The associations between physical fitness and cardiometabolic risk and body-size phenotypes in perimenopausal women

Maturitas 92 (2016) 162–167

Contents lists available at ScienceDirect

Maturitas journal homepage: www.elsevier.com/locate/maturitas

The associations between physical fitness and cardiometabolic risk and body-size phenotypes in perimenopausal women E. Gregorio-Arenas a,b , P. Ruiz-Cabello a , D. Camiletti-Moirón a,c , N. Moratalla-Cecilia a,b , P. Aranda a , M. López-Jurado a , J. Llopis a , V.A Aparicio a,d,∗ a

Department of Physiology, Faculty of Pharmacy, Faculty of Sport Sciences, and Institute of Nutrition and Food Technology, University of Granada, Spain Pinos Puente Clinical Management Unit, Granada, Spain c Department of Physical Education, School of Education, University of Cádiz, Spain d Department of Public and Occupational Health, EMGO+ Institute for Health and Care Research, VU University Medical Centre, Amsterdam, The Netherlands b

a r t i c l e

i n f o

Article history: Received 31 March 2016 Received in revised form 1 August 2016 Accepted 11 August 2016 Keywords: Metabolic syndrome Cardiorespiratory fitness Flexibility Fitness testing

a b s t r a c t Objective: To study the association between physical fitness and body-size phenotypes, and to test which aspects of physical fitness show the greatest independent association with cardiometabolic risk in perimenopausal women. Study design: This cross-sectional study involved 228 women aged 53 ± 5 years from southern Spain. Main outcome measurements: Physical fitness was assessed by means of the Senior Fitness Test Battery (additionally including handgrip strength and timed up-and-go tests). Anthropometry, resting heart rate, blood pressure and plasma markers of lipid, glycaemic and inflammatory status were measured by standard procedures. The harmonized definition of the ‘metabolically healthy but obese’ (MHO) phenotype was employed to classify individuals. Results: The overall prevalence of the MHO phenotype was 13% but was 43% among the obese women. Apart from traditional markers, metabolically healthy non-obese women had lower levels of C-reactive protein than women with the other phenotypes (p < 0.001), and levels of glycosylated haemoglobin were lower in MHO women than in metabolically abnormal non-obese women (overall p = 0.004). Most of the components of physical fitness differed with body-size phenotypes. The 6-min walk and the back-scratch tests presented the most robust differences (both p < 0.001). Moreover, the women’s performance on the back-scratch (␤ = 0.32; p < 0.001) and the 6-min walk (␤ = 0.22; p = 0.003) tests was independently associated with the clustered cardiometabolic risk. The back-scratch test explained 10% of the variability (step 1, p < 0.001), and the final model, which also included the 6-min walk test (step 2, p = 0.003), explained 14% of the variability. Conclusion: Low upper-body flexibility was the most important fitness indicator of cardiometabolic risk in perimenopausal women, but cardiorespiratory fitness also played an important role. © 2016 Elsevier Ireland Ltd. All rights reserved.

1. Introduction Cardiovascular disease is the main cause of mortality worldwide. In the last decade, a new concept, the cardiometabolic risk, appeared due to the association between cardiovascular disease and metabolic abnormalities [1]. These metabolic risk factors are

Abbreviations: MANO, metabolically abnormal and not obese; MAO, metabolically abnormal and obese; MHNO, metabolically healthy and not obese; MHO, metabolically healthy but obese. ∗ Corresponding author at: Department of Physiology, School of Pharmacy, Campus Universitario de Cartuja s/n. Granada, 18071, Spain. E-mail address: [email protected] (V.A Aparicio). http://dx.doi.org/10.1016/j.maturitas.2016.08.008 0378-5122/© 2016 Elsevier Ireland Ltd. All rights reserved.

clustered in a phenomenon called the metabolic syndrome, which directly promotes cardiovascular disease [2]. Menopause also impairs cardiometabolic markers due to the significant decline in the oestrogens levels and the testosterone predominance [3]. This period is frequently associated with weight gain and central body-fat accumulation, which may contribute to the development of hypertension, dyslipidaemia and insulin resistance [4]. Consequently, the menopause transition is associated with higher incidence of metabolic syndrome [5]. Nonetheless, despite obesity is an important contributor of cardiovascular disease and has increased in pandemic proportions [6], a group of obese people seem to be protected against many obesity-related cardiometabolic complications. These individuals

