6 82.5
18.9 4.6 37.5
17.2 1.9 87.5
17.9 3.4 43.5
0.33a 0.001a 0.02a
All
Mean Energy expenditure at work, METS hour/week Sitting or standing still 11.1 Carrying or lifting objects 7.2 Walking 46.1 Active at wok 53.2 Energy expenditure outside of work, METS hour/week Leisure physical activity 18.5 Commuting to or from work 3.4 Total physical activity, METS hour/week 86.2
Active at work: sum of energy expenditure of walking at work and carrying or lifting objects. The cumulative total physical activity was also determined as the addition of energy expenditure at work, commuting to and from work and leisure-time physical activity. Results expressed in percentage. a Results of K-Wallis test.
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Table 3 Relation between different types of physical activity and eGFR, linear regression analysis according to models A and B. eGFR/ml/min/1.73 m2 (model A)
N = 1058
β Standardized Energy expenditure at work, METS hour/week Sitting or standing still −1.1071 Carrying or lifting objects 1.4601 Walking 0.2088 Active at work 1.0476 Energy expenditure outside of work, METS hour/week Leisure physical activity 0.0941 Commuting to or from work 1.5012 Total physical activity, METS hour/week 1.1474
eGFR/ml/min/1.73 m2 (model B)
SE
P value
β Standardized
SE
P value
0.469 0.468 0.471 0.469
0.018 0.002 0.65 0.026
−0.9583 1.3077 0.0091 0.8628
0.498 0.481 0.488 0.503
0.05 0.007 0.98 0.08
0.471 0.468 0.468
0.84 0.001 0.01
0.1421 1.3621 0.9728
0.473 0.473 0.4991
0.76 0.004 0.04
Model A: adjusted for age only. Model B: adjusted for age, metabolic syndrome, treatment of high blood pressure, educational level, tobacco and alcohol consumption β Standardized for one standard deviation change. No interaction between variables of interest and adjustment factors; multi-co-linearity checked: none significant.
discernible concerning “hard” events. The analyses of the potential mediating effects of physical activity uncovered no significant impact on angina or hard events, whatever the types of physical activity considered Table 5. 4. Discussion Advanced impairment of kidney function is now recognized as a major risk factor for CVD and since it can remain silent and undiagnosed for many years, it is increasingly becoming the focus of public health policy. Our interest has been on the early stages of reduced renal function, since modification of risk factors, such as occupational or leisure physical activity. Three main messages emerge from this study. Firstly, the results have highlighted the specific association of different types of physical activity, in particular those linked to work and eGFR. In this employed population, for the first time, these original results have demonstrated that among men without a history of CVD or mellitus diabetes and after adjustment to confounding factors, high total levels of habitual physical activity during a normal week, (including at work, commuting and leisure activities) are associated with a greater eGFR. Sedentary behavior comparable to sitting at work
Table 4 Relation between different type of occupational physical activity and eGFR, stratified by the levels of leisure physical activity (tertile): linear regression analysis, adjusted on model B. N = 1058
Leisure physical activity Ith, 0–7.6 METS hour/week Sitting or standing still Carrying or lifting objects Walking Active at work Commuting to or from work Leisure physical activity IIth, 7.6–20.2 METS Hour week Sitting or standing still Carrying or lifting objects Walking. Active at work Commuting to or from work Leisure physical activity IIIth, N20.2 METS hour/week Sitting or standing still Carrying or lifting objects Walking Active at work Commuting to or from work
eGFR ml/min/1.73 m2 (model B) β Standardized
SE
P value
−2.2461 0.9844 1.159 1.694 2.1519
0.912 0.778 0.819 0.839 0.844
0.01 0.21 0.15 0.04 0.01
−1.3112 1.1342 0.5322 1.2244 0.5756
0.728 0.736 0.765 0.781 0.826
0.07 0.125 0.48 0.12 0.48
0.6794 2.068 −2.101 −0.795 1.3084
0.939 1.076 0.963 1.01 0.818
0.47 0.05 0.03 0.43 0.11
Model B: adjusted for age, metabolic syndrome, treatment of high blood pressure, educational level, tobacco and alcohol consumption. β standardized for one standard deviation change.
increased the risk of impaired renal function. In contrast, active tasks such as carrying heavy or light objects or being active at work tended to have protective effects on eGFR. The link between these different activities and eGFR remains after adjustment for major factors known to modify kidney function (age, metabolic syndrome, treatment of Hypertension, lifestyle habits and educational level). By calculating the aggregate energy expenditure per week (physical activity at commuting, at work and for leisure), these results broadly corroborate crosssectional National Health and Nutrition Examination Survey (NHANES) findings, showing a protective effect of total physical activity on early stages of CKD [17]. Although the assessment of total physical activity involved objective quantitative methods (accelerometry), the qualitative aspect of different types of physical activity was not distinguished. Our outcomes show which type of physical activity could interact, and which ones could have a potential role in preventing impaired renal function among employed men. Secondly, in this selected population of 50–60-year-old men, leisure time physical activity did not exert a significant relation on slight decrease of eGFR. From NHANES, these relationships held only in younger participants (b44 years old) and those without metabolic syndrome [15]. The authors attempted to explain this pattern of association by assuming that kidney function lost a certain degree of ‘plasticity’ with age. Moreover, it is recognized that tolerance for physical activity decreases as kidney function falls [13]. In contrast, from a survey in Nord-Trondelag [30] or from the Cardiovascular Health Study [16], leisure time physical inactivity was associated with lower eGFR (under 45 ml/min) and higher levels of leisure time PA slowed the progression of kidney function decline, in older populations (N 65 years). [16].Thus, the effect of leisure physical activity on modest declines of eGFR is not well established [12] and there is currently no guideline that advocates physical activity for renal function protection in early stages [14]. Moreover, our results have shown that the association between different types of occupational activity and eGFR could be even more significant when leisure-time physical activity is low. We can suppose that for people who have no time [31] or no opportunity for leisuretime physical activity, being active at work could be even more important for reno-protection. At least with respect to the prevention of early stages of decreased renal function, we can hypothesize that both periods of occupational and leisure time physical activities of sufficient and regular duration are of public health importance. Different mechanisms have been proposed as partial mediators of the relationship between physical activity and declining renal function, including insulin resistance, inflammatory processes, renal endothelial damage, oxidative stress, and stimulation of the renin angiotensin and sympathetic system [32–34]. Given the fact that multiple pathways might be involved, determining exactly how PA could be implicated in improving renal function remains a challenge for the future. Thus, PA at work offers relevant opportunities for a primary prevention strategy to conserve
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Table 5 Mediator effect of different types of physical activity on the relation between cardiovascular events and eGFR stages, over 10-year follow-up. N = 1058
Angina events
Hard events
HR 95% CI of eGFR stages (model B)
HR 95% CI of eGFR stages (model B)
Stage 1: reference
Stage 2
Stage 3
Mediator effect
Stage 2
Stage 3
Mediator effect
Total effect, P value for trend Sitting or standing still, P value for trend Carry or lifting objects, P value for trend Active at work, P value for trend Leisure PA, P value for trend Commuting to work, P value for trend Total physical activity, P value for trend
1.94 (1.01–3.71)⁎, 0.038 1.97 (1.02–3.77)⁎, 0.035 1.94 (1.01–3.71)⁎, 0.036 1.95 (1.02–3.73)⁎, 0.038 1.94 (1.01–3.71)⁎, 0.038 1.99 (1.03–3.82)⁎, 0.034 1.94 (1.01–3.72)⁎, 0.038
2.20 (0.72–6.65) 2.23 (0.74–6.77) 2.23 (0.74–6.77) 2.20 (0.73–6.66) 2.20 (0.73–6.66) 2.26 (0.74–6.89) 2.20 (0.72–6.66)
35.4 (–30.4; 101.3) 8.7 (−11.5; 28.7) 13.2 (−12.9; 39.5) −1.8 (−5.6; 1.2) 2.6 (−9.9; 15.2) 15.4 (−14.4; 45.2)
0.95 (0.49–1.83), 0.91 0.95 (0.49–1.82), 0.91 0.95 (0.49–1.83), 0.90 0.96 (0.49–1.82), 0.92 0.96 (0.51–1.86), 0.89 0.96 (0.50–1.85), 0.87 0.95 (0.49–1.83), 0.91
1.22 (0.36–4.15) 1.22 (0.36–4.15) 1.24 (0.36–4.21) 1.20 (0.35–4.08) 1.22 (0.35–4.11) 1.24 (0.36–4.27) 1.22 (0.36–4.15)
−22.8 (−65.1; 19.3) −19.1 (−65.7; 27.4) −16.5 (−47.2; 14.1) −13.1 (−32.9; 6.8) −9.8 (−32; 51.8) −9.1 (−24.1; 5.8)
Model B: Cox analysis adjusted on age, metabolic syndrome, treatment of HBP, alcohol and tobacco consumptions, educational level. HR and 95% CI of eGFR stages (1, 2, 3). Stage 1: reference. Mediator effect: proportion of total effect explained by mediator (PE) (total effect − potential effect)/total effect * 100), mean and 95% CI. Bootstrap sampling (500). To evaluate proportion of mediator effect, interactions between eGFR and each mediator were checked and none were significant. ⁎ P value b 0.05.
