QJM Advance Access originally published online on July 23, 2008
QJM 2008 101(9):713-721; doi:10.1093/qjmed/hcn084
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Physical activity is negatively associated with the metabolic syndrome in the elderly
From the Department of Internal Medicine, Aging and Nephrological Diseases, University of Bologna, Bologna, Italy
Address correspondence to Dr G. Bianchi, Department of Internal Medicine, Aging and Nephrological Diseases, University of Bologna, S. Orsola–Malpighi Hospital, Via Albertoni 15, I-40138 Bologna, Italy. email: giampaolo.bianchi{at}unibo.it
Received 29 October 2007 and in revised form 30 May 2008
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Summary |
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Background: An inverse association between physical activity and metabolic syndrome has been reported in several cohorts, but very few specific studies are available in the elderly, in whom neurological and musculo-skeletal diseases are expected to lead to a remarkable age-related decline of physical activity.
Aim and Design: The relationships among physical activity, insulin resistance and metabolic syndrome were assessed in a cross-sectional study concerning 1144 subjects aged 65–91 years resident in Pianoro (northern Italy). Household and leisure-time activities were assessed by a self-administered questionnaire (Physical Activity Scale for Elderly—PASE). Routine clinical and biochemical data (including fasting insulin) were used to assess insulin resistance [Homeostasis Model Assessment (HOMA) method] and the prevalence of metabolic syndrome.
Results: All PASE scores were inversely correlated with waist circumference, triglycerides and HOMA index, with highest significance for leisure-time activities (P 
0.005). The PASE score for household activities was also correlated inversely with blood glucose (P < 0.05), and directly with HDL cholesterol (P < 0.001). In logistic regression analysis, the metabolic syndrome was more prevalent among sedentary subjects (corresponding to the low tertile of leisure-time activities) than in the remaining more active population (odds ratio 1.51, 95% confidence interval 1.12–2.03, P = 0.007), independently of possible confounders.
Conclusion: Physical activity is inversely associated with insulin resistance and the metabolic syndrome even in the elderly. Community programs favoring physical activity are expected to significantly improve the health status in these subjects.
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Introduction |
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Metabolic syndrome (MS), as defined by the clustering of abdominal obesity, high triglycerides, blood glucose and blood pressure, and low high-density lipoprotein (HDL) cholesterol, is a condition with a high prevalence in Western countries,1–3 which is associated with an increased risk of cardiovascular mortality and morbidity.4–6 Insulin resistance is thought to be its central pathophysiological feature.2,7 MS is associated with a reduced muscular strength and cardio-respiratory fitness, with following limited mobility,8,9 especially when obesity, one of the components of the syndrome, occurs.8
Changes in lifestyle have been shown to modify the prevalence and severity of MS, as well as to reduce insulin resistance, in adults.10 Among modifications in lifestyle, the increase in physical activity plays a relevant role.11 Conversely, patients with reduced mobility due to neurological disorders have high fasting glucose levels, low HDL-cholesterol and large abdominal sagittal diameters.12 Similarly, elderly subjects with reduced physical activity and mobility, due to limitations of the musculo-skeletal system,13 are expected to have a high prevalence of MS features, but very few studies have specifically addressed this subject.4,8,13
The present investigation was performed to assess the relationships involving physical activity, insulin resistance and MS in a wide non-selected population of Italian elderly subjects. In particular, household and leisure-time activities were considered both separately and as a global physical activity.
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Materials and methods |
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Research design and subjects—The Pianoro Study
This study reports a cross-sectional sub-analysis of a larger study aimed at measuring and promoting physical activity in elderly subjects, in order to reduce the rate and the impact of cardiovascular events.
In November 2003, a postal questionnaire was sent to all subjects over 65 years old of both sexes (3255 people) resident in Pianoro, a mountain municipality near Bologna (northern Italy). Of these, 2023 subjects (62%) sent back the questionnaire, which provided information concerning risk factors for atherosclerosis, lifestyles, previous cardiovascular diseases, quality of life, degree of autonomy, prescribed medications and physical activity during the last week.