E. Gregorio-Arenas et al. / Maturitas 92 (2016) 162–167

are described as metabolically healthy but obese (MHO) [7] and some studies have evidenced that higher physical fitness should be considered an inherent characteristic of this phenotype [8]. Nowadays, almost 40% deaths attributable to cardiovascular disease can be explained by physical inactivity [9] and most of adult women are physically inactive [10]. To note is that contrary to general knowledge, cardiorespiratory fitness, which is comprised of physical activity and genetic endowment, is the strongest predictor of life expectancy beyond other traditional risk factors such as tobacco or inadequate dietary habits [9]. Since physical fitness is a modifiable factor powerfully related to cardiovascular disease, it is important to comprehensively characterize the extent to which different components of fitness (cardiorespiratory fitness, muscle strength, flexibility and balance) present independent associations with the cardiometabolic risk when all the fitness components are simultaneously considered. Therefore, the aims of the present study were to study the association of physical fitness with body-size phenotypes, and to test which physical fitness components show the greatest independent association with the cardiometabolic risk in perimenopausal women. 2. Materials and methods 2.1. Participants and study design We recruited 228 women from primary care centres from Granada, southern Spain. All participants were informed about the aims, the study procedures and signed a written informed consent before taking part in the study. The inclusion criteria were: 1) to be between 45 and 65 years old; 2) not to have presence of neuromuscular disease or drugs affecting neuromuscular function; 3) able to ambulate without assistance; 4) not to have suffered a major cardiovascular event (i.e. myocardial infarction, angina, or stroke) in the past 6 months. 2.2. Procedures 2.2.1. Socio-demographic and clinical data Socio-demographic information was recorded using self-report questionnaires that included age, smoking and alcohol consumption, marital status and educational level, among others. Current diagnosis or being under treatment for diabetes, hypertension, or hypercholesterolemia was recorded. Hypertension, hypercholesterolemia and diabetes mellitus were considered present if the participant had been previously diagnosed by a doctor or showed an average blood pressure ≥140/90 mmHg, total cholesterol ≥240 mg/dL and fasting glucose ≥126 mg/dL. Smoking (yes/no) was defined if the patient was a current smoker or had regularly smoked in the last year. Information related to clinical history of cardiovascular risk factors, acute illness or neuromuscular disease was recorded by physicians involved in the present study. 2.2.2. Anthropometry and body composition A portable eight-polar tactile-electrode impedanciometer (InBody R20, Biospace, Seoul, Korea) was used to measure body fat (%). Height (cm) was measured using a stadiometer (Seca 22, Hamburg, Germany). Body mass index was calculated as weight (kg) divided by height (m) squared. 2.2.3. Resting blood pressure and heart rate Systolic and diastolic blood pressure and resting heart rate were measured after 5 min of rest, twice (2 min apart), with the person sitting down (Omron Health Care Europe B.V. Hoolddorp,

163

The Netherlands). The lowest value of the two measurements was selected for the analysis. 2.2.4. Physical fitness assessment Lower-body muscle strength was measured by the “30-s chair stand” test. This test consists of rising to a full stand from a seated position with back straight and feet flat on the floor and crossed arms at the chest level as many times as possible in 30 s [11]. Upper-body muscle strength was assessed by means of the “handgrip strength” test. Handgrip strength was measured with a digital dynamometer (TKK-5101 Grip-D; Takei, Tokyo, Japan), alternately with both hands, resting 1 min between measures. Optimal grip span was calculated using the formula: y = x/5 + 1.5, where “y” is the grip length and “x” the hand size [12]. The average of the better of 2 attempts for each hand was used in the data analysis Lower-body flexibility was measured through the “sit and reach” test, which requires the use of the sit-and-reach standardized box with a slide ruler attached to the top [13]. The participant is required to sit with knees straight and legs together, and feet placed against the box. The participant slowly reach forward as far as possible [13] and the final position reached in centimetres is the test score. The better of 2 attempts was used in the data analysis. Upper-body flexibility was assessed by means of the “back scratch” test. The back scratch test involves a combination of shoulder adduction, abduction, and external and internal rotation, measuring the distance between the middle fingers behind the back [11]. The better of 2 attempts was used in the data analysis. Motor agility/dynamic balance was measured by means of the “timed up and go” test [14]. The participant, seated in a chair with arms and trunk supported, is instructed to stand up on the word “go” and walk 3 m in a straight line, turn 180◦ , walk back to the chair and sit down again. The time from the start until the participant sit down in the chair with back support is measured. The better of 2 attempts was used in the data analysis. Cardiorespiratory fitness was assessed with the “6-min walk” test. This test involves determining the maximum distance that can be walked in 6 min [11]. 2.2.5. Biochemical analyses Venous blood samples after all night fasting were collected. Blood was collected (with heparin as anticoagulant) and a count cell was performed in a Coulter to measure leukocytes (units × 109 /L). Plasma triglycerides, C-reactive protein, high-density lipoproteincholesterol, low-density lipoprotein-cholesterol, total cholesterol and glucose were estimated by using an autoanalyzer (Olympus Diagnostic, Hamburg, Germany). Glycosylated haemoglobin was determined by immunoturbidimetry (HORIBA-ABX Diagnostics). 2.2.6. Body size phenotypes criteria We employed the harmonized definition of the MHO and metabolically abnormal and obese (MAO) phenotypes, which is a consensus from major international organizations to define metabolic syndrome, i.e. 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 [15], and excluded waist circumference [8]. A person would be classified as MHO if meeting 0 or 1 of the remaining metabolic syndrome criteria (i.e. after excluding waist circumference), which would be the following: triglycerides ≥150 mg/dL (or drug treatment), high-density lipoprotein-cholesterol <50 mg/dL (or drug treatment), systolic blood pressure ≥130 mmHg or diastolic blood pressure ≥85 mmHg (or antihypertensive treatment), and fasting glucose ≥100 mg/dL (or drug treatment). Consistent with previous literature on this topic [8,16] waist circumference was excluded as a criterion since

