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Changes in cardiorespiratory fitness through adolescence predict metabolic syndrome in young adults
Journal Pre-proof Changes in cardiorespiratory fitness through adolescence predict metabolic syndrome in young adults Evelin Mäestu, Jaanus Harro, Toomas Veidebaum, Triin Kurrikoff, Jaak Jürimäe, Jarek Mäestu PII:
S0939-4753(19)30458-2
DOI:
https://doi.org/10.1016/j.numecd.2019.12.009
Reference:
NUMECD 2200
To appear in:
Nutrition, Metabolism and Cardiovascular Diseases
Received Date: 24 June 2019 Revised Date:
16 December 2019
Accepted Date: 18 December 2019
Please cite this article as: Mäestu E, Harro J, Veidebaum T, Kurrikoff T, Jürimäe J, Mäestu J, Changes in cardiorespiratory fitness through adolescence predict metabolic syndrome in young adults, Nutrition, Metabolism and Cardiovascular Diseases, https://doi.org/10.1016/j.numecd.2019.12.009. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2019 Published by Elsevier B.V. on behalf of The Italian Society of Diabetology, the Italian Society for the Study of Atherosclerosis, the Italian Society of Human Nutrition, and the Department of Clinical Medicine and Surgery, Federico II University.
Changes in cardiorespiratory fitness through adolescence predict metabolic syndrome in young adults Evelin Mäestua, Jaanus Harrob, Toomas Veidebaumc, Triin Kurrikoffd, Jaak Jürimäea, Jarek Mäestua a
Department of Exercise Biology, Institute of Sport Sciences and Physiotherapy, Faculty of
Medicine, University of Tartu, Tartu, Estonia; b
Department of Neuropsychopharmacology, Institute of Psychology, Faculty of Social
Sciences, University of Tartu, Tartu, Estonia; c
National Institute for Health Development, Tallinn, Estonia;
d
Institute of Social Studies, Faculty of Social Sciences, University of Tartu, Tartu, Estonia
Corresponding author: Evelin Mäestu, PhD, Institute of Sport Sciences and Physiotherapy, Faculty of Medicine, University of Tartu, Ülikooli 18, 50090 Tartu, Estonia. Phone +372 737 5373 E-mail: [email protected]
Text word counts: 3681 Abstract word counts: 250 Number of references: 44 Number of Figures: 1 Number on Tables: 4 Supplementary material: 1
ABSTRACT Background and Aims: Higher cardiorespiratory fitness (CRF) has been suggested to reduce the risk of metabolic syndrome (MetS). We aimed to examine longitudinally the changes of CRF on MetS and its risk factors from adolescence to adulthood. Methods and Results: At the age of 15 years, 1076 subjects were recruited from two cohorts. CRF was measured on a cycle ergometer. MetS was classified as having at least three of the following parameters above the threshold of risk factors: waist circumference; triglycerides, high-density lipoprotein cholesterol (HDL), high blood pressure (BP) and fasting glucose. In addition, insulin, total cholesterol and low-density lipoprotein cholesterol were measured and homeostasis model assessment of insulin resistance (HOMA-IR) was calculated. Persistently high, increasing, decreasing and persistently low CRF groups were formed according to change in CRF from adolescence to adulthood. Longitudinal increase in CRF was positively associated with change in HDL and negatively associated with change in insulin, HOMA-IR, triglycerides, BP and prevalence of MetS after adjustment for potential confounders. Subjects with persistently low CRF had 11.5- to 34.4-time higher risk of MetS at the age of 25 and 33 years compared to subjects with persistently high CRF and 14.6- to 15.9-time higher risk compared to increasing CRF group. Conclusion: Higher CRF is strongly related to lower values of MetS risk factors. Increasing CRF from adolescence to adulthood reduces the risk to have MetS later in adulthood. High CRF in adolescence that decreases during adulthood has similar risks to MetS compared to individuals with persistently low CRF.
Key words: Cardiorespiratory fitness; Cohort study; Metabolic syndrome risk factors; Metabolic health; young adulthood
Introduction One of the most significant public health concerns is the increasing occurrence of metabolic syndrome (MetS) and its risk factors such as central adiposity, elevated blood pressure, high levels of cholesterol and triglycerides and insulin resistance. Most if not all of these risk factors are strongly predicted by low cardiorespiratory fitness (CRF) [1, 2, 3, 4]. CRF assessment evaluates capacities of numerous physiological systems [5], and thus provides a quantitative measure of overall health. Higher CRF during childhood has been found to be associated with healthier metabolic profile in adolescence [6, 7, 8, 9, 10], as well as in adulthood [11, 12, 13]. However, some studies have found limited associations between CRF and MetS or its risk factors. For example, associations were found between CRF and body fatness parameters [14, 15] and blood pressure [14], but the associations with other MetS risk parameters were not so evident. Other studies found that MetS was affected by body fatness components rather than CRF in children [16] and adults [17]. Furthermore, it has been found that very high CRF levels may not provide significant health benefits, suggesting that the largest benefits for metabolic health occur between the least fit and the next least fit group [4]. Importantly, tracking of CRF from childhood to adolescence [8, 18, 19], and from adolescence to adulthood [19, 20] has been found high, suggesting that acquiring high CRF should be targeted already at childhood. To date most studies have measured CRF in childhood or adolescence and predict MetS risk factors in adulthood. However, to find out how changes in these parameters are longitudinally related with each other it is important to understand the implications of CRF in childhood or adolescence, before MetS or its risk factors appear, and to measure both CRF and MetS risk factors in childhood and adolescence as well as in adulthood. Multiple measurements of CRF also allow for the assessment of relationship between changes in CRF and the given outcome
variables taking into account the potential confounding factors like heredity, pre-existing disease or changes in overall physical activity [21]. The aim of the current study was to examine the importance of CRF and its changes from adolescence to adulthood in relation to adulthood MetS. We hypothesised that changes in CRF from adolescence to adulthood will reflect long-term MetS progression. Hence, we aimed: (i) to analyse the tracking of CRF from adolescence to adulthood; (ii) to examine the effect of CRF change from adolescence to adulthood on the adult MetS and its risk factors; (iii) to examine whether having persistently low CRF or decreasing CRF from adolescence to adulthood is related to the higher risk of prevalence of MetS.
Methods Study design and participants. The study was carried out on the Estonian sample of the European Youth Heart Study in 1998/99 that was subsequently incorporated into the longitudinal Estonian Children Personality, Behaviour and Health Study (ECPBHS). The rationale and procedure of sample formation have been described elsewhere [22, 23, 24]. Available data for both cohorts is from 15 years of age. This earliest measurement of the older cohort took place in 1998/99 (n = 593) and the measurement at corresponding age for the younger cohort in autumn/winter 2004 (n = 483). Follow-up studies for the older cohort took place during age 18 years (n = 442), 25 years (n = 515) and 33 years (n = 492). Follow-up studies for the younger cohort took place during age 18 years (n = 454) and 25 years (n = 433). These numbers refer to subjects for whom all data for the presented analyses were available. No differences were found between participants who provided valid data and those who had some missing data point and were excluded from the study. The overview of the participation rate and age of participants are presented in Figure 1. All participants were thoroughly informed about the purpose and
content of the study, and their written informed consent was obtained before participation; in case of minors, also from their parents. This study was approved by the Research Ethics Committee of the University of Tartu (Estonia) and was conducted in accordance with the Declaration of Helsinki. [Insert Figure 1 about here] Cardiorespiratory fitness Cardiorespiratory fitness (CRF) was measured on a cycle-ergometer (Tunturi, Finland) test with progressively increasing workload until exhaustion and was defined as maximal power output (Wmax) and calculated for kilogram of body mass (W·kg-1). The cycle-ergometer saddle was adjusted for each individual, and after 3-min warming up without resistance, participants pedalled at a self-selected rate between 60 and 80 rpm. The cycle-ergometer was run in a mode that kept pedalling rate independent of the workload. The origin of the protocol for aerobic fitness was from the European Youth Heart Study [25]. At the age of 15 and 18 years, the initial workload in males was set at 50 W, with the increments of 50 W in every 3 min, while in females the initial workload was set at 40 W, with the increments of 40 W in every 3 min until exhaustion. At the age of 25 and 33 years, the initial workload in males was set at 70 W, with the increments of 60 W in every 3 min, while in females the initial workload was set at 50 W, with the increments of 40 W in every 3 min until exhaustion. Heart rate (HR) was recorded continuously throughout the test using a HR monitor (Polar Vantage, Polar Electro, Kempele, Finland). Criteria for exhaustion were: 1) HR > 185 bpm; 2) failure to maintain a pedalling frequency of at least 50 rpm; or 3) subjective judgment by the observer that the individual could no longer continue, even after encouragement. Maximal power output (Wmax) was calculated for each individual according to the formula: W1 + (W2 x t/180), where W1 was the work rate at fully completed stage, W2 was the increase of the work rate before final incomplete stage, and t was the time in seconds at final incomplete stage.