renal function in employed men. Future work could help shed light on whether reno-protective effects operate through local hemodynamic mechanisms in the kidney or by delaying arteriosclerosis and insulin resistance among people who present a moderate decrease of eGFR. Thirdly, our results confirmed that a moderate decrease of eGFR was a significant risk factor for angina events (i.e. according to the stage of eGFR; P for trend was significant), but not for hard events such as myocardial infarction or coronary death. This may reflect a stress effect on atheroma processes among these middle-age working men but probably not of sufficient severity for acute events like myocardial infarction (ischemic process).The influence on “hard” effects needs to be re-examined in future studies with longer follow-up. Indeed, while the impact of the end stages of CKD on cardiovascular risk is welldocumented, the threshold for early impairment of eGFR at which cardiovascular risk increases significantly remains to be determined clearly. The level at b60 ml/min/1.73 m2 is put forward by the KDIGO guideline, while one recent study suggests a level of b90 ml/min/ 1.73 m2 as a threshold for risk [35]. We have tested the potential mediating effect of occupational physical activity on the relationship between stages of CKD and CVDs over 10 years follow-up. None of the different types of physical activity have shown a mediating impact on these CVD. Any explanation for this may need to account for the fact that these men are at a transitional period in their lives and with the cessation of occupational activity after retirement (as it could be in this population after 10 year's follow-up), the positive or negative impact of some occupational physical activity could be temporary, and may not have a long-term effect or sustained effect on the incidence of CHD. In other words, being active or sedentary during the working period could influence the impact of declining renal function on the development of CVD, but these effects may fade with time. To confirm this hypothesis, longitudinal studies with repeated measures of the different levels of physical activity are needed. Study limitation: As in other studies focused on impact of physical activity on early reductions of kidney function, the first part of our findings are limited by the initial cross sectional analysis from which we cannot ascertain a causal relation. Thus, longitudinal studies and randomized trials assessing the role of physical activity in modifying the early development of CKD are required to confirm these results. Few studies have focused on the impact of physical activity on early stages of eGFR and among the three recent cross-sectional studies, two evaluated eGFR according to the MDRD formula [17,30] and one used the Cockroft–Gault equation [15]. Recently, it has emerged that the more accurate and accessible equation for estimating GFR is that based on serum creatinine by CKD-EPI equation [21,22], as used here. Even if a more accurate formula to calculate eGFR was used, proteinuria was not available in PRIME study and strictly speaking damage to the kidney could not evaluated. However, leisure-time physical activity seemed to have no effects on proteinuria [15].
5. Conclusion Chronic kidney disease is an important public health challenge, requiring an early prevention strategy. GFR represents a key determinant of renal function to define CKD. The results of this study suggested that occupational physical activity could have the potential to exert dual roles on early impairments of renal function, in employed men. Sedentary behaviors such as sitting could increase the risk, while activities such as lifting and carrying, could have a protective effect. Managing occupational physical activity could be an efficient way for preventing subtle declines of GFR, even if the impact on the relation between cardiovascular diseases over 10 years follow-up has not been not confirmed. Authors' Individual Contribution Conducted the literature review and prepared the material and methods and results and the discussion sections of the text: Y.E., F.K.; drafted this article: Y.E.; designed the study and directed its implementation in each center, including quality and control, design: J.F, J.B.R, F.K.; revised the article critically for important intellectual content: M.T., D.F., J.F., M.Q., M.H; helped in statistical analyses and results: J.B.R., Y.E. All authors have contributed substantially to this article and approved the final version. Competing interests The authors declare no potential conflict of interest, including related consultancies, shareholdings and funding grants. Ethics approval Participants were invited to participate voluntarily in this study and signed a consent form: an approval from Belfast ethics committee was obtained (06/NIR02/107). Acknowledgment We thank the following organizations that facilitated the recruitment of participants for the Prospective Epidemiological Study of Myocardial Infarction (PRIME): the health screening centers organized by the Social Security of Lille (Institut Pasteur), Strasbourg, Toulouse, and Tourcoing; the occupational medicine services of Haute-Garonne; the Urban Community of Strasbourg; the Association Inter-entreprises des Services Médicaux du Travail de Lille et environs; the Comité pour le Développement de la Médecine du Travail; the Mutuelle Générale des Postes, Télégraphes et Téléphones du Bas-Rhin; the Laboratoired'Analyses de l'Institut de Chimie Biologique de la Faculté de Médecine de Strasbourg; the Department of Health (NI); and the Northern Ireland Chest
Y. Esquirol et al. / International Journal of Cardiology 177 (2014) 1036–1041
Heart and Stroke Association. We also thank the Alliance Partnership Programme for its financial support and the following members of the event validation committees: L. Guize; C. Morrison; M.-T. Guillanneuf; and M. Giroud. Funding: PRIME was supported by grants from the Institut National de la Santé et de la Recherche Médicale (INSERM), Merck, Sharp & Dohme-Chibret Laboratory and the Department of Health and Social Services and Personal Safety for Northern Ireland. Ethic committees in PRIME STUDY : for French centers : CNIL 252269 and agreement 04/ 06/91/60 from ethic committee and for Belfast center : 06/NIR02/107. Funding sources had no role in collection, analysis, interpretation of data, writing of the report, or the decision to submit the paper for publication. F.K., M.T. and M.H. received funding from the UKCRC Centre of Excellence for Public Health Research, Northern Ireland. Ethic committees in PRIME STUDY: for French centers: CNIL 252269 and agreement 04/06/91/60 from ethic committee and for Belfast center: 06/ NIR02/107. References [1] Esquirol Y, Yarnell J, Ferrieres J, Evans A, Ruidavets JB, Wagner A, et al. Impact of occupational physical activity and related tasks on cardiovascular disease: emerging opportunities for prevention? Int J Cardiol 2013;168:4475–8. [2] Foster MC, Rawlings AM, Marrett E, Neff D, Grams ME, Kasiske BL, et al. Potential effects of reclassifying CKD as a coronary heart disease risk equivalent in the US population. Am