164

E. Gregorio-Arenas et al. / Maturitas 92 (2016) 162–167

our main purpose was to study the prognosis of obese people who, regardless of their adiposity levels (both total and central), have a better metabolic profile, i.e. MHO phenotype.

2.3. Statistical analysis Descriptive statistics (mean (standard deviation) or percentage) were used to describe the characteristics of the study participants (Table 1). Student’s t-tests were conducted to explore differences in age and anthropometric continuous variables. Differences in categorical variables such as sociodemographic characteristics were assessed by using Chi-square test. One-way analysis of covariance (ANCOVA) was used to assess the differences across body-size phenotypes after adjustment for age, smoking and marital status and educational level (Table 2). The Bonferroni’s correction for multiple comparisons across phenotype groups was applied to test pairwise differences (e.g. MHO vs metabolically healthy non-obese (MHNO)). A clustered cardiometabolic risk (Z-score) constituted by the standardized scores [(value-mean)/standard deviation] of the metabolic syndrome markers (i.e. triglycerides, mean blood pressure, fasting glucose and inverted high-density lipoproteincholesterol) was created. To test which fitness component are independently associated with the cardiometabolic risk (Table 3), a forward stepwise regression analysis was undertaken including the clustered cardiometabolic risk as dependent variable in a separate model. The aforementioned confounders and the six physical fitness tests were simultaneously introduced into the model by using a stepwise procedure. This procedure introduces each variable step-by-step into the model (when p < 0.05) according to the strength of their association with the outcome (i.e. clustered cardiometabolic risk). The model is reassessed with the addition of every new fitness test and variables are left out of the model if p > 0.10. All analyses were performed using the Statistical Package for Social Sciences (IBM-SPSS for Windows, version 22.0, Amonk, NY), and the level of significance was set at p < 0.05.

3. Results The descriptive characteristics of the study participants are shown in Table 1. The mean age of the sample was 53 ± 5 years and the MHNO phenotype was younger than the rest of phenotypes (p < 0.001). Most of the participants were married and had finished secondary school or University but the groups differed in educational level (p = 0.001) and marital status (p = 0.030). The presence of metabolic syndrome among the entire sample was 36% and most of them reported not to have currently regular menstruation. The prevalence of MHO phenotype was 13% (43% of the obese subjects). Cardiometabolic markers plus physical fitness parameters by body-size phenotypes are shown in Table 2. The MHO group presented higher plasma high-density lipoprotein-cholesterol than MAO and lower triglycerides than metabolically abnormal nonobese (MANO) and MAO groups. No differences between body-size phenotypes were observed in total and low-density lipoprotein cholesterol (all p > 0.05). Groups differed in inflammatory profile as measured by C-reactive protein (overall p < 0.001) but did not in leukocytes count. Pairwise comparisons showed higher levels of C-reactive protein among all body-size phenotypes compared to the MHNO. The MHO group had lower fasting glucose than both metabolically abnormal groups. Glycosylated haemoglobin also differed among groups (overall p = 0.004) and was lower in MHO and MHNO compared to the MANO group. The MHNO group showed lower diastolic blood pressure compared to the rest of groups, and

lower systolic blood pressure compared to the metabolically abnormal groups (both overall p < 0.001). Most of the physical fitness components studied differed among body-size phenotypes (except for handgrip and sit and reach tests). Pairwise comparisons showed differences mainly between the MHNO with the rest of groups, and the 6-min walk and the backscratch tests presented the most robust differences (both overall p < 0.001). Stepwise regression analysis assessing which fitness components were independently associated with the clustered cardiometabolic risk is shown in Table 3. The back-scratch (␤ = 0.32; p < 0.001), followed by the 6-min walk (␤ = 0.22; p = 0.003) tests were independently associated with the clustered cardiometabolic risk. The back-scratch test explained 10% of the variability in the clustered cardiometabolic risk (step 1, p < 0.001), and the final model, also including the 6-min walk test (step 2, p = 0.003) explained 14% of the variability.

