Atherosclerotic lesions start to develop at an early age with intimal lesions in the aorta and coronary arteries being present as early as 2 years (6,43). It has been suggested that arterial structure indices, such as carotid intima-media thickness (cIMT), could be used in children as a part of the overall risk assessment for early atherosclerosis in some clinical settings and in the research domain (15). The Association for European Paediatric Cardiology Working Group has recommended to use cIMT as a valid surrogate marker for evaluation of cardiovascular risk in adolescent patients (10). Several studies have shown both retrospectively and prospectively that cardiovascular risk factors such as body mass index (BMI), high body fat percentage, obesity, systolic blood pressure, diastolic blood pressure, and smoking in adolescence are important predictors of arterial structure in adulthood (6,27,39,41). At the same time, recent studies have found opposite effects of lean body mass (LBM) and fat mass on cIMT, proposing that the changes in arterial structure might predominantly involve the media and represent physical adaptations as opposed to subclinical atherosclerosis (9).
Suggestions have been posed on using assessments on vascular aging to improve cardiovascular risk stratification. Pulse wave velocity (PWV) and augmentation index (AI) as arterial function parameters are considered valuable in the assessment of vascular aging with PWV being the gold standard. AI has been found more influenced by pathophysiological conditions, medications, heart rate, and age and thus less reliable when not taking these factors into consideration (31,46). Arterial function parameters have been shown to be more dependent on diastolic blood pressure, LBM, and smoking habits (41). For some time, it was established that high BMI and obesity in childhood predict worse outcomes for arterial health in the future (29,40), but further research has shown the associations with fat mass and arterial function not to be that simple. The Avon Longitudinal Study of Parents and Children (ALSPAC) cohort analyses have suggested a potential “arterial paradox,” as healthy arterial function was found to be independently associated with increased total and trunk fat mass and vice versa (1). Arterial health parameters, like systolic blood pressure, carotid-femoral PWV (cf-PWV), and reactive hyperemia have interactions with fat mass, but this association was also shown to be dependent on the amount of LBM (43).
Although high cardiorespiratory fitness level (CRF) and physical activity in otherwise healthy and low-risk men have proven to be beneficial in preventing cardiovascular disease occurrence (19,20,24), recent studies in children and adolescents in the ALSPAC cohort (7) have shown the potentially more complex interactions of risk factors and their effects on arterial structure and function. Allometrically scaled CRF per body mass was directly related to distensibility coefficient, while allometrically scaled CRF per LBM was inversely associated with pulse wave velocity, and the results remained significant in bidirectional analyses. CRF was found to partially mediate the associations of cardiometabolic health with flow-mediated dilation, distensibility coefficient, and PWV (1). Other studies have also suggested that CRF level has a mediating effect for obesity effects on cardiovascular disease risk (33).
Physical activity at an early age has been shown to decrease IMT in adulthood in a retrospective study (26), but at the same time higher training load has shown associations with thicker cIMT, but better elasticity indices suggesting that higher training load leads to functional adaptation of the carotid artery in youth (5). Longitudinal analysis on children and adults data has shown that physical activity could have beneficial effects on arterial elasticity, but the data in this study were collected via a questionnaire, not objectively (36). To our best knowledge, longitudinal studies on objectively measured physical activity and arterial health parameters have not been performed so far.
The objective of this study was to determine the relationship of late adolescence arterial structure and function indicators with objectively measured physical activity, CRF, body composition, and other cardiorespiratory risk factors measured through puberty to young adulthood in healthy males. We hypothesized that higher cumulative levels of physical activity and CRF are associated with better arterial structure and function status at the age of 18 years irrespective of body composition measures and other cardiovascular risk factors.
Methods
Study Participants
Data were from longitudinal prospective study performed in Estonia 2009–2018, “Risk factors for metabolic syndrome in boys during pubertal development: a longitudinal study with special attention to physical activity and fitness.” The study included healthy adolescent White males at between 9 and 12 years of age attending schools in the city and the surroundings of Tartu (45). The number of participants in the initial study cohort is shown on Figure 1. Current analysis excluded time point T0 due to missing data on physical evaluation. The study was conducted following the Declaration of Helsinki, and the protocol for analysis was approved by the Ethics Committee of the University of Tartu (Consent No 327-T20, 19.10.2020). Written informed consent was received from each study participant and their legal guardian where applicable.
Analysis of data for anthropometry, sexual maturation, physical activity, CRF, and lifestyle factors was performed. Arterial structure and arterial function measurements were performed at the last study time point at the mean age of 18 years.
Participants were asked to fill a questionnaire about their health status and used medications. This information was checked to make sure that they were indeed healthy. None of the participants had diagnosed diabetes or any other endocrine disorder. Five reported hypertension or increased blood pressure in their questionnaire at T4 but did not use any blood pressure–lowering medications. As their blood pressure was normal at the last study visit, they were included in the analysis. No other clinically significant health problems were reported. Individuals were asked about their smoking habits (whether they smoke, how frequently and at what age they started smoking) at T4.
For this longitudinal analysis, we included 102 individuals for whom arterial structure and function measurements were performed at T4. Two individuals out of 104 who completed all time points were excluded due to missing data for arterial health measurements. The study cohort of current study is representative to the individuals initially included at baseline regarding the mean age, anthropometry, body composition, CRF, physical activity, and arterial parameters for which no significant differences were present for the whole cohort and individuals included in this analysis.
Pulse Wave Analysis, Pulse Wave Velocity, and Augmentation Index
Radial artery pressure waveforms were obtained with a high-fidelity applanation tonometer (SPT-301B; Millar Instruments) from the wrist of the left hand. After 20 sequential waveforms had been acquired, the integral software (SphygmoCor Px, version 7.0, AtCor Medical) was used to generate a corresponding central (ascending aortic) waveform using a generalized transfer function, which has been prospectively validated for assessment of central blood pressure (42).
The AI was calculated as the difference between the second and the first systolic peaks, divided by central pulse pressure and expressed in percentages (51). The AI values were adjusted to a heart rate of 75 beats per minute (AIxHR75) using a SphygmoCor built-in algorithm.
The cf-PWV was measured by the foot-to-foot method, using the SphygmoCor device. The cf-PWV was determined by sequentially recording electrocardiograph-gated carotid and femoral artery waveforms using the SphygmoCor software (11). Wave transit time was calculated as the time delay between the arrival of the pulse wave at the common carotid artery and the common femoral artery using the R wave of a simultaneously recorded electrocardiograph as the reference frame. The surface distance between the 2 recording sites was measured as the distance from the suprasternal notch to the common femoral artery minus the distance from the suprasternal notch to the common carotid artery (51). Brachial blood pressure was registered in the supine position from the left arm using an automated digital oscillometric blood pressure monitor (OMRON M4-I, Omron Healthcare Europe).
Carotid Artery Ultrasound
Carotid ultrasound for evaluation of cIMT was performed using SonoSite M-Turbo portable ultrasound device (SonoSite, Bothell, WA) coupled to a 5- to 10-MHz multifrequency high-resolution linear transducer. The measurements were obtained with the participant lying down, with the head extended and slightly turned opposite to the examined carotid. Measurements were made on both common carotid arteries after the examination of a longitudinal section of 10 mm at a distance of 1 cm from the bifurcation, performing measurements in the far wall in the lateral, anterior, and posterior projections (total of 6 measurements per participant). The within-observer and between-observer coefficients of variation for mean IMT were 2.2% and 5.9%, respectively. SonoCalc software was used for calculation of arithmetic mean and maximum values of cIMT.
Anthropometry and Sexual Maturation
Body height (in centimeters) was measured to the nearest 0.1 cm using the Martin metal anthropometer according to the standard technique (GPM anthropological instruments). Body mass (in kilograms) was measured to the nearest 0.05 kg using a medical electronic scale (A&D Instruments Ltd) with the participant wearing light clothes. BMI (in kilogram per square meter) was calculated as body mass divided by the square of body height.
The pubertal development of the participants was determined using a self-report questionnaire of pubertal stages according to the Tanner classification method (30), which has been previously validated (14). The participants were given photographs, figures, and descriptions representing genitalia and pubic hair development stages and were asked to choose the one that most closely matched their own development. In the case of discrepancies between the 2 variables, the Tanner stage of the participant was determined according to the self-estimation of genitalia development (14). Pubertal development was not assessed on the last time point (T4), as it was estimated based on T3 results that most if not all participants had developed into Tanner stages 4 to 5.
Body Composition
Body fat mass and LBM were measured by DEXA scan Discovery (Hologic QDR Series) at T4 and by DEXA scan (aDPX-IQ; Lunar Corporation) at all other time points as described in the original cohort analysis (45). The participants were scanned in the supine position wearing minimal clothing and medium scan mode was used for the measurement.
Physical Activity
Physical activity was measured objectively using an ActiGraph accelerometer designed to register vertical accelerations. Model GT3X (ActiGraph LLC) at T4 and model GT1M in all other time points was used. All participants were instructed to wear the accelerometer on the right hip for 7 consecutive days during the wake-up time. For the analyses of accelerometer data, all night activity (24:00–6:00 h) and all sequences of 10 minutes or more of consecutive zero counts were excluded from each individual recording. At least 2 weekdays and 1 weekend day of recording with a minimum of 10 hours per day was set as an inclusion criterion. The accelerometer was programmed to record activity counts in 15-second epochs. Time spent in physical activity with different intensity levels was calculated (34). Evenson cutoff points for physical activity intensity levels were initially developed on 5- to 8-year-olds (16), but in the latest decade they have been shown to be valid also in a wider age range of children and adolescents (48), and thus the following cutoffs were used: sedentary time ≤ 100 counts per minute, light 101 to 2295 counts per minute, moderate 2296 to 4011 counts per minute, and vigorous physical activity ≥ 4012 counts per minute. The time spent in at least moderate-intensity physical activity was calculated as moderate to vigorous physical activity (MVPA). MVPA values in regression models were used as continuous variables, but model adjustments were done for a categorical variable according to World Health Organization daily recommendations (>60 or <60 min/d) (53).
