Unhealthy lifestyle impacts on biological systems involved in stress response: hypothalamic–pituitary–adrenal axis, inflammation and autonomous nervous system : International Clinical Psychopharmacology

Secondary Logo

Journal Logo

Original article

Unhealthy lifestyle impacts on biological systems involved in stress response: hypothalamic–pituitary–adrenal axis, inflammation and autonomous nervous system

Mandelli, Lauraa; Milaneschi, Yurib; Hiles, Sarahb,c; Serretti, Alessandroa; Penninx, Brenda W.b

Author Information
International Clinical Psychopharmacology: November 11, 2022 - Volume - Issue - 10.1097/YIC.0000000000000437
doi: 10.1097/YIC.0000000000000437

Abstract

Introduction

In medicine, lifestyle refers to the set of habits and customs, including the use of substances such as alcohol and tobacco, dietary habits and exercise, which have important implications for health and are influenced by both environmental and heritable factors (Heller et al., 1988).

Lifestyle is critically involved in the cause and maintenance of several noncommunicable and chronic diseases such as cancer, cardiovascular disease, diabetes, as well as neurodegenerative and psychiatric conditions (Sarris et al., 2014; Arena et al., 2017). Lifestyle is also responsible for the high comorbidity between mental and physical disorders (Möller-Leimkühler, 2010).

There is indeed a close relationship between mental disorders and lifestyle: those who suffer from them have a more unhealthy lifestyle, and interventions aimed at changing lifestyle can have therapeutic effects (Sarris et al., 2014,2020; Walsh, 2011). Although the underlying mechanism is not uniformly clear, an unhealthy lifestyle seems to be associated with alterations of the biological systems of stress response (Lopresti et al., 2013; Badini et al., 2020), alterations that are also present in several somatic diseases (McEwen, 1998).

The physiological response to stress involves a complex physiological system, located both in the central nervous system (hypothalamus and brainstem) and in the periphery of the body [outflow of the hypothalamic–pituitary–adrenal (HPA) axis and autonomous nervous system (ANS)]. Further, the HPA axis and ANS closely interact with the immune system, which is the third main actor of the stress response system (Nater et al., 2013).

The functioning of these interacting systems, as mentioned above, is also influenced by lifestyle factors. As for the HPA axis, nicotine exposure provokes acute activation of the HPA system (Kishioka et al., 2014). Alcohol also has activating effects and interacts with glucocorticoid receptors (Blaine and Sinha, 2017). Almost all abuses of drugs, including opioid and psychostimulant drugs, sedative hypnotics and cannabinoids, deeply impact on HPA-functioning, both in acute- and in long-term use (Sarnyai et al., 2001). However, the regular use of nicotine, alcohol and drugs can lead to HPA-axis tolerance, resulting in blunted cortisol response to substances or stress (Sarnyai et al., 2001; Blaine and Sinha, 2017). Acute exercise also increases cortisol levels, whereas regular physical activity, although upregulating the basal activity of the HPA axis, can decrease its reactivity in front of eliciting stimuli (e.g. stress), leading to an overall hyporeactivity of the HPA axis (Chen et al., 2017). The relationship between sleep and HPA axis is less clear; however, several studies have shown higher levels of cortisol, especially in the evening and at awakening, in people who suffer from insomnia (Vgontzas et al., 2001), in subjects exposed to experimental sleep restriction (Spiegel et al., 1999) and those who sleep little habitually (Späth-Schwalbe et al., 1993).

Likewise, the ANS is influenced by the use of nicotine, alcohol and drugs use, physical activity and sleep. Nicotine produces an increase of sympathetic activity: for instance, healthy smokers, although in a normal range, have a higher heart rate (HR) and blood pressure than healthy nonsmokers (Zhou et al., 2016). Nevertheless, the sympathetic activity can be attenuated in chronic smokers (Middlekauff et al., 2014). An excessive use of alcohol is associated with several cardiac disorders and this association is likely mediated by an increase of the sympathetic activity of the ANS, as seen in alcohol abuse (Gardner and Mouton, 2015) although not consistently (Boschloo et al., 2011). Insomnia is also highly comorbid with cardiovascular disorder, potentially because poor sleep increases sympathetic ANS activity (Javaheri and Redline, 2017). On the contrary, regular physical activity (moderate-to-high) can decrease the sympathetic ANS activity in the long term (Lanfranchi and Somers, 2002).