4. Discussion The prevalence of MHO phenotype among this populationbased sample of perimenopausal women from Southern Spain was 13% (43% of the obese subjects), and 36% of the entirely sample presented metabolic syndrome. Apart from traditional markers, MHNO women showed lower C-reactive protein than the rest of phenotypes and glycosylated haemoglobin was lower in MHO compared to MANO group. Physical fitness was worse in obese and metabolically unhealthy groups. Upper-body flexibility followed by cardiorespiratory fitness were inversely and independently associated with the clustered cardiometabolic risk, and the final model explained 14% of its variability. A lower rate of all-cause mortality among MHO compared to MAO individuals has been reported [17]. Nonetheless, despite the strong evidence that physical fitness is a major predictor of cardiometabolic risk [9], it is usually underestimated when examining the cardiometabolic characteristics of the MHO phenotype and its prognosis [8,18]. Besides, cardiorespiratory fitness seems to play a key role in the prognosis of MHO individuals [8,18], and when adjusting for fitness, MHO subjects present 30–50% lower risk of all-cause and cardiovascular disease mortality than their MAO counterparts [19]. The importance of cardiorespiratory fitness is often overlooked from a clinical perspective compared to obesity [20] despite several prospective studies indicate that high cardiorespiratory fitness attenuate the increased risk of death associated with obesity [20]. A potential mechanism to explain why MHO subjects showed higher cardiorespiratory fitness than MAO is that compared to MAO, MHO women use to spend less time in sedentary behaviour and more in physical activity and active transportation [21]. In fact, the clinically meaningful lower resting heart rate showed by the MHO in relation to the MANO and MAO groups can be promoted by physical activity, and lower resting heart rate is well known as a predictor of all-cause and cardiovascular disease mortality in the middle aged population [22]. Moreover, insulin resistance is an important risk factor for cardiovascular disease [23], and glycemic profile was also better in the MHO compared to the MANO and MAO groups. Previous studies have observed associations between physical fitness and metabolic syndrome markers [24,25], as well as they have contrasted the usefulness of fitness testing to predict metabolic abnormalities [26]. Similar studies developed in lower sample sizes found that muscle strength and cardiorespiratory fitness predicted insulin resistance in men, and cardiorespiratory fitness did it in women [27]. A recent study [24] assessed the same fitness testing battery than us in elderly women and they found that those with the metabolic syndrome scored worse in the timed up-

E. Gregorio-Arenas et al. / Maturitas 92 (2016) 162–167

165

Table 1 Descriptive characteristics of the study participants (n = 228). Metabolically healthy non-obese (n = 119)

Metabolically abnormal and non-obese (n = 39)

Metabolically healthy but obese (n = 30)

Metabolically abnormal and obese (n = 40)

p

51.5 (4.1)abc

53.9 (4.8)a

54.0 (3.7)b

54.9 (5.1)c

<0.001

64.4 (7.6)ab 25.4 (2.6)ab 38.8 (4.8)ab

66.8 (7.2)cd 26.4 (2.3)cd 39.6 (5.4)cd

81.3 (14.5)ac 34.0 (4.3)ac 43.8 (5.1)ac

83.0 (10.6)bd 35.1 (4.3)bd 46.4 (4.2)bd

<0.001 <0.001 <0.001

Marital status (%) Married/with a partner Single Separated/divorced/widow

71.8 16.5 11.8

55.6 11.1 33.3

73.3 6.7 20.0

94.7 0.0 5.3

0.030

Educational level (%) No studies/primary school Secondary school/professional training University degree Regular menstruation (yes, %) Smoking status (yes, %)

37.1 30.2 32.8 31.8 29.7

52.6 36.8 10.5 25.9 18.7

63.0 18.5 18.5 6.7 6.7

71.8 20.5 7.7 26.3 16.7

0.001

Age (years) Anthropometry Weight (kg) Body mass index (kg/m2 ) Body fat (%)

0.251 0.440

Values showed as mean (standard deviation) unless otherwise indicated.