All subjects were then invited to their general practitioner's office to sign the informed consent to participate in the study, to check or fill in the questionnaire, to measure weight, height and blood pressure, to show the documentation concerning their clinical history and to perform a cognitive test.
Finally, they were also invited to come to our hospital division, 1 or 2 weeks later, where a fasting blood sampling, a general physical examination (including anthropometry), an electrocardiogram, an abdominal ultrasound assessment and a sub-maximal step-test were performed. The study was approved by the joint University–Hospital Ethical Committee.
In all, 1144 subjects performed all clinical steps of the study without missing data. They are the population considered in the present report: 550 men and 594 women, aged 65–91 years (median 71).
A total of 861 subjects sent back the questionnaire but did not come to our laboratories, and therefore were not included in the study. Among the questionnaires, 788 were fully completed and allowed the analysis of the differences from the 1144 participants: non-participants had a greater prevalence of previous myocardial infarction (9.2% vs. 5.3%; P = 0.002), stroke (5.4% vs. 2.9%; P = 0.009) and peripheral artery disease (10.0% vs. 6.4%, P = 0.005), and were more often women (56.6% vs. 51.9%, P = 0.029). Moreover, the same subjects reported a lower physical activity [Physical Activity Scale for the Elderly (PASE)-global: 37.4 (0–151) vs. 53.5 (0–183), P < 0.001], a lower alcohol consumption (26.1% vs. 33.8% drinking more than one drink a day, P < 0.001), a lower body mass index (BMI) [25.8 (4.2) vs. 26.3 (3.8); P = 0.002] and a lower prevalence of hypercholesterolemia (25.1% vs. 34.6%; P < 0.001). On the other hand, there were no differences with the participants as far as age is concerned and in the prevalence of diabetes, hypertension and former or current smoking.
Variables examined
PASE
Physical activity was evaluated with the PASE14 in its validated Italian version.15 PASE scores are obtained by a self-administered questionnaire consisting of two sections. Section I considers the individual performance of household activities, while Section II estimates recreational physical activity. The score of Section I is derived from the sum of the activity weights during the whole day, where every household activity (mainly heavy or light household work, gardening and home repairs) has a specific weight, regardless of its duration. The score of Section II is calculated by multiplying the weekly time (hours) spent in every single recreational activity (mainly walking, jogging, outdoor or indoor cycling, home or gym exercising, dancing and swimming) with the activity weight. The weights had been obtained in previous validation studies.14,16,17 The PASE global score is produced by summing the scores of the two sections.16,17
Mini Mental State Examination (MMSE)
The MMSE18 is the most extensively studied cognitive assessment test in elderly people. It was corrected for the influence of aging (by increasing the score in the most advanced ages) and educational level (by decreasing the score in the most educated subjects).19,20
Anthropometry
BMI was calculated as the ratio between weight (kg) and the square of height (m). Waist circumference, an index of central fat distribution, was measured in centimeters with the patient standing at the umbilicus level.
Diabetes and MS definition
Subjects with a history of diabetes according to the American Diabetes Association Criteria,21 and/or on anti-diabetes drugs were considered diabetic. The presence of MS was defined according to the National Cholesterol Education Program-Adult Treatment Panel III (NCEP-ATP III).22 MS occurs when at least three of the following five criteria are fulfilled: (i) waist circumference >102 cm in men or >88 cm in women; (ii) Blood glucose 
100 mg/dl (5.6 mmol/l) or current treatment with anti-diabetes drugs; (iii) triglycerides 150 mg/dl (1.7 mmol/l) or current treatment with fibrates; (iv) HDL cholesterol <40 mg/dl (1.0 mmol/l) in men or <50 mg/dl (1.3 mmol/l) in women; (v) systolic blood pressure 130 mmHg and/or diastolic blood pressure 85 mmHg or current treatment with anti-hypertensive drugs.
Insulin resistance
Insulin resistance was estimated, according to the homeostasis model assessment (HOMA), with the formula: (fasting insulin [mU/l] x fasting glucose [mmol/l])/22.5.23,24 In a previous study concerning an Italian population, the cut-off of HOMA values indicative of insulin resistance (above the 75th percentile) was set at 2.7.25
Laboratory assays
Fasting venous blood samples were collected in vacutainer tubes with gel clot activator. Blood glucose, creatinine, total cholesterol, HDL-cholesterol and triglycerides were assayed with enzymatic methods on the same day of blood sampling.
Insulin was measured a few months later, in aliquots of serum frozen at –80°C, by an ElectroChemiLuminescence Immuno Assay (ECLIA, Elecsys Insulin, Roche Diagnostics Co., Mannheim, Germany) with an analytical sensitivity of 0.2 mU/l.
Statistical analysis
Data are reported as mean (standard deviation) for the variables with normal or near-normal distribution, otherwise data are reported as median (range). PASE scores were treated both as continuous and dichotomous variables (having the limit between low and middle tertile, in the whole population, as cut-off point).
Since most variables did not share a normal distribution, the groups were compared with non-parametric tests (Mann–Whitney's U-test or Kruskal–Wallis’ test). Percentages were compared with 
2-test (with Yates’ correction) or with Fisher's exact test when appropriate. Simple correlations were assessed by Spearman's test and calculation of 
coefficients.
To ascertain which variables were independently associated with the MS, a backward logistic regression analysis was performed, including the low tertile of PASE-leisure time as indicator of sedentariness, together with gender, ever smoking, alcohol consumption, living alone, Charlson's index26 (an indicator of comorbidities) and treatment with statins or anti-hypertensive drugs as covariates. For each variable the adjusted relative risk OR of having MS and 95% CI were also computed.
Two-tailed tests were used throughout, considering significant P-values <0.05. The statistical analysis was performed with SPSS software (version 8.0) (SPSS Inc. Headquarters, Chicago, IL, USA).
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Results |
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Table 1 shows the main lifestyle, social and health characteristics of the study population.
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The prevalence of MS among the subjects that completed the study was 40.5%. Approximately 14% of the subjects were still working (irrespective of official retirement), 19% lived alone, >45% were former or current smokers and two-thirds were alcohol drinkers, mostly of wine (>95%) (Table 1). The prevalence of alcohol consumption and smoking was significantly higher in males than in females (data not shown).
The subjects with MS were more frequently females than males, which was in contrast to the higher HOMA index in males than in females [respectively, 2.01 (0.27–22.00) vs. 1.90 (0.18–18.20); P = 0.03]. Moreover, the subjects with MS differed from the subjects without the syndrome for a higher prevalence of previous smokers, for performing less leisure-time activities, for having more comorbidities and for taking more ACE-inhibitors, statins and β-blockers. In particular, Table 1 reports the different prevalence of the low tertile of the three PASE scores (as determined in the whole population) among the subjects with and without MS. The medians of the absolute values were, respectively, 51.4 vs. 55.5, P = 0.17 (PASE-global); 40.5 vs. 42.3, P = 0.73 (PASE-household activities) and 10.9 vs. 13.2, P = 0.005 (PASE-leisure time). The subjects with MS also tended to live alone more frequently and to be current smokers less frequently than the subjects without MS, but these differences were not significant. MS was not associated with age, alcohol consumption, working status and cognitive status as assessed by MMSE.
PASE-global score was higher in females than in males [55.8 (0–155) vs. 51.0 (0–183), P < 0.001], mostly due to the contribution of household activities [45.5 (0–135) vs. 37.2 (0–136), P < 0.001]. Instead, PASE-leisure time score was higher in males than in females [14.1 (0–100) vs. 10.4 (0–70), P < 0.001].
In females aging reduced PASE-leisure time and PASE global scores (P < 0.001 for both variables), while PASE-household activities did not change significantly (P = 0.07) (Figure 1). In males no age-related decline of PASE scores was observed (P > 0.46 for all scores) (Figure 1).