Finally, as for the inflammatory system, smoking increases the levels of several circulating inflammatory markers and cytokines, such as tumor necrosis factor-alpha (TNF-α) and interleukin-1 (IL-1), -6 (IL-6) and -8 (IL-8), while reducing the levels of anti-inflammatory cytokines such as IL-10 (Rom et al., 2013). Similarly, several substances of abuse interact with the immune system and alter neuroimmune gene expression and signaling (Cui et al., 2014). Physical exercise also increases the release of inflammatory cytokines. However, the inflammatory response induced by regular physical exercise seems to be tissue-specific: increased in the skeletal muscles cells and reduced in the periphery and adipose tissue. Regular exercise, globally, can be associated with anti-inflammatory effects, mediated mainly by a decrease in the production and release of TNF-α in the periphery of the body (Peake et al., 2015). Poor sleep is associated with an increase of inflammatory processes. In particular, sleep seems to be involved in TNF-α and IL-6 release (Grandner et al., 2016). However, we previously reported an opposite association between long sleep duration and increased levels of IL-6 and C-reactive protein (CRP) (Prather et al., 2015).

In previous investigations from the Netherlands Study of Depression and Anxiety (NESDA), we found evidence of underlying associations between lifestyle factors and dysregulations in these biological systems related to stress response, though not always consistently with previous literature or in the expected direction (Vreeburg et al., 2009; Vogelzangs et al., 2012; van Reedt Dortland et al., 2013; Hu et al., 2017; Phillips and Fahimi, 2018; Kuzminskaite et al., 2020; Elnazer et al., 2021).

However, the relevance of individual behaviors on biological systems implicated in several disorders is limited by the fact that lifestyle includes a number of habits, harmful and protective, which may have, as a whole, differential influence on these systems. Further, it is known that unhealthy lifestyle factors tend to correlate, and the presence of a risky habit predicts the occurrence of one or more other risky habits (Schuit et al., 2002; Coups et al., 2004; Poortinga, 2007; Silva et al., 2013). The impact of multiple unfavorable lifestyle factors on mechanisms underlying disease risk can, therefore, be more informative than that of single behaviors (King et al., 2015). Therefore, we assume that a lifestyle that is overall more unhealthy, understood as containing multiple unhealthy habits, can result in more substantial dysregulations in biological systems of stress response. Using data from the large-scale NESDA, we tested whether a summary lifestyle index, examining drug and excessive alcohol use, smoking, poor sleep and physical inactivity, is indeed linearly associated with dysregulations of the HPA axis, inflammation and autonomic nervous systems.

Methods

Study sample

Data were from the NESDA, a large ongoing longitudinal cohort study on 2981 adults (18–65 years). Study details were previously described (Penninx et al., 2008). Briefly, respondents were recruited between September 2004 and February 2007 from the community, primary care and in specialized mental health care settings, including persons with a lifetime diagnosis of a depressive or anxiety disorder and healthy controls. Exclusion criteria were represented by: (a) a primary clinically overt diagnosis of other psychiatric conditions such as psychotic, obsessive compulsive, bipolar or severe addiction disorder, and (b) not being fluent in Dutch. Baseline data collection consisted of a medical examination, blood draw, self-report questionnaires and a detailed interview. Assessments were conducted by specially trained research staff. The research protocol was approved by the ethical committee of participating universities and all respondents provided written informed consent.

Of all participants, 2783 persons with self-reported lifestyle factors were included (93.4% of overall sample). For specific physiological stress markers, however, we had to exclude subjects with missing data for that assessment: 881 missing data for HPA-axis measures, 114 missing data for ANS and 31 missing data points for inflammatory markers.

