Participants of this study were Belgian children recruited for the longitudinal ChiBS study (year 2010 and 2012). Children were between 4.4 and 11.0 years old at baseline and between 6.7 and 12.2 years old at follow-up. More details on the ChiBS study and its measurements have been described elsewhere (Michels et al., 2012). Since blood withdrawal was optional, not all participant could be included in the current analysis. In 2010, 270 from the 520 children had information on all variables for the current analyses: serum values and HRV but this number was diminished to 249 after checking all confounders. Of them, 230 also had information on HRV and serum values in 2012 but this number was diminished to 223 after checking all confounders. On the examination day in 2010 and 2012, fasting blood withdrawal was executed (i.a. for leptin and adiponectin level determination) and HRV was measured. The study was conducted according to the guidelines laid down in the Declaration of Helsinki and the project protocol was approved by the Ethics Committee of the Ghent University Hospital. A written informed consent was obtained from the parents and a verbal assent from the children.

Leptin and adiponectin were measured using a venous blood sample after an overnight fast. The venipuncture was done at the level of the antecubital vein in the left arm. Serum tubes were stored at room temperature for 30 min to allow clotting. Processing of the blood samples was performed within 4 h after collection. All blood samples were centrifuged at 2,500 g for 10 min and were stored at −80°C until further analyses of the extracted serum. Leptin serum concentrations were measured in 2010 using a Meso Scale Discovery sandwich electrochemiluminescence immunoassay (inter-assay CV 2.4% for low, 3.9% for high controls; intra-assay CV 2.7% for low, 5.1% for high controls) and in 2012 using a Millipore radioimmunoassay in a certified laboratory (inter-assay CV 3.0% for low, 6.2% for high controls; intra-assay CV 3.4% for low, 8.3% for high controls). Millipore radioimmunoassay was also used to measure adiponectin serum concentrations, in both 2010 and 2012.

To define HRV, each child was individually examined in a quiet room in supine position (i.e., lying down with the face up) during 10 min. Children were asked to refrain from strenuous physical activity on the measurement day. The child was encouraged to be calm, to breath normally and not to speak or move during the 10 min of HRV measurement. The heart rate belt was fixed around the chest and measurements were started after a couple of minutes when the signal was stabilized. RR-intervals (RRI) were recorded at a sampling rate of 1,000 Hz with the elastic electrode belt Polar Wear link 31 using a Wind link infrared computer transmitter. This low-cost device has a proven validity in supine position compared to the gold standard of an electrocardiogram device (r > 0.97; Gamelin et al., 2006), also in children (r > 0.99; Gamelin et al., 2008).

The middle 5 min were manually checked on their quality and if necessary, another appropriate 5 min interval was chosen. Quality was defined as no large RRI outliers, an equidistance between consecutive RRI points and unimodal and Gaussians RRI and heart rate distribution graphics. As such, disturbing phenomena like the Valsalva maneuvre were excluded. Data processing to obtain time-domain and frequency-domain parameters was performed with the free, professional HRV Analysis Software of the University of Kuopio, Finland (Niskanen et al., 2004). The RR series were de-trended using the Smoothness priors method with alpha = 300 and a cubic interpolation at the default rate of 4 Hz was done (Task Force, 1996). Low frequency (LF) and high frequency (HF) bands were analyzed between 0.04–0.15 and 0.15–0.4 Hz, as default (Task Force, 1996) using the autoregression method. In addition, the LF/HF ratio was calculated as approximate parameter of the sympathovagal balance. HF was expressed in power units and was used as a marker of the parasympathetic activity. LF was expressed in normalized units [=LF/(total power − very low power)] to roughly represent the sympathetic activity (although it might be a mixture of sympathetic and parasympathetic activity). In the time domain analysis, pNN50 (percentage of successive normal sinus RR intervals >50 ms) was used as marker of the parasympathetic activity. In Supplementary Tables 1–3 the results are shown for 4 other HRV parameters that do not give additional information next to the 4 others mentioned above: root mean square of successive differences (RMSSD), standard deviation of normal RR intervals (SDNN), HF in normalized units, LF in power.

Sex, age, parental education level, body fat%, negative emotions and mean heart rate were considered as potential confounding factors, based on previous research (Porges et al., 1994; Blum et al., 1997). When analyzing leptin, pubertal status was considered as extra confounding factor in 2012. The children's sex and birth date were reported by the parent. To represent socio-economic status, parental education level was assessed by questionnaire according to the International Standard Classification of Education (Unesco, 2010). Six groups (1 = low income, 6 = high income) were distinguished. Due to the limited percentage of children in the lowest groups, group 1 until 4 and groups 5 and 6 were respectively recoded in “low income” and “high income.” Body fat% was reliably measured using air-displacement plethysmography (BOD POD®) in both 2010 and 2012. Children were measured following standardized procedures: twice in tight-fitting bathing suit with swim cap to rule out air trapped in clothes or hair and child-specific conversion factors were applied (McCrory et al., 1995). For negative emotions, children were questioned about their recent feelings by rating the feelings anger, anxiety, and sadness on a 0 to 10 Likert-scale (0 “not at all” to 10 “very strong;” Zimmer-Gembeck et al., 2009). To help the children understand these distinct feelings, pictures of a social skills training game for very young children were displayed next to the question. The sum of the three negative emotions was used as confounder. Pubertal status was assessed in 2012 using Tanner classification based on staging of pubic hair distribution and genital development for boys, and pubic hair distribution and breast development for girls. Due to the limited percentage of children in the Tanner stages higher than stage I, these data were recoded into two groups (“no sign of puberty” vs. “signs of puberty”). Heart rate was defined in analogy to HRV: in supine position with an electrode band and infrared computer transmission.

Statistical analyses were performed using SPSS 23 (SPSS Inc., Chicago, IL) and all p < 0.05 were considered significant. The association between the biomarkers (predictor) and HRV (outcome) was tested with cross-sectional linear regression using 2010 and 2012 variables. When the regression residuals were not normally distributed, outcome variables were transformed by calculating the natural logarithm. Since all variables were available in both 2010 and 2012, the association between adiponectin and leptin (predictors) and HRV (outcome) was also tested with longitudinal mixed model linear regression. This multilevel method enables testing the longitudinal change in the slope (two measurements within the individual) by including predictor and outcome factors of the two time points; thus having the advantage of not using difference scores but slopes for longitudinal analyses. The longitudinal effect is reflected by the time^*predictor variable (while also including the separate time and predictor variable in the model). Adiponectin was also tested as outcome of HRV, since literature reports unclearly about the role of adiponectin in relationship to autonomic activity.

Adjustment for confounding factors was performed in several consecutive steps. First, we adjusted for age, sex, and parental education as general confounders. Secondly, body fat% was added as additional confounder. Thirdly, negative emotions were added as additional confounder since HRV might also be influenced by stress/emotions (Porges et al., 1994; Michels et al., 2013). In case of leptin, adjustment for pubertal status was included in the analyses of 2012. At last, heart rate was included as additional confounding factor. A stepwise regression model did not show different results compared to this consecutive confounder model. Since sex interaction terms (i.e., sex^*predictor) were often significant in our sample, analyses were split by sex.

Descriptive values of leptin levels, adiponectin levels, HRV indices, and confounding factors can be found in Tables 1A,B. Some sex differences were detected with girls having significantly higher leptin levels at baseline and at follow-up; while pNN50 and HF at follow-up were significantly higher in boys compared to girls. When comparing data at baseline and at follow up, leptin was significantly increased over time in both sexes, while adiponectin and LF/HF ratio were significantly increased over time in girls only, and LF in boys only. Leptin was positively correlated with age, while LF and LF/HF were negatively correlated with age. At baseline, 6.74% of the boys and 10.13% of the girls were overweight or obese. At follow-up, respectively 6.54 and 12.07% of the children weighed too much. As expected, leptin was positively correlated with body fat% (r = 0.448 p < 0.001 at baseline; r = 0.776 p < 0.001 at follow-up) but adiponectin was not.

Results of leptin as predictor of the autonomic balance at baseline are shown in Table 2. In girls, a positive significant association was found for leptin with LF and LF/HF, also after adjustment for confounding factors. At follow-up (see Table 3), leptin was negatively associated with pNN50 and HF in boys. After adjustment for body fat%, the significance for HF disappeared, but the association with pNN50 remained all the way. Longitudinally, a negative significant effect of leptin on pNN50 and HF was detected in boys (Table 4).

As seen in Table 2, there were no significant associations between adiponectin and HRV at baseline. At follow up (see Table 3), a negative association between adiponectin and HF was seen in boys, which disappeared after adjusting for mean heart rate. However, significant associations in girls were seen after adjustment for mean heart rate: positive association with HF and borderline significant with pNN50. Longitudinal analyses (Table 4) showed similar results as the 2012 cross-sectional analyses. The negative effect on pNN50 and HF in boys disappeared after adjustment for mean heart rate. In girls, a positive effect of adiponectin on HF was visible after adjustment for mean heart rate. The analyses considering adiponectin as outcome of HRV (data not shown), did not show any significant results.

In girls, there was a positive association of leptin with LF/HF and LF at baseline, roughly representing higher sympathetic activity. Interestingly, the association was stronger after correction for the tested confounding factors (e.g., from β = 0.384 to β = 0.453 for LF/HF), with the biggest impact after adjusting for body fat% and mean heart rate. The confounding role of body fat% seems straightforward as leptin is produced by adipose tissue increasingly with bigger or more fat cells (Fried et al., 2000) and as weight decrease has previously been related to HRV improvements probably due to changed free fatty acid levels (Li et al., 2009). Those findings also imply the importance of so-called negative confounders: confounders that cause an underestimation of the association. For example, age was negatively correlated with LF and LF/HF in this study and significantly positively with leptin.

The positive association between leptin and LF or LF/HF in girls is analogous to the findings of Flanagan et al. who showed a positive association in adult women between leptin and sympathetic activity (Flanagan et al., 2007), although others found a positive association between leptin and sympathetic activity in men (Paolisso et al., 2000; Pieterse et al., 2014). In contrast, a negative association between leptin and sympathetic activity was found in both women and men with a body fat% >25.5%. This is remarkable since body fat% is expected to have a positive correlation with both leptin and sympathetic activity (Charles et al., 2015).

Still, a positive leptin-sympathetic association seems logic from a physiological viewpoint. Leptin stimulates energy-expenditure in fat mass and enhances sympathetic activity in non-thermogenic organs and glands (e.g., kidney and adrenal gland) by an increase in noradrenalin concentrations, the neurotransmitter of the sympathetic nervous system (Vinik et al., 2011). The fact that the effects of leptin are mediated by the sympathetic nervous system, is also supported by a study in animals, where blockage of adrenal activity destroyed the effects of leptin (Vinik et al., 2011).

At follow-up and longitudinally, we found a significant and negative association between leptin and parameters of parasympathetic activity (HF and pNN50) in boys. The association with pNN50 was independent of body fat% and mean heart rate. This association was also found by Flanagan et al. in women and was also reported by Paolisso et al. in men (Paolisso et al., 2000; Flanagan et al., 2007). Our study did not show this significant association in girls. The significantly higher values for pNN50 and HF in boys at follow-up could possibly explain this discrepancy. The inverse association between leptin and the parasympathetic nervous system, also known as the anti-inflammatory component of the autonomic system, is supported by the bi-directional relationship between leptin and inflammation. After all, leptin increases the competence of the immune system and the synthesis of leptin is stimulated by inflammatory cytokines (Jung et al., 2012).

The cross-sectional and longitudinal results confirmed the hypothesis that adiponectin is a positive predictor of parasympathetic activity, but this was only true in girls and after adjustment for heart rate. Contrary, adiponectin was a negative predictor of parasympathetic activity in boys but no longer after adjustment for heart rate.