VO2peak was calculated using the following equations: For 15-year olds [26]: VO2peak (L·min-1) = 0.465 + (0.0112 x Wmax) + (0.172 x sex), where sex = 0 for girls and 1 for boys. For 18-33-years-olds [27, 28]: Males: VO2max (L·min-1) = [(10.51 x Wmaz) + (6.35 x body weight (kg)) – (10.49 x age) + 519.3 ml·min-1]/1000 Females: VO2max (L/min) = [(9.39 x Wmaz) + (7.7 x body weight (kg)) – (5.88 x age) + 136.7 ml·min-1]/1000 Based on VO2peak, the VO2peak per kilogram of body mass (VO2peak/kg) was calculated. According to sex specific quartile values of VO2peak/kg participants were divided into high, high-moderate, low-moderate and low CRF group (Supplementary material Table 1). Predictive equations provide reliable estimation of CRF in a wide range of settings and participants [4, 5]. Metabolic syndrome and its risk factors Venous blood samples were taken from the antecubital vein after an overnight fast at 8:008:30 immediately after the subjects reported to the lab. Total cholesterol, high-density lipoprotein cholesterol (HDL), low-density lipoprotein cholesterol (LDL), triglycerides, glucose and insulin were measured by conventional methods. All blood biochemical parameters were analyzed on the same day when the sample was collected. Homeostasis model assessment (HOMA-IR) of insulin resistance was calculated as (fasting glucose x fasting insulin)/22.5. Resting systolic (BPsyst) and diastolic blood pressure (BPdiast) were measured on the left arm with the subject in sitting position (Dinamap XL, Critikron, Inc, Tampa, FL, USA). Five measurements were taken at 2-min intervals and the mean of the last three measurements was used for analysis.
Participants were classified as having MetS at the age 18, 25 and 33 years according to the harmonized definition [29] if having at least 3 of the following parameters: waist circumference ≥94 cm (males) and ≥80 cm (females) (according to the IDF suggestion of population of European origin); triglycerides ≥1.70 mmol·L-1 or drug treatment for elevated triglycerides; HDL cholesterol <1.04 mmol·L-1 (males) and <1.30 mmol·L-1 (females) or treatment for this lipid abnormality; high blood pressure (systolic ≥130 mm Hg or diastolic ≥85 mm Hg) or treatment of diagnosed hypertension; fasting glucose ≥5.6 mmol·L-1 or treatment for elevated blood glucose. For children younger than 16 years metabolic syndrome was diagnosed with abdominal obesity (≥90th percentile or adult cut-off if lower) and the presence of two or more of the following parameters [29]: elevated triglycerides, low HDL cholesterol, high blood pressure or increased plasma glucose (the criteria adhere to the absolute values of the adult definition, except for HDL cholesterol level where one cut-off (<1.03 mmol·L-1) is used). For children older than 16 years, the adult criteria were used. Additional measurements Height and body mass of the participants were measured using the Martin metal anthropometer (± 0.5 cm) and medical electronic scales (A&D Instruments, UK, ± 0.05 kg), respectively. The body mass index (BMI; kg·m-2) was calculated. Age adjusted BMI cut-off points were used to define overweight subjects under 18 years [30]. According to the World Health Organization the cut-off point ≥25.00 kg·m-2 was used to define overweight in adults [31]. In addition, participation in sports trainings (0 = participating in sports trainings, 1 = not participating in sports trainings) and participation in vigorous physical activities of over 30 minutes per week (0 = yes, 1 = no) was assessed using a validated questionnaire. Maternal education was used as an indicator of socioeconomic status as used in different studies [19, 32] and was coded as 0 = university and 1 = below university. Statistical analysis
Statistical analysis was performed using Statistical Package for the Social Sciences (SPSS) version 24. Participant characteristics at all measurement points are presented as mean and standard deviations (SD) for continuous variables. Numbers and proportions are reported for categorical data. All variables were checked for normality of distribution with the Kolmogorov-Smirnov test before the analysis and not normally distributed data were log transformed for further analysis. At ages 15, 18 and 25 years, two cohorts were analysed together, while only one cohort was analysed at age 33 years. Untransformed data are presented in the manuscript to enhance readability. Tracking of CRF from adolescence to adulthood was presented as Pearson’s product correlation coefficients and was adjusted for length of follow-up (in years). Tracking is often used to represent the overall stability of a given variable through time [18], and the predictability of future values by early measurements [19, 33]. Multilevel mixed-effects regression models were used for testing interactions with 4 time points. The MetS risk factors and the prevalence of MetS were inserted separately as dependent variables. The CRF parameter (VO2peak/kg) was inserted as fixed variable and subject ID as a random factor. Two different models were performed: Model 1 – adjusted for length of follow-up (years; as random slope and random intercept), sex and cohort; Model 2 – as Model 1 but additional adjustments for BMI, participating in sports trainings, vigorous physical activity over 30 min/week and maternal education. Mixed modelling procedure allows the inclusion of subjects with different number of observations, maximizing the power in the analyses. Variance components were estimated using the restricted maximum likelihood estimate method. Additionally, the composite CRF variable was created. Subjects who were in the lowmoderate and low CRF group (two lowest quartile) at the age 15 and at the age 25 and 33 years were classified as “persistently low CRF” and those who changed from high-moderate
or high CRF group (two highest quartiles) to low-moderate or low CRF group were classified as “decreasing CRF”. Subjects who were in high or moderate-high CRF groups at the age 15 and also at the age 25 and 33 years were classified as “persistently high CRF” and those who increased their CRF from low or low-moderate CRF to high or high-moderate were classified as “increasing CRF”. Logistic regression models were performed to assess the role of changes in CRF from adolescence to adulthood in acquiring MetS at the age of 25 and 33 years. All the models were adjusted for length of follow-up and sex and in MetS at age 25 years also for cohort. The level of significance was set at P < 0.05 for all the analyses.
Results Descriptive characteristics of the subjects at different ages are presented in Table 1. With increasing age, the prevalence of overweight/obese subjects increased (11.7%, 15.3%, 31.9% and 46.2% at the age of 15, 18, 25 and 33 years, respectively). [Insert Table 1 about here] Table 2 shows Pearson partial correlation coefficients for the tracking of CRF between different ages. All tracking coefficients revealed high correlation (r = 0.720-0.855; P < 0.05). Tracking tended to be stronger between closer measurement times and the strongest correlation was between CRF at the age of 25 years and CRF at the age of 33 years (P < 0.05). [Insert Table 2 about here]
Multilevel mixed models indicated that changes in CRF (measured as VO2peak/kg) had positive longitudinal association with HDL (P < 0.001) and negative longitudinal association with changes in all other MetS risk factors (except glucose) (P ≤ 0.008) after adjustment for sex and cohort (Table 3, Model 1). If additional adjustment was made for BMI, participation in
trainings, vigorous PA over 30 min per week and mother’s education, the longitudinal changes in CRF were positively associated with HDL and negatively with insulin, HOMA-IR, triglycerides, BPdiast and prevalence of MetS (P ≤ 0.011) (Table 3, Model 2). Associations between changes in CRF and glucose, total cholesterol, LDL and BPsyst changed nonsignificant in Model 2 (P ≥ 0.166). [Insert Table 3 about here] The subjects who had persistently low CRF had 34.4 and 11.5 times and the subjects with decreased CRF had 22.6- and 8.4-times higher likelihood of having MetS at the age of 25 and 33 years, respectively, compared to those who had persistently high CRF (P ≤0.003; Table 4). There was no higher risk for having MetS at the age of 25 and 33 years for those who had increased CRF compared to subjects with persistently high CRF (OR = 2.3 and 0.7, respectively; P ≥ 0.560). Subjects with decreasing CRF had 9.4- and 11.6-times higher risk and subjects with persistently low CRF had 14.7- and 15.9-times higher risk to have MetS at age 25 and 33, respectively (P ≤0.037) compared to subjects with increasing CRF. There were no differences in the likelihood to have MetS if the subject had decreasing CRF or persistently low CRF (P =0.257). [Insert Table 4 about here]
Discussion The main study finding was that those subjects who had persistently low or decreasing CRF had up to 34 times higher risk of having MetS in later in adulthood compared to those who had persistently high CRF. Furthermore, having high CRF in adolescence, that decreased during adulthood does not provide an advantage compared to those subjects having low CRF at all times.
