Journal Logo


Exercise Periodization over the Year Improves Metabolic Syndrome and Medication Use


Author Information
Medicine & Science in Sports & Exercise: October 2018 - Volume 50 - Issue 10 - p 1983-1991
doi: 10.1249/MSS.0000000000001659
  • Free


It is well established that having metabolic syndrome (MetS) increases the risk of suffering cardiovascular diseases (1). Namely, MetS doubles cardiovascular diseases prevalence and increases all-cause mortality 1.5 times (2). Furthermore, the early detection of MetS is important because its prevalence increases with aging and with the maintenance of obesity in time (3). Lifestyle changes (i.e., diet and exercise) and pharmacological interventions could halt the progression of MetS to cardiovascular diseases and diabetes (4–6). Within lifestyle modifications, increased physical activity may be more effective than calorie restriction to slow MetS development since it has been proposed that MetS may be a condition more related to underutilization of energy rather than overconsumption (7). On the other hand, there are data to support that weight loss is more efficient, at least in the short term, for reducing MetS components than exercise training (8).

Most of the information on the effects of exercise training in MetS factors arise from experiments lasting 12 to 20 wk with end-point measures taken 24 to 48 h after the last exercise bout. A limited number of studies have examined which training adaptations persist after short-term detraining (15–30 d) in MetS individuals. Those studies coincide in that if body fat losses elicited with training are maintained during detraining, the improvements in blood pressure (BP) (9) and insulin sensitivity (10,11) are preserved. Probably, because of these enduring health effects of training, we have recently reported that the progression of MetS could be slowed if a 16-wk aerobic interval training program is repeated yearly (12). However, in that study, we did not address if this positive MetS evolution with exercise training is related to the improvement in cardiorespiratory fitness (CRF) induced by training, as suggested in the literature (13).

Lifestyle changes to increase physical activity and reduce body weight in MetS are hard to maintain in the long term. Therefore, when lifestyle interventions are not effective, primary care physicians start treatment with pharmacological agents with time trend to increase in dose and number. However, it has been shown in patients diagnosed with MetS that the prescription of multiple medication (polypharmacy) is not cost-effective (14). Furthermore, in patients with diabetes, polypharmacy is associated with decreased quality of life (15), adverse drug side effects, and increased visits to the doctor (16). In our previous study (12), we did not examine if those consecutive yearly programs had an impact on medication use, although this outcome is central from a clinical and economical point of view.

Permanent inclusion of exercise into lifestyle is recommended for diabetic, obese, dyslipidemic, and hypertensive individuals and therefore for individuals with MetS. However, involvement in exercise programs is not habitually permanent but intermittent with breaks due to holidays, season changes (winter-summer), work related travelling, injuries, gym availability, and others. Those breaks, if long enough, may result in losses of some of the health improvements by detraining (9–11), whereas the repetition of the training programs may result in the retention of some physiological adaptations despite detraining (12). The purpose of this study is to determine if a 2-yr seasonal training program chronically improves CRF and MetS. Furthermore, few studies have addressed the effects of exercise training in medication use in MetS patients. We hypothesized that yearly training could reduce the progression toward increased medication use in MetS individuals.



Forty-four middle-age (53 ± 9 yr) obese subjects initially sedentary (<120 min·wk−1 of moderate-intensity activity [17] assessed by 7-d International Physical Activity Questionnaire [18]) with body mass index (BMI) of 32.7 ± 4.0 kg·m−2 and MetS completed this study. MetS was defined as the presence of three of the following five risk factors as agreed by the International Diabetes Federation in 2009; elevated waist circumference (WC) (≥94 cm for males and ≥80 cm for females), BP (>130 mm Hg for systolic or >85 mm Hg for diastolic or Rx) fasting blood glucose (≥100 mg·dL−1 or Rx), triglycerides (TG) (≥150 mg·dL−1 or Rx) and reduced high-density lipoprotein cholesterol (HDL-c) (<40 mg·dL−1 for males and <50 mg·dL−1 for females or Rx) (19). Exclusion criteria included untreated cardiovascular or renal disease, or any condition associated with exercise intolerance. All subjects provided written, witnessed, informed consent of the protocol approved by the local Hospital’s Ethics Committee in accordance with the world medical association Declaration of Helsinki.