J Kidney Dis 2014;63:753–60. [3] Levey AS, Atkins R, Coresh J, Cohen EP, Collins AJ, Eckardt KU, et al. Chronic kidney disease as a global public health problem: approaches and initiatives—a position statement from Kidney Disease Improving Global Outcomes. Kidney Int 2007;72:247–59. [4] Black C, Sharma P, Scotland G, McCullough K, McGurn D, Robertson L, et al. Early referral strategies for management of people with markers of renal disease: a systematic review of the evidence of clinical effectiveness, cost-effectiveness and economic analysis. Health Technol Assess 2010;14:1–184. [5] Matsushita K, van der Velde M, Astor BC, Woodward M, Levey AS, de Jong PE, et al. Association of estimated glomerular filtration rate and albuminuria with all-cause and cardiovascular mortality in general population cohorts: a collaborative meta-analysis. Lancet 2010;375:2073–81. [6] Chrysohoou C, Panagiotakos DB, Pitsavos C, Skoumas J, Toutouza M, Papaioannou I, et al. Renal function, cardiovascular disease risk factors' prevalence and 5-year disease incidence; the role of diet, exercise, lipids and inflammation markers: the ATTICA study. QJM 2010;103:413–22. [7] Mahmoodi BK, Matsushita K, Woodward M, Blankestijn PJ, Cirillo M, Ohkubo T, et al. Associations of kidney disease measures with mortality and end-stage renal disease in individuals with and without hypertension: a meta-analysis. Lancet 2012;380: 1649–61. [8] Matsushita K, Selvin E, Bash LD, Franceschini N, Astor BC, Coresh J. Change in estimated GFR associates with coronary heart disease and mortality. J Am Soc Nephrol 2009;20:2617–24. [9] Debella YT, Giduma HD, Light RP, Agarwal R. Chronic kidney disease as a coronary disease equivalent—a comparison with diabetes over a decade. Clin J Am Soc Nephrol 2011;6:1385–92. [10] Tonelli M, Wiebe N, Culleton B, House A, Rabbat C, Fok M, et al. Chronic kidney disease and mortality risk: a systematic review. J Am Soc Nephrol 2006;17:2034–47. [11] Heiwe S, Jacobson SH. Exercise training for adults with chronic kidney disease. Cochrane Database Syst Rev 2011:CD003236. [12] Rognant N, Pouliquen E, Fave S, Jolivot A, Laville M. Physical activity and chronic kidney disease: an update in 2013? Nephrol Ther 2014;10:86–93. [13] Johansen KL, Painter P. Exercise in individuals with CKD. Am J Kidney Dis 2012;59: 126–34. [14] Smith AC, Burton JO. Exercise in kidney disease and diabetes: time for action. J Ren Care 2012;38(Suppl. 1):52–8.
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[15] Finkelstein J, Joshi A, Hise MK. Association of physical activity and renal function in subjects with and without metabolic syndrome: a review of the Third National Health and Nutrition Examination Survey (NHANES III). Am J Kidney Dis 2006;48: 372–82. [16] Robinson-Cohen C, Katz R, Mozaffarian D, Dalrymple LS, de Boer I, Sarnak M, et al. Physical activity and rapid decline in kidney function among older adults. Arch Intern Med 2009;169:2116–23. [17] Hawkins MS, Sevick MA, Richardson CR, Fried LF, Arena VC, Kriska AM. Association between physical activity and kidney function: National Health and Nutrition Examination Survey. Med Sci Sports Exerc 2011;43:1457–64. [18] Ducimetiere P, Ruidavets JB, Montaye M, Haas B, Yarnell J. Five-year incidence of angina pectoris and other forms of coronary heart disease in healthy men aged 50-59 in France and Northern Ireland: the Prospective Epidemiological Study of Myocardial Infarction (PRIME) Study. Int J Epidemiol 2001;30:1057–62. [19] Yarnell JW. The PRIME study: classical risk factors do not explain the severalfold differences in risk of coronary heart disease between France and Northern Ireland. Prospective Epidemiological Study of Myocardial Infarction. QJM 1998;91:667–76. [20] Levey AS, Coresh J, Greene T, Marsh J, Stevens LA, Kusek JW, et al. Expressing the Modification of Diet in Renal Disease Study equation for estimating glomerular filtration rate with standardized serum creatinine values. Clin Chem 2007;53: 766–72. [21] Matsushita K, Mahmoodi BK, Woodward M, Emberson JR, Jafar TH, Jee SH, et al. Comparison of risk prediction using the CKD-EPI equation and the MDRD study equation for estimated glomerular filtration rate. JAMA 2012;307:1941–51. [22] Levey AS, Stevens LA, Schmid CH, Zhang YL, Castro 3rd AF, Feldman HI, et al. A new equation to estimate glomerular filtration rate. Ann Intern Med 2009;150:604–12. [23] Levin A, Stevens PE. Summary of KDIGO 2012 CKD Guideline: behind the scenes, need for guidance, and a framework for moving forward. Kidney Int 2014;85:49–61. [24] Roeykens J, Rogers R, Meeusen R, Magnus L, Borms J, de Meirleir K. Validity and reliability in a Flemish population of the WHO-MONICA Optional Study of Physical Activity Questionnaire. Med Sci Sports Exerc 1998;30:1071–5. [25] Wagner A, Simon C, Ducimetiere P, Montaye M, Bongard V, Yarnell J, et al. Leisuretime physical activity and regular walking or cycling to work are associated with adiposity and 5 y weight gain in middle-aged men: the PRIME Study. Int J Obes Relat Metab Disord 2001;25:940–8. [26] Wagner A, Simon C, Evans A, Ferrieres J, Montaye M, Ducimetiere P, et al. Physical activity and coronary event incidence in Northern Ireland and France: the Prospective Epidemiological Study of Myocardial Infarction (PRIME). Circulation 2002;105: 2247–52. [27] Alberti KG, Eckel RH, Grundy SM, Zimmet PZ, Cleeman JI, Donato KA, et al. Harmonizing the metabolic syndrome: a joint interim statement of the International Diabetes Federation Task Force on Epidemiology and Prevention; National Heart, Lung, and Blood Institute; American Heart Association; World Heart Federation; International Atherosclerosis Society; and International Association for the Study of Obesity. Circulation 2009;120:1640–5. [28] Ruidavets JB, Ducimetiere P, Evans A, Montaye M, Haas B, Bingham A, et al. Patterns of alcohol consumption and ischaemic heart disease in culturally divergent countries: the Prospective Epidemiological Study of Myocardial Infarction (PRIME). BMJ 2010;341:c6077. [29] Beulens JW, van der Schouw YT, Moons KG, Boshuizen HC, van der AD, Groenwold RH. Estimating the mediating effect of different biomarkers on the relation of alcohol consumption with the risk of type 2 diabetes. Ann Epidemiol 2013;23:193–7. [30] Hallan S, de Mutsert R, Carlsen S, Dekker FW, Aasarod K, Holmen J. Obesity, smoking, and physical inactivity as risk factors for CKD: are men more vulnerable? Am J Kidney Dis 2006;47:396–405. [31] Kirk MA, Rhodes RE. Occupation correlates of adults' participation in leisure-time physical activity: a systematic review. Am J Prev Med 2011;40:476–85. [32] Raimundo M, Lopes JA. Metabolic syndrome, chronic kidney disease, and cardiovascular disease: a dynamic and life-threatening triad. Cardiol Res Pract 2011;2011: 747861. [33] Stump CS. Physical activity in the prevention of chronic kidney disease. Cardiorenal Med 2011;1:164–73. [34] Nitta K. Possible link between metabolic syndrome and chronic kidney disease in the development of cardiovascular disease. Cardiol Res Pract 2010:2011. [35] Moody WE, Edwards NC, Chue CD, Ferro CJ, Townend JN. Arterial disease in chronic kidney disease. Heart 2013;99:365–72.