The association between CRF assessed during adolescence and long-term risk of having MetS and its risk factors during adulthood is not well known. However, there is the trend for declining CRF among adolescents and youth [34, 35], suggesting a greater proportion of young adults with lower CRF, and thus have the elevated risk of cardiovascular disease in the near future [21]. In this study we measured both CRF and MetS risk factors in a time span of 15-18 years from early adolescence to adulthood, therefore we could assess the influence of changes in CRF on MetS risk factors. The results from the linear mixed model indicated that the change in CRF was strongly and inversely related to MetS and its risk factors, even after adjustment for potential confounders. In previous studies, the assessment of CRF has mostly been carried out one-time, and thus the conclusions remain subject to uncertainty about the influence of potential changes in CRF during follow-up. Prospective studies that include multiple assessment of CRF allow for the assessment of relationship between changes in CRF and the given outcome thereby controlling for potentially confounding factors [21]. Previous studies have found higher childhood fitness level associated with a reduced risk of adult MetS compared with low childhood fitness [36]. Studies that have assessed changes in fitness from childhood to adolescence have established that increasing fitness associated only with lower diastolic blood pressure seven years later [11] and with total cholesterol twenty years later [37]. There are several factors that might influence CRF, like genetics, sex, age, health condition, body composition [38] and overall physical activity. It is known that fatness and physical activity are strongly associated with CRF [39, 40]. In our study, after adjustment for fatness and physical activity some factors previously correlated (glucose, LDL, total cholesterol, BPsyst) lost their association, whereas others (insulin, HOMA-IR, HDL, BPdiast) increased their correlation value, suggesting that the relationship between CRF and MetS risk factors is affected by fatness as previously shown in adults with MetS [17] .
We found high levels of tracking of CRF between all measurement time points. Tracking results indicate that childhood CRF is an important predictor of adult CRF, suggesting that a fit child is more likely to be fit as an adult. It is important to notice that the results of tracking depend on the length of the measurement time, being usually stronger between shorter measurement points [33]. In addition, the risk of having MetS at the age of 25 and 33 years was between 11.5 and 34.4 times higher for the subjects with persistently low CRF and between 8.4 and 22.0 times higher for subjects with decreasing CRF compared to participants with persistently high CRF. Previous studies have indicated that higher level of childhood fitness is a good predictor for not developing MetS later [11, 12, 13]. The novelty of our study is in the finding that the decrease from initially high CRF to low fitness has the similar effect on developing MetS in early adulthood compared to persistently low fitness values. In contrast, those subjects who increased their fitness also decreased their MetS risk similar to subjects with persistently high CRF. Therefore, from adolescence to adulthood we should focus on maintaining fitness when the initial level is at least satisfactory however, further improvement of fitness is suggested if the respective levels in adolescence are low. For example, Carnethon et al. [41] found that young adults with low fitness levels were 3- to 6-fold more likely to develop diabetes, hypertension, and the MetS after 15-years compared to participants with high fitness. This is supported by the findings that the improved fitness over 7-years period was associated with a reduced risk of developing diabetes and the MetS [41]. Knaeps et al. [42] also found that increases in CRF in middle-aged (baseline age 46.1 ± 9.5 years) subjects during 10-year period (two measurement points) were associated with favourable changes in MetS risk factors like fasting glucose, HDL, triglycerides, diastolic and systolic blood pressure. Similarly, Laukkanen et al. [43] found that decrease in fitness levels over 11 years was associated with an increased risk of mortality in all caused death, including parameters of MetS. Thus, it is important to evaluate and improve CRF not
only in childhood but also during adulthood, ensuring better metabolic health and to include counselling of the physical activity into primary medical care. This study had several strengths that should be mentioned. Firstly, relatively large cohorts were followed for 10- or 18-year periods with three or two follow-up measurements for the older and younger cohorts, respectively. Secondly, all MetS risk factors and CRF were measured at all measurement times. Several covariates (BMI as fatness parameter, mother’s education as sociodemographic variable and participation in sports trainings and vigorous physical activities for over 30 minutes per week as physical activity parameters) were included into the analysis as parameters possibly affecting CRF. There are also some limitations in this study. Firstly, CRF ie. VO2peak was not measured directly but calculated by regression equations that might be less accurate. However, we predicted CRF by using maximal exercise test that is more valid compared to non-exercise prediction equations for estimating CRF. In addition, studies have shown that predicting equations provide reliable estimation of CRF in a wide range of settings and participants [4, 44]. Secondly, the general growth process itself affects some of the measured variables, for example, the changes in muscle, bone or fat tissue and this interaction was not taken into account. Thirdly, low incidence of MetS may affect the strength of the correlation. Our main finding was that high CRF in adolescence followed by the decrease during adulthood does not have an advantage over those who had persistently low CRF at all times. Higher CRF is strongly related to lower values of MetS risk factors. Furthermore, increasing CRF from adolescence to adulthood reduces the risk to have MetS later in adulthood. This suggests that it is important to improve CRF throughout the entire life, since it has a very strong protective effect against MetS risk factors and MetS. The measurement of CRF would be suggested for national health care priority, and not only during childhood and adolescence
(when it is easier to measure), but regular monitoring has to be conducted during adulthood as well.
Conflicts of interest All authors declared no conflict of interest. Funding This study was supported by the Estonian Research Council, Grant number: IUT 20-58, IUT 42-2, PUT1395 and IUT 20-40.
Appendix A. Supplementary data Supplemental Data File 1. Table (.docx)
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Figure legends
Figure 1. Schematic overview of the follow-up of the two cohorts. n, number of subjects with complete data on cardiorespiratory fitness and metabolic syndrome parameters available.