Experimental design

We followed a single-blind, randomized block-controlled trial design. Participants were recruited, clinically screened, randomized, and tested as depicted in Figure 1 in compliance with Consolidated Standards of Reporting Trials statement (20). Volunteers were studied during two consecutive years (November 2015 to November 2017). Knowing that seasons may affect MetS factors incidence (21), testing was conducted at the same time of the year (early November). Subjects were divided into two group (blocks) of similar characteristics in age, weight, BMI and number of women per group (Table 1). One group (TRAIN; n = 22) underwent supervised high-intensity interval training (HIIT) with a frequency of three times per week for 16 wk. Training consisted on pedaling for 10-min as warm-up at 70% of their individual peak heart rate (HRPEAK; Seego; Realtrack Systems, Almeria, Spain) followed by 4 × 4-min intervals at 90% HRPEAK interspersed with 3-min active recovery at 70% HRPEAK and a 5-min cool-down period for a total of 43 min per workout. Exercise intensity was increased as training adaptations developed to maintain target heart rate (HR) (22). Participants were required to attend at least 90% of all the exercise sessions. The control group (CONT; n = 22), from an intervention waiting list, remained sedentary for the duration of the study. All subjects were advised to maintain their normal dietary and physical activity habits during the whole study. Monthly, during the intervention period, (4 months per year) subjects filled out a 3-day nutritional diary that was analyzed for caloric intake and macronutrient composition with a software that included common Spanish foodstuff (CESNICD v1.0; Barcelona, Spain). Likewise, every month, subjects wore a wrist band activity monitor (Polar Loop Electro, Kempele, Finland) for 48 h to monitor steps per day, standing time and supine resting time. During the intervention, feedback to prevent fluctuations in caloric intake or physical activity was delivered to the subject monthly.

Anthropometric, evolution of MetS factors, additional clinical variables and predicted 10-yr atherosclerotic cardiovascular disease risk during two yearly 16-wk program of Aerobic Interval Training (TRAIN group) or sedentary lifestyle (CONT group).
CONSORT schematic representation of the study procedures. CONSORT, Consolidated Standards of Reporting Trials.

Clinical investigation

Patients arrived to the laboratory in the morning after an overnight fast and at least 72 h after the last training bout. Nude body weight (Hawk; Mettler, Toledo, USA) and body composition using bio-impedance were assessed. Then, height (Stadiometer; Secca 217, Hamburg, DE) and waist circumference (2 cm above the iliac crest) was assessed as a surrogate of abdominal obesity. Data from body weight and height was used to calculate BMI. After 20 min of supine rest, resting blood pressure was measured using a calibrated ECG-gated electro sphygmomanometer (Tango; Suntech Medical, Morrisville, NC) in triplicate. After peak oxygen consumption (V˙O2PEAK) was assessed using a maximal graded exercise test with 12-lead electrocardiography. At least 48 h separated the exercise testing from a fasting morning blood sample collection that served for the determination of glucose, insulin and lipids (i.e., TG, HDL-c, low-density lipoprotein cholesterol, and total cholesterol).

Cardiorespiratory fitness and body composition

Peak aerobic capacity (V˙O2PEAK) was assessed on an electronically braked cycle ergometer (Ergoselect 200; Ergoline, Germany) during a graded exercise testing using indirect calorimetry, (Quark b2; Cosmed, Italy) with 12-lead ECG monitoring (Quark T12; Cosmed). After 3 min of warm-up at 30 W for women and 50 W for men, workload was increased every minute (15 W women and 20 W men) until volitional exhaustion. In cases of absence of criteria for V˙O2 plateau, secondary criteria for maximal tests, such as RPE ≥ 17, RER >1.1, and an HR >85% of estimated maximal HR, were considered (23). The highest HR value obtained during the test was deemed HRPEAK. Likewise, maximal power output reached was considered the POPEAK. Fat mass (FM) and fat-free mass (FFM) were determined by bioelectrical impedance analysis (Tanita BC-418; Tanita Corp, Tokyo, Japan).

Blood analyses and medicine use score

Plasma glucose was analyzed using the glucose oxidase peroxidase method with intra–inter assay coefficient of variation (iCV) of 0.9% ± 1.2%. HDL-c using accelerator selective detergent method (iCV, 1.7% ± 2.9%). Blood TG with glycerol-3-phosphate oxidize method (iCV, 0.8% ± 1.7%). Total serum cholesterol by an enzymatic method with a single aqueous reagent (iCV, 1.1%–1.4%). Low-density lipoprotein cholesterol was calculated as proposed by Friedewald (24). All the above analyses were run in an automated Mindray BS 400 Chemistry Analyzer (Mindray Medical Instrumentation, Shenzhen, China). Insulin concentration was measured in duplicate using chemiluminescent micro particle immunoassay (iCV, 2.0%–2.8%) in an automated immunoassay analyzer (Architect ci4100; Abbott Laboratories, USA). Insulin sensitivity was calculated using the homeostasis model assessment (HOMA-IR; [25]). All participants were under the supervision of their primary care physicians. Doctors followed the national primary care guidelines for the treatment of each of the components of the MetS (26). Those guidelines require lifestyle advice and counseling, blood analysis every 6 months, and pharmacological prescription adjusted to blood chemistry, BP values and body weight evolution. Participants brought all prescription medication to the baseline, 1- and 2-yr visits to ensure recording accuracy. Only medicines to control hyperglycemia, hypertension and hyperlipemia (main factors of MetS) were computed. To account not only for the number of medicines used but also for the evolution in its dose over the 2-yr period, a medicine use score was devised as follow:

The effective dose was extracted from pharmacokinetic data for each prescription according to the Anatomical Therapeutic Chemical Classification System of the World Health Organization adapted for medications sold in our country (27).