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Table 2 reports the absolute values of MS components, together with BMI, HOMA index and diabetes prevalence, both in the whole population and in the subgroups with and without MS. The table also reports the prevalence of MS components in agreement with the ATP III criteria for MS. As far as total cholesterol is concerned, the subjects with MS had slightly lower levels than the subjects without MS [215.0 (39.6) vs. 218.3 (37.0); P = 0.048].
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The correlations of PASE scores with MS components, total cholesterol and HOMA index are shown in Table 3. Physical activity was inversely correlated with waist circumference, insulin resistance and triglycerides in each of the three PASE categories (leisure-time activities, household activities and global activities), although the significance was generally higher for leisure time with respect to household activities. Conversely, the inverse relationship with blood glucose and the direct correlation with HDL cholesterol seemed to concern household activities only. No significant relationship was found between systolic or diastolic blood pressure and PASE scores.
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Figure 2 shows the relationships of PASE-leisure time score and PASE-global score with HOMA index. PASE-leisure time was significantly correlated with HOMA index both in males (
= –0.124, P = 0.004) and in females ( = –0.096, P = 0.022), while the association between PASE-household activities and HOMA index was no longer significant when the population was split according to gender.
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The variables listed in Table 1 that in univariate analysis were associated with the MS with P-values <0.20 (female gender, previous/current smoking, low tertile of PASE-leisure time score, living alone, having a Charlson's index >2, and treatment with statins, β-blockers and ACE inhibitors) were included in a multiple logistic regression model, with the presence of MS as dependent variable. The variables that remained significantly associated with the MS after a backward elimination procedure are shown in Table 4. Statins, ACE inhibitors and β-blockers were the most significant covariates, as they reflected two important components of the MS, i.e. dyslipidemia and hypertension. After them, the main factor independently associated with the MS was a low leisure-time activity: the subjects with the corresponding PASE score in the low tertile had a relative risk of 1.51 of having MS, with respect to the remaining more active subjects. Finally, also the female gender remained feebly associated with MS.
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Discussion |
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Our study shows that sedentariness, concerning, in particular, leisure time activities, is associated with a higher prevalence of MS also in the elderly, similar to what is known to occur in younger individuals.27,28 The elderly subjects of Pianoro with high PASE-scores tended to have low values of most features of the MS, suggesting that physical activity may be a disease-modifying factor for MS.
Our multivariate analysis included all lifestyle, social and health characteristics that were associated with MS with P values <0.20, and that were not related to the definition of MS. After drug treatment (which included variables highly associated with MS, since the use of drugs reflected the presence of hypertension and dyslipidemia), the factor most strongly (inversely) associated with MS was leisure-time physical activity. Instead, household physical activity was not associated with MS, even in univariate analysis. Possibly, household physical activity, differently from leisure-time activity, may be overestimated by the PASE, or may not involve a significant amount of aerobic exercise. Clearly, the possibility also exists that the subjects with three or more of the items describing MS were less willing to perform leisure-time activities.
The other factor independently associated with MS was the female sex. This appears in contrast with the fact that HOMA index is higher in men, and is probably due to the definition itself of MS, which settles less restrictive thresholds of waist circumference and HDL cholesterol for women.
As already pointed out, MS has insulin resistance as a putative pathogenic factor for every single feature.29 In our elderly subjects the HOMA index was inversely correlated with physical activity. Physical activity would thus appear to favorably affect insulin resistance. This remark confirms previous observations emphasizing the protective effects of physical activity with respect to insulin-resistance.10,30 The training in resistance activities produces an increase in muscular mass, an increase in glucose uptake per unit of muscular mass and a significant increase in insulin activity in skeletal muscles.30
The pathway connecting physical activity to a reduced prevalence of MS also includes the documented actions on triglycerides and HDL-cholesterol levels,31 blood pressure levels32 and central obesity.33
Many epidemiologic observational studies have reported a consistent inverse association between physical activity and the risk of incident cardiovascular diseases.4 Even in an advanced age, becoming more active still confers a reduction in coronary heart disease mortality.34,35 The cardio-protective effect of physical activity may be related to its beneficial effects on body weight, blood pressure, serum cholesterol and glucose tolerance.36–38
The ability of PASE score in effectively describing the individual physical activity in the elderly emerged in all validation studies of the PASE questionnaire.16,17 Washburn and coworkers17 showed that in subjects less than 70 years old PASE leisure-time score was the parameter best correlated with the data records obtained by accelerometer. The population scores reported by Washburn14,16,17 and others39 were higher than the scores obtained in our subjects, possibly because of greater mobility and fitness of northern populations. Any leisure-time activity is of benefit (walking, dancing or sport activity). Apparently, this kind of physical activity is able to modify the metabolic status, improving insulin sensitivity. The thresholds of intensity, frequency and duration of this physical activity, beyond which the beneficial effects begin, have not been clearly determined. Recent reports indicate that even a moderate-intensity activity or brisk walking leads to an improved insulin sensitivity.37
The PASE questionnaire appears to be an easy, fast and valid tool for the assessment of physical activity in epidemiologic studies concerning elderly populations14. Its very low ‘cost’, in terms of consumed resources, renders it winning a semiquantitative appraisal of physical activity. In our hands, it provided reasonable and consistent data to assess the association of physical activity with MS.
In this study we had the opportunity to test the hypothesis of a possible association between MS and cognitive impairment. However, no association was found between MMSE score and MS, suggesting a substantial independence of cognitive status from insulin resistance.
The main limit of this study is its cross-sectional nature, which does not allow any assessment of the cause–effect relationships for the associations found. We are now trying to promote and increase physical activity in the elderly of the Pianoro community, in order to ascertain whether such a strategy may lead to a reduced prevalence of MS and associated diseases.
In conclusion, our study demonstrates that physical activity is inversely associated with insulin resistance and several related risk factors, and that a low leisure-time activity is an independent predictor of the presence of MS in the elderly. The engagement into a regular physical workload, possibly of recreational type, could simultaneously modify several predisposing factors, with beneficial effects following, even at an advanced age.
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Pianoro Study Group
Dipartimento di Medicina interna, Invecchiamento e Malattie Nefrologiche—University of Bologna
Steering committee: Marco Zoli (Chief), Giampaolo Bianchi, Donatella Magalotti, Antonio Muscari.
Medical staff: Annalisa Berzigotti, Nicola Castaldini, Susanna Dapporto, Gianmarco Drago, Claudia Giannoni, Cosimo Ludovico, Fausta Montesi, Francesco Nicolino, Stefano Ramilli, Dario Sbano, Paola Zappoli.
Technical staff: Raffaela Chianese, Franca Ferri, Giorgia Passerini.
Dipartimento di Istologia, Embriologia e Biologia Applicata—University of Bologna
Physical training staff: Pasqualino Maietta, Claudio Tentoni, Erika Nerozzi, Carlo Ravaioli, Paola Masi, Irene Negrini.
Dipartimento di Scienze Statistiche—University of Bologna
Statistical staff: Giulia Cavrini, Andrea Mattivi.
Azienda USL Bologna
Epidemiological staff: Marilina Colombo, Paolo Pandolfi.
Laboratory staff: Amedeo Ligabue, Loretta Dini, Annalisa Zacchini.
General practitioners: Alberto Melli, John Sonnino, Antonio Ajuti, Stefano Bacci, Luca Bruni, Gemma Chiarini, Giampaolo Corradini, Fulvia Nucci, Elisabetta Ongaro, Elisabetta Scandellari, Serena Selva.
Municipality of Pianoro
Simonetta Saliera (Mayor), Gianalberto Cavazza, Antonella Grazia, Maria Pia Mezzini, Daniela Mignogna, Emanuela Torchi.
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*Members of the Pianoro Study Group are listed in the appendix.
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The study was supported by grants from Fondazione Cassa di Risparmio in Bologna and Regione Emilia Romagna, Piani per la Salute.
Conflict of interest: None declared.
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References |
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