Table 1. Descriptive characteristics of the analyzed study sample in different age groups during the longitudinal study 15-year-old
18-year-old
25-year-old
33-year-old #
Sample size (n)
1076
896
948
492
Female (%)
55.2
56.6
56.0
57.3
Male
20.4 ± 0.1
22.5 ± 0.2a
25.0 ± 0.2ab
26.7 ± 0.2abc
Female
20.6 ± 0.1
21.8 ± 0.1*a
23.0 ± 0.2*ab
23.9 ± 0.2*abc
Waist circumference Male
71.4 ± 0.3
77.8 ± 0.3a
85.9 ± 0.4ab
90.7 ± 0.6abc
(cm)
Female
66.7 ± 0.3*
70.3 ± 0.3*a
75.0 ± 0.4*ab
77.6 ± 0.5*abc
Overweight (%)
Male
12.9
17.3
43.1ab
63.8abc
Female
10.4
13.3
20.6*ab
28.5*abc
BMI (kg·m-2)
VO2peak/kg (mL·min-
Male
48.6 ± 0.3
42.3 ± 0.4a
40.1 ± 0.4ab
45.5 ± 0.6abc
1
Female
36.0 ± 0.2*
27.8 ± 0.2*a
27.1 ± 0.2*ab
31.6 ± 0.4*abc
Male
11.4 ± 0.3
8.0 ± 0.4a
7.9 ± 0.3a
9.9 ± 0.5abc
Female
12.0 ± 0.2
8.2 ± 0.3a
7.6 ± 0.3a
7.6 ± 0.4*abc
Male
5.2 ± 0.02
5.0 ± 0.02a
5.12 ± 0.02b
5.36 ± 0.03abc
Female
4.9 ± 0.02*
4.7 ± 0.02*a
4.8 ± 0.02*ab
5.0 ± 0.03*bc
Male
2.67 ± 0.08
1.79 ± 0.09a
1.83 ± 0.08a
2.41 ± 0.14bc
Female
2.67 ± 0.06
1.73 ± 0.06a
1.64 ± 0.06*a
1.71 ± 0.9*ac
Total cholesterol
Male
3.84 ± 0.03
3.97 ± 0.04a
4.43 ± 0.04ab
4.88 ± 0.06abc
(mmol·L-1)
Female
4.26 ± 0.03*
4.36 ± 0.04*a
4.57 ± 0.03*ab
4.61 ± 0.05*a
HDL (mmol·L-1)
Male
1.36 ± 0.01
1.35 ± 0.01
1.31 ± 0.02a
1.36 ± 0.02c
Female
1.51 ± 0.01*
1.62 ± 0.02*a
1.69 ± 0.02*ab
1.77 ± 0.02*abc
Male
2.19 ± 0.05
2.30 ± 0.03
2.72 ± 0.04ab
3.29 ± 0.05abc
Female
2.47 ± 0.05*
2.46 ± 0.03*
2.57 ± 0.03*b
2.77 ± 0.03*ab
·kg-1)
Insulin (µUm·L-1)
Glucose (mmol·L-1)
HOMA-IR
LDL (mmol·L-1)
Triglycerides
Male
0.79 ± 0.03
0.88 ± 0.03a
1.13 ± 0.04ab
1.39 ± 0.05ab
(mmol·L-1)
Female
0.84 ± 0.02
0.84 ± 0.03
0.94 ± 0.02*ab
0.98 ± 0.03*ab
BPsyst (mm Hg)
Male
116 ± 0.6
122 ± 0.6a
125 ± 0.6ab
124 ± 0.9ab
Female
107 ± 0.4*
109 ± 0.5*
109 ± 0.4*a
108 ± 0.7*b
Male
62 ± 0.3
62 ± 0.4
68 ± 0.4ab
71 ± 0.6abc
Female
63 ± 0.3
61 ± 0.3*a
65 ± 0.3*ab
65 ± 0.5*ab
Male
18/3.7
17/4.4
34/8.2
42/20.0
Female
6/1.0*
7/1.4*
16/3.0*
18/6.4*
BPdiast (mm Hg)
MetS (n/ %)
Data presented in mean ± SE or prevalence (n) / percentage. Values are adjusted for cohort; P < 0.05 #
Only older cohort
*Significant difference between sexes a
Significantly different from the age of 15 years
b
Significantly different from the age of 18 years
c
Significantly different from the age of 25 years
BMI, body mass index; HOMA-IR, homeostasis model assessment of insulin resistance; HDL, high-density lipoprotein cholesterol; LDL, low-density lipoprotein cholesterol; BPsyst, systolic blood pressure; BPdiast, diastolic blood pressure; MetS, metabolic syndrome
Table 2. Pearson correlation coefficients for tracking of cardiorespiratory fitness (VO2peak/kg) over four measurement times.
CRF at age 15 CRF at age 18
CRF at age 18
CRF at age 25
CRF at age 33
0.796
0.757
0.782
0.790
0.720
CRF at age 25
0.855
All correlation coefficients were adjusted for length to follow-up. All correlations were significant at P < 0.001.
Table 3. Multilevel analysis of changes in cardiorespiratory fitness and its impact on changes in metabolic syndrome and its risk factors Estimate ± SE
95% CI
P
Model 1
-0.033 ± 0.016
-0.064; -0.003
0.001
Model 2
-0.174 ± 0.031
-0.235; -0.112
<0.001
Model 1
0.006 ± 0.001
0.003; 0.008
<0.001
Model 2
-0.004 ± 0.004
-0.011; 0.003
0.265
Model 1
-0.006 ± 0.004
-0.014; 0.002
0.001
Model 2
-0.043 ± 0.008
-0.059; -0.027
<0.001
Insulin
Glucose
HOMA-IR
Total cholesterol Model 1
-0.008 ± 0.002
-0.012; -0.005
<0.001
Model 2
-0.008 ± 0.006
-0.020; 0.004
0.166
Model 1
0.005 ± 0.001
0.003; 0.006
<0.001
Model 2
0.009 ± 0.004
0.002; 0.016
0.011
Model 1
-0.008 ± 0.002
-0.011; -0.004
<0.001
Model 2
-0.006 ± 0.005
-0.016; 0.004
0.167
Model 1
-0.011 ± 0.002
-0.014; -0.008
<0.001
Model 2
-0.034 ± 0.011
-0.056; -0.014
0.001
HDL
LDL
Triglycerides
BPsyst
Model 1
-0.261 ± 0.028
-0.316; -0.205
<0.001
Model 2
-0.106 ± 0.083
-0.270; 0.057
0.203
Model 1
-0.050 ± 0.019
-0.088; -0.012
0.008
Model 2
-0.210 ± 0.065
-0.339; -0.082
0.001
Model 1
-0.013 ± 0.001
-0.015; -0.011
<0.001
Model 2
-0.006 ± 0.002
-0.009; -0.003
<0.001
BPdiast
MetS
Model 1: adjusted for length to follow-up, sex and cohort Model 2: in addition to Model 1 adjusted for BMI, participation in trainings, vigorous physical activity over 30 min in week and mother’s education SE, standard error; CI, confidence intervals; HOMA-IR, homeostasis model assessment of insulin resistance; HDL, high-density lipoprotein cholesterol; LDL, low-density lipoprotein cholesterol; BPsyst, systolic blood pressure; BPdiast, diastolic blood pressure; MetS, metabolic syndrome.
Table 4. Effect of change in cardiorespiratory fitness from adolescence to adulthood on metabolic syndrome at the age of 25 (n=948) and 33 (n=492) years for all subjects. MetS at age 25 n*
OR
95% CI
Persistently high CRF
5/328
Ref
Increasing CRF
6/149
2.30
0.141; 37.45
Decreasing CRF
15/159
22.57
Persistently low CRF
31/255
34.43
MetS at age 33 P
n*
OR
95% CI
P
7/189
Ref
0.560
4/49
0.73
0.08; 6.86
0.785
2.83; 180.13
0.003
17/84
8.35
2.58; 27.00
<0.001
4.62; 256.85
0.001
28/114
11.52
3.79; 34.98
<0.001
Increasing CRF
Ref
Decreasing CRF
9.39
1.14; 77.43
0.037
11.57
1.42; 94.17
0.022
Persistently low CRF
14.71
1.94; 111.38
0.009
15.89
2.02; 124.90
0.009
0.63; 2.97
0.424
Decreasing CRF
Ref
Persistently low CRF
1.59
Ref
Ref 0.71; 3.52
0.257
1.37
All models were adjusted for length to follow-up, sex (1-male, ref; 2-female) and additionally in MetS at age 25 for cohort (1 -older cohort, ref; 2-younger cohort). *prevalence / absence of MetS in the each CRF group OR, odds ratio; CI, confidence intervals; MetS, metabolic syndrome; CRF, cardiorespiratory fitness
1998 Older cohort
Younger cohort
n = 593 (male = 260) (15.4 ± 0.6 yrs)
2001
2004
2007
n = 442 (male = 187) (18.4 ± 0.9 yrs)
2008
2014
n = 515 (male = 224) (25.2 ± 0.7 yrs) n = 483 (male = 222) (15.3 ± 0.5 yrs)
n = 454 (male = 202) (18.3 ± 0.5 yrs)
2016 n = 492 (male = 210) (33.5 ± 0.7 yrs)
n = 433 (male = 193) (25.3 ± 0.5 yrs)
Highlights
•
High CRF strongly relates to lower scores of metabolic syndrome (MetS) risk factors
•
Increase in CRF through adolescence reduces the risk of MetS later in adulthood
•
The decrease in CRF during adolescence has the risk of MetS similarly to persistently low CRF
S0939-4753(19)30458-2
DOI:
https://doi.org/10.1016/j.numecd.2019.12.009
Reference:
NUMECD 2200
To appear in:
Nutrition, Metabolism and Cardiovascular Diseases
Received Date: 24 June 2019 Revised Date:
16 December 2019
Accepted Date: 18 December 2019
Please cite this article as: Mäestu E, Harro J, Veidebaum T, Kurrikoff T, Jürimäe J, Mäestu J, Changes in cardiorespiratory fitness through adolescence predict metabolic syndrome in young adults, Nutrition, Metabolism and Cardiovascular Diseases, https://doi.org/10.1016/j.numecd.2019.12.009. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2019 Published by Elsevier B.V. on behalf of The Italian Society of Diabetology, the Italian Society for the Study of Atherosclerosis, the Italian Society of Human Nutrition, and the Department of Clinical Medicine and Surgery, Federico II University.
Changes in cardiorespiratory fitness through adolescence predict metabolic syndrome in young adults Evelin Mäestua, Jaanus Harrob, Toomas Veidebaumc, Triin Kurrikoffd, Jaak Jürimäea, Jarek Mäestua a
Department of Exercise Biology, Institute of Sport Sciences and Physiotherapy, Faculty of
Medicine, University of Tartu, Tartu, Estonia; b
Department of Neuropsychopharmacology, Institute of Psychology, Faculty of Social
Sciences, University of Tartu, Tartu, Estonia; c
National Institute for Health Development, Tallinn, Estonia;
d
Institute of Social Studies, Faculty of Social Sciences, University of Tartu, Tartu, Estonia
Corresponding author: Evelin Mäestu, PhD, Institute of Sport Sciences and Physiotherapy, Faculty of Medicine, University of Tartu, Ülikooli 18, 50090 Tartu, Estonia. Phone +372 737 5373 E-mail: [email protected]
Text word counts: 3681 Abstract word counts: 250 Number of references: 44 Number of Figures: 1 Number on Tables: 4 Supplementary material: 1
ABSTRACT Background and Aims: Higher cardiorespiratory fitness (CRF) has been suggested to reduce the risk of metabolic syndrome (MetS). We aimed to examine longitudinally the changes of CRF on MetS and its risk factors from adolescence to adulthood. Methods and Results: At the age of 15 years, 1076 subjects were recruited from two cohorts. CRF was measured on a cycle ergometer. MetS was classified as having at least three of the following parameters above the threshold of risk factors: waist circumference; triglycerides, high-density lipoprotein cholesterol (HDL), high blood pressure (BP) and fasting glucose. In addition, insulin, total cholesterol and low-density lipoprotein cholesterol were measured and homeostasis model assessment of insulin resistance (HOMA-IR) was calculated. Persistently high, increasing, decreasing and persistently low CRF groups were formed according to change in CRF from adolescence to adulthood. Longitudinal increase in CRF was positively associated with change in HDL and negatively associated with change in insulin, HOMA-IR, triglycerides, BP and prevalence of MetS after adjustment for potential confounders. Subjects with persistently low CRF had 11.5- to 34.4-time higher risk of MetS at the age of 25 and 33 years compared to subjects with persistently high CRF and 14.6- to 15.9-time higher risk compared to increasing CRF group. Conclusion: Higher CRF is strongly related to lower values of MetS risk factors. Increasing CRF from adolescence to adulthood reduces the risk to have MetS later in adulthood. High CRF in adolescence that decreases during adulthood has similar risks to MetS compared to individuals with persistently low CRF.
Key words: Cardiorespiratory fitness; Cohort study; Metabolic syndrome risk factors; Metabolic health; young adulthood
Introduction One of the most significant public health concerns is the increasing occurrence of metabolic syndrome (MetS) and its risk factors such as central adiposity, elevated blood pressure, high levels of cholesterol and triglycerides and insulin resistance. Most if not all of these risk factors are strongly predicted by low cardiorespiratory fitness (CRF) [1, 2, 3, 4]. CRF assessment evaluates capacities of numerous physiological systems [5], and thus provides a quantitative measure of overall health. Higher CRF during childhood has been found to be associated with healthier metabolic profile in adolescence [6, 7, 8, 9, 10], as well as in adulthood [11, 12, 13]. However, some studies have found limited associations between CRF and MetS or its risk factors. For example, associations were found between CRF and body fatness parameters [14, 15] and blood pressure [14], but the associations with other MetS risk parameters were not so evident. Other studies found that MetS was affected by body fatness components rather than CRF in children [16] and adults [17]. Furthermore, it has been found that very high CRF levels may not provide significant health benefits, suggesting that the largest benefits for metabolic health occur between the least fit and the next least fit group [4]. Importantly, tracking of CRF from childhood to adolescence [8, 18, 19], and from adolescence to adulthood [19, 20] has been found high, suggesting that acquiring high CRF should be targeted already at childhood. To date most studies have measured CRF in childhood or adolescence and predict MetS risk factors in adulthood. However, to find out how changes in these parameters are longitudinally related with each other it is important to understand the implications of CRF in childhood or adolescence, before MetS or its risk factors appear, and to measure both CRF and MetS risk factors in childhood and adolescence as well as in adulthood. Multiple measurements of CRF also allow for the assessment of relationship between changes in CRF and the given outcome
variables taking into account the potential confounding factors like heredity, pre-existing disease or changes in overall physical activity [21]. The aim of the current study was to examine the importance of CRF and its changes from adolescence to adulthood in relation to adulthood MetS. We hypothesised that changes in CRF from adolescence to adulthood will reflect long-term MetS progression. Hence, we aimed: (i) to analyse the tracking of CRF from adolescence to adulthood; (ii) to examine the effect of CRF change from adolescence to adulthood on the adult MetS and its risk factors; (iii) to examine whether having persistently low CRF or decreasing CRF from adolescence to adulthood is related to the higher risk of prevalence of MetS.
Methods Study design and participants. The study was carried out on the Estonian sample of the European Youth Heart Study in 1998/99 that was subsequently incorporated into the longitudinal Estonian Children Personality, Behaviour and Health Study (ECPBHS). The rationale and procedure of sample formation have been described elsewhere [22, 23, 24]. Available data for both cohorts is from 15 years of age. This earliest measurement of the older cohort took place in 1998/99 (n = 593) and the measurement at corresponding age for the younger cohort in autumn/winter 2004 (n = 483). Follow-up studies for the older cohort took place during age 18 years (n = 442), 25 years (n = 515) and 33 years (n = 492). Follow-up studies for the younger cohort took place during age 18 years (n = 454) and 25 years (n = 433). These numbers refer to subjects for whom all data for the presented analyses were available. No differences were found between participants who provided valid data and those who had some missing data point and were excluded from the study. The overview of the participation rate and age of participants are presented in Figure 1. All participants were thoroughly informed about the purpose and
content of the study, and their written informed consent was obtained before participation; in case of minors, also from their parents. This study was approved by the Research Ethics Committee of the University of Tartu (Estonia) and was conducted in accordance with the Declaration of Helsinki. [Insert Figure 1 about here] Cardiorespiratory fitness Cardiorespiratory fitness (CRF) was measured on a cycle-ergometer (Tunturi, Finland) test with progressively increasing workload until exhaustion and was defined as maximal power output (Wmax) and calculated for kilogram of body mass (W·kg-1). The cycle-ergometer saddle was adjusted for each individual, and after 3-min warming up without resistance, participants pedalled at a self-selected rate between 60 and 80 rpm. The cycle-ergometer was run in a mode that kept pedalling rate independent of the workload. The origin of the protocol for aerobic fitness was from the European Youth Heart Study [25]. At the age of 15 and 18 years, the initial workload in males was set at 50 W, with the increments of 50 W in every 3 min, while in females the initial workload was set at 40 W, with the increments of 40 W in every 3 min until exhaustion. At the age of 25 and 33 years, the initial workload in males was set at 70 W, with the increments of 60 W in every 3 min, while in females the initial workload was set at 50 W, with the increments of 40 W in every 3 min until exhaustion. Heart rate (HR) was recorded continuously throughout the test using a HR monitor (Polar Vantage, Polar Electro, Kempele, Finland). Criteria for exhaustion were: 1) HR > 185 bpm; 2) failure to maintain a pedalling frequency of at least 50 rpm; or 3) subjective judgment by the observer that the individual could no longer continue, even after encouragement. Maximal power output (Wmax) was calculated for each individual according to the formula: W1 + (W2 x t/180), where W1 was the work rate at fully completed stage, W2 was the increase of the work rate before final incomplete stage, and t was the time in seconds at final incomplete stage.