MetS Z score and 10-yr cardiovascular risk scores

Sex-specific Z-score equations were used to calculate MetS Z score and assess the continuous evolution of MetS risk factors with the treatments (8,28). The sum of the Z-scores for each MetS components was divided by five to compile the MetS risk score with units of standard deviation (29). Ten-year atherosclerotic cardiovascular disease risk score was determined using a published algorithm (30).

Statistical analysis

Data are presented as mean ± SEM. Smirnov–Kolmogorov test revealed that data were normally distributed. Preintervention differences between groups were studied using Student’s t-test for independent samples. Differences in percent of subjects using a group of medicines (i.e., Table 2) were analyzed using Chi-square. Mixed-design ANOVA was used to analyze differences across time (repeated measures) and between experimental groups (TRAIN vs CONT) in all reported variables. When the time–group interaction was significant, vertical multiple comparisons among pairwise group were performed using Bonferroni post hoc testing. Effect size (ES) of time and time–group effects were calculated using partial eta-squared (η2) which was considered large if ≥0.14, moderate 0.06 to 0.14, and small if <0.06 (31). SPSS, v22 (IBM Corporation, Armonk, New York, NY) was used for statistical analysis. Significance set at P ≤ 0.05.

Evolution in percent of subjects using a group of medicines during two yearly 16-wk program of aerobic interval training (TRAIN group) or sedentary lifestyle (CONT group).


Subjects’ characteristic

Participants were all Caucasians. There were no significant differences in response to training and/or medication between sexes (68% males and 32% females). Thus, group data was analyzed without sex differences. Upon questioning at the onset of each yearly intervention, no subject referred getting involved in any type of seasonal increase in physical activity (gardening, hiking, walking, active commuting, etc.). We could not detect significant changes in calorie intake or physical activity between groups or from baseline to 2 yr. Subjects in average ingested 2308 ± 91 kcal·d−1 and 2211 ± 85 kcal·d−1 at baseline and at 2 yr, respectively (P > 0.05). Macronutrient distribution remained constant (43% ± 2% carbohydrate, 36% ± 1% fat [40% saturated fat] and 21% ± 1% protein) in both groups throughout the study. In addition at baseline subject completed 5369 ± 241 steps per day, were standing 190 ± 115 min·d−1 and in supine rest 498 ± 197 min·d−1. Similar values were found after 2 yr with 5281 ± 237 steps per day, 188 ± 96 min·d−1 standing, and 501 ± 189 min·d−1 in supine rest (all P > 0.05). No differences existed between CONT and TRAIN at preintervention baseline in age (53 ± 2 yr vs 53 ± 2 yr) number of MetS factors (3.6 ± 0.2 vs 3.7 ± 0.2), BMI (32.2 ± 0.8 vs 33.2 ± 0.9 kg·m−2), and 10 yr cardiovascular disease risk score (7.8% ± 1.3% vs 8.1% ± 1.5%; Table 1). TRAIN attendance to the exercise sessions averaged 95%. As shown in Table 1, BMI and body weight decreased significantly after 24 months in the TRAIN group (−0.72 ± 0.36 kg·m−2 and −1.9 ± 1.1 kg·m−2; both P < 0.05), while tended to increase in CONT group (0.43 ± 0.36 kg·m−2 after 24 months, P = 0.640). Fat mass and FFM did not change from baseline after 24 months in the CONT group (0.3 ± 0.2 kg for FM and −0.2 ± 0.1 kg for FFM, respectively; both P > 0.05). However, in TRAIN, FM decreased (−0.9 ± 0.3 kg; P = 0.048), whereas FFM remained unchanged (0.2 ± 0.1 kg; P = 0.82). Insulin concentration and HOMA-IR increased significantly after 12 months in CONT group (1.81 ± 0.48 μIU·mL−1 and 0.25 ± 0.06 units respectively; both P = 0.02), whereas the TRAIN group values tended to decrease being different from CONT. At 24 months, there were no differences between groups.

MetS factors and 10-yr cardiovascular risk scores

In CONT, WC increased significantly after 12 and 24 months (2.86 ± 0.32 and 3.83 ± 1.07 cm, respectively; P = 0.002 and 0.004; Table 1). Blood glucose, TG, and HDL-c concentrations did not change in any group from baseline after 12 and 24 months (Table 1). However, in CONT, the 10-yr cardiovascular risk score increased significantly after 12 and 24 months follow-up (1.15% ± 0.12% and 2.64% ± 0.91%, respectively; P = 0.002 and 0.003; Table 1). Mean arterial BP decreased in TRAIN after 12 and 24 months (−4.1 ± 1.7 and −7.3 ± 2.2 mm Hg, respectively; P = 0.008 and 0.002; Table 1). A significant time–group interaction between TRAIN and CONT was observed at 24 months in WC, mean arterial pressure, and 10-yr cardiovascular disease risk score.

Medicine use score and MetS Z-score

Table 2 shows the percent of subjects using each group of medicines (i.e., antihypertensive, cholesterol lowering, glucose lowering and TG lowering) and its evolution during 2 yr. In CONT, the use of glucose lowering medicines augmented during 2 yr. Likewise, cholesterol-lowering medicine use was significantly higher in CONT than TRAIN at 2 yr time-point. After 1 and 2 yr, the percent of subjects using two medications was higher in the CONT than in the TRAIN group. The reverse was true for the use of one medication. Figure 2 depicts the changes in MetS Z-score and medicine use score in each group during 2 yr. In CONT, MetS Z-score was maintained during the duration of the study (0.35 ± 0.11, 0.30 ± 0.12 and 0.31 ± 0.11, at baseline 12 and 24 months, respectively; all P = 0.90). However, in CONT, medicine use score increased significantly from baseline, at 12 months, and 24 months (1.59 ± 0.35, 1.83 ± 0.41 to 2.23 ± 0.43; P = 0.001).

Combined evolution of MetS Z-score and medicine use score during the 2-yr follow-up. Data are presented as mean ± SEM for 44 MetS patients divided into the TRAIN (16 wk·yr−1) and CONT groups. †Significant change from baseline within each group. ‡Significant change from 12 months within each group. #Significant difference between groups at that time point (all P < 0.05).

In contrast, in TRAIN MetS Z-score at 12 and 24 months was reduced below baseline (P = 0.001 and P = 0.017). In TRAIN medicine use score remained without differences from baseline after 12 and 24 months of the study (1.18 ± 0.22, 1.23 ± 0.0.24, 1.27 ± 0.22, respectively; P = 0.92). As a consequence, medicine use score was lower in TRAIN than CONT after 24 months (1.27 ± 0.22 vs 2.23 ± 0.43, respectively; P = 0.048).

CRF and exercise parameters

In CONT, CRF (i.e., V˙O2PEAK) tended to decrease after 12 and 24 months (−0.04 ± 0.02 L·min−1 and −0.12 ± 0.04 L·min−1, respectively; Fig. 3A). In CONT, POPEAK was significantly reduced from baseline after 12 and 24 months (−10 ± 1 W and −21 ± 4 W, respectively; P = 0.013 and 0.001; Fig. 3C), whereas HRPEAK remained at baseline levels. TRAIN maintained V˙O2PEAK at baseline levels after 12 and 24 months (Fig. 3A and B). HR PEAK and POPEAK increased significantly from baseline after 12 months (3 ± 1 bpm and 21 ± 4 W) and 24 months (3 ± 1 bpm and 14 ± 5 W; both P < 0.05; Fig. 3B and C). At baseline (0 months), there were no differences between TRAIN and CONT groups in V˙O2 PEAK (2.18 ± 0.14 L·min−1 vs 2.11 ± 0.14 L·min−1; P = 0.73), HRPEAK (161 ± 3 vs 156 ± 3; P = 0.45) and POPEAK (179 ± 12 vs 174 ± 11 W; P = 0.74). However, POPEAK was lower in CONT than TRAIN after 12 (200 ± 14 vs 164 ± 10 W; P = 0.037) and 24 months (193 ± 12 vs 153 ± 10 W; P = 0.013; Fig. 3C). After 24 months, V˙O2PEAK in CONT was lower than in TRAIN (2.32 ± 0.14 L·min−1 vs 1.98 ± 0.11 L·min−1; P = 0.044; Fig. 3A).

Evolution of peak. (A) Oxygen consumption, (B) HR, and (C) power output during the 2 yr follow up. Data are presented as mean ± SEM for 44 MetS patients divided into the TRAIN (16 wk·yr−1) and CONT groups. †Significant change from baseline within each group. ‡Significant change from 12 months within each group. #Significant difference between TRAIN and CONT groups at that time point (all P < 0.05).


In this study, we followed the evolution of sedentary individuals with MetS for 2 yr while in a randomized fashion, half of the group was treated with yearly 4-month HIIT program. The main finding of this study is that exercise training prevented the increases in whole body (BMI as proxy) and abdominal obesity (WC as proxy) observed in the sedentary CONT group (Table 1). Along with the maintenance of body weight, the TRAIN group lowered their BP and finally their MetS Z-score (i.e., continuous rather than dichotomous measure of MetS evolution). We have previously reported that two consecutive 4-month long training programs are effective on reducing MetS (12). However, in our previous study, we did not follow subject’s CRF or medicine use evolution. Variations in medicine dose could have affected MetS Z-score with increases or decreases wrongly ascribed to exercise training or sedentary lifestyle, respectively.

A clinically relevant finding of this study is the increased medicine use within 2 yr in the sedentary CONT group. This increase hinged in elevations in glucose and cholesterol lowering medication (i.e., metformin and statins; Table 2). Figure 2 reveals that a sedentary lifestyle in middle-age MetS individuals (i.e., CONT) necessitates from progressive increases in medicine use to prevent worsening of their MetS condition. In contrast, a yearly program of aerobic intense exercise during only 4 months per year (TRAIN) improves MetS condition while maintaining medicine use score from increasing. As mentioned in the methods, all subjects were under the supervision of their primary care doctor and prescriptions adjusted according to guidelines based on the evolution of BP, body weight and blood chemistry. The unchanging MetS Z-score in CONT despite increases in WC and BMI was likely due to the increased pharmacological treatment. The costs derived from the medical management of MetS in this group are not quantified in our study but includes qualified personnel hours (nurse, doctors, and pharmacists), biochemistry analytical and the costs of the progressive increase in medications. Furthermore, two consecutive years of sedentary behavior (CONT) raised the 10-yr prediction of suffering a cardiovascular disease based on the Framingham Heart Study (30).

Our data are unique in that in a relatively short period of time (i.e., 2 yr), we could detect positive effects of exercise training in MetS health. However, those effects were not enough to lower medication use in the TRAIN group. In contrast, it has been reported that 12 month lifestyle intervention that included 5- to 6-d exercise per week and 6 kg body weight loss, resulted in reductions in antidiabetic medication (32). Thus, exercise combined with diet has the potential to reduce MetS factors to a level where physicians may consider reducing the dose or intake of some medicines. Conversely, augmenting medicine use halted MetS progression (Fig. 2). However, it is uncertain if increases in medication could maintain MetS factors from increasing in the long-term. A 10-yr follow-up study reported worsening of MetS Z-score with aging in 76% of participants even after initiating medication. This led authors to conclude that medication did not prevent MetS progression (33) in the long term. Our data supports increased medication use to prevent MetS worsening in sedentary individuals. However, baseline medication score was low in our subjects (i.e., 1.4 in average) and likely the progressive increases in the dose and number of medicines could not be sustained without a plateau treatment effect.

Although both groups of subjects started from a similar CRF level (CRF; V˙O2PEAK) at baseline, after 2 yr, the TRAIN group had a 14% higher CRF than CONT (i.e., 0.33 L O2·min−1; Fig. 3A). It is well described that aging is related to a progressive reduction in V˙O2PEAK which, in turn, seems to be associated with declines in physical activity and lean body mass (34). However, we could not detect with our wristband monitors any reduction in physical activity in CONT or reductions in lean body mass measured by bio impedance. The factors behind the reductions in V˙O2max with aging involve both central and peripheral deconditioning. Within the central factors, HRMAX declines with age independently of exercise training and accounts from 40% to 100% of the decrease in maximal cardiac output (35) and thus oxygen delivery. We observed a 1% reduction in HRPEAK in the CONT group, whereas TRAIN group maintained HRPEAK (Fig. 3B) which could account for some of the reported differences in V˙O2PEAK. Furthermore, maximal power output was enhanced in TRAIN which is another central mechanism (36) that may have allowed the differences in V˙O2PEAK.

Not only oxygen delivery to the muscle declines with age but also oxygen utilization by the skeletal muscle (37). Although there is evidence that high-intensity exercise training, of the type undertaken by master athletes, could reduce the rates of V˙O2max decline during middle age (38), it is unclear if exercise training of the type used in this exercise training program (i.e., HIIT) could also result in V˙O2max maintenance. Our results confirm that HIIT during 4 months per year could maintain CRF during 2 yr in MetS patients. Results from our laboratory measuring skeletal muscle mitochondria respiration suggest that this 16-wk aerobic interval training program improves mitochondria biogenesis in MetS patients (9). In endurance-trained individuals, 3 months of detraining reduces mitochondrial enzyme activity, but levels remain above sedentary levels allowing higher oxygen extraction (39). It is then possible that even after 8 months of detraining, lingering muscle mitochondrial adaptations are contributing to V˙O2PEAK maintenance in the TRAIN group.

Participants initial V˙O2PEAK was in the bottom 10 percentile for their age and gender (40) and significant V˙O2PEAK improvements were expected and measured with each training program (~10%, chronic adaptations not shown). After 8 months of detraining, these values decreased with respect to the end of each training program, but remained slightly higher (not significantly) than baseline values. We suspect that V˙O2PEAK did not fully return to pretraining values after 8 months of detraining because the 4 months of HIIT elicited structural cardiac adaptations. A recent article from our laboratory agrees that during submaximal exercise cardiac output and stroke volume increases after 4 months of HIIT in MetS participants (41).

Blood pressure was the MetS factor that better responded to the 4 months per year HIIT exercise program. We have recently reported that this type of training acts simultaneously reducing arterial stiffness while improving endothelial factors in the peripheral circulation in MetS individuals (42). However, general practitioners in charge of our participants’ health, did not reduce antihypertensive medicine in the TRAIN group (Table 2). We provided doctors with a morning BP assessment but continuous 24-h ambulatory BP analysis (ABP) was not conducted in this experiment. Thus, it was unclear if the effects of exercise on restraining BP persisted during the night. Nocturnal BP is a significant risk factor for mortality and cardiovascular morbidity in both hypertensive patients and the general population (43) which explains doctors’ reluctance to lower antihypertensive medication without ABP data.

We strived to start our interventions with subjects of similar characteristics in both groups (i.e., randomized-block) and to maintain diet and physical activity constant and similar in both groups. Our measurement of caloric intake (3-d diary) and physical activity (wrist band activity monitors) did not reveal any significant change in those parameters in 2 yr. Thus, the increase in BMI and WC in CONT may have been due to the persistence of a small imbalance between calorie intake and expenditure than manifested after 24 months of continuous energy-excess accumulation. Some medications, although improving their targeted disarrangement, have side effects in other health component. For instance, it has been published that statins curtail the normal improvement in CRF with exercise training (44). In turn, metformin could interfere with the insulin sensitizing actions of a bout of exercise (45). Thus, although we carefully monitored the evolution in the number and doses of the medications that our subjects were taken the side effects of medications may be a confounding factor.

We measured the chronic adaptations to two consecutive yearly training programs (4 months each) in MetS individuals and found a sustained enhancement of their CRF. This took place along with reductions in MetS Z-score owing to reductions in abdominal obesity and BP. Of note, inclusion of a yearly 4-month exercise training program achieved a maintenance in medication use during 2 yr. In contrast, a sedentary lifestyle in middle-age MetS individuals (i.e., CONT) necessitates from progressive increases in medicine use to prevent worsening of their MetS condition. Thus, MetS individuals should be treated with exercise for an easier clinical management of this condition and to reduce health costs in the treatment of this syndrome.

We would like to state that the results of the study are presented clearly, honestly, and without fabrication, falsification, or inappropriate data manipulation. The results of the present study do not constitute endorsement by ACSM. We wish to thank the volunteers for their dedication to the training. This work has not been previously presented. This study was partially funded by a grant from the Spanish Ministry of Economy, Industry and Competivity (DEP-2017-83244-R). The authors have no conflicts of interest. identifier: NCT03019796.


1. Dekker JM, Girman C, Rhodes T, et al. Metabolic syndrome and 10-year cardiovascular disease risk in the Hoorn Study. Circulation. 2005;112(5):666–73.
2. Mottillo S, Filion KB, Genest J, et al. The metabolic syndrome and cardiovascular risk a systematic review and meta-analysis. J Am Coll Cardiol. 2010;56(14):1113–32.
3. Roos V, Elmståhl S, Ingelsson E, Sundström J, Ärnlöv J, Lind L. Metabolic syndrome development during aging with special reference to obesity without the metabolic syndrome. Metab Syndr Relat Disord. 2017;15(1):36–43.
4. American Heart A, National Heart L, Blood I, et al. Diagnosis and management of the metabolic syndrome. An American Heart Association/National Heart, Lung, and Blood Institute Scientific Statement. Executive summary. Cardiol Rev. 2005;13(6):322–7.
5. Grundy SM, Cleeman JI, Daniels SR, et al. Diagnosis and management of the metabolic syndrome: an American Heart Association/National Heart, Lung, and Blood Institute Scientific Statement. Circulation. 2005;112(17):2735–52.
6. Kahn R, Buse J, Ferrannini E, Stern M. The metabolic syndrome: time for a critical appraisal. Joint statement from the American Diabetes Association and the European Association for the Study of Diabetes. Diabetologia. 2005;48(9):1684–99.
7. Fruge AD, Byrd SH, Fountain BJ, Cossman JS, Schilling MW, Gerard P. Increased physical activity may be more protective for metabolic syndrome than reduced caloric intake. An analysis of estimated energy balance in U.S. adults: 2007–2010 NHANES. Nutr Metab Cardiovasc Dis. 2015;25(6):535–40.
8. Mora-Rodriguez R, Ortega JF, Guio de Prada V, et al. Effects of simultaneous or sequential weight loss diet and aerobic interval training on metabolic syndrome. Int J Sports Med. 2016;37(4):274–81.
9. Mora-Rodriguez R, Ortega JF, Hamouti N, et al. Time-course effects of aerobic interval training and detraining in patients with metabolic syndrome. Nutr Metab Cardiovasc Dis. 2014;24(7):792–8.
10. Bajpeyi S, Tanner CJ, Slentz CA, et al. Effect of exercise intensity and volume on persistence of insulin sensitivity during training cessation. J Appl Physiol (1985). 2009;106(4):1079–85.
11. Slentz CA, Houmard JA, Johnson JL, et al. Inactivity, exercise training and detraining, and plasma lipoproteins. STRRIDE: a randomized, controlled study of exercise intensity and amount. J Appl Physiol (1985). 2007;103(2):432–42.
12. Morales-Palomo F, Ramirez-Jimenez M, Ortega JF, Lopez-Galindo PL, Fernandez-Martin J, Mora-Rodriguez R. Effects of repeated yearly exposure to exercise-training on blood pressure and metabolic syndrome evolution. J Hypertens. 2017;35(10):1992–9.
13. Hassinen M, Lakka TA, Hakola L, et al. Cardiorespiratory fitness and metabolic syndrome in older men and women: the dose responses to exercise training (DR’s EXTRA) study. Diabetes Care. 2010;33(7):1655–7.
14. Zomer E, Owen A, Magliano DJ, Ademi Z, Reid CM, Liew D. Predicting the impact of polypill use in a metabolic syndrome population: an effectiveness and cost-effectiveness analysis. Am J Cardiovasc Drugs. 2013;13(2):121–8.
15. Huang ES, Brown SE, Ewigman BG, Foley EC, Meltzer DO. Patient perceptions of quality of life with diabetes-related complications and treatments. Diabetes Care. 2007;30(10):2478–83.
16. Cramer JA. A systematic review of adherence with medications for diabetes. Diabetes Care. 2004;27(5):1218–24.
17. Bennett JA, Winters-Stone K, Nail LM, Scherer J. Definitions of sedentary in physical-activity-intervention trials: a summary of the literature. J Aging Phys Act. 2006;14(4):456–77.
18. Craig CL, Marshall AL, Sjostrom M, et al. International physical activity questionnaire: 12-country reliability and validity. Med Sci Sports Exerc. 2003;35(8):1381–95.
19. Alberti KG, Eckel RH, Grundy SM, et al. International Diabetes Federation Task Force on Epidemiology and Prevention; Hational Heart, Lung, and Blood Institute; American Heart Association; World Heart Federation; International Atherosclerosis Society; International Association for the Study of Obesity. 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(16):1640–5.
20. Begg C, Cho M, Eastwood S, et al. Improving the quality of reporting of randomized controlled trials. The CONSORT statement. JAMA. 1996;276(8):637–9.
21. Kamezaki F, Sonoda S, Nakata S, et al. Association of seasonal variation in the prevalence of metabolic syndrome with insulin resistance. Hypertens Res. 2013;36(5):398–402.
22. Morales-Palomo F, Ramirez-Jimenez M, Ortega JF, Pallares JG, Mora-Rodriguez R. Cardiovascular drift during training for fitness in patients with metabolic syndrome. Med Sci Sports Exerc. 2017;49(3):518–26.
23. Balady GJ, Arena R, Sietsema K, et al. American Heart Association Exercise, Cardiac Rehabilitation, and Prevention Committee of the Council on Clinical Cardiology; Council on Epidemiology and Prevention; Council on Peripheral Vascular Disease; Interdisciplinary Council on Quality of Care and Outcomes Research. Clinician’s Guide to cardiopulmonary exercise testing in adults: a scientific statement from the American Heart Association. Circulation. 2010;122(2):191–225.
24. Friedewald WT, Levy RI, Fredrickson DS. Estimation of the concentration of low-density lipoprotein cholesterol in plasma, without use of the preparative ultracentrifuge. Clin Chem. 1972;18(6):499–502.
25. Matthews DR, Hosker JP, Rudenski AS, Naylor BA, Treacher DF, Turner RC. Homeostasis model assessment: insulin resistance and beta-cell function from fasting plasma glucose and insulin concentrations in man. Diabetologia. 1985;28(7):412–9.
26. Vilaseca Canals J, Espinàs Boquet J. Guía terapéutica en Atención Primaria; basada en la selección razonada de medicamentos. 6th ed. Barcelona (Spain): semFYC ediciones; 2016. pp. 584.
27. Canales MJ, Pachon ML, Galindo P, Sanchez-Tercero B. VADEMECUM Internacional. 14th ed. Madrid (Spain): UBM Medica; 2014.
28. Ortega JF, Morales-Palomo F, Fernandez-Elias V, et al. Dietary supplementation with omega-3 fatty acids and oleate enhances exercise training effects in patients with metabolic syndrome. Obesity (Silver Spring). 2016;24(8):1704–11.
29. Brage S, Wedderkopp N, Ekelund U, et al. Features of the metabolic syndrome are associated with objectively measured physical activity and fitness in Danish children: the European Youth Heart Study (EYHS). Diabetes Care. 2004;27(9):2141–8.
30. D’Agostino RB Sr, Vasan RS, Pencina MJ, et al. General cardiovascular risk profile for use in primary care: the Framingham Heart Study. Circulation. 2008;117(6):743–53.
31. Cohen J. Statistical Power Analysis for the Behavioral Sciences. Taylor & Francis; 1988.
32. Johansen MY, MacDonald CS, Hansen KB, et al. Effect of an intensive lifestyle intervention on glycemic control in patients with type 2 diabetes: a randomized clinical trial. JAMA. 2017;318(7):637–46.
33. Vishnu A, Gurka MJ, DeBoer MD. The severity of the metabolic syndrome increases over time within individuals, independent of baseline metabolic syndrome status and medication use: the Atherosclerosis Risk in Communities Study. Atherosclerosis. 2015;243(1):278–85.
34. Hawkins S, Wiswell R. Rate and mechanism of maximal oxygen consumption decline with aging: implications for exercise training. Sports Med. 2003;33(12):877–88.
35. Rivera AM, Pels AE 3rd, Sady SP, Sady MA, Cullinane EM, Thompson PD. Physiological factors associated with the lower maximal oxygen consumption of master runners. J Appl Physiol (1985). 1989;66(2):949–54.
36. Morales-Alamo D, Losa-Reyna J, Torres-Peralta R, et al. What limits performance during whole-body incremental exercise to exhaustion in humans? J Physiol. 2015;593(20):4631–48.
37. Proctor DN, Joyner MJ. Skeletal muscle mass and the reduction of V˙O2max in trained older subjects. J Appl Physiol (1985). 1997;82(5):1411–5.
38. Hawkins SA, Marcell TJ, Victoria Jaque S, Wiswell RA. A longitudinal assessment of change in VO2max and maximal heart rate in master athletes. Med Sci Sports Exerc. 2001;33(10):1744–50.
39. Coyle EF, Martin WH 3rd, Sinacore DR, Joyner MJ, Hagberg JM, Holloszy JO. Time course of loss of adaptations after stopping prolonged intense endurance training. J Appl Physiol Respir Environ Exerc Physiol. 1984;57(6):1857–64.
40. Armstrong LE, Balady GJ, Berry MJ, et al. Health-related physical testing and interpretation. In: Whaley MH editor. ACSM’s Gudelines for Exercise Testing and Prescription. Philadelphia, Pennsylvania, USA: LIppincott, Williams and Willkins; 2000, pp. 55–89.
41. Mora-Rodriguez R, Fernandez-Elias VE, Morales-Palomo F, Pallares JG, Ramirez-Jimenez M, Ortega JF. Aerobic interval training reduces vascular resistances during submaximal exercise in obese metabolic syndrome individuals. Eur J Appl Physiol. 2017;117(10):2065–73.
42. Mora-Rodriguez R, Ramirez-Jimenez M, Fernandez-Elias VE, et al. Effects of aerobic interval training on arterial stiffness and microvascular function in patients with metabolic syndrome. J Clin Hypertens (Greenwich). 2018;20(1):11–8.
43. Hansen TW, Li Y, Boggia J, Thijs L, Richart T, Staessen JA. Predictive role of the nighttime blood pressure. Hypertension. 2011;57(1):3–10.
44. Mikus CR, Boyle LJ, Borengasser SJ, et al. Simvastatin impairs exercise training adaptations. J Am Coll Cardiol. 2013;62(8):709–14.
45. Sharoff CG, Hagobian TA, Malin SK, et al. Combining short-term metformin treatment and one bout of exercise does not increase insulin action in insulin-resistant individuals. Am J Physiol Endocrinol Metab. 2010;298(4):E815–23.


© 2018 American College of Sports Medicine