Table 2 Cardiometabolic markers and physical fitness by body-size phenotype groups. Metabolically healthy non-obese (n = 119)

Metabolically abnormal and non-obese (n = 39)

Metabolically healthy but obese (n = 30)

Metabolically abnormal and obese (n = 40)

p

223.2 (3.7) 64.5 (1.4)ab 140.0 (3.3) 92.3 (4.9)ab

227.7 (6.3) 58.2 (2.5)a 135.8 (6.2) 148.6 (8.9)ac

207.9 (7.1) 61.9 (2.6)c 129.1 (6.4) 92.1 (9.5)cd

212.8 (6.5) 50.2 (2.5)bc 129.7 (5.7) 158.1 (8.7)bd

0.100 <0.001 0.309 <0.001

1.84 (0.36)abc 6.4 (0.19)

4.08 (0.67)a 7.0 (0.35)

3.56 (0.72)b 6.5 (0.42)

4.97 (0.76)c 6.7 (0.46)

<0.001 0.449

84.0 (1.20)ab 5.46 (0.05)a

95.4 (2.12)acd 5.84 (0.09)ab

84.3 (2.26)ce 5.45 (0.10)b

104.6 (2.17)bde 5.65 (0.13)

<0.001 0.004

Blood pressure and heart rate Systolic blood pressure (mmHg) Diastolic blood pressure (mmHg) Resting heart rate (bpm)

120.8 (1.5)ab 73.5 (0.9)abc 74.0 (1.2)

131.0 (2.5)a 79.1 (1.5)a 78.5 (2.0)

124.9 (2.9)c 78.1 (1.7)b 71.9 (2.6)

133.1 (2.5)bc 81.4 (1.5)c 78.4 (2.4)

<0.001 <0.001 0.066

Physical fitness tests 6-min walk (m) Handgrip strength (kg) 30-s chair stand (rep) Back-scratch (cm) Sit and reach (cm) Timed up and go (s)#

603.2 (6.7)abc 25.2 (0.44) 15.2 (0.27)ab −1.9 (0.74)abc 25.9 (0.76)a 5.11 (0.08)a

570.5 (11.7)ad 25.7 (0.77) 14.8 (0.47) −4.8 (1.3)a 23.3 (1.3) 5.26 (0.15)

549.0 (13.1)b 25.9 (0.86) 13.7 (0.53)a −5.5 (1.4)b 23.7 (1.5) 5.49 (0.17)

525.2 (11.5)cd 25.4 (0.77) 14.1 (0.47)b −8.2 (1.3)c 21.8 (1.4)a 5.63 (0.15)a

<0.001 0.895 0.045 <0.001 0.060 0.022

Lipid profile Total cholesterol (mg/dL) High-density lipoprotein-cholesterol (mg/dL) Low-density lipoprotein-cholesterol (mg/dL) Triglycerides (mg/dL) Inflammatory profile C-reactive protein (mg/L) Leukocytes (units x 109 /L) Glycaemic profile Fasting glucose (mg/dL) Glycosylated haemoglobin (%)

Values shown as mean (standard error); Analyses were performed with ANCOVA with age, smoking (yes/no), marital status and educational level as covariates; The upper case letters indicate significant differences among the groups with the same letter. # Lower score indicates better performance.

Table 3 Stepwise regression analysis assessing which fitness components were independently associated with the clustered cardiometabolic risk in perimenopausal women. Clustered cardiometabolic risk ␤ Step 1 Back-scratch test Step 2 Back-scratch test 6-min walk test

B

SE

P

−0.319

−0.026

0.006

<0.001

−0.246 −0.218

−0.020 −0.002

0.006 0.001

0.001 0.003

R2

R2 change

P

0.102

0.102

<0.001

0.144

0.042

0.003

SE: Standard Error; ␤, standardized regression coefficient; B, nonstandardized regression coefficient; R2 , adjusted coefficient of determination, expressing the percent variability of the dependent variable explained by each model; R2 change, additional percent variability explained by the model due to the inclusion of the new term.

and-go, 6-min walk, sit and reach and 30-s chair stand tests, which concurs with our findings. In the same line, our group previously reported that Moroccan women with the metabolic syndrome per-

formed worse in most of the fitness tests studied, and that the 6-min walk test was the most associated to metabolic syndrome [26]. Cardiorespiratory fitness (as measured with the 6-min walk

166

E. Gregorio-Arenas et al. / Maturitas 92 (2016) 162–167

test) was also associated to the clustered cardiometabolic risk in the present study sample. In this context, the “6-min walk” test is a good marker of cardiorespiratory fitness which has been suggested as a powerful predictor of survival for patients in clinical settings [28]. Finally, in agreement with our present findings, a recent study [29] has contrasted the strong potential of flexibility on predicting metabolic syndrome in 628 community-dwelling elders. Reduced flexibility was independently associated with metabolic syndrome regardless of age, sex, or body composition [29]. Consequently, they suggested that flexibility should be included in the complete evaluation for metabolic syndrome [29]. Among the hypothesis that could explain the inverse association found between upperbody flexibility and cardiometabolic risk we primary speculated that an increase in arms and trunk obesity hampered the ability to reach in the back-scratch test, resulting in decreased upper-body flexibility in obese participants. However, the MANO group also showed lower upper-body flexibility than the MHNO. Other potential explanatory mechanism might be the fact that upper-body flexibility has been inversely linked with anxiety levels [30,31], and anxiety is strongly associated with the cardiometabolic risk (i.e. elevated risk of cardiovascular events including stroke, coronary heart disease, heart failure and cardiovascular death) [32]. Most people with anxiety (a common feature in climacteric [33]) exhibit increased muscle tension [34] with special emphasis in the trapezius muscles [35] which may decrease the upper-body range of movement. The connection between physical activity and health has been clearly established and exercise should be treated as a cost-effective medication [36] prescribed as a first line treatment [37]. Indeed, the American Heart Association recently highlighted the importance of promoting exercise as medicine, and stated that no one can argue that cardiorespiratory fitness and physical activity, should feel right at home in healthcare [38]. Moreover, greater emphasis should be placed on improving fitness rather than weight loss in the prevention of cardiovascular diseases [39]. Since poor fitness is a common feature of the MAO phenotype that contribute to cardiovascular disease, exercise programs can prevent the transition of MHO to MAO but also promote the transition from MAO to MHO phenotypes [40]. Therefore, the study of the effects of fitness-based interventions rather than weight-loss driven approaches to reduce cardiometabolic risk in MAO and MANO perimenopausal women is warranted. The limitations of the present study include the inability to establish causal relationships in the associations found, due to its cross-sectional design. The study lacked of a high sample size, was of convenience and was carried out in a single area, so may not be representative of the wider population. Moreover, we lack prognostic information on the different body-size phenotypes to enable us to make a more robust recommendation for clinical practise. Finally, we did not register dietary patterns and physical activity levels which would have been of interest for the better interpretation of the present results. On the other hand, we assessed a large number of physical fitness components through validated fitness tests, which can provide insights for future observational and interventional research to further characterize the extent to which improve specific physical fitness components might attenuate adverse cardiometabolic effects of obesity in this population. Overall, physical fitness was impaired in both obese and metabolically abnormal perimenopausal women. Among the physical fitness components studied, upper-body flexibility and cardiorespiratory fitness were independently associated with the clustered cardiometabolic risk. The results from the present study could facilitate the improvement of adequate exercise-based preventive strategies to allow these women the achievement of a better cardiometabolic status after menopause.