Measurements

Lifestyle factors

In this study, the following self-reported lifestyle factors were considered: smoking, alcohol use, use of drugs, physical activity and amount of sleep.

According to the original NESDA protocol (Penninx, et al., 2008), participants self-assessed as current smoker, exsmoker and never smoker. The level of alcohol consumption was evaluated by the Alcohol Use Disorders Identification Test (Saunders et al., 1993) and classified as abstinence (0 drinks per week), moderate use (1–21 for males and 1–14 for females) or heavy use of alcohol (>21 for males and >14 for females), according to the Dutch alcohol use guidelines (Health Council of the Netherlands, 2015). The level of general physical activity was assessed by the International Physical Activity questionnaire (Ainsworth, et al., 2000; Craig, et al., 2003), which calculates an overall level of physical activity, weighted by the intensity of the activities. For drug use, people indicated whether they had used cannabis, ecstasy, speed, cocaine, heroin or LSD in the previous month. Finally, the amount of sleep was calculated as the average number of self-reported hours of sleep per night in the last 4 weeks.

Unhealthy behaviors were dichotomized as follows: current smoker, heavy use of alcohol, use of drugs in the past month, low level of physical activity and an average of less than 7 h of sleep. An index of unhealthy lifestyle was derived from the sum of each of the unhealthy behaviors (Hiles et al., 2017). For descriptive purposes, subjects were grouped into three categories of lifestyle, according to the tertiles of the distribution of the unhealthy lifestyle index in the sample: healthy pattern (no self-reported unhealthy behavior), intermediate pattern (one self-reported unhealthy behavior) and unhealthy pattern (two or more self-reported unhealthy behaviors).

Biological stress measures

Methods of evaluation of HPA-axis function, inflammation levels and ANS were previously reported [see e.g. (Barakat et al., 2012; Duivis et al., 2013; Lamers et al., 2013)].

Briefly, for HPA-axis functioning, respondents were instructed to collect seven saliva samples at home on a regular (preferably working) day using Salivettes (Sarstedt, Nümbrecht, Germany) at different time points. Cortisol analysis was performed by competitive electrochemiluminescence immunoassay (Roche, Basel, Switzerland) (van Aken et al., 2003). We measured the cortisol awakening curve (cortisol curve during first hour after awakening) by calculating the area under the curve with respect to the ground (AUCg, N = 1737) and with respect to the increase (AUCi, n = 1077), the mean evening cortisol level (MEC, average of levels at 10 and 11 p.m., N = 1892) and the cortisol suppression ratio (CSR) after dexamethasone intake (by the ratio of cortisol values at awakening on the day before and the day after ingestion of 0.5 mg dexamethasone, N = 1789) [see (Vreeburg et al., 2013) for more details]. The AUCg is an estimate of the total cortisol secretion over the first hour after awakening, whereas the AUCi is a measure of the dynamic of the cortisol awakening response over time, more indicative of the sensitivity of the system (Pruessner et al., 2003); MEC levels are indicative of basal HPA-axis activity, because cortisol levels are generally low at the end of the day; the dexamethasone CSR examines the adequacy of the negative feedback of the HPA axis. A higher CSR indicates suppression by dexamethasone, which occurs when the feedback loop functions adequately, a lower ratio indicates nonsuppression of the HPA axis (Carroll, 1984).

For inflammation, circulating plasma levels of CRP (N = 2742), IL-6 (N = 2743) and TNF-α (N = 2726) were assessed from fasting blood samples [see (Vogelzangs et al., 2012) for more details].

For ANS activity, the HR (N = 2669), the pre-ejection period (PEP, N = 2645) and respiratory sinus arrhythmia (RSA, N = 2669) were extracted from the combined dZ and ECG signals (Licht et al., 2012). HR is an indicator of combined sympathetic and parasympathetic nervous system activity; PEP is a measