Edited by: Michael Noll-Hussong, Saarland University Hospital, Germany
Reviewed by: Regien G. Schoemaker, University Medical Center Groningen, Netherlands; Karl Bechter, University of Ulm, Germany
*Correspondence: Frank M. Schmidt,
This article was submitted to Psychosomatic Medicine, a section of the journal Frontiers in Psychiatry
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Cytokines are mediators of inflammation that contribute to a low-grade inflammation in different disorders like major depression and obesity. It still remains unclear which psychological and medical factors interact with cytokine regulation. In the current investigation, the association between levels of pro-and anti-inflammatory cytokines and anthropometrics, mood state (depressiveness), physical activity and sleep were investigated in a sample of community-dwelled adults.
Forty-nine subjects met the inclusion criteria for analyses and were assessed at two time-points (baseline (T1) and follow-up (T2), average T1-T2-interval = 215 days). Serum cytokine measures included the pro-inflammatory cytokines interleukin (IL)-2, IL-12, IFN-γ and TNF-α, the anti-inflammatory cytokines IL-4, IL-5, IL-10 and IL-13 and the granulocyte-macrophage colony-stimulating factor (GM-CSF); anthropometrics were assessed
Correlation analyses showed low-to moderate significant relationships between the majority of cytokines and the BDI2 at T1, positive correlation with weight and BMI at T1 and T2, and negative correlations with the number of steps and METs at T2 and T2. Regression analyses for T1 revealed that the BDI2 score was the best positive predictor for the concentrations of all nine cytokines, followed by the number of steps and the nightsleep duration as negative predictors. At T2, the amount of steps was found to be negatively associated with IL-4, IL5, IL-10, GM-CSF, IFN-γ, and TNF-α, whereas the BMI could significantly predict IL-12 and IL-13. The BDI2-score was not significantly associated with any of the cytokines. No associations could be found between dynamics in cytokines from T1 and T2 and changes in any of the variables.
The present results indicate an influence of physical activity, subjective well-being and body composition on inflammatory mediators. Since there was no standardized intervention targeting the independent variables between T1 and T2, no assumptions on causality can be drawn from the association results.
Cytokines are a category of heterogeneous peptides produced by various cells, such as macrophages, T lymphocytes, B lymphocytes and mast cells, which are critically involved in cell signaling and immune response. Previously, it could be demonstrated that both, subjects suffering from major depression and subjects with obesity show elevations in their cytokine profiles (
Although a lot of research has been performed to shed light on the multilateral facets of cytokines, and meta-analyses confirm certain assumptions [e.g. elevations of cytokines in depression (
To shed light on different factors that may be related to cytokine regulation, the associations between nine different pro- and anti-inflammatory cytokines and anthropometrics, mood states (depressiveness), physical activity, energy expenditure and parameters of sleep were examined in a sample of community-dwelled adults at two different time points. We further aimed to investigate if dynamics in these parameters between the two measurements were associated with changes in cytokine levels.
For the present study, data were used from the OBDEP sub-project (Obesity and Depression: pathogenic role of sleep and wakefulness regulation, motor activity level and neurochemical aspects), which was conducted at the Department of Psychiatry and Psychotherapy of the University Hospital Leipzig within the framework of the Integrated Research and Treatment Center for Adiposity Diseases Leipzig (IFB Adiposity). The study was approved by the Leipzig University Ethics Committee (#015–10–18012009). All participants were aged 18 to 70 years and gave written informed consent. The OBDEP project comprised of two assessment points (baseline and follow-up), in which participants were requested to fill out questionnaires related to mood and diet (including the German version of the revised Beck Depression Inventory, second edition (BDI2) (
In total, 304 participants had been recruited for the OBDEP project either from the outpatient clinic of the IFB Adiposity, from the Department of Psychiatry and Psychotherapy of the University Hospital Leipzig and
Participants included into the OBDEP project underwent a full physical examination (including blood sampling) by qualified healthcare professionals. Weight [kg] was determined in underwear and without shoes using a digital scale calibrated and standardized using a weight of known mass. Height [cm] was recorded using a stadiometer with participants standing on a flat surface at a right angle to the vertical board of the stadiometer. BMI [kg/m2] was defined as body weight [kg] divided by the square of height [m2].
For the present study, we selected all participants who had partaken in the optional follow-up assessment (N=107). Since only 35% of the original sample took part in the follow-up assessment we compared those subjects with the non-participants. There were no statistical differences concerning sex (62% versus 66% females, p=0.401), but those subjects completing the follow-up were significantly older (41.5 versus 38.2 years, p=0.032) and had a lower T1-BMI (34.0 versus 37.2, p=0.027). We then excluded those subjects for whom at least one of the following exclusion criteria was present: a) retest interval < 150 or > 300 days (N=4); b) cytokine levels had not been measured at both assessments (N=6); c) missing BMI data (N=5); d) missing data in BDI2 questionnaires (N=10) or e) actigraphy data not fulfilling analysis quality criteria (see below) (N=36). In addition, two subjects were excluded post-hoc from the final sample because the levels of the majority of cytokines were rated as extreme outliers (> 2 standard deviations compared to the overall group average, which could indicate a current illness). In total, 49 participants were included in the subsequent analyses. When comparing the included with the excluded participants, there was a trend for differences concerning sex (53% versus 69% females, p=.092), but no statistical differences in age (41.7 versus 41.4 years, p=.925) or T1-BMI (33.4 versus 34.5, p=.607).
Immediately after blood drawing, serum probes were centrifuged at 3,000 rpm for 10 min. The supernatant was aliquoted and stored in non-absorbing polypropylene tubes of 300 ml, which were subsequently snap-frozen in liquid N2 and stored in freezers at −80°C until further measurement. Cytokines were measured at the Institute of Laboratory Medicine of the University Hospital of the Ludwig-Maximilians-University Munich using the Bio-Plex Pro™ human cytokine Th1/Th2 immunoassay (Bio Rad, Germany), a 96-well kit that includes coupled magnetic beads and detection antibodies. This multiplex assay detects pro-inflammatory IL-2, IL-12, GM-CSF, IFN-γ, TNF-α and anti-inflammatory IL-4, IL-5, IL-10, IL-13. The intraassay coefficient of variation (CV) for cytokines was between 1.6% and 3.8%. If cytokine levels were lower than the detection cut-off of the immunoassay (< OOR), there were assigned a value of 0.
On both occasions (baseline and follow-up), a 1-week actigraphy recording was performed, using the SenseWear® Pro 3 actigraph (SWA; BodyMedia Inc.; Pittsburgh, Pennsylvania). The SWA is attached to the upper right arm and records 2-axis body acceleration, skin temperature, heat flux and galvanic skin response. Furthermore, SWA detects periods in which it is not worn (off-arm periods). Actigraphic data were analyzed using SenseWear® Professional Software Version 7 (BodyMedia Inc.). Based on validated proprietary scoring algorithms included in the software, each minute of the recorded data is scored as laying down [yes/no] or sleep [yes/no]. Furthermore, amount of steps and MET levels are given for each 1-min timeframe. Several studies have demonstrated that the SWA provides accurate estimates of energy expenditure during rest and daily life activities, comparable to the gold standards of indirect calorimetry and doubly labeled water (
Scored data were entered into a customized Excel-Template for further data preparation. Participants had kept a sleep and activity diary throughout the recording period and according to the respective information provided by the participants, nightsleep intervals (NSI) was estimated (ranging from first minute to last minute scored as laying down in close proximity to the noted bed times within the sleep diary). Accordingly, the daytime interval (DTI) was determined as duration between two consecutive NSI. Sleep duration (SD) was calculated for each NSI and each DTI (=sum of minutes scored as sleep within the NSI or DTI). Total sleep duration (TSD) was calculated by adding night sleep duration within one NSI (e.g. nightsleep from Friday to Saturday) with daytime sleep duration of the following DTI (e.g. daysleep on Saturday). In addition, for each DTI, the variables
The Kolmogorov-Smirnov (K-S) test was used to examine whether the cytokine levels were normally distributed or not. Therefore, in a first step, outliers cases (± 2 standard deviations) were detected and removed from the respective analysis. Since most outliers (e.g. 9 out of 10 outliers at T2) could be attributed to two specific participants, these two participants were post-hoc excluded from all analyses. After outlier removal, the K-S test still attested for non-Gaussian distribution, therefore cytokine values were normalized using a square root (SQR) or logarithmic (LN) transformation. Based on K-S results, the best transformation was determined for each cytokine level (SQR transformation: IL-2, IL-4, IL-10, IL-12, IFN-γ, TNF-α; LN transformation: IL-5, IL-13, GM-CSF).
The association between cytokine levels (response variables) and anthropometric, psychometric and actigraphic parameters (independent variables) were tested using multiple linear regression (MLR) models. The initial MLR models included all independent variables. A stepwise backwards linear regression method was used: the variables which contributed least to the aforementioned model had been removed in subsequent steps until only statistically significant variables remained (p < 0.05). In order to assess multicollinearity of the independent variables, tolerance statistics like the variance inflating factor were also calculated.
Changes between baseline (T1) and follow-up (T2) were quantified by calculation difference scores (Δ=T2-T1). The K-S test showed a Gaussian distribution in all cytokine change scores but Δ IL-5. This non-Gaussian distribution could be attributed to one outlier case which was therefore excluded from the respective MLR analysis.
For correlation analyses of cytokine levels and independent variables, Spearman rank correlation coefficients were calculated, since most independent variables also exhibited a non-Gaussian distribution. Therefore, non-transformed cytokine levels were used for all correlation analyses. For similar reasons, differences between T1 and T2 values were tested using the Wilcoxon test. All analyses were conducted by using the statistical software SPSS 23 (IBM SPSS Statistics for Windows, version 23 (IBM Corp., Armonk, N.Y., USA)). Also, the significance level was taken to be α=0.05 (two-tailed).
Forty-nine participants were included in the final analyses in this study (N=23 males (46.9%), mean age 41.64 years (SD=11.8 years, range 22–62 years). On average, the follow-up assessment (T2) of the included participants was performed 215.76 days (SD=27.24, range 170–288) after their baseline assessment (T1).
Results concerning the anthropometrics (weight, BMI), psychometric test (BDI2 score), and actigraphy results (night sleep duration, total sleep duration, as well as number of steps and average METs during the wake phase) measured at T1 and T2 are depicted in
Comparison of baseline (T1) and follow-up (T2) values of anthropometrics, psychometric and actigraphic parameters.
Baseline (T1) | Follow up (T2) | Wilcoxon | |||||||
---|---|---|---|---|---|---|---|---|---|
Mean | SD | Min | Max | Mean | SD | Min | Max | Z/p | |
|
99.2 | 36.25 | 52 | 179 | 97.4 | 33.57 | 53 | 170 | −0.527/0.598 |
|
33.4 | 11.15 | 18.3 | 61.4 | 32.7 | 10.31 | 18.9 | 61.3 | −0.954/0.340 |
|
6.4 | 6.68 | 0 | 25 | 6.7 | 8.06 | 0 | 31 | −0.350/0.726 |
|
5:53 | 1:03 | 2:53 | 7:56 | 6:01 | 1:02 | 3:51 | 7:45 | −0.741/0.459 |
|
6:18 | 1:08 | 3:13 | 8:23 | 6:23 | 1:02 | 4:00 | 8:13 | −0.453/0.651 |
|
10087.1 | 4891.49 | 1787.6 | 21407.3 | 9725.7 | 4259.84 | 2355.5 | 18357.67 | −0.831/0.406 |
|
1.68 | 0.56 | 0.83 | 3.02 | 1:62 | 0.42 | 0.93 | 2.54 | −1.457/0.145 |
Comparison of cytokine levels at baseline (T1) and follow-up (T2).
Baseline (T1) | Follow up (T2) | Wilcoxon | |||||||
---|---|---|---|---|---|---|---|---|---|
Mean | SD | Min | Max | Mean | SD | Min | Max | Z/p | |
|
|||||||||
|
7.16 | 7.11 | 0.00 | 25.96 | 5.76 | 5.62 | 0.00 | 18.19 | −1.885/0.059 |
|
10.33 | 8.33 | 0.00 | 40.90 | 8.79 | 6.79 | 0.00 | 31.96 |
|
|
38.03 | 20.91 | 3.83 | 97.78 | 35.02 | 16.05 | 9.10 | 65.55 | −1.323/0.186 |
|
121.19 | 64.64 | 0.00 | 280.09 | 112.85 | 57.15 | 19.44 | 269.73 | −1.169/0.242 |
|
31.19 | 18.73 | 3.71 | 84.87 | 28.21 | 14.78 | 6.34 | 64.95 | −1.323/0.186 |
|
|||||||||
|
4.56 | 2.57 | 0.09 | 10.32 | 4.13 | 2.10 | 0.79 | 9.84 | −1.263/0.206 |
|
4.02 | 3.09 | 0.00 | 18.03 | 3.60 | 3.79 | 0.00 | 18.22 | −0.850/0.395 |
|
3.02 | 2.53 | 0.00 | 13.10 | 2.56 | 1.62 | 0.00 | 6.87 | −1.550/0.121 |
|
5.55 | 4.98 | 0.00 | 18.16 | 4.59 | 3.87 | 0.00 | 13.90 | −1.707/0.088 |
In bold: p > 0.05.
Correlations between cytokine levels and BMI, BDI2 score and actigraphy parameters at T1 are depicted in
Spearman Rank correlation coefficients between cytokine levels and antropometric, psychometric and actigraphic parameters at T1.
Pro-inflammatory | Anti-inflammatory | ||||||||
---|---|---|---|---|---|---|---|---|---|
IL-2 | IL-12 | GM-CSF | IFN-γ | TNF-α | IL-4 | IL-5 | IL-10 | IL-13 | |
|
0.326* | 0.387** | 0.224 | 0.273 | 0.278 | 0.248 | 0.277 | 0.371* | 0.387** |
|
0.339* | 0.423** | 0.236 | 0.277 | 0.289* | 0.245 | 0.277 | 0.409** | 0.423** |
|
0.313* | 0.330* | 0.322* | 0.311* | 0.348* | 0.281 | 0.278 | 0.334* | 0.330* |
|
−0.251 | −0.161 | −0.199 | −0.227 | −0.223 | −0.214 | −0.197 | −0.260 | −0.161 |
|
−0.244 | −0.171 | −0.232 | −0.260 | −0.219 | −0.218 | −0.184 | −0.260 | −0.171 |
|
−0.375** | −0.317* | −0.264 | −0.298* | −0.350* | −0.299* | −0.305* | −0.388** | −0.317* |
|
−0.330* | −0.307* | −0.203 | −0.279 | −0.273 | −0.252 | −0.192 | −0.375** | −0.307* |
Annotations: * = p < 0.05; ** = p < 0.01.
Correlations between cytokine levels and BMI, BDI2 score and actigraphy parameters at T2 are depicted in
Spearman Rank correlation coefficients between cytokine levels and antropometric, psychometric and actigraphic parameters at T2.
Pro-inflammatory | Anti-inflammatory | ||||||||
---|---|---|---|---|---|---|---|---|---|
IL-2 | IL-12 | GM-CSF | IFN-γ | TNF-α | IL-4 | IL-5 | IL-10 | IL-13 | |
|
0.484** | 0.494** | 0.348* | 0.387** | 0.415** | 0.373** | 0.394** | 0.431** | 0.407** |
|
0.475** | 0.479** | 0.352* | 0.377** | 0.402** | 0.361* | 0.363* | 0.428** | 0.427** |
|
0.075 | 0.154 | 0.029 | 0.025 | 0.058 | −0.014 | 0.110 | 0.119 | −0.007 |
|
−0.027 | −0.118 | 0.006 | −0.038 | −0.030 | −0.057 | −0.037 | −0.086 | −0.092 |
|
−0.027 | −0.094 | −0.013 | −0.062 | −0.023 | −0.072 | −0.056 | −0.083 | −0.022 |
|
−0.517** | −0.408** | −0.438** | −0.478** | −0.497** | −0.453** | −0.486** | −0.499** | −0.317* |
|
−0.508** | −0.442** | −0.413** | −0.483** | −0.471** | −0.447** | −0.432** | −0.459** | −0.348* |
Annotations: * = p < 0.05; ** = p < 0.01.
When changes in cytokine levels between T1 and T2 were correlated with changes in the independent variables (see
Spearman Rank correlation coefficients between difference values (T2-T1) in cytokine levels and antropometric, psychometric and actigraphic parameters.
Pro-inflammatory | Anti-inflammatory | ||||||||
---|---|---|---|---|---|---|---|---|---|
Δ IL-2 | Δ IL-12 | Δ GM-CSF | Δ IFN-γ | Δ TNF-α | Δ IL-4 | Δ IL-5 | Δ IL-10 | Δ IL-13 | |
Δ |
−0.153 | −0.124 | −0.077 | −0.035 | −0.096 | −0.030 | −0.051 | −0.113 | −0.201 |
Δ |
−0.103 | −0.061 | −0.038 | −0.003 | −0.048 | 0.023 | 0.017 | −0.050 | −0.122 |
Δ |
−0.021 | −0.073 | 0.000 | 0.004 | 0.040 | −0.016 | −0.009 | −0.066 | −0.069 |
Δ |
−0.155 | −0.234 | −0.219 | −0.222 | −0.284* | −0.236 | −0.209 | −0.072 | −0.094 |
Δ |
−0.162 | −0.262 | −0.205 | −0.245 | −0.267 | −0.210 | −0.208 | −0.058 | −0.108 |
Δ |
0.066 | 0.177 | 0.060 | −0.003 | −0.039 | 0.077 | 0.045 | 0.004 | 0.184 |
Δ |
0.079 | 0.184 | 0.045 | 0.022 | −0.032 | 0.067 | 0.031 | 0.086 | 0.190 |
Annotations: * = p < 0.05; ** = p < 0.01.
The relationship between cytokine levels and anthropometric, psychometric and actigraphic parameters (independent variables) were tested using multiple linear regression (MLR) models. As expected, several of the independent variables highly correlated (weight/BMI: rhoT1 = 0.954, rhoT2 = 0.950, rhoΔ=0.961; night/total sleep duration: rhoT1 = 0.934, rhoT2 = 0.908, rhoΔ=0.899; steps/METs: rhoT1 = 0.861, rhoT2 = 0.821, rhoΔ=0.659). To avoid multicollinearity, only 4 independent variables (BMI, BDI2 scores, night sleep duration, and step counts during wake phase) were included in the initial MLR models.
Findings regarding initial as well as final MLR models for cytokine levels at baseline (T1) are summarized in
Regression coefficients of cytokine levels on the predictor variables recorded at baseline (T1).
Predictors | Primary Model (Include) | Final Model (stepwise backwards) | ||||||||
---|---|---|---|---|---|---|---|---|---|---|
Beta | CI− | CI+ | P-Value | Stand. Coeff. | Beta | CI− | CI+ | P-Value | Stand. Coeff. | |
IL-2 (SQR-trans.) | Model 1: N = 49/R2 =.263/corr. R2 = 0.196 | Model 3: R2 = 0.216/corr. R2 = 0.182 | ||||||||
BMI | −0.011 | −0.059 | 0.037 | 0.643 | −0.097 | |||||
BDI2-Score | 0.057 | −0.004 | 0.117 | 0.065 | 0.297 | 0.053 | 0.000 | 0.105 | 0.049 | 0.275 |
Nightsleep Duration | −0.275 | −0.607 | 0.056 | 0.101 | −0.228 | |||||
Steps | −8.2E-5 | −1.8E-4 | 1.0E-4 | 0.081 | −0.315 | −7.9E-5 | −1.5E-4 | −0.8E-5 | 0.031 | −0.304 |
IL-12 (SQR-trans.) | Model 1: N = 49/R2 = 0.224/corr. R2 = 0.153 | Model 3: R2 = 0.222/corr. R2 = 0.188 | ||||||||
BMI | −0.001 | −0.048 | 0.047 | 0.982 | −0.005 | |||||
BDI2-Score | 0.062 | 0.001 | 0.122 | 0.046 | 0.331 | 0.063 | 0.014 | 0.112 | 0.013 | 0.340 |
Nightsleep Duration | −0.330 | −0.661 | 0.001 | 0.051 | −0.280 | −0.337 | −0.647 | −0.027 | 0.034 | −0.287 |
Steps | −1.0E-5 | −1.0E-4 | 8.2E-5 | 0.821 | −0.041 | |||||
GMCSF (LGn-trans.) | Model 1: N = 49/R2 = 0.193/corr. R2 = 0.119 | Model 4: R2 = 0.107/corr. R2 = 0.088 | ||||||||
BMI | −0.011 | −0.035 | 0.012 | 0.342 | −0.209 | |||||
BDI2-Score | 0.030 | 0.000 | 0.060 | 0.049 | 0.333 | 0.030 | 0.005 | 0.055 | 0.022 | 0.327 |
Nightsleep Duration | −0.110 | −0.275 | 0.055 | 0.185 | −0.192 | |||||
Steps | −3.7E-5 | 0.112 | −0.298 | |||||||
IFN-γ (SQR-trans.) | Model 1: N = 49/R2 = 0.222/corr. R2 = 0.151 | Model 3: R2 = 0.177/corr. R2 = 0.142 | ||||||||
BMI | −0.037 | −0.158 | 0.038 | 0.539 | −0.132 | |||||
BDI2-Score | 0.146 | −0.007 | 0.298 | 0.060 | 0.311 | 0.148 | 0.020 | 0.275 | 0.024 | 0.315 |
Nightsleep Duration | −0.666 | −1.500 | 0.168 | 0.114 | −0.225 | −0.717 | −1.520 | 0.086 | 0.079 | −0.242 |
Steps | −1.7E-4 | −4.1E-4 | 5.6E-5 | 0.134 | −0.276 | |||||
TNF-α (SQR-trans.) | Model 1: N = 49/R2 = 0.288/corr. R2 = 0.224 | Model 3: R2 = 0.233/corr. R2 = 0.200 | ||||||||
BMI | −0.024 | −0.085 | 0.037 | 0.429 | −0.163 | |||||
BDI2-Score | 0.086 | 0.009 | 0.163 | 0.030 | 0.346 | 0.074 | 0.007 | 0.141 | 0.031 | 0.300 |
Nightsleep Duration | −0.375 | −0.797 | 0.046 | 0.080 | −0.240 | |||||
Steps | −1.2E-4 | −2.4E-4 | 9.9E-8 | 0.050 | −0.348 | −1.0E-4 | −1.9E-4 | −1.1E-5 | 0.029 | −0.303 |
IL-4 (SQR-trans.) | Model 1: N = 49/R2 = 0.223/corr. R2 = 0.152 | Model 3: R2 = 0.175/corr. R2 = 0.139 | ||||||||
BMI | −0.008 | −0.029 | 0.014 | 0.478 | −0.152 | |||||
BDI2-Score | 0.024 | −0.002 | 0.051 | 0.074 | 0.295 | 0.024 | 0.002 | 0.047 | 0.036 | 0.292 |
Nightsleep Duration | −0.130 | −0.278 | 0.017 | 0.082 | −0.249 | −0.138 | −0.281 | 0.004 | 0.057 | −0.264 |
Steps | −3.3E-5 | −7.4E-5 | 0.8E-5 | 0.114 | −0.291 | |||||
IL-5 (LGn-trans.) | Model 1: N = 49/R2 = 0.151/corr. R2 = 0.073 | Model 4: R2 = 0.087/corr. R2 = 0.068 | ||||||||
BMI | −0.005 | −0.030 | 0.019 | 0.659 | −0.099 | |||||
BDI2-Score | 0.025 | −0.006 | 0.056 | 0.110 | 0.275 | 0.027 | 0.001 | 0.053 | 0.039 | 0.295 |
Nightsleep Duration | −0.124 | −0.294 | 0.046 | 0.148 | −0.215 | |||||
Steps | −2.1E-5 | 0.383 | −0.166 | |||||||
IL-10 (SQR-trans.) | Model 1: N = 49/R2 = 0.245/corr. R2 = 0.176 | Model 3: R2 = 0.205/corr. R2 = 0.171 | ||||||||
BMI | −0.007 | −0.042 | 0.028 | 0.677 | −0.088 | |||||
BDI2-Score | 0.036 | −0.009 | 0.080 | 0.113 | 0.257 | 0.033 | −0.005 | 0.071 | 0.090 | 0.238 |
Nightsleep Duration | −0.182 | −0.426 | 0.061 | 0.138 | −0.208 | |||||
Steps | −6.3E-5 | −1.3E-4 | 0.5E-5 | 0.068 | −0.333 | −6.1E-5 | −1.1E-4 | −0.9E-5 | 0.023 | −0.324 |
IL-13 (LGn-trans.) | Model 1: N = 49/R2 = 0.217/corr. R2 = 0.146 | Model 4: R2 = 0.155/corr. R2 = 0.137 | ||||||||
BMI | 0.007 | −0.027 | 0.040 | 0.693 | 0.085 | |||||
BDI2-Score | 0.037 | −0.004 | 0.079 | 0.078 | 0.292 | 0.050 | 0.016 | 0.085 | 0.005 | 0.393 |
Nightsleep Duration | −0.116 | −0.345 | 0.113 | 0.314 | −0.143 | |||||
Steps | −2.2E-5 | −8.6E-5 | 4.2E-5 | 0.490 | −0.126 |
Results of the (initial and final) MLR models for cytokine levels at T2 are presented in
Regression coefficients of cytokine levels on the predictor variables recorded at follow-up (T2).
Predictors | Primary Model (Include) | Final Model (stepwise backwards) | ||||||||
---|---|---|---|---|---|---|---|---|---|---|
Beta | CI− | CI+ | P-Value | Stand. Coeff. | Beta | CI− | CI+ | P-Value | Stand. Coeff. | |
IL-2 (SQR-trans.) | Model 1: N = 49/R2 = 0.340/corr. R2 = 0.281 | Model 3: R2 = 0.314/corr. R2 = 0.284 | ||||||||
BMI | 0.036 | −0.001 | 0.074 | 0.059 | 0.345 | 0.029 | −0.003 | 0.061 | 0.079 | 0.271 |
BDI2-Score | −0.008 | −0.047 | 0.031 | 0.677 | −0.060 | |||||
Nightsleep Duration | 0.169 | −0.103 | 0.440 | 0.216 | −0.162 | |||||
Steps | 9.2E-5 | −1.7E-4 | −1.1E-5 | 0.027 | −0.359 | −9.1E-5 | −1.7E-4 | −1.3E-5 | < 0.001 | −0.356 |
IL-12 (SQR-trans.) | Model 1: N = 49/R2 = 0.170/corr. R2 = 0.095 | Model 4: R2 = 0.137/corr. R2 = 0.118 | ||||||||
BMI | 0.019 | −0.022 | 0.060 | 0.355 | 0.187 | 0.038 | 0.010 | 0.065 | 0.009 | 0.370 |
BDI2-Score | 0.015 | 0.027 | 0.057 | 0.483 | 0.113 | |||||
Nightsleep Duration | 0.000 | −0.293 | 0.294 | 0.999 | 0.000 | |||||
Steps | −5.5E-5 | −1.4E-4 | 3.3E-5 | 0.213 | −0.223 | |||||
GM-CSF (LN-trans.) | Model 1: N = 49/R2 = 0.198/corr. R2 = 0.169 | Model 4: R2 = 0.169/corr. R2 = 0.151 | ||||||||
BMI | 0.008 | −0.010 | 0.026 | 0.394 | 0.169 | |||||
BDI2-Score | −0.007 | −0.025 | 0.012 | 0.455 | −0.118 | |||||
Nightsleep Duration | 0.060 | −0.069 | 0.189 | 0.355 | 0.133 | |||||
Steps | −3.9E-5 | −7.8E-5 | −7.9E-7 | 0.046 | −0.356 | −4.528 | −7.5E-5 | −1.6E-5 | 0.003 | −0.411 |
IFN-γ (SQR-trans.) | Model 1: N = 49/R2 = 0.204/corr. R2 = 0.132 | Model 4: R2 = 0.189/corr. R2 = 0.171 | ||||||||
BMI | 0.042 | −0.058 | 0.143 | 0.401 | 0.166 | |||||
BDI2-Score | −0.30 | −0.133 | 0.074 | 0.566 | −0.091 | |||||
Nightsleep Duration | 0.149 | −0.575 | 0.873 | 0.681 | 0.059 | |||||
Steps | −2.2E-4 | −4.4E-4 | −0.8E-5 | 0.043 | −0.360 | −2.7E-4 | −4.3E-4 | −1.1E-4 | 0.002 | −0.434 |
TNF-α (SQR-trans.) | Model 1: N = 49/R2 = 0.283/corr. R2 = 0.217 | Model 4: R2 = 0.250/corr. R2 = 0.234 | ||||||||
BMI | 0.028 | −0.021 | 0.077 | 0.261 | 0.211 | |||||
BDI2-Score | −0.016 | −0.067 | 0.034 | 0.512 | −0.098 | |||||
Nightsleep Duration | 0.171 | −0.180 | 0.522 | 0.331 | 0.132 | |||||
Steps | −1.3E-4 | −2.4E-4 | −2.8E-5 | 0.014 | −0.419 | −1.5E-4 | −2.4E-4 | −7.8E-5 | < 0.001 | −0.500 |
IL-4 (SQR-trans.) | Model 1: N = 49/R2 = 0.204/corr. R2 = 0.132 | Model 4: R2 = 0.185/corr. R2 = 0.168 | ||||||||
BMI | 0.007 | −0.010 | 0.024 | 0.410 | 0.163 | |||||
BDI2-Score | −0.006 | −0.024 | 0.011 | 0.487 | −0.110 | |||||
Nightsleep Duration | 0.036 | −0.087 | 0.159 | 0.560 | 0.083 | |||||
Steps | −3.9E-5 | −7.5E-5 | −0.2E-5 | 0.040 | −0.366 | −4.5E-5 | −7.3E-5 | −1.7E-5 | 0.002 | −0.430 |
IL-5 (LN-trans.) | Model 1: N = 49/R2 = 0.220/corr. R2 = 0.149 | Model 4: R2 = 0.205/corr. R2 = 0.188 | ||||||||
BMI | 0.005 | −0.013 | 0.023 | 0.589 | 0.105 | |||||
BDI2-Score | 0.000 | −0.019 | 0.018 | 0.965 | −0.007 | |||||
Nightsleep Duration | 0.052 | −0.078 | 0.181 | 0.424 | 0.113 | |||||
Steps | −4.7E-5 | −8.6E-5 | −0.9E-5 | 0.018 | −0.420 | −5.1E-5 | −8.0E-5 | −2.1E-5 | 0.001 | −0.453 |
IL-10 (SQR-trans.) | Model 1: N = 49/R2 = 0.251/corr. R2 = 0.183 | Model 4: R2 = 0.236/corr. R2 = 0.220 | ||||||||
BMI | 0.015 | −0.017 | 0.046 | 0.352 | 0.178 | |||||
BDI2-Score | −0.006 | −0.038 | 0.026 | 0.716 | −0.056 | |||||
Nightsleep Duration | 0.024 | −0.200 | 0.249 | 0.828 | 0.030 | |||||
Steps | −7.8E-5 | −1.5E-4 | −1.1E-5 | 0.023 | −0.394 | −9.6E-5 | −1.5E-4 | −4.5E-5 | < 0.001 | −0.486 |
IL-13 (LN-trans.) | Model 1: N = 49/R2 = 0.178/corr. R2 = 0.103 | Model 4: R2 = 0.145/corr. R2 = 0.127 | ||||||||
BMI | 0.038 | 0.006 | 0.070 | 0.021 | 0.475 | 0.031 | 0.009 | 0.052 | 0.007 | 0.381 |
BDI2-Score | −0.021 | −0.054 | 0.012 | 0.210 | −0.202 | |||||
Nightsleep Duration | 0.004 | −0.226 | 0.234 | 0.971 | 0.005 | |||||
Steps | −4.6E-7 | −6.9E-5 | 6.8E-5 | 0.989 | −0.002 |
MLR models for changes in cytokine levels (T2–T1) are presented in
Regression coefficients of changes in cytokine levels (T2-T1) on the changes of predictor variables.
Psredictor | Primary Model (Include) | Final Model (stepwise backwards) | ||||||||
---|---|---|---|---|---|---|---|---|---|---|
Beta | CI− | CI+ | P-Value | Stand. Coeff. | Beta | CI− | CI+ | P-Value | Stand. Coeff. | |
Δ IL−2 | Model 1: N = 49/R2 = 0.045/corr. R2 = −0.041 | Model 5: R2 = 0.000/corr. R2 = 0.000 | ||||||||
Δ BMI | −0.326 | −1.014 | 0.362 | 0.345 | −0.147 | |||||
Δ BDI2-Score | 0.029 | −0.203 | 0.260 | 0.802 | 0.038 | |||||
Δ Night SD | −0.900 | −2.489 | 0.689 | 0.260 | −0.174 | |||||
Δ Steps | −1.5E-4 | −0.001 | 3.1E-4 | 0.517 | −0.099 | |||||
Δ IL-12 | Model 1: N = 49/R2 = 0.045/corr. R2 = −0.042 | Model 5: R2 = 0.000/corr. R2 = 0.000 | ||||||||
Δ BMI | −0.029 | −0.729 | 0.671 | 0.934 | −0.013 | |||||
Δ BDI2-Score | 0.006 | −0.230 | 0.241 | 0.962 | 0.007 | |||||
Δ Night SD | −1.027 | −2.644 | 0.591 | 0.208 | −0.195 | |||||
Δ Steps | 1.1E-4 | −3.7E-4 | 0.001 | 0.650 | 0.069 | |||||
Δ GM-CSF | Model 1: N = 49/R2 = 0.056/corr. R2 = −0.029 | Model 5: R2 = 0.000/corr. R2 = 0.000 | ||||||||
Δ BMI | −0.419 | −2.727 | 1.889 | 0.716 | −0.056 | |||||
Δ BDI2-Score | 0.078 | −0.699 | 0.855 | 0.841 | 0.030 | |||||
Δ Night SD | −4.194 | −9.525 | 1.138 | 0.120 | −0.240 | |||||
Δ Steps | −2.0E-4 | −0.002 | 0.001 | 0.799 | −0.039 | |||||
Δ IFN-γ | Model 1: N = 49/R2 = 0.064/corr. R2 = −0.021 | Model 4: R2 = 0.057/corr. R2 = 0.037 | ||||||||
Δ BMI | −0.116 | −7.485 | 7.253 | 0.975 | −0.005 | |||||
Δ BDI2-Score | 0.042 | −2.439 | 2.552 | 0.973 | 0.005 | |||||
Δ Night SD | −13.859 | −30.882 | 3.164 | 0.108 | −0.247 | −13.371 | −29.327 | 2.586 | 0.098 | −0.239 |
Δ Steps | −0.001 | −0.006 | 0.004 | 0.571 | −0.086 | |||||
Δ TNF-α | Model 1: N = 48/R2 = 0.091/corr. R2 = 0.009 | Model 4: R2 = 0.062/corr. R2 = 0.042 | ||||||||
Δ BMI | −0.557 | −2.523 | 1.409 | 0.571 | −0.086 | |||||
Δ BDI2-Score | 0.144 | −0.518 | 0.806 | 0.663 | 0.064 | |||||
Δ Night SD | −4.153 | −8.695 | 0.389 | 0.072 | −0.274 | −3.768 | −8.078 | 0.542 | 0.085 | −0.248 |
Δ Steps | −0.001 | −0.002 | 0.001 | 0.322 | −0.148 | |||||
Δ IL-4 | Model 1: N = 49/R2 = 0.061/corr. R2 = −0.025 | Model 4: R2 = 0.057/corr. R2 = 0.037 | ||||||||
Δ BMI | −0.054 | −0.354 | 0.246 | 0.720 | −0.055 | |||||
Δ BDI2-Score | 0.004 | −0.097 | 0.105 | 0.935 | 0.012 | |||||
Δ Night SD | −0.573 | −1.266 | 0.120 | 0.102 | −0.252 | −0.541 | −1.190 | 0.107 | 0.100 | −0.238 |
Δ Steps | −2.8E-5 | −2.3E-4 | 1.7E-4 | 0.779 | −0.042 | |||||
Δ IL-5 | Model 1: N = 48/R2 = 0.024/corr. R2 = −0.067 | Model 5: R2 = 0.000/corr. R2 = 0.000 | ||||||||
Δ BMI | −0.012 | −0.206 | 0.182 | 0.904 | −0.019 | |||||
Δ BDI2-Score | 0.003 | −0.062 | 0.068 | 0.929 | 0.014 | |||||
Δ Night SD | −0.201 | −0.649 | 0.247 | 0.370 | −0.141 | |||||
Δ Steps | −3.4E-5 | −1.7E-4 | 1.0E-4 | 0.616 | −0.078 | |||||
Δ IL-10 | Model 1: N = 49/R2 = 0.016/corr. R2 = −0.073 | Model 5: R2 = 0.000/corr. R2 = 0.000 | ||||||||
Δ BMI | −0.002 | −0.371 | 0.367 | 0.992 | −0.002 | |||||
Δ BDI2-Score | 0.004 | −0.120 | 0.128 | 0.948 | 0.010 | |||||
Δ Night SD | −0.288 | −1.140 | 0.564 | 0.500 | −0.105 | |||||
Δ Steps | −6.3E-5 | −3.1E-4 | 1.9E-4 | 0.614 | −0.078 | |||||
Δ IL-13 | Model 1: N = 49/R2 = 0.032/corr. R2 = −0.056 | Model 5: R2 = 0.000/corr. R2 = 0.000 | ||||||||
Δ BMI | −0.096 | −0.540 | 0.349 | 0.667 | −0.067 | |||||
Δ BDI2-Score | 0.030 | −0.120 | 0.179 | 0.692 | 0.060 | |||||
Δ Night SD | −0.334 | −1.361 | 0.693 | 0.516 | −0.101 | |||||
Δ Steps | 1.1E-4 | −1.9E-4 | 4.1E-4 | 0.450 | 0.116 |
In this investigation, we examined the impact of the four factors BMI, depressiveness, physical activity, and sleep duration on the serum levels of nine different pro- and anti-inflammatory cytokines in a sample of 49 community-dwelled subjects at two different time points. Further, we investigated if dynamics in any of the variables between the two time points were accountable for changes in cytokine levels.
Regarding the correlation analyses, the majority of cytokines were found to weakly correlate with the BMI and weight in a positive direction at both time points. The number of steps and METs correlated in a negative direction at both T1 and T2. The results for the BDI2, however, were inconsistent with significant weak correlations at T1 which were absent at T2. In the regression analyses, the physical activity could explain IL-2, IL-10, and TNF-α at T1 and was negatively involved in all cytokines except IL-12 and IL-13 at T2. On the other hand, the mood state assessed with the BDI2 showed a disparity between the two cross-sectional time points, as the significant positive association with all cytokines at T1 could not be repeated for any of the cytokines in the follow-up assessment. For all analyses, the direction of the association of the parameters did not depend upon the pro-or anti-inflammatory characteristics of the cytokines. Concerning changes in any of the four factors included into the analyses, none was found associated with dynamics in cytokines from T1 and T2.
Although no conclusions on causality can be drawn from association studies, our results still support the assumption that physical activity could influence mediators of inflammation (the analyses on of the baseline assessment of the same study sample (
An association between the amount of adipose tissue and pro-inflammatory cytokine levels has been reported earlier (
Concerning the link between mood states and cytokines, the findings between the two times of measures are inconsistent. Since the BDI2, as well as the levels of cytokines, did not change relevantly between the two time points and we could not identify other factors that may have impeded the results, this connection, at least in mentally healthy subjects, has to remain open for debate. It needs to be kept in mind that the subjects included in this analysis did not suffer from a major depression but showed no or only little symptom load and for whom the plain BDI2 sum score may not be the suitable indicator. For community-dwelled samples without a clinical depression, previous results on the association between depressive symptoms and cytokine levels were also both positive (
Cytokines have previously been described to have sleep- or wakefulness-promoting effects (
A number of limitations have to be considered when interpreting our results: As a consequence of the applied quality criteria for our analysis, especially with regard to the actigraphy, the number of subjects included into the final analyses was relatively small to fully rule out type-II-errors, impeded sub-group-analyses as well as the inclusion of further parameters. The observational period varied between the subjects, which could interact with the results at T2 and the dynamics between the time points. Further, the subjects did not undergo a systematic interventional program and, thus, the changes in the psycho-biological parameters between the two time points varied distinctly. A group undergoing interventional programs concerning the factors could have added valuable information to the findings in this observational setting.
In conclusion, this investigation on the association between physical activity, subjective well-being, sleep parameters and body composition and inflammatory mediators revealed that predominately, the degree of physical activity is associated with certain cytokines. This could be relevant when focusing on anti-inflammatory treatment strategies. Since the results differed between the two assessments, although no specific intervention, but rather clinical management including a range of therapies in some patients had been performed in between, the conclusions for an association between the parameters cannot be considered as definite but give good reason for further research on this highly relevant field.
The datasets generated for this study are available on request to the corresponding author.
The studies involving human participants were reviewed and approved by Leipzig University Ethics Committee. The patients/participants provided their written informed consent to participate in this study.
FS and CS designed the study. JM, FS, and HH recruited the participants. FS, CS, RM, and HH wrote the manuscript. LH and DT conducted the chemical analyses. CS, RM, and FS performed the statistical analyses. JM, RM, and CS revised the manuscript. All authors contributed to the article and approved the submitted version.
This work was supported by the Federal Ministry of Education and Research (BMBF), Germany, FKZ: 01EO1001. This study was supported by the Integrated Research and Treatment Centre for Adiposity Diseases (IFB), University of Leipzig which is funded by the Federal Ministry of Education and Research (BMBF; Grant Number: 01EO1001). The authors acknowledge support from the German Research Foundation (DFG) and Universität Leipzig within the program of Open Access Publishing. HH has received salary support from the National Institute for Health Research (NIHR) Biomedical Research Centre (BRC) at South London and Maudsley NHS Foundation Trust (SLaM) and King’s College London. The funding sources had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; and preparation, review, or approval of the manuscript.
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.
We thank Wolfgang Wilfert for technical support. We thank Julia Thormann and Tobias Chittka for the help in recruiting the participants.