Contributors VAA was involved in the acquisition, analysis and interpretation of the data, and drafting the manuscript. EG-A and JL were involved in organizing the study and interpretation of the data. PA and ML-J were involved in the conception, planning and design of the study. DC-M, NM-C and PR-C were involved in the acquisition of the data. All authors read and approved the final manuscript. Conflict of interest The authors declare that they have no conflict of interest. Funding This study was financed by the Ministry of Health of the Andalusian Junta, Spain (PI-0339/2008 and PI-0667-2013). VAA was also supported by the Andalucía Talent Hub Program launched by the Andalusian Knowledge Agency, co-funded by the European Union’s Seventh Framework Program, Marie Skłodowska-Curie actions (COFUND–Grant Agreement no 291780) and the Ministry of Economy, Innovation, Science and Employment of the Junta de Andalucía, Spain. Ethical approval The study was reviewed and approved by the Ethics Committee of the “Hospital Virgen de las Nieves” (Granada, Spain). Informed consent was obtained from all participants. Provenance and peer review This article has undergone peer review. References [1] J.P. Despres, I. Lemieux, J. Bergeron, P. Pibarot, P. Mathieu, E. Larose, et al., Abdominal obesity and the metabolic syndrome: contribution to global cardiometabolic risk, Arterioscler. Thromb. Vasc. Biol. 28 (2008) 1039–1049. [2] S.M. Grundy, J.I. Cleeman, S.R. Daniels, K.A. Donato, R.H. Eckel, B.A. Franklin, et al., Diagnosis and management of the metabolic syndrome − an American heart association/national heart, lung, and blood institute scientific statement, Circulation 112 (2005) 2735–2752. [3] F. Ramezani Tehrani, S. Behboudi-Gandevani, A. Ghanbarian, F. Azizi, Effect of menopause on cardiovascular disease and its risk factors: a 9-year follow-up study, Climacteric 17 (2014) 164–172. [4] C. Zhang, K.M. Rexrode, R.M. van Dam, T.Y. Li, F.B. Hu, Abdominal obesity and the risk of all-cause, cardiovascular, and cancer mortality: sixteen years of follow-up in US women, Circulation 117 (2008) 1658–1667. [5] I. Janssen, L.H. Powell, S. Crawford, B. Lasley, K. Sutton-Tyrrell, Menopause and the metabolic syndrome: the Study of Women’s Health Across the Nation, Arch. Intern. Med. 168 (2008) 1568–1575. [6] A. Bombak, Obesity, health at every size, and public health policy, Am. J. Public Health 104 (2014) e60–7. [7] A.D. Karelis, Metabolically healthy but obese individuals, Lancet 372 (2008) 1281–1283. [8] F.B. Ortega, C. Cadenas-Sanchez, X. Sui, S.N. Blair, C.J. Lavie, Role of fitness in the metabolically healthy but obese phenotype: a review and update, Prog. Cardiovasc. Dis. 58 (2015) 76–86. [9] X. Sui, H. Li, J. Zhang, L. Chen, L. Zhu, S.N. Blair, Percentage of deaths attributable to poor cardiovascular health lifestyle factors: findings from the aerobics center longitudinal study, Epidemiol. Res. Int. 2013 (2013). [10] P.C. Hallal, L.B. Andersen, F.C. Bull, R. Guthold, W. Haskell, U. Ekelund, Global physical activity levels: surveillance progress, pitfalls, and prospects, Lancet 380 (2012) 247–257. [11] R.E. Rikli, C.J. Jones, Development and validation of a functional fitness test for community-residing older adults, J. Aging Phys. Act. 7 (1999) 129–161. [12] J. Ruiz-Ruiz, J.L. Mesa, A. Gutiérrez, M.J. Castillo, Hand size influences optimal grip span in women but not in men, J. Hand Surg. 27 (2002) 897–901. [13] F.A. Rodriguez, N. Gusi, A. Valenzuela, S. Nacher, J. Nogues, M. Marina, Evaluation of health-related fitness in adults (I): background and protocols of

E. Gregorio-Arenas et al. / Maturitas 92 (2016) 162–167

[14]

[15]

[16]

[17]

[18]

[19]

[20]

[21]

[22]

[23]

[24]

[25]

[26]

the AFISAL-INEFC Battery [in Spanish], Apunts Educacion Fisica y Deportes 52 (1998) 54–76. D.L. Spagnuolo, S.P. Jurgensen, A.M. Iwama, V.Z. Dourado, Walking for the assessment of balance in healthy subjects older than 40 years, Gerontology 56 (2010) 467–473. K.G. Alberti, R.H. Eckel, S.M. Grundy, P.Z. Zimmet, J.I. Cleeman, K.A. Donato, 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 120 (2009) 1640–1645. V. Primeau, L. Coderre, A.D. Karelis, M. Brochu, M.E. Lavoie, V. Messier, et al., Characterizing the profile of obese patients who are metabolically healthy, Int. J. Obes.(2005) 35 (2011) 971–981. C.M. Durward, T.J. Hartman, S.M. Nickols-Richardson, All-cause mortality risk of metabolically healthy obese individuals in NHANES III, J. Obes. 2012 (2012) 460321. F. Poelkens, T.M. Eijsvogels, P. Brussee, R.J. Verheggen, C.J. Tack, M.T. Hopman, Physical fitness can partly explain the metabolically healthy obese phenotype in women, Exp. Clin. Endocrinol. Diabetes 122 (2014) 87–91. F.B. Ortega, D.C. Lee, P.T. Katzmarzyk, J.R. Ruiz, X. Sui, T.S. Church, et al., The intriguing metabolically healthy but obese phenotype: cardiovascular prognosis and role of fitness, Eur. Heart J. 34 (2013) 389–397. D.C. Lee, E.G. Artero, X. Sui, S.N. Blair, Mortality trends in the general population: the importance of cardiorespiratory fitness, J. Psychopharmacol. 24 (2010) 27–35. S.M. Camhi, S.E. Crouter, L.L. Hayman, A. Must, A.H. Lichtenstein, Lifestyle behaviors in metabolically healthy and unhealthy overweight and obese women: a preliminary study, PLoS One 10 (2015) e0138548. M. Woodward, R. Webster, Y. Murakami, F. Barzi, T.H. Lam, X. Fang, et al., The association between resting heart rate, cardiovascular disease and mortality: evidence from 112,680 men and women in 12 cohorts, Eur. J. Preventive Cardiol. 21 (2014) 719–726. G. Reaven, The metabolic syndrome or the insulin resistance syndrome? Different names, different concepts, and different goals, Endocrinol. Metab. Clin. North Am. 33 (2004) 283–303. D.C. Vieira, R.A. Tibana, V. Tajra, C. Nascimento Dda, D.L. de Farias, O. Silva Ade, et al., Decreased functional capacity and muscle strength in elderly women with metabolic syndrome, Clin. Interv. Aging 8 (2013) 1377–1386. K.S. Mileski, J.L. Leitao, A. Lofrano-Porto, Grossi Porto LG. Health-related physical fitness in middle-aged men with and without metabolic syndrome, J. Sports Med. Phys. Fitness 55 (2015) 223–230. V.A. Aparicio, A. Carbonell-Baeza, M. Senhaji, S. Martin, D. Camiletti-Moiron, P. Aranda, Usefulness of fitness testing to establish metabolic syndrome in perimenopausal Moroccan women, Eur. J. Cardiovasc. Nurs. 13 (2014) 524–531.

167

[27] C.N. Chen, L.M. Chuang, Y.T. Wu, Clinical measures of physical fitness predict insulin resistance in people at risk for diabetes, Phys. Ther. 88 (2008) 1355–1364. [28] M. Tabata, R. Shimizu, D. Kamekawa, M. Kato, K. Kamiya, A. Akiyama, et al., Six-minute walk distance is an independent predictor of hospital readmission in patients with chronic heart failure, Int. Heart J. 55 (2014) 331–336. [29] K.V. Chang, C.Y. Hung, C.M. Li, Y.H. Lin, T.G. Wang, K.S. Tsai, et al., Reduced flexibility associated with metabolic syndrome in community-dwelling elders, PLoS One 10 (2015) e0117167. [30] M.B.R.O. Gavi, D.V. Vassalo, F.T. Amaral, D.C.F. Macedo, P.L. Gava, E.M. Dantas, et al., Strengthening exercises improve symptoms and quality of life but do not change autonomic modulation in fibromyalgia: a randomized clinical trial, PLoS One 9 (2014) e90767. [31] S. Cordoba-Torrecilla, V.A. Aparicio, A. Soriano-Maldonado, F. Estevez-Lopez, V. Segura-Jimenez, I. Alvarez-Gallardo, et al., Physical fitness is associated with anxiety levels in women with fibromyalgia: the al-Andalus project, Qual. Life Res. (2015). [32] C.A. Emdin, A. Odutayo, C.X. Wong, J. Tran, A.J. Hsiao, B.H. Hunn, Meta-analysis of anxiety as a risk factor for cardiovascular disease, Am. J. Cardiol. (2016). [33] M. Amore, P. Di Donato, A. Berti, A. Palareti, C. Chirico, A. Papalini, et al., Sexual and psychological symptoms in the climacteric years, Maturitas 56 (2007) 303–311. [34] R. Hoehn-Saric, Psychic and somatic anxiety: worries, somatic symptoms and physiological changes, Acta Psychiatr. Scand. 98 (1998) 32–38. [35] U. Lundberg, R. Kadefors, B. Melin, G. Palmerud, P. Hassmén, M. Engström, et al., Psychophysiological stress and EMG activity of the trapezius muscle, Int. J. Behav. Med. 1 (1994) 354–370. [36] S.A. Carlson, J.E. Fulton, M. Pratt, Z. Yang, E.K. Adams, Inadequate physical activity and health care expenditures in the United States, Prog. Cardiovasc. Dis. 57 (2015) 315–323. [37] R. Sallis, B. Franklin, L. Joy, R. Ross, D. Sabgir, J. Stone, Strategies for promoting physical activity in clinical practice, Prog. Cardiovasc. Dis. 57 (2015) 375–386. [38] R. Arena, R.A. Harrington, J.P. Despres, A message from modern-day healthcare to physical activity and fitness: welcome home!, Prog. Cardiovasc. Dis. 57 (2015) 293–295. [39] C.J. Lavie, A. De Schutter, R.V. Milani, Healthy obese versus unhealthy lean: the obesity paradox, Nat. Rev. Endocrinol. 11 (2015) 55–62. [40] L.C. Dalleck, G.P. Van Guilder, T.B. Richardson, D.L. Bredle, J.M. Janot, A community-based exercise intervention transitions metabolically abnormal obese adults to a metabolically healthy obese phenotype, Diabetes Metab. Syndr. Obes. 7 (2014) 369–380.