VO2peak was calculated using the following equations: For 15-year olds [26]: VO2peak (L·min-1) = 0.465 + (0.0112 x Wmax) + (0.172 x sex), where sex = 0 for girls and 1 for boys. For 18-33-years-olds [27, 28]: Males: VO2max (L·min-1) = [(10.51 x Wmaz) + (6.35 x body weight (kg)) – (10.49 x age) + 519.3 ml·min-1]/1000 Females: VO2max (L/min) = [(9.39 x Wmaz) + (7.7 x body weight (kg)) – (5.88 x age) + 136.7 ml·min-1]/1000 Based on VO2peak, the VO2peak per kilogram of body mass (VO2peak/kg) was calculated. According to sex specific quartile values of VO2peak/kg participants were divided into high, high-moderate, low-moderate and low CRF group (Supplementary material Table 1). Predictive equations provide reliable estimation of CRF in a wide range of settings and participants [4, 5]. Metabolic syndrome and its risk factors Venous blood samples were taken from the antecubital vein after an overnight fast at 8:008:30 immediately after the subjects reported to the lab. Total cholesterol, high-density lipoprotein cholesterol (HDL), low-density lipoprotein cholesterol (LDL), triglycerides, glucose and insulin were measured by conventional methods. All blood biochemical parameters were analyzed on the same day when the sample was collected. Homeostasis model assessment (HOMA-IR) of insulin resistance was calculated as (fasting glucose x fasting insulin)/22.5. Resting systolic (BPsyst) and diastolic blood pressure (BPdiast) were measured on the left arm with the subject in sitting position (Dinamap XL, Critikron, Inc, Tampa, FL, USA). Five measurements were taken at 2-min intervals and the mean of the last three measurements was used for analysis.
Participants were classified as having MetS at the age 18, 25 and 33 years according to the harmonized definition [29] if having at least 3 of the following parameters: waist circumference ≥94 cm (males) and ≥80 cm (females) (according to the IDF suggestion of population of European origin); triglycerides ≥1.70 mmol·L-1 or drug treatment for elevated triglycerides; HDL cholesterol <1.04 mmol·L-1 (males) and <1.30 mmol·L-1 (females) or treatment for this lipid abnormality; high blood pressure (systolic ≥130 mm Hg or diastolic ≥85 mm Hg) or treatment of diagnosed hypertension; fasting glucose ≥5.6 mmol·L-1 or treatment for elevated blood glucose. For children younger than 16 years metabolic syndrome was diagnosed with abdominal obesity (≥90th percentile or adult cut-off if lower) and the presence of two or more of the following parameters [29]: elevated triglycerides, low HDL cholesterol, high blood pressure or increased plasma glucose (the criteria adhere to the absolute values of the adult definition, except for HDL cholesterol level where one cut-off (<1.03 mmol·L-1) is used). For children older than 16 years, the adult criteria were used. Additional measurements Height and body mass of the participants were measured using the Martin metal anthropometer (± 0.5 cm) and medical electronic scales (A&D Instruments, UK, ± 0.05 kg), respectively. The body mass index (BMI; kg·m-2) was calculated. Age adjusted BMI cut-off points were used to define overweight subjects under 18 years [30]. According to the World Health Organization the cut-off point ≥25.00 kg·m-2 was used to define overweight in adults [31]. In addition, participation in sports trainings (0 = participating in sports trainings, 1 = not participating in sports trainings) and participation in vigorous physical activities of over 30 minutes per week (0 = yes, 1 = no) was assessed using a validated questionnaire. Maternal education was used as an indicator of socioeconomic status as used in different studies [19, 32] and was coded as 0 = university and 1 = below university. Statistical analysis
Statistical analysis was performed using Statistical Package for the Social Sciences (SPSS) version 24. Participant characteristics at all measurement points are presented as mean and standard deviations (SD) for continuous variables. Numbers and proportions are reported for categorical data. All variables were checked for normality of distribution with the Kolmogorov-Smirnov test before the analysis and not normally distributed data were log transformed for further analysis. At ages 15, 18 and 25 years, two cohorts were analysed together, while only one cohort was analysed at age 33 years. Untransformed data are presented in the manuscript to enhance readability. Tracking of CRF from adolescence to adulthood was presented as Pearson’s product correlation coefficients and was adjusted for length of follow-up (in years). Tracking is often used to represent the overall stability of a given variable through time [18], and the predictability of future values by early measurements [19, 33]. Multilevel mixed-effects regression models were used for testing interactions with 4 time points. The MetS risk factors and the prevalence of MetS were inserted separately as dependent variables. The CRF parameter (VO2peak/kg) was inserted as fixed variable and subject ID as a random factor. Two different models were performed: Model 1 – adjusted for length of follow-up (years; as random slope and random intercept), sex and cohort; Model 2 – as Model 1 but additional adjustments for BMI, participating in sports trainings, vigorous physical activity over 30 min/week and maternal education. Mixed modelling procedure allows the inclusion of subjects with different number of observations, maximizing the power in the analyses. Variance components were estimated using the restricted maximum likelihood estimate method. Additionally, the composite CRF variable was created. Subjects who were in the lowmoderate and low CRF group (two lowest quartile) at the age 15 and at the age 25 and 33 years were classified as “persistently low CRF” and those who changed from high-moderate
or high CRF group (two highest quartiles) to low-moderate or low CRF group were classified as “decreasing CRF”. Subjects who were in high or moderate-high CRF groups at the age 15 and also at the age 25 and 33 years were classified as “persistently high CRF” and those who increased their CRF from low or low-moderate CRF to high or high-moderate were classified as “increasing CRF”. Logistic regression models were performed to assess the role of changes in CRF from adolescence to adulthood in acquiring MetS at the age of 25 and 33 years. All the models were adjusted for length of follow-up and sex and in MetS at age 25 years also for cohort. The level of significance was set at P < 0.05 for all the analyses.
Results Descriptive characteristics of the subjects at different ages are presented in Table 1. With increasing age, the prevalence of overweight/obese subjects increased (11.7%, 15.3%, 31.9% and 46.2% at the age of 15, 18, 25 and 33 years, respectively). [Insert Table 1 about here] Table 2 shows Pearson partial correlation coefficients for the tracking of CRF between different ages. All tracking coefficients revealed high correlation (r = 0.720-0.855; P < 0.05). Tracking tended to be stronger between closer measurement times and the strongest correlation was between CRF at the age of 25 years and CRF at the age of 33 years (P < 0.05). [Insert Table 2 about here]
Multilevel mixed models indicated that changes in CRF (measured as VO2peak/kg) had positive longitudinal association with HDL (P < 0.001) and negative longitudinal association with changes in all other MetS risk factors (except glucose) (P ≤ 0.008) after adjustment for sex and cohort (Table 3, Model 1). If additional adjustment was made for BMI, participation in
trainings, vigorous PA over 30 min per week and mother’s education, the longitudinal changes in CRF were positively associated with HDL and negatively with insulin, HOMA-IR, triglycerides, BPdiast and prevalence of MetS (P ≤ 0.011) (Table 3, Model 2). Associations between changes in CRF and glucose, total cholesterol, LDL and BPsyst changed nonsignificant in Model 2 (P ≥ 0.166). [Insert Table 3 about here] The subjects who had persistently low CRF had 34.4 and 11.5 times and the subjects with decreased CRF had 22.6- and 8.4-times higher likelihood of having MetS at the age of 25 and 33 years, respectively, compared to those who had persistently high CRF (P ≤0.003; Table 4). There was no higher risk for having MetS at the age of 25 and 33 years for those who had increased CRF compared to subjects with persistently high CRF (OR = 2.3 and 0.7, respectively; P ≥ 0.560). Subjects with decreasing CRF had 9.4- and 11.6-times higher risk and subjects with persistently low CRF had 14.7- and 15.9-times higher risk to have MetS at age 25 and 33, respectively (P ≤0.037) compared to subjects with increasing CRF. There were no differences in the likelihood to have MetS if the subject had decreasing CRF or persistently low CRF (P =0.257). [Insert Table 4 about here]
Discussion The main study finding was that those subjects who had persistently low or decreasing CRF had up to 34 times higher risk of having MetS in later in adulthood compared to those who had persistently high CRF. Furthermore, having high CRF in adolescence, that decreased during adulthood does not provide an advantage compared to those subjects having low CRF at all times.
The association between CRF assessed during adolescence and long-term risk of having MetS and its risk factors during adulthood is not well known. However, there is the trend for declining CRF among adolescents and youth [34, 35], suggesting a greater proportion of young adults with lower CRF, and thus have the elevated risk of cardiovascular disease in the near future [21]. In this study we measured both CRF and MetS risk factors in a time span of 15-18 years from early adolescence to adulthood, therefore we could assess the influence of changes in CRF on MetS risk factors. The results from the linear mixed model indicated that the change in CRF was strongly and inversely related to MetS and its risk factors, even after adjustment for potential confounders. In previous studies, the assessment of CRF has mostly been carried out one-time, and thus the conclusions remain subject to uncertainty about the influence of potential changes in CRF during follow-up. Prospective studies that include multiple assessment of CRF allow for the assessment of relationship between changes in CRF and the given outcome thereby controlling for potentially confounding factors [21]. Previous studies have found higher childhood fitness level associated with a reduced risk of adult MetS compared with low childhood fitness [36]. Studies that have assessed changes in fitness from childhood to adolescence have established that increasing fitness associated only with lower diastolic blood pressure seven years later [11] and with total cholesterol twenty years later [37]. There are several factors that might influence CRF, like genetics, sex, age, health condition, body composition [38] and overall physical activity. It is known that fatness and physical activity are strongly associated with CRF [39, 40]. In our study, after adjustment for fatness and physical activity some factors previously correlated (glucose, LDL, total cholesterol, BPsyst) lost their association, whereas others (insulin, HOMA-IR, HDL, BPdiast) increased their correlation value, suggesting that the relationship between CRF and MetS risk factors is affected by fatness as previously shown in adults with MetS [17] .
We found high levels of tracking of CRF between all measurement time points. Tracking results indicate that childhood CRF is an important predictor of adult CRF, suggesting that a fit child is more likely to be fit as an adult. It is important to notice that the results of tracking depend on the length of the measurement time, being usually stronger between shorter measurement points [33]. In addition, the risk of having MetS at the age of 25 and 33 years was between 11.5 and 34.4 times higher for the subjects with persistently low CRF and between 8.4 and 22.0 times higher for subjects with decreasing CRF compared to participants with persistently high CRF. Previous studies have indicated that higher level of childhood fitness is a good predictor for not developing MetS later [11, 12, 13]. The novelty of our study is in the finding that the decrease from initially high CRF to low fitness has the similar effect on developing MetS in early adulthood compared to persistently low fitness values. In contrast, those subjects who increased their fitness also decreased their MetS risk similar to subjects with persistently high CRF. Therefore, from adolescence to adulthood we should focus on maintaining fitness when the initial level is at least satisfactory however, further improvement of fitness is suggested if the respective levels in adolescence are low. For example, Carnethon et al. [41] found that young adults with low fitness levels were 3- to 6-fold more likely to develop diabetes, hypertension, and the MetS after 15-years compared to participants with high fitness. This is supported by the findings that the improved fitness over 7-years period was associated with a reduced risk of developing diabetes and the MetS [41]. Knaeps et al. [42] also found that increases in CRF in middle-aged (baseline age 46.1 ± 9.5 years) subjects during 10-year period (two measurement points) were associated with favourable changes in MetS risk factors like fasting glucose, HDL, triglycerides, diastolic and systolic blood pressure. Similarly, Laukkanen et al. [43] found that decrease in fitness levels over 11 years was associated with an increased risk of mortality in all caused death, including parameters of MetS. Thus, it is important to evaluate and improve CRF not
only in childhood but also during adulthood, ensuring better metabolic health and to include counselling of the physical activity into primary medical care. This study had several strengths that should be mentioned. Firstly, relatively large cohorts were followed for 10- or 18-year periods with three or two follow-up measurements for the older and younger cohorts, respectively. Secondly, all MetS risk factors and CRF were measured at all measurement times. Several covariates (BMI as fatness parameter, mother’s education as sociodemographic variable and participation in sports trainings and vigorous physical activities for over 30 minutes per week as physical activity parameters) were included into the analysis as parameters possibly affecting CRF. There are also some limitations in this study. Firstly, CRF ie. VO2peak was not measured directly but calculated by regression equations that might be less accurate. However, we predicted CRF by using maximal exercise test that is more valid compared to non-exercise prediction equations for estimating CRF. In addition, studies have shown that predicting equations provide reliable estimation of CRF in a wide range of settings and participants [4, 44]. Secondly, the general growth process itself affects some of the measured variables, for example, the changes in muscle, bone or fat tissue and this interaction was not taken into account. Thirdly, low incidence of MetS may affect the strength of the correlation. Our main finding was that high CRF in adolescence followed by the decrease during adulthood does not have an advantage over those who had persistently low CRF at all times. Higher CRF is strongly related to lower values of MetS risk factors. Furthermore, increasing CRF from adolescence to adulthood reduces the risk to have MetS later in adulthood. This suggests that it is important to improve CRF throughout the entire life, since it has a very strong protective effect against MetS risk factors and MetS. The measurement of CRF would be suggested for national health care priority, and not only during childhood and adolescence
(when it is easier to measure), but regular monitoring has to be conducted during adulthood as well.
Conflicts of interest All authors declared no conflict of interest. Funding This study was supported by the Estonian Research Council, Grant number: IUT 20-58, IUT 42-2, PUT1395 and IUT 20-40.
Appendix A. Supplementary data Supplemental Data File 1. Table (.docx)
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Figure legends
Figure 1. Schematic overview of the follow-up of the two cohorts. n, number of subjects with complete data on cardiorespiratory fitness and metabolic syndrome parameters available.
Table 1. Descriptive characteristics of the analyzed study sample in different age groups during the longitudinal study 15-year-old
18-year-old
25-year-old
33-year-old #
Sample size (n)
1076
896
948
492
Female (%)
55.2
56.6
56.0
57.3
Male
20.4 ± 0.1
22.5 ± 0.2a
25.0 ± 0.2ab
26.7 ± 0.2abc
Female
20.6 ± 0.1
21.8 ± 0.1*a
23.0 ± 0.2*ab
23.9 ± 0.2*abc
Waist circumference Male
71.4 ± 0.3
77.8 ± 0.3a
85.9 ± 0.4ab
90.7 ± 0.6abc
(cm)
Female
66.7 ± 0.3*
70.3 ± 0.3*a
75.0 ± 0.4*ab
77.6 ± 0.5*abc
Overweight (%)
Male
12.9
17.3
43.1ab
63.8abc
Female
10.4
13.3
20.6*ab
28.5*abc
BMI (kg·m-2)
VO2peak/kg (mL·min-
Male
48.6 ± 0.3
42.3 ± 0.4a
40.1 ± 0.4ab
45.5 ± 0.6abc
1
Female
36.0 ± 0.2*
27.8 ± 0.2*a
27.1 ± 0.2*ab
31.6 ± 0.4*abc
Male
11.4 ± 0.3
8.0 ± 0.4a
7.9 ± 0.3a
9.9 ± 0.5abc
Female
12.0 ± 0.2
8.2 ± 0.3a
7.6 ± 0.3a
7.6 ± 0.4*abc
Male
5.2 ± 0.02
5.0 ± 0.02a
5.12 ± 0.02b
5.36 ± 0.03abc
Female
4.9 ± 0.02*
4.7 ± 0.02*a
4.8 ± 0.02*ab
5.0 ± 0.03*bc
Male
2.67 ± 0.08
1.79 ± 0.09a
1.83 ± 0.08a
2.41 ± 0.14bc
Female
2.67 ± 0.06
1.73 ± 0.06a
1.64 ± 0.06*a
1.71 ± 0.9*ac
Total cholesterol
Male
3.84 ± 0.03
3.97 ± 0.04a
4.43 ± 0.04ab
4.88 ± 0.06abc
(mmol·L-1)
Female
4.26 ± 0.03*
4.36 ± 0.04*a
4.57 ± 0.03*ab
4.61 ± 0.05*a
HDL (mmol·L-1)
Male
1.36 ± 0.01
1.35 ± 0.01
1.31 ± 0.02a
1.36 ± 0.02c
Female
1.51 ± 0.01*
1.62 ± 0.02*a
1.69 ± 0.02*ab
1.77 ± 0.02*abc
Male
2.19 ± 0.05
2.30 ± 0.03
2.72 ± 0.04ab
3.29 ± 0.05abc
Female
2.47 ± 0.05*
2.46 ± 0.03*
2.57 ± 0.03*b
2.77 ± 0.03*ab
·kg-1)
Insulin (µUm·L-1)
Glucose (mmol·L-1)
HOMA-IR
LDL (mmol·L-1)
Triglycerides
Male
0.79 ± 0.03
0.88 ± 0.03a
1.13 ± 0.04ab
1.39 ± 0.05ab
(mmol·L-1)
Female
0.84 ± 0.02
0.84 ± 0.03
0.94 ± 0.02*ab
0.98 ± 0.03*ab
BPsyst (mm Hg)
Male
116 ± 0.6
122 ± 0.6a
125 ± 0.6ab
124 ± 0.9ab
Female
107 ± 0.4*
109 ± 0.5*
109 ± 0.4*a
108 ± 0.7*b
Male
62 ± 0.3
62 ± 0.4
68 ± 0.4ab
71 ± 0.6abc
Female
63 ± 0.3
61 ± 0.3*a
65 ± 0.3*ab
65 ± 0.5*ab
Male
18/3.7
17/4.4
34/8.2
42/20.0
Female
6/1.0*
7/1.4*
16/3.0*
18/6.4*
BPdiast (mm Hg)
MetS (n/ %)
Data presented in mean ± SE or prevalence (n) / percentage. Values are adjusted for cohort; P < 0.05 #
Only older cohort
*Significant difference between sexes a
Significantly different from the age of 15 years
b
Significantly different from the age of 18 years
c
Significantly different from the age of 25 years
BMI, body mass index; HOMA-IR, homeostasis model assessment of insulin resistance; HDL, high-density lipoprotein cholesterol; LDL, low-density lipoprotein cholesterol; BPsyst, systolic blood pressure; BPdiast, diastolic blood pressure; MetS, metabolic syndrome
Table 2. Pearson correlation coefficients for tracking of cardiorespiratory fitness (VO2peak/kg) over four measurement times.
CRF at age 15 CRF at age 18
CRF at age 18
CRF at age 25
CRF at age 33
0.796
0.757
0.782
0.790
0.720
CRF at age 25
0.855
All correlation coefficients were adjusted for length to follow-up. All correlations were significant at P < 0.001.
Table 3. Multilevel analysis of changes in cardiorespiratory fitness and its impact on changes in metabolic syndrome and its risk factors Estimate ± SE
95% CI
P
Model 1
-0.033 ± 0.016
-0.064; -0.003
0.001
Model 2
-0.174 ± 0.031
-0.235; -0.112
<0.001
Model 1
0.006 ± 0.001
0.003; 0.008
<0.001
Model 2
-0.004 ± 0.004
-0.011; 0.003
0.265
Model 1
-0.006 ± 0.004
-0.014; 0.002
0.001
Model 2
-0.043 ± 0.008
-0.059; -0.027
<0.001
Insulin
Glucose
HOMA-IR
Total cholesterol Model 1
-0.008 ± 0.002
-0.012; -0.005
<0.001
Model 2
-0.008 ± 0.006
-0.020; 0.004
0.166
Model 1
0.005 ± 0.001
0.003; 0.006
<0.001
Model 2
0.009 ± 0.004
0.002; 0.016
0.011
Model 1
-0.008 ± 0.002
-0.011; -0.004
<0.001
Model 2
-0.006 ± 0.005
-0.016; 0.004
0.167
Model 1
-0.011 ± 0.002
-0.014; -0.008
<0.001
Model 2
-0.034 ± 0.011
-0.056; -0.014
0.001
HDL
LDL
Triglycerides
BPsyst
Model 1
-0.261 ± 0.028
-0.316; -0.205
<0.001
Model 2
-0.106 ± 0.083
-0.270; 0.057
0.203
Model 1
-0.050 ± 0.019
-0.088; -0.012
0.008
Model 2
-0.210 ± 0.065
-0.339; -0.082
0.001
Model 1
-0.013 ± 0.001
-0.015; -0.011
<0.001
Model 2
-0.006 ± 0.002
-0.009; -0.003
<0.001
BPdiast
MetS
Model 1: adjusted for length to follow-up, sex and cohort Model 2: in addition to Model 1 adjusted for BMI, participation in trainings, vigorous physical activity over 30 min in week and mother’s education SE, standard error; CI, confidence intervals; HOMA-IR, homeostasis model assessment of insulin resistance; HDL, high-density lipoprotein cholesterol; LDL, low-density lipoprotein cholesterol; BPsyst, systolic blood pressure; BPdiast, diastolic blood pressure; MetS, metabolic syndrome.
Table 4. Effect of change in cardiorespiratory fitness from adolescence to adulthood on metabolic syndrome at the age of 25 (n=948) and 33 (n=492) years for all subjects. MetS at age 25 n*
OR
95% CI
Persistently high CRF
5/328
Ref
Increasing CRF
6/149
2.30
0.141; 37.45
Decreasing CRF
15/159
22.57
Persistently low CRF
31/255
34.43
MetS at age 33 P
n*
OR
95% CI
P
7/189
Ref
0.560
4/49
0.73
0.08; 6.86
0.785
2.83; 180.13
0.003
17/84
8.35
2.58; 27.00
<0.001
4.62; 256.85
0.001
28/114
11.52
3.79; 34.98
<0.001
Increasing CRF
Ref
Decreasing CRF
9.39
1.14; 77.43
0.037
11.57
1.42; 94.17
0.022
Persistently low CRF
14.71
1.94; 111.38
0.009
15.89
2.02; 124.90
0.009
0.63; 2.97
0.424
Decreasing CRF
Ref
Persistently low CRF
1.59
Ref
Ref 0.71; 3.52
0.257
1.37
All models were adjusted for length to follow-up, sex (1-male, ref; 2-female) and additionally in MetS at age 25 for cohort (1 -older cohort, ref; 2-younger cohort). *prevalence / absence of MetS in the each CRF group OR, odds ratio; CI, confidence intervals; MetS, metabolic syndrome; CRF, cardiorespiratory fitness
1998 Older cohort
Younger cohort
n = 593 (male = 260) (15.4 ± 0.6 yrs)
2001
2004
2007
n = 442 (male = 187) (18.4 ± 0.9 yrs)
2008
2014
n = 515 (male = 224) (25.2 ± 0.7 yrs) n = 483 (male = 222) (15.3 ± 0.5 yrs)
n = 454 (male = 202) (18.3 ± 0.5 yrs)
2016 n = 492 (male = 210) (33.5 ± 0.7 yrs)
n = 433 (male = 193) (25.3 ± 0.5 yrs)
Highlights
•
High CRF strongly relates to lower scores of metabolic syndrome (MetS) risk factors
•
Increase in CRF through adolescence reduces the risk of MetS later in adulthood
•
The decrease in CRF during adolescence has the risk of MetS similarly to persistently low CRF