Studies exploring the association between adiponectin and the autonomic balance in healthy subjects are limited. However, it is expected that the physiological mechanisms in healthy subjects are similar as in patients with metabolic and cardiovascular diseases, though extrapolation of study results of patients into a healthy population should be done with caution. Literature mainly reports a positive association between adiponectin and parasympathetic activity (Fasshauer et al., 2003; Takahashi et al., 2007; Boer-Martins et al., 2011; Barbosa-Ferreira et al., 2015). This positive link between adiponectin and parasympathetic activity was also seen in girls in our cross-sectional and longitudinal analyses but only after correcting for mean heart rate. The results after correction for mean heart rate correspond to what is seen in literature and also seem logic considering the knowledge about adiponectin (lower adiponectin levels as well as lower parasympathetic activity in people with overweight and obesity). Consequently, correction for heart rate seems to have an important influence on the association between HRV and adiponectin and supports the hypothesis that HRV cannot be interpreted correctly without taking heart rate into account (Sacha and Pluta, 2005, 2008; Billman, 2013a).

Based on literature, it is unclear whether adiponectin should be considered as predictor or outcome of HRV. Several studies suggest that sympathetic hyperactivity diminishes the expression of adiponectin in fat tissue (Delporte et al., 2002; Fasshauer et al., 2003; Wakabayashi and Aso, 2004; Takahashi et al., 2007; Hoyda et al., 2009). However, there is doubt whether the low adiponectin concentrations are the cause or consequence of the sympathetic hyperactivity (Takahashi et al., 2007; Hoyda et al., 2009). On the other hand, some studies support the hypothesis that adiponectin is a predictor of HRV. For example, Hoyda et al. described the autonomic effects of adiponectin by depolarization of parvocellular neurons in the paraventricular nucleus and emphasized the importance of adiponectin in the energy and autonomic homeostasis regulation (Hoyda et al., 2009). Our longitudinal analyses confirm adiponectin as predictor of HRV rather than HRV as predictor of adiponectin. Consequently, HRV cannot be used as non-invasive measure for adiponectin levels.

Remarkable in the results of this study is the lower amount of significant findings at baseline compared to follow-up. At first sight, it might be because the follow-up study comprised somewhat less participants. Though, even less associations were significant when analyzing the group of children with both data at baseline and follow up. A possible explanation for this discrepancy could be the difference in age: children were ~2 years older at follow-up. Biological changes in puberty could influence the physiology of the association between adiponectin and the autonomic balance. Possibly, the fat tissue is endocrinologically more active in puberty. This hypothesis is supported by our findings: adiponectin concentrations were significantly higher at follow-up in comparison to baseline, mainly in girls. When comparing boys and girls, significant associations differed as well. As we already suggested for the association between HRV and leptin, this could be due to the significant higher values for pNN50 and HF in boys at follow-up.

Compared to existing research, our study had several strengths. First of all, the size of the studied cohort (at baseline 249; at follow-up 223 children) is larger than most other published studies. Secondly, this study responds to the lack of studies examining the association between the autonomic balance and the adipokines leptin and adiponectin in healthy people. Especially a study examining this association in children is unique. Another strength is the correction for confounding factors. This study distinguishes itself by correcting for mean heart rate. Splitting the analyses by sex is also an added value of this study. A final strength is the longitudinal analysis to suggest some directionality.

Of course, this study is not without limitations. First of all, results might be difficult to generalize because of a selection bias with more high-educated parents. At baseline, the selected population consisted of less girls and older children than in the total cohort of the ChiBS-study. Because of the exclusion of low-quality data and some technical problems with the measurements, HRV data were not available for all children. Furthermore, LF is no perfect sympathetic parameter as it reflects both sympathetic and parasympathetic activity; and also the traditional interpretation of LF/HF has been criticized (Billman, 2013b). Also, monitoring respiration might be recommended for the frequency domain measures. In the longitudinal analyses, there is the limitation that the leptin analysis kit was different at baseline and at follow-up but a previous study showed that different leptin analysis kits yielded almost indistinguishable concentrations (Carlson et al., 1999).

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.