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International Journal of Cardiology journal homepage: www.elsevier.com/locate/ijcard
Could occupational physical activity mitigate the link between moderate kidney dysfunction and coronary heart disease?☆ Yolande Esquirol a,b,⁎, Mark Tully c, Jean-Bernard Ruidavets a, Damian Fogarty d, Jean Ferrieres a,e, Michael Quinn d, Maria Hughes c, Frank Kee c, on behalf of, the Prime study a
Université Paul Sabatier de Toulouse III, UMR1027, Toulouse F-31073, France CHU Toulouse, SMPE, Toulouse F-31059, France UK Clinical Research Collaboration Centre of Excellence for Public Health, Queen's University Belfast, Belfast, UK d Belfast Health and Social Care Trust, Belfast, UK e CHU Toulouse, Department of Cardiology B, Toulouse F-31059, France b c
a r t i c l e
i n f o
Article history: Received 4 August 2014 Received in revised form 17 September 2014 Accepted 20 September 2014 Available online 30 September 2014 Keywords: Glomerular filtration rate Work Exercise
a b s t r a c t Background: Chronic kidney disease is now regarded as a risk factor for cardiovascular disease. The impact of occupational or non-occupational physical activity (PA) on moderate decreases of renal function is uncertain. Objectives: We aimed to identify the potential association of PA (occupational and leisure-time) on early decline of estimated glomerular filtration rate (eGFR) and to determine the potential mediating effect of PA on the relationship between eGFR and heart disease. Methods: From the PRIME study analyses were conducted in 1058 employed men. Energy expended during leisure, work and commuting was calculated. Linear regression analyses were used to determine the link between types of PA and moderate decrements of eGFR determined with the KDIGO guideline at the baseline assessment. Cox proportional hazards analyses were used to explore the potential effect of PA on the relationship between eGFR and heart disease, ascertained during follow-up over 10 years. Results: For these employed men, and after adjustment for known confounders of GFR change, more time spent sitting at work was associated with increased risk of moderate decline in kidney function, while carrying objects or being active at work was associated with decreased risk. In contrast, no significant link with leisure PA was apparent. No potential mediating effect of occupational PA was found for the relationship between eGFR and coronary heart disease. Conclusion: Occupational PA (potential modifiable factors) could provide a dual role on early impairment of renal function, without influence on the relationship between early decrease of e-GFR and CHD risk. © 2014 Elsevier Ireland Ltd. All rights reserved.
1. Introduction Evidence has emerged on the potential impact of occupational physical activity (PA) on the incidence of cardiovascular diseases (CVD), with negative or positive effects depending on the types of tasks undertaken at work [1]. Chronic kidney disease (CKD) is acknowledged as a risk factor for coronary heart disease [2], and since its prevalence has been increasing, discovering strategies for early prevention is imperative and has been described as a global public health challenge [3–5]. Even though the link between CKD and cardiovascular mortality and morbidity has been well described for advanced stages of CKD [5–9], it now also exists for earlier stages of disease when reductions
☆ These authors take responsibility for all aspects of the reliability and freedom from bias of the data presented and their discussed interpretation. ⁎ Corresponding author at: Faculté de Médecine 37 allées jules guesde 31059 Toulouse, France. Tel.: +33 5 61 77 21 90; fax: +33 5 61 77 75 61. E-mail address: [email protected] (Y. Esquirol).
http://dx.doi.org/10.1016/j.ijcard.2014.09.102 0167-5273/© 2014 Elsevier Ireland Ltd. All rights reserved.
in estimated glomerular filtration rate (eGFR) are comparatively modest [5,10]. A recent Cochrane review has highlighted the potential positive effects of regular leisure exercise on advanced CKD mainly through the improvement of cardiovascular risk (diabetes, high blood pressure) [11–14] and aerobic capacity [11]. However, the effect of physical activity on early stages of CKD is not well-established [12], and few studies have focused specifically on it [15–17]. The eGFR is one of the essential determinants that characterize CKD. According to KDIGO (Kidney Disease: Improving Global Outcomes) in 2012, early impairment of renal function, estimated by eGFR carries a risk of unfavorable prognosis (progress towards end-stage CKD and/or CVD events). However we have little information that distinguishes the different types and contexts for physical activity. It is assumed that occupational physical activity could have an impact on modest impairment of eGFR, and that, this may differ according to the “background” leisure-time physical activity levels. Moreover, the potential effect of each type of physical activity acting directly on any early decrease of eGFR or indirectly through cardiovascular factors is unknown.
Y. Esquirol et al. / International Journal of Cardiology 177 (2014) 1036–1041
Thus, the objectives of this study are to assess the potential relationship between different types of physical activity (occupational and leisure) and glomerular filtration rate and also to analyze the potential mediating effect of each type of physical activity on the relationship between the modest declines in eGFR and CHD, over 10 years follow-up.
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Total participants from Belfast centre In PRIME Study N=2745
2. Methods 2.1. Population study From the prospective PRIME study, (Prospective epidemiological study of myocardial infarction), we followed up 2745 men (50–60 years old) from the Belfast center (Northern Ireland) for ten years [18,19]. Serum creatinine was available only in this center and for 1858 subjects. Participants provided signed informed consent and approval from the Belfast ethics committee was obtained.The study protocol conforms to the ethical guidelines of the 1975 Declaration of Helsinki. Because of our interest in the consequences of occupational physical activity on kidney function, unemployed, retired, or men with current disability or sickness were excluded. In addition, men with a history of CVD or history of diabetes mellitus or specific treatment at baseline were also excluded. The analyses were conducted only when serum creatinine and data for confounding factors were available. Because the focus was on the evaluation of factors affecting subtle decreases of kidney function, participants with eGFR b 30 ml/min/1.73 m2 were excluded (Fig. 1). 2.2. Assessment of kidney function Non standardized serum creatinine was measured with the Jaffe method using a kinetic assay, expressed in mg/dl. To obtain standardized measurements, creatinine was adjusted (×0.95) using the calibration proposed by Levey et al. [20] in 2007 and as used in a recent meta-analysis [21]. To be more accurate, eGFR was evaluated by using the recent CKD-EPI formulae [22], taking account standardized creatinine levels referenced in the KDIGO guideline [23]: Standardized serum creatinine ≤ 0.9 mg/dl: GFR = 141 × (Scr/0.9)_0.411 × (0.993)Age; Standardized serum creatinine N 0.9 mg/dl: GFR = 141 × (Scr/0.9)_1.209 × (0.993)Age. The classification of stages proposed by KDIGO was used. 2.3. Assessment of physical activity: leisure, occupational and commuting physical activities From the MONICA Optional Study of physical activity questionnaire [24], levels of all types of physical activity were estimated using the updated (2011) compendium of physical activity and their associated energy expenditures, expressed in METS (Metabolic equivalent of tasks) hour/week [25,26]. Thus, occupational physical activity was evaluated from listed tasks for a typical day at work, expressed in METS hour/week. The responses to the question: ‘how much time do you spend sitting or standing still at work’ were used as variables to define sedentary behavior at work. For participants carrying or lifting light or heavy objects taken separately, a variable (carrying or lifting objects) was created by compiling the energy expenditure of both. Moreover, to describe the overall level of activity of each job, the sum of the energy expenditure during walking at work and carrying or lifting objects was calculated, defined by a new variable ‘active at work’. Leisure time physical activity was calculated according to time spent in different kinds of physical activity regularly carried out per week and for the stated number of months during the previous year. For a typical working day, physical activity undertaken during commuting to work was considered as the time spent walking or biking to or from work. The cumulative total physical activity was also determined as the addition of energy expenditure at work, commuting to and from work and leisure time physical activity, expressed in METS hour/week. 2.4. Assessment of potential confounding factors on kidney function Age and demographics were collected as described before [19]. From a fasting plasma sample, glucose level and lipid parameters were determined. Blood pressure was measured after a 5 minute rest while sitting with an automatic blood pressure analyzer. Body mass index (BMI) (weight/height2) was calculated. In our European study population, each component of metabolic syndrome (Met-S) was evaluated according to an updated consensus definition of metabolic syndrome. The variable metabolic syndrome was dichotomized into two classes if 3 or more components were present [27]. Daily alcohol consumption was categorized into two classes: ≤ 30/N30 g/d. Tobacco consumption was also dichotomised: current smoker or not. Two categories of educational level were provided: b12/≥ 12 years of school. 2.4.1. Follow-up at 10 years Methods of ascertainment of CVD have been described previously [18,28]. The first episode of angina pectoris (without MI or coronary death) and ‘Hard’ CVD (defined by counting only the first MI, or coronary death) were recorded according to the codes of International Classification of Diseases, 9th Revision. In addition, a Medical Committee reviewed the classification of each event.
Participants with serum creatinine data available N=1858
Employed participants (Excluded Unemployed, retired, or men with current disability or sickness) N=1538
Participants without CVD history and diabetes or treatment N=1388
Participants with eGFR≥30 mL/min/1.73m2 N=1376
Final population (excluded data missing from confounding factors) N= 1058
Fig. 1. study design.
2.5. Statistical methods Basic descriptive statistics on the qualitative and quantitative variables using frequency counts or means and standard deviation were performed. Comparisons of groups were undertaken using chi-squared tests for contingency tables or one-way analysis of variance. Homogeneity of variance and normality of distributions were tested and appropriate analyses applied. Physical activity parameters, both occupational and leisure-time, were dealt with as continuous variables. Multivariate linear regression was used to determine the relationship between potential explanatory factors and the increase of eGFR (continuous variable), expressed by β coefficients standardized per one standard deviation change and 95% CI. Finally, interactions were also tested between predictors of interest and potential confounding factors. Two models were constructed depending on confounding factors that were integrated in the model: model A adjusted on age and model B adjusted on age, metabolic syndrome, treatment for hypertension, behavioral habits (tobacco and alcohol consumption), and social factors (educational level). To analyze the relationship between eGFR and occupational physical activity according to the accompanying levels of leisure-time physical activity, stratified analyses were conducted by different levels of leisure-time physical activity (divided into tertiles to compare the distribution). The hazard ratios (HR) and 95% CI for each predictor variable and their effects on incidence of ‘hard’ CVD or angina pectoris were calculated in a proportional hazards Cox analysis, with the proportionality assumption tested by examining the Schoenfeld residuals. To be compared with the relevant literature and according to usual medical
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Table 1 Clinical, biological and lifestyle characteristics according to eGFR classes defined by CKD creatinine equation. N = 1058
All
Stage 1: eGFR ≥90 ml/min/1.73 m2 Stage 2: 60 b eGFR b 90 ml/min/1.73 m2 Stage 3: 30 b eGFR b 60 ml/min/1.73 m2 P value (N = 590) (N = 413) (N = 55)
Age (years), mean/SD Current smoker, % Alcohol intake N 30 g/day, % Education level N 12 years, % Metabolic syndrome, yes, % Creatinine (mg/dL), mean (SD) eGFR, mean (SD)
54.5 (2.9) 26.4 60.6 39.5 26.5 0.96 (0.2) 88.8 (15.5)
53.8 (2.7) 31.2 62.5 36.3 23.9 0.85 (0.1) 98.5 (10.1)
55.4 (2.7) 20.3 57.4 45.5 29.3 1.05 (0.1) 79.8 (7.9)
b0.001 b0.001 0.23 0.003 0.09 b0.001 b0.001
55.4 (2.9) 20.00 63.6 29.1 32.7 1.51 (0.2) 51.9 (6.4)
eGFR, estimated glomerular filtration rate. Results expressed by mean and standard deviation or percentage. Chi-squared tests for contingency tables and one-way analysis of variance were used to compare means for variables that had a normal distribution.
practice, for these analyses eGFR was categorized into three classes according to stages defined by KDIGO: ≥90, 89–60, 30–59 ml/min/1.73 m2. To analyze the potential mediating effect of the different types of physical activity on CVD, a method already described elsewhere was used [29]. The proportion of the effect (PE) on eGFR stages explained by the potential mediators (different types of physical activity) was calculated as PE = (Total effect − direct effect)/total effect * 100, and expressed by mean and 95% IC. We used bootstrap sampling (500 bootstrap samples) to obtain 95% confidence intervals (CIs), adjusted on model B. Interactions between classes of eGFR and the type of physical activity were checked.
3. Results Serum creatinine levels were available in 1858 men. Populations excluded and included were comparable in age (54.6/55.1 years old respectively), in cardiovascular history (P = 0.11), and in frequency of ‘hard’ cardiovascular and angina events, with P value at 0.4 and 0.1 respectively. In this selected population, 1538 participants were working men, 132 men had a previous history of CVD. After excluding men with missing data for potential confounders, and any history of mellitus diabetes or treatment and participants with an estimated GFR b 30 mL/min/1.73 m2, the remaining population was composed of 1058 active participants (Fig. 1). From the CKD-EPI creatinine equation [22,23], 55.8% of this population had normal renal function (with GFR ≥ 90 ml/min/1.73 m2); 39.1% had stage 2 with a modest reduction in eGFR to levels between 60 and 89 ml/min/1.73 m2; and 5.2% had stage 3 with a moderate decease of eGFR to levels between 30 and 59 ml/min/1.73 m2. Just over 26% of men satisfied the definition of metabolic syndrome, but this proportion was higher among men with impaired renal function (Table 1). Consistently, men classified in stages 2 or 3 spent more energy sitting or standing still at work and expended less energy carrying or lifting objects compared to men classified in stage 1. Levels of leisure
physical activity did not differ significantly between those with normal and mild or moderately reduced renal function. Energy expenditure while commuting to work decreased inversely with the eGFR stage of CKD. Total energy expenditure from physical activity (all types combined) decreased in the groups of reduced renal function (Table 2). Table 3 shows the results of the multivariate linear regression, adjusting for several confounding factors (known to interact with renal function), calculated to explain the relation between the increase of eGFR and the different types of physical activity. They confirmed an inverse effect between time spent sitting or standing still at work and the increase in eGFR and a positive effect of time spent carrying or lifting objects and being active at work. Energy expenditure outside work for commuting at work had a positive and significant effect on renal function in this analysis, while leisure physical activity had not. To untangle the role of occupational activity and the levels of leisure physical activity, stratified analyses were conducted (Table 4). The effects of occupational physical activity were more pronounced when the level of leisure-time physical activity was low. Being sedentary had a negative effect, while being active had a positive effect on the levels of eGFR. These effects tended to disappear as levels of leisuretime physical activity increased Table 4. To analyze the potential mediating effect of the different types of physical activity on the relationship between CVD and eGFR, Cox proportional hazards analyses were conducted adjusted for several confounding factors. During 10 years follow-up, 45 first angina events and 44 ‘hard’ events (MI or coronary death) were recorded. The incidence rate/1000 people's years and 95% CI were respectively 4.6% (3.4–6.1) and 4.4% (3.2–5.8). According to stage of eGFR, for angina events and ‘Hard’ events, the incidences were respectively: stage 1: 3.2%/4.2%; stage 2: 6.1%/4.3%; and stage 3: 8.1%/5.8%. After adjustment on confounding factors, the results confirmed that the risk of incident angina rose with increasing stage of eGFR, while no impact was
Table 2 Energy expenditure of different tasks at work, commuting, leisure-time physical activity according to eGFR classes defined by CKD creatinine equation. N = 1058
Stage 1: eGFR ≥90 ml/min/1.73 m2 (N = 590)
Stage 2: 60 b eGFR b 90 ml/min/1.73 m2 (N = 413)
Stage 3: 30 b eGFR b 60 ml/min/1.73 m2 (N = 55)
P value
SD
Mean
SD
Mean
SD
Mean
SD
P value
6.7 23.3 31.8 38.8
10.5 8.3 47.6 55.9
6.5 23.3 31.7 39.1
11.9 6.1 42.7 48.8
6.7 24.4 30.3 37.9
11.1 3.4 53.8 57.1
7.8 11.6 39.8 40.9
0.01a 0.01a 0.01a 0.007a
18.5 6.1 38.4
18.1 4.1 88.7
18.2 6.9 38.4
19.2 2.6 82.5
18.9 4.6 37.5
17.2 1.9 87.5
17.9 3.4 43.5
0.33a 0.001a 0.02a
All
Mean Energy expenditure at work, METS hour/week Sitting or standing still 11.1 Carrying or lifting objects 7.2 Walking 46.1 Active at wok 53.2 Energy expenditure outside of work, METS hour/week Leisure physical activity 18.5 Commuting to or from work 3.4 Total physical activity, METS hour/week 86.2
Active at work: sum of energy expenditure of walking at work and carrying or lifting objects. The cumulative total physical activity was also determined as the addition of energy expenditure at work, commuting to and from work and leisure-time physical activity. Results expressed in percentage. a Results of K-Wallis test.
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Table 3 Relation between different types of physical activity and eGFR, linear regression analysis according to models A and B. eGFR/ml/min/1.73 m2 (model A)
N = 1058
β Standardized Energy expenditure at work, METS hour/week Sitting or standing still −1.1071 Carrying or lifting objects 1.4601 Walking 0.2088 Active at work 1.0476 Energy expenditure outside of work, METS hour/week Leisure physical activity 0.0941 Commuting to or from work 1.5012 Total physical activity, METS hour/week 1.1474
eGFR/ml/min/1.73 m2 (model B)
SE
P value
β Standardized
SE
P value
0.469 0.468 0.471 0.469
0.018 0.002 0.65 0.026
−0.9583 1.3077 0.0091 0.8628
0.498 0.481 0.488 0.503
0.05 0.007 0.98 0.08
0.471 0.468 0.468
0.84 0.001 0.01
0.1421 1.3621 0.9728
0.473 0.473 0.4991
0.76 0.004 0.04
Model A: adjusted for age only. Model B: adjusted for age, metabolic syndrome, treatment of high blood pressure, educational level, tobacco and alcohol consumption β Standardized for one standard deviation change. No interaction between variables of interest and adjustment factors; multi-co-linearity checked: none significant.
discernible concerning “hard” events. The analyses of the potential mediating effects of physical activity uncovered no significant impact on angina or hard events, whatever the types of physical activity considered Table 5. 4. Discussion Advanced impairment of kidney function is now recognized as a major risk factor for CVD and since it can remain silent and undiagnosed for many years, it is increasingly becoming the focus of public health policy. Our interest has been on the early stages of reduced renal function, since modification of risk factors, such as occupational or leisure physical activity. Three main messages emerge from this study. Firstly, the results have highlighted the specific association of different types of physical activity, in particular those linked to work and eGFR. In this employed population, for the first time, these original results have demonstrated that among men without a history of CVD or mellitus diabetes and after adjustment to confounding factors, high total levels of habitual physical activity during a normal week, (including at work, commuting and leisure activities) are associated with a greater eGFR. Sedentary behavior comparable to sitting at work
Table 4 Relation between different type of occupational physical activity and eGFR, stratified by the levels of leisure physical activity (tertile): linear regression analysis, adjusted on model B. N = 1058
Leisure physical activity Ith, 0–7.6 METS hour/week Sitting or standing still Carrying or lifting objects Walking Active at work Commuting to or from work Leisure physical activity IIth, 7.6–20.2 METS Hour week Sitting or standing still Carrying or lifting objects Walking. Active at work Commuting to or from work Leisure physical activity IIIth, N20.2 METS hour/week Sitting or standing still Carrying or lifting objects Walking Active at work Commuting to or from work
eGFR ml/min/1.73 m2 (model B) β Standardized
SE
P value
−2.2461 0.9844 1.159 1.694 2.1519
0.912 0.778 0.819 0.839 0.844
0.01 0.21 0.15 0.04 0.01
−1.3112 1.1342 0.5322 1.2244 0.5756
0.728 0.736 0.765 0.781 0.826
0.07 0.125 0.48 0.12 0.48
0.6794 2.068 −2.101 −0.795 1.3084
0.939 1.076 0.963 1.01 0.818
0.47 0.05 0.03 0.43 0.11
Model B: adjusted for age, metabolic syndrome, treatment of high blood pressure, educational level, tobacco and alcohol consumption. β standardized for one standard deviation change.
increased the risk of impaired renal function. In contrast, active tasks such as carrying heavy or light objects or being active at work tended to have protective effects on eGFR. The link between these different activities and eGFR remains after adjustment for major factors known to modify kidney function (age, metabolic syndrome, treatment of Hypertension, lifestyle habits and educational level). By calculating the aggregate energy expenditure per week (physical activity at commuting, at work and for leisure), these results broadly corroborate crosssectional National Health and Nutrition Examination Survey (NHANES) findings, showing a protective effect of total physical activity on early stages of CKD [17]. Although the assessment of total physical activity involved objective quantitative methods (accelerometry), the qualitative aspect of different types of physical activity was not distinguished. Our outcomes show which type of physical activity could interact, and which ones could have a potential role in preventing impaired renal function among employed men. Secondly, in this selected population of 50–60-year-old men, leisure time physical activity did not exert a significant relation on slight decrease of eGFR. From NHANES, these relationships held only in younger participants (b44 years old) and those without metabolic syndrome [15]. The authors attempted to explain this pattern of association by assuming that kidney function lost a certain degree of ‘plasticity’ with age. Moreover, it is recognized that tolerance for physical activity decreases as kidney function falls [13]. In contrast, from a survey in Nord-Trondelag [30] or from the Cardiovascular Health Study [16], leisure time physical inactivity was associated with lower eGFR (under 45 ml/min) and higher levels of leisure time PA slowed the progression of kidney function decline, in older populations (N 65 years). [16].Thus, the effect of leisure physical activity on modest declines of eGFR is not well established [12] and there is currently no guideline that advocates physical activity for renal function protection in early stages [14]. Moreover, our results have shown that the association between different types of occupational activity and eGFR could be even more significant when leisure-time physical activity is low. We can suppose that for people who have no time [31] or no opportunity for leisuretime physical activity, being active at work could be even more important for reno-protection. At least with respect to the prevention of early stages of decreased renal function, we can hypothesize that both periods of occupational and leisure time physical activities of sufficient and regular duration are of public health importance. Different mechanisms have been proposed as partial mediators of the relationship between physical activity and declining renal function, including insulin resistance, inflammatory processes, renal endothelial damage, oxidative stress, and stimulation of the renin angiotensin and sympathetic system [32–34]. Given the fact that multiple pathways might be involved, determining exactly how PA could be implicated in improving renal function remains a challenge for the future. Thus, PA at work offers relevant opportunities for a primary prevention strategy to conserve
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Table 5 Mediator effect of different types of physical activity on the relation between cardiovascular events and eGFR stages, over 10-year follow-up. N = 1058
Angina events
Hard events
HR 95% CI of eGFR stages (model B)
HR 95% CI of eGFR stages (model B)
Stage 1: reference
Stage 2
Stage 3
Mediator effect
Stage 2
Stage 3
Mediator effect
Total effect, P value for trend Sitting or standing still, P value for trend Carry or lifting objects, P value for trend Active at work, P value for trend Leisure PA, P value for trend Commuting to work, P value for trend Total physical activity, P value for trend
1.94 (1.01–3.71)⁎, 0.038 1.97 (1.02–3.77)⁎, 0.035 1.94 (1.01–3.71)⁎, 0.036 1.95 (1.02–3.73)⁎, 0.038 1.94 (1.01–3.71)⁎, 0.038 1.99 (1.03–3.82)⁎, 0.034 1.94 (1.01–3.72)⁎, 0.038
2.20 (0.72–6.65) 2.23 (0.74–6.77) 2.23 (0.74–6.77) 2.20 (0.73–6.66) 2.20 (0.73–6.66) 2.26 (0.74–6.89) 2.20 (0.72–6.66)
35.4 (–30.4; 101.3) 8.7 (−11.5; 28.7) 13.2 (−12.9; 39.5) −1.8 (−5.6; 1.2) 2.6 (−9.9; 15.2) 15.4 (−14.4; 45.2)
0.95 (0.49–1.83), 0.91 0.95 (0.49–1.82), 0.91 0.95 (0.49–1.83), 0.90 0.96 (0.49–1.82), 0.92 0.96 (0.51–1.86), 0.89 0.96 (0.50–1.85), 0.87 0.95 (0.49–1.83), 0.91
1.22 (0.36–4.15) 1.22 (0.36–4.15) 1.24 (0.36–4.21) 1.20 (0.35–4.08) 1.22 (0.35–4.11) 1.24 (0.36–4.27) 1.22 (0.36–4.15)
−22.8 (−65.1; 19.3) −19.1 (−65.7; 27.4) −16.5 (−47.2; 14.1) −13.1 (−32.9; 6.8) −9.8 (−32; 51.8) −9.1 (−24.1; 5.8)
Model B: Cox analysis adjusted on age, metabolic syndrome, treatment of HBP, alcohol and tobacco consumptions, educational level. HR and 95% CI of eGFR stages (1, 2, 3). Stage 1: reference. Mediator effect: proportion of total effect explained by mediator (PE) (total effect − potential effect)/total effect * 100), mean and 95% CI. Bootstrap sampling (500). To evaluate proportion of mediator effect, interactions between eGFR and each mediator were checked and none were significant. ⁎ P value b 0.05.
renal function in employed men. Future work could help shed light on whether reno-protective effects operate through local hemodynamic mechanisms in the kidney or by delaying arteriosclerosis and insulin resistance among people who present a moderate decrease of eGFR. Thirdly, our results confirmed that a moderate decrease of eGFR was a significant risk factor for angina events (i.e. according to the stage of eGFR; P for trend was significant), but not for hard events such as myocardial infarction or coronary death. This may reflect a stress effect on atheroma processes among these middle-age working men but probably not of sufficient severity for acute events like myocardial infarction (ischemic process).The influence on “hard” effects needs to be re-examined in future studies with longer follow-up. Indeed, while the impact of the end stages of CKD on cardiovascular risk is welldocumented, the threshold for early impairment of eGFR at which cardiovascular risk increases significantly remains to be determined clearly. The level at b60 ml/min/1.73 m2 is put forward by the KDIGO guideline, while one recent study suggests a level of b90 ml/min/ 1.73 m2 as a threshold for risk [35]. We have tested the potential mediating effect of occupational physical activity on the relationship between stages of CKD and CVDs over 10 years follow-up. None of the different types of physical activity have shown a mediating impact on these CVD. Any explanation for this may need to account for the fact that these men are at a transitional period in their lives and with the cessation of occupational activity after retirement (as it could be in this population after 10 year's follow-up), the positive or negative impact of some occupational physical activity could be temporary, and may not have a long-term effect or sustained effect on the incidence of CHD. In other words, being active or sedentary during the working period could influence the impact of declining renal function on the development of CVD, but these effects may fade with time. To confirm this hypothesis, longitudinal studies with repeated measures of the different levels of physical activity are needed. Study limitation: As in other studies focused on impact of physical activity on early reductions of kidney function, the first part of our findings are limited by the initial cross sectional analysis from which we cannot ascertain a causal relation. Thus, longitudinal studies and randomized trials assessing the role of physical activity in modifying the early development of CKD are required to confirm these results. Few studies have focused on the impact of physical activity on early stages of eGFR and among the three recent cross-sectional studies, two evaluated eGFR according to the MDRD formula [17,30] and one used the Cockroft–Gault equation [15]. Recently, it has emerged that the more accurate and accessible equation for estimating GFR is that based on serum creatinine by CKD-EPI equation [21,22], as used here. Even if a more accurate formula to calculate eGFR was used, proteinuria was not available in PRIME study and strictly speaking damage to the kidney could not evaluated. However, leisure-time physical activity seemed to have no effects on proteinuria [15].
5. Conclusion Chronic kidney disease is an important public health challenge, requiring an early prevention strategy. GFR represents a key determinant of renal function to define CKD. The results of this study suggested that occupational physical activity could have the potential to exert dual roles on early impairments of renal function, in employed men. Sedentary behaviors such as sitting could increase the risk, while activities such as lifting and carrying, could have a protective effect. Managing occupational physical activity could be an efficient way for preventing subtle declines of GFR, even if the impact on the relation between cardiovascular diseases over 10 years follow-up has not been not confirmed. Authors' Individual Contribution Conducted the literature review and prepared the material and methods and results and the discussion sections of the text: Y.E., F.K.; drafted this article: Y.E.; designed the study and directed its implementation in each center, including quality and control, design: J.F, J.B.R, F.K.; revised the article critically for important intellectual content: M.T., D.F., J.F., M.Q., M.H; helped in statistical analyses and results: J.B.R., Y.E. All authors have contributed substantially to this article and approved the final version. Competing interests The authors declare no potential conflict of interest, including related consultancies, shareholdings and funding grants. Ethics approval Participants were invited to participate voluntarily in this study and signed a consent form: an approval from Belfast ethics committee was obtained (06/NIR02/107). Acknowledgment We thank the following organizations that facilitated the recruitment of participants for the Prospective Epidemiological Study of Myocardial Infarction (PRIME): the health screening centers organized by the Social Security of Lille (Institut Pasteur), Strasbourg, Toulouse, and Tourcoing; the occupational medicine services of Haute-Garonne; the Urban Community of Strasbourg; the Association Inter-entreprises des Services Médicaux du Travail de Lille et environs; the Comité pour le Développement de la Médecine du Travail; the Mutuelle Générale des Postes, Télégraphes et Téléphones du Bas-Rhin; the Laboratoired'Analyses de l'Institut de Chimie Biologique de la Faculté de Médecine de Strasbourg; the Department of Health (NI); and the Northern Ireland Chest
Y. Esquirol et al. / International Journal of Cardiology 177 (2014) 1036–1041
Heart and Stroke Association. We also thank the Alliance Partnership Programme for its financial support and the following members of the event validation committees: L. Guize; C. Morrison; M.-T. Guillanneuf; and M. Giroud. Funding: PRIME was supported by grants from the Institut National de la Santé et de la Recherche Médicale (INSERM), Merck, Sharp & Dohme-Chibret Laboratory and the Department of Health and Social Services and Personal Safety for Northern Ireland. Ethic committees in PRIME STUDY : for French centers : CNIL 252269 and agreement 04/ 06/91/60 from ethic committee and for Belfast center : 06/NIR02/107. Funding sources had no role in collection, analysis, interpretation of data, writing of the report, or the decision to submit the paper for publication. F.K., M.T. and M.H. received funding from the UKCRC Centre of Excellence for Public Health Research, Northern Ireland. Ethic committees in PRIME STUDY: for French centers: CNIL 252269 and agreement 04/06/91/60 from ethic committee and for Belfast center: 06/ NIR02/107. References [1] Esquirol Y, Yarnell J, Ferrieres J, Evans A, Ruidavets JB, Wagner A, et al. Impact of occupational physical activity and related tasks on cardiovascular disease: emerging opportunities for prevention? Int J Cardiol 2013;168:4475–8. [2] Foster MC, Rawlings AM, Marrett E, Neff D, Grams ME, Kasiske BL, et al. Potential effects of reclassifying CKD as a coronary heart disease risk equivalent in the US population. Am J Kidney Dis 2014;63:753–60. [3] Levey AS, Atkins R, Coresh J, Cohen EP, Collins AJ, Eckardt KU, et al. Chronic kidney disease as a global public health problem: approaches and initiatives—a position statement from Kidney Disease Improving Global Outcomes. Kidney Int 2007;72:247–59. [4] Black C, Sharma P, Scotland G, McCullough K, McGurn D, Robertson L, et al. Early referral strategies for management of people with markers of renal disease: a systematic review of the evidence of clinical effectiveness, cost-effectiveness and economic analysis. Health Technol Assess 2010;14:1–184. [5] Matsushita K, van der Velde M, Astor BC, Woodward M, Levey AS, de Jong PE, et al. Association of estimated glomerular filtration rate and albuminuria with all-cause and cardiovascular mortality in general population cohorts: a collaborative meta-analysis. Lancet 2010;375:2073–81. [6] Chrysohoou C, Panagiotakos DB, Pitsavos C, Skoumas J, Toutouza M, Papaioannou I, et al. Renal function, cardiovascular disease risk factors' prevalence and 5-year disease incidence; the role of diet, exercise, lipids and inflammation markers: the ATTICA study. QJM 2010;103:413–22. [7] Mahmoodi BK, Matsushita K, Woodward M, Blankestijn PJ, Cirillo M, Ohkubo T, et al. Associations of kidney disease measures with mortality and end-stage renal disease in individuals with and without hypertension: a meta-analysis. Lancet 2012;380: 1649–61. [8] Matsushita K, Selvin E, Bash LD, Franceschini N, Astor BC, Coresh J. Change in estimated GFR associates with coronary heart disease and mortality. J Am Soc Nephrol 2009;20:2617–24. [9] Debella YT, Giduma HD, Light RP, Agarwal R. Chronic kidney disease as a coronary disease equivalent—a comparison with diabetes over a decade. Clin J Am Soc Nephrol 2011;6:1385–92. [10] Tonelli M, Wiebe N, Culleton B, House A, Rabbat C, Fok M, et al. Chronic kidney disease and mortality risk: a systematic review. J Am Soc Nephrol 2006;17:2034–47. [11] Heiwe S, Jacobson SH. Exercise training for adults with chronic kidney disease. Cochrane Database Syst Rev 2011:CD003236. [12] Rognant N, Pouliquen E, Fave S, Jolivot A, Laville M. Physical activity and chronic kidney disease: an update in 2013? Nephrol Ther 2014;10:86–93. [13] Johansen KL, Painter P. Exercise in individuals with CKD. Am J Kidney Dis 2012;59: 126–34. [14] Smith AC, Burton JO. Exercise in kidney disease and diabetes: time for action. J Ren Care 2012;38(Suppl. 1):52–8.
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