Cardiorespiratory Fitness
Cardiorespiratory fitness was determined by a stepwise incremental exercise test until volitional exhaustion performed on an electrically braked bicycle ergometer (Corival V3, Lode). Initial work rate was 60 W, with 25-W increments after every 3 minutes until volitional exhaustion. Pedaling frequency was set 60 to 70 rpm. Participants were verbally encouraged to produce maximal effort. Respiratory gas exchange variables were measured throughout the test using breath-by-breath mode with data being stored in 10-second intervals. During all tests, participants breathed through a face mask. Oxygen consumption (VO2), carbon dioxide output, and minute ventilation were continuously measured using a portable open-air spirometry system (MetaMax I, Cortex). The analyzer was calibrated with gases of known concentration before the test according to the manufacturer’s guidelines. All data were calculated by computer analysis using standard software (MetaMax-Analysis, version 3.21, Cortex). Peak oxygen consumption (VO2peak; L/min) was measured. The exercise test was considered acceptable if it met any one of following criteria; respiratory exchange ratio ≥ 0.99, defined plateau of VO2, max heart rate > 200 beats per minute, or signs of intense effort (eg, hyperpnea, facial flushing, or difficulties in keeping up the speed of the bicycle [3]).
For children and youth, an allometric expression of VO2peak relative to LBM has been shown to be the best expression of CRF as it removes the effect of body composition on fitness (28,49) and therefore a log linear regression model with LBM as the independent variable and VO2peak as the dependent variable was used. Usage of additional independent variables (age, Tanner stage) in the model did not provide additional significant value for the model and were therefore not included. The exponent (b) was 0.82 (95% confidence interval, 0.78–0.86). Allometric scaling resulted in diminishing the significant association between LBM and VO2peak (r = .01, P = .815), providing a better CRF measure than ratio scale. VO2peak was therefore expressed allometrically scaled for LBM (mL/min/kg LBM0.82).
Statistical Analyses
Statistical analyses were performed using R software (version 1.3.1093) for iOS (38). Normally distributed continuous variables are described as a mean and ± 1 SD, and not normally distributed variables as a median and 25th and 75th percentile. As the age of the participants is found to affect IMT less than body size, IMT mean was standardized to height (IMT-SDS), but not to age (12,13,34). BMI values were standardized to age and z scores were calculated according to World Health Organization growth charts (34,52). We aimed to evaluate both cross-sectional (from 12 y of age and late adolescence) and longitudinal effects of risk factors on arterial structure (IMT-SDS) and function (AIxHR75 and cf-PWV), and thus different linear regression models were developed using risk factors longitudinally (sum of a risk variable at all 4 time points), at puberty (at 12 y), and at late adolescence (at 18 y). Independent variables checked for associations included the following: BMI z score, body fat percentage, fat mass (in kilograms), LBM (in kilograms), sedentary time (in minutes per day), vigorous physical activity (in minutes per day) and MVPA (in minutes per day and category of >60 min/d or <60 min/d), CRF (in mL/min/kg LBM0.82), and smoking (in years). Multivariable linear regression models were adjusted for late adolescence smoking, systolic blood pressure, Tanner stage, CRF, MVPA category (>60 or <60 min/d), and LBM depending on the predictor variable. Independent variables that showed multicollinearity were excluded from the model (r = .5) and therefore for models with body composition variable as the independent variable, LBM adjustment was not used. Physical activity and CRF correlations did not exceed the set limit (r < .25 for all physical activity variables) and were used together in the adjusted model. To better understand whether the cumulative and 12-year physical activity results were independent of late adolescence results, we used the MVPA categorical variable for adjustments (or MVPA cumulative variable in case the MVPA category was the independent variable) at these time points.
Missing values for independent variables were imputed using the multiple imputation method (8); P < .05 was regarded as significant.
Results
Basic Characteristics
The basic characteristics of the participants at different time points are summarized in Table 1. As expected, most of the characteristics changed significantly over time except body fat percentage and BMI z score. Time spent in vigorous physical activity and sedentary time increased, whereas the time in MVPA and allometrically scaled CRF decreased over the 6-year study period. Seventy-eight participants (76%) reported that they have tried smoking at least once, at the mean age of 10.7 ± 6.3 years. Fifty-six (55%) reported smoking also in the last year less then few times, 12 boys (12%) weekly to monthly, and 9 boys (9%) almost daily. The mean arterial structure and function parameters at late adolescence in 102 participants are presented in Table 2.
Basic Characteristics of the Participants (N = 102) at Different Time Points
| Characteristics | T1 N = 102 | T2 N = 102 | T3 N = 102 | T4 N = 102 | Pa |
|---|---|---|---|---|---|
| Age, y | 12.0 (0.7) | 13.1 (0.7) | 14.0 (0.7) | 18.0 (0.7) | <.001 |
| Height, cm | 154.7 (8.3) | 163.1 (9.3) | 169.6 (8.7) | 181.3 (6.4) | <.001 |
| Body mass, kg | 47.2 (12.9) | 54.0 (14.6) | 59.5 (14.0) | 73.5 (12.1) | <.001 |
| Body fat percentage | 22.8 (10.7) | 21.6 (10.4) | 19.5 (9.7) | 18.0 (4.9) | .025 |
| Fat mass, kg | 11.1 (8.3) | 12.1 (9.0) | 11.7 (8.5) | 13.3 (5.9) | <.001 |
| LBM, kg | 33.5 (6.3) | 39.5 (8.2) | 44.8 (8.4) | 55.6 (7.2) | <.001 |
| BMI z score | 0.5 (1.3) | 0.4 (1.3) | 0.3 (1.2) | 0.1 (1.0) | .079 |
| Tanner stage | |||||
| 1 | 1 (1.0%) | 0 (0%) | 0 (0%) | ||
| 2 | 37 (36%) | 10 (9.8%) | 2 (2.0%) | ||
| 3 | 53 (52%) | 48 (47%) | 21 (21%) | ||
| 4 | 11 (11%) | 35 (34%) | 39 (39%) | ||
| 5 | 0 (0%) | 9 (8.8%) | 38 (38%) | ||
| VO2peak, L/min | 2.3 (0.5) | 2.5 (0.5) | 2.9 (0.6) | 3.3 (0.7) | <.001 |
| VO2peak/LBM0.82, mL/min/kg0.82 | 129.9 (13.2) | 124.6 (15.8) | 126.1 (13.9) | 121.8 (20.8) | <.001 |
| Sedentary time, min/d | 532.8 (62.5) | 539.5 (81.9) | 570.7 (84.7) | 622.8 (86.4) | <.001 |
| Vigorous physical activity, min/d | 18.8 (14.5) | 20.2 (13.4) | 19.4 (15.0) | 27.7 (18.0) | <.001 |
| MVPA, min/d | 66.4 (26.9) | 62.0 (23.8) | 55.5 (23.7) | 55.4 (24.1) | .004 |
| MVPA > 60 min/d | 41 (44%) | 46 (48%) | 33 (37%) | 28 (36%) |
Abbreviations: BMI, body mass index; LBM, lean body mass; MVPA, moderate to vigorous physical activity; VO2peak, peak oxygen consumption. Note: The values are presented as mean (SD) and n (%).
aKruskal–Wallis rank sum test.
Arterial Structure and Function Parameters at 18 Years in 102 Participants
| Arterial variables | Mean (SD) |
|---|---|
| Peripheral systolic BP, mm Hg | 122.6 (9.2) |
| Peripheral diastolic BP, mm Hg | 67.1 (7.0) |
| Heart rate, bpm | 62.3 (9.9) |
| cIMT mean, mm | 0.5 (0.1) |
| IMT-SDS | −0.9 (2.1) |
| AIxHR75, % | −2.6 (12.6) |
| cf-PWV, m/s | 5.6 (0.9) |
Abbreviations: AIxHR75, augmentation index adjusted to heart rate of 75×/min; BP, blood pressure; cf-PWV, carotid-femoral pulse wave velocity; cIMT, carotid intima media thickness; IMT-SDS, intima media thickness standard score adjusted to height.
Body Composition and Arterial Parameters
Detailed results for linear regression models are presented in Appendix Table A1 (exposure variables at late adolescence), Appendix Table A2 (exposure variables at T1), and Appendix Table A3 (cumulative exposure of each risk variable).
Standardized BMI, fat mass, and body fat percentage showed continuous associations with the arterial structure variable IMT-SDS in cross-sectional analyses at T1 and at late adolescence as well as with cumulative variable from puberty to late adolescence with the only exception of BMI being not significant at 18 years. These risk factors at T1 (β = 0.392, P = .021 for BMI z score; β = 0.052, P = .012 for body fat percentage; and β = 0.066, P = .001 for fat mass) and cumulatively (β = 0.106, P = .028 for BMI z score; β = 0.018, P = .006 for body fat percentage; β = 0.020, P = .007 for fat mass) remained significant after adjustment for cofactors (MVPA group, CRF, smoking, Tanner stage, and systolic blood pressure). At late adolescence, the association for fat mass and body fat percentage with arterial function (AIxHR75) diminished after adjustment for covariables.
The LBM was not related to IMT-SDS, AIxHR75, or cf-PWV at any time points nor with a cumulative risk factor.
Physical Activity and Arterial Parameters
Time spent in vigorous physical activity at T1 was significantly associated with IMT-SDS (β = −0.047, P = .001) and with AIxHR75 (β = −0.213, P = .020), but after adjustment for covariates the significance disappeared. Late adolescence vigorous physical activity showed no relationship with arterial health, whereas cumulative exposure was associated with arterial structure (β = −0.011, P =.027). This association was not independent of cofactors.
Time spent in MVPA at T1 correlated significantly with IMT-SDS (β = −0.020, P = .012) and with AIxHR75 (β = −0.102, P = .046), but after adjustment for other risk factors the correlations were not statistically significant. The same type of association was present for the cumulative exposure of MVPA and IMT-SDS (β = −0.008, P = .013), but adjustment to risk factors resulted in nonsignificant associations. While looking at whether study participants fulfilled the suggestion of doing MVPA more than 60 minutes per day, we found an association with the independent variable at T1 (β = −1.091, P = .026) and arterial structure parameter even after adjustment for other risk factors, including MVPA at late adolescence. This strong relationship was not present in late adolescence or for arterial function parameters.
Time spent in sedentary time at T1, late adolescence, and cumulatively did not show any associations with arterial structure and function variables.
Cardiorespiratory Fitness and Arterial Parameters
The VO2peak per LBM0.82 in late adolescence was inversely associated with IMT-SDS (β = −0.031, P = .010) after adjustment for risk factors. The same type of strong relationship was shown for the cumulative exposure measure and arterial structure (β = −0.011, P = .036), independent of physical activity, LBM, Tanner stage, smoking habits, and systolic blood pressure. The relationship of cumulative CRF and AIxHR75 was present (β = −0.061, P = .035) but diminished after adjustment for other risk factors.
Discussion
The findings of this longitudinal study in a homogenous cohort of healthy Estonian boys followed through puberty to late adolescence showed that body composition, namely, higher fat mass and body fat percentage, both at the age of 12 years and as cumulative burden influences arterial structure at the age of 18 years. Fulfillment of physical activity recommendations of at least 60 minutes per day at the age of 12 years has a strong association with arterial structure later in life and this result is independent of other risk factors, including physical activity level at 18 years. Cumulative CRF and CRF at late adolescence were both associated with IMT-SDS. It is well known that physical activity significantly improves CRF (27), but our results show that MVPA and CRF associations with arterial structure are independent of each other.
Although earlier studies based on The Cardiovascular Risk in Young Finns cohort analyses seemed to have established that obesity in childhood is a risk factor for adulthood atherosclerosis (25,39) recent studies in the ALSPAC cohort have started questioning whether BMI, as a measure of obesity, really predicts arterial structural changes properly or the effects of BMI on arterial health should be divided into 2 components, namely, LBM and fat mass effect (2,9). The results from the latest analysis on the Bogalusa Heart Study cohort showed that the life course cumulative burden of BMI has an impact on arterial wall thickening and this impact starts in the early life (17). Our analysis supplements these results by showing that the BMI results could be mainly driven by the fat mass component. The fact that the results were only present at the age of 12 years and cumulatively, but not at late adolescence, suggests that the remodeling effects take time to develop.
A large study on a cohort of children and adults did not find any association between compliance with physical activity recommendations and arterial thickness (54), but in that study the authors did not control for previous physical activity levels, but rather analyzed the data cross sectionally while performing vascular evaluation. In a retrospective study Werneck et al showed that the relationship between early sports participation and arterial thickness exists independently of current physical activity level and other risk factors (50). Our study has successfully replicated the results using objective measurement of physical activity by accelerometry. Furthermore, we have shown that these results are independent of CRF, which is considered a potential mediator in the associations tested. A study in athletic children and adolescents showed that physical activity duration and intensity might have adverse effects on arterial thickness (5), but this was not shown in our cohort of moderately active youngsters. The mechanism for the direct physical activity effect on arteries is unknown, but it is proposed that in critical periods, such as childhood, the stress generated by sports participation can stimulate epigenetic modifications, leading to lowering of the basal level of inflammation and factors contributing to chronic diseases independent of current lifestyle and/or biological conditions in adulthood (21). The seemingly favorable response of the arterial system to physical activity could also be related to increased nitric oxide bioavailability. This is thought to result from the upregulation of endothelial nitric oxide synthase gene expression in response to vascular wall shear stress caused by exercise-related increases in systemic blood flow (22,23). As basal nitric oxide levels also contribute to arterial compliance, this may partially explain the positive effect of physical activity on augmentation index (18).
There is a large gap of studies investigating the associations between CRF and arterial health in children going through puberty, although a relationship between fitness levels and cardiovascular risk has been proposed (35,48). Cardiorespiratory fitness in adolescents with obesity is shown to have an inverse association with cIMT, even after controlling for sedentary time (4). These results are supported by our study in healthy late adolescent males. In addition, we showed that CRF has a cumulative effect on arterial structure independently of LBM and physical activity, while the CRF at the beginning of puberty might not be so meaningful for arteria structure in late adolescence.
There are some limitations to the study. First, the arterial structure and function were studied only in late adolescence. Having these data also from puberty would have allowed to analyze the changes in the indices and their determinants. Second, the study cohort is representative of only male gender and specific region of interest, not allowing it to be extended to general population. Third, the fact that our participants were healthy, with most having normal arterial parameters and BMI, may restrict the predictive capacity of the measured outcomes on vascular dysfunction.
However, this study is unique as most of the variables were measured objectively (physical activity, CRF, arterial structure and function, and body composition) and standardized where appropriate (BMI, IMT, and VO2peak/LBM0.82).
The results of our study support the importance of early age physical activity and body composition effects on arterial structure at the age of 18 years independently of the late adolescence physical activity. The study confirms the independent relationship between cumulative CRF from puberty to late adolescence and arterial structure of 18-year-old healthy males. We did not find independent associations between arterial function parameters and studied risk factors. Further research is necessary to clarify whether these longitudinal associations are seen also in female adolescents.
Perspective
The results of our study suggest a higher than expected role of MVPA in childhood to late adolescence arterial structure in boys. The combination of the roles of MVPA and CRF at different time points as well as their cumulative effects suggests an independent relationship of the 2 in mediating arterial thickness. It was shown that these results are independent of other important cardiovascular risk factors.
It is known that arterial health is affected by physical activity, but the fact that the longitudinal effects remain for so long is a new insight and should provide additional emphasis on the importance of physical activity in childhood. Strategies promoting physical activity in children could provide important assistance in decreasing cardiovascular disease in later life. Further evidence in multinational studies would be necessary to confirm these results on a larger scale and allow for more active engagement for policy makers.
Acknowledgments
The study was supported by the grant from the Estonian Research Council PRG1120 and PRG1428. All authors participated in designing of the study, discussed the results and implications, and commented on the paper at all stages. Research data will be made available upon request to the corresponding author or the article. The data are not publicly available due to privacy or ethical restrictions. Author Contributions: Tillmann and Jürimäe conceived and designed research. Tamme, Remmel, Mäestu, and Zagura conducted experiments and collected the data. Kraav designed analysis plan, analyzed data, and wrote the manuscript. All authors read and approved the manuscript.
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Appendix Table A1: Linear Regression Examining Association Between Risk Factors and Arterial Parameters at 18 Years
| Model 1 | Model 2 | |||||
|---|---|---|---|---|---|---|
| β | SE | P | β | SE | P | |
| Dependent variable: IMT-SDS | ||||||
| BMI z scorea | 0.346 | 0.210 | .103 | 0.342 | 0.225 | .131 |
| Fat percentagea | 0.106 | 0.043 | .015 | 0.070 | 0.049 | .158 |
| Fat mass,a kg | 0.072 | 0.036 | .048 | 0.055 | 0.039 | .159 |
| Lean mass,a kg | −0.007 | 0.030 | .818 | 0.013 | 0.030 | .673 |
| Sedentary time, min/d | −0.001 | 0.004 | .767 | −0.003 | 0.003 | .280 |
| Vigorous physical activity, min/d | −0.001 | 0.013 | .915 | 0.005 | 0.018 | .790 |
| MVPA,b min/d | −0.008 | 0.010 | .434 | 0.006 | 0.015 | .698 |
| MVPA > 60 min/d (compared to <60) | −1.003 | 0.485 | .042 | −0.639 | 0.755 | .413 |
| VO2peak, mL/min/kg0.82 | −0.034 | 0.010 | .001 | −0.031 | 0.012 | .010 |
| Smoking, y | 0.020 | 0.037 | .578 | 0.007 | 0.037 | .849 |
| Age, y | −0.251 | 0.339 | .460 | −0.535 | 0.448 | .236 |
| Dependent variable: AIxHR75 | ||||||
| BMI z score | 0.195 | 1.294 | .881 | −0.108 | 1.301 | .934 |
| Body fat percentage | 0.665 | 0.253 | .010 | 0.524 | 0.271 | .056 |
| Fat mass, kg | 0.425 | 0.213 | .049 | 0.338 | 0.218 | .125 |
| Lean mass, kg | −0.130 | 0.175 | .459 | −0.050 | 0.177 | .777 |
| Sedentary time, min/d | −0.011 | 0.015 | .493 | −0.004 | 0.019 | .821 |
| Vigorous physical activity, min/d | −0.047 | 0.086 | .586 | 0.013 | 0.103 | .906 |
| MVPA, min/d | −0.045 | 0.061 | .462 | 0.011 | 0.076 | .891 |
| MVPA > 60 min/d (compared to <60) | −4.065 | 2.776 | .148 | −5.278 | 4.276 | .239 |
| VO2peak, mL/min/kg0.82 | −0.113 | 0.068 | .109 | −0.085 | 0.072 | .250 |
| Smoking, y | 0.017 | 0.208 | .934 | −0.008 | 0.233 | .972 |
| Age, y | −0.075 | 1.919 | .698 | 2.807 | 2.457 | .257 |
| Dependent variable: cf-PWV | ||||||
| BMI z score | 0.048 | 0.108 | .655 | 0.094 | 0.124 | 0.452 |
| Body fat percentage | 0.000 | 0.020 | .986 | 0.000 | 0.026 | 0.990 |
| Fat mass, kg | 0.000 | 0.017 | .987 | 0.002 | 0.021 | 0.916 |
| Lean mass, kg | 0.006 | 0.013 | .682 | 0.01 | 0.016 | .525 |
| Sedentary time, min/d | 0.000 | 0.001 | .670 | 0.000 | 0.002 | .912 |
| Vigorous physical activity, min/d | 0.004 | 0.006 | .490 | −0.001 | 0.008 | .909 |
| MVPA, min/d | −0.001 | 0.004 | .856 | −0.001 | 0.006 | .827 |
| MVPA > 60 min/d (compared to <60) | −0.100 | 0.220 | .653 | 0.023 | 0.450 | .961 |
| VO2peak, mL/min/kg0.82 | −0.004 | 0.005 | .445 | −0.005 | 0.008 | .547 |
| Smoking, y | 0.013 | 0.016 | .406 | 0.006 | 0.020 | .775 |
| Age, y | 0.014 | 0.149 | .925 | 0.112 | 0.251 | .660 |
Abbreviations: AIxHR75, augmentation index adjusted to heart rate of 75×/min; BMI, body mass index; cf-PWV, carotid-femoral pulse wave velocity; IMT-SDS, intima media thickness standard score adjusted to height; MVPA, moderate to vigorous physical activity; VO2peak, peak oxygen consumption. Note: Model 1—unadjusted; Model 2—adjusted for lean mass, VO2peak, smoking, tanner, systolic blood pressure at 18 years depending on the predictor variable. aFor body composition variables, lean mass adjustment was not used.
Bold values indicate that p < 0.05.
Appendix Table A2: Linear Regression Models Examining Association Between Independent Variables at 12 Years and Arterial Parameters Measured at 18 Years
| Model 1 | Model 2 | |||||
|---|---|---|---|---|---|---|
| β | SE | P | β | SE | P | |
| Dependent variable: IMT-SDS | ||||||
| BMI z scorea | 0.441 | 0.162 | .013 | 0.392 | 0.167 | .021 |
| Fat percentagea | 0.055 | 0.020 | .006 | 0.052 | 0.020 | .012 |
| Fat mass,a kg | 0.073 | 0.025 | .004 | 0.066 | 0.025 | .010 |
| Lean mass,a kg | −0.002 | 0.034 | .964 | 0.009 | 0.037 | .815 |
| Sedentary time, min/d | 0.003 | 0.003 | .425 | 0.002 | 0.004 | .555 |
| Vigorous physical activity, min/d | −0.047 | 0.014 | .001 | −0.031 | 0.016 | .063 |
| MVPA, min/db | −0.020 | 0.008 | .012 | −0.016 | 0.008 | .067 |
| MVPA > 60 min/db (compared to <60) | −1.125 | 0.408 | .007 | −1.091 | 0.475 | .026 |
| VO2peak, mL/min/kg0.82 | −0.020 | 0.016 | .215 | −0.025 | 0.017 | .152 |
| Dependent variable: AIxHR75 | ||||||
| BMI z score | 0.435 | 0.987 | .660 | −0.022 | 1.011 | .983 |
| Body fat percentage | 0.126 | 0.119 | .291 | 0.056 | 0.121 | .642 |
| Fat mass, kg | 0.195 | 0.152 | .204 | 0.162 | 0.152 | .290 |
| Lean mass, kg | 0.012 | 0.200 | .952 | 0.193 | 0.213 | .366 |
| Sedentary time, min/d | 0.010 | 0.021 | .615 | 0.018 | 0.028 | .536 |
| Vigorous physical activity, min/d | −0.213 | 0.090 | .020 | −0.113 | 0.096 | .248 |
| MVPA, min/d | −0.102 | 0.050 | .046 | −0.031 | 0.053 | .567 |
| MVPA > 60 min/d (compared to <60) | −5.788 | 2.622 | .031 | −3.703 | 2.781 | .188 |
| VO2peak, mL/min/kg0.82 | −0.115 | 0.098 | .245 | −0.098 | 0.106 | .360 |
| Dependent variable: cf-PWV | ||||||
| BMI z score | 0.020 | 0.081 | .809 | 0.023 | 0.124 | .853 |
| Fat percentage | 0.000 | 0.009 | .974 | −0.002 | 0.012 | .883 |
| Fat mass, kg | 0.000 | 0.012 | .982 | −0.001 | 0.015 | .964 |
| Lean mass, kg | −0.002 | 0.015 | .888 | 0.008 | 0.024 | .747 |
| Sedentary time, min/d | −0.001 | 0.002 | .464 | 0.000 | 0.002 | .833 |
| Vigorous physical activity, min/d | 0.002 | 0.007 | .733 | 0.001 | 0.010 | .887 |
| MVPA, min/d | 0.004 | 0.004 | .309 | 0.002 | 0.005 | .638 |
| MVPA > 60 min/d (compared to <60) | 0.254 | 0.199 | .205 | 0.259 | 0.292 | .388 |
| VO2peak, mL/min/kg0.82 | −0.007 | 0.008 | .343 | −0.006 | 0.011 | .550 |
Abbreviations: AIxHR75, augmentation index adjusted to heart rate of 75×/min; BMI, body mass index; cf-PWV, carotid-femoral pulse wave velocity; IMT-SDS, intima media thickness standard score adjusted to height; MVPA, moderate to vigorous physical activity; VO2peak, peak oxygen consumption. Note: Model 1—unadjusted; Model 2—adjusted for lean mass, VO2peak, smoking, Tanner scale, systolic blood pressure at 18 years depending on the predictor variable.
aFor body composition variables, lean mass adjustment was not used. bFor MVPA at 12 years, adjustment for 18-year MVPA was used as a continuous/categorical variable depending on the independent variable.
Bold values indicate that p < 0.05.
Appendix Table A3: Linear Regression Models Examining Association Between Independent Variables Cumulative Exposure From Puberty to Late Adolescence and Arterial Parameters Measured at 18 Years
| Model 1 | Model 2 | |||||
|---|---|---|---|---|---|---|
| β | SE | P | β | SE | P | |
| Dependent variable: IMT-SDS | ||||||
| BMI z scorea | 0.113 | 0.046 | .016 | 0.106 | 0.047 | .028 |
| Body fata percentage | 0.020 | 0.006 | .002 | 0.018 | 0.006 | .006 |
| Fat mass,a kg | 0.022 | 0.007 | .002 | 0.020 | 0.007 | .007 |
| Lean mass,a kg | −0.003 | 0.008 | .733 | 0.001 | 0.009 | .930 |
| Sedentary time, min/d | 0.001 | 0.001 | .668 | 0.000 | 0.001 | .778 |
| Vigorous physical activity, min/d | −0.011 | 0.005 | .027 | −0.008 | 0.006 | .232 |
| MVPA,b min/d | −0.008 | 0.003 | .013 | −0.005 | 0.004 | .141 |
| VO2peak, mL/min/kg0.82 | −0.009 | 0.005 | .067 | −0.011 | 0.005 | .036 |
| Dependent variable: AIxHR75 | ||||||
| BMI z score | 0.104 | 0.282 | .713 | −0.013 | 0.285 | .963 |
| Body fat percentage | 0.061 | 0.037 | .107 | 0.029 | 0.040 | .463 |
| Fat mass, kg | 0.070 | 0.042 | .106 | 0.333 | 0.216 | .126 |
| Lean mass, kg | −0.022 | 0.045 | .635 | 0.018 | 0.051 | .722 |
| Sedentary time, min/d | 0.001 | 0.006 | .920 | 0.002 | 0.008 | .787 |
| Vigorous physical activity, min/d | −0.053 | 0.030 | .076 | −0.017 | 0.032 | .600 |
| MVPA, min/d | −0.040 | 0.018 | .027 | −0.016 | 0.021 | .456 |
| VO2peak, mL/min/kg0.82 | −0.061 | 0.028 | .035 | −0.065 | 0.034 | .064 |
| Dependent variable: cf-PWV | ||||||
| BMI z score | 0.006 | 0.023 | .779 | 0.015 | 0.030 | .635 |
| Fat percentage | 0.001 | 0.003 | .757 | 0.000 | 0.004 | .967 |
| Fat mass, kg | 0.001 | 0.003 | .796 | 0.001 | 0.004 | .903 |
| Lean mass, kg | −0.001 | 0.003 | .790 | 0.003 | 0.005 | .579 |
| Sedentary time, min/d | 0.000 | 0.000 | .930 | 0.000 | 0.001 | .883 |
| Vigorous physical activity, min/d | 0.000 | 0.002 | .843 | 0.000 | 0.003 | .908 |
| MVPA, min/d | 0.000 | 0.001 | .761 | 0.000 | 0.002 | .947 |
| VO2peak, mL/min/kg0.82 | −0.003 | 0.003 | .221 | 0.003 | 0.004 | .442 |
Abbreviations: AIxHR75, augmentation index adjusted to heart rate of 75x/min; BMI, body mass index; cf-PWV, carotid-femoral pulse wave velocity; IMT-SDS, intima media thickness standard score adjusted to height; MVPA, moderate to vigorous physical activity; VO2peak, peak oxygen consumption. Note: Model 1—unadjusted; Model 2—adjusted for lean mass, VO2peak, smoking, Tanner scale, systolic blood pressure at 18 years depending on the predictor variable.
aFor body composition variables, lean mass adjustment was not used. bFor MVPA adjustment 18-year MVPA was used as categorical variable.
Bold values indicate that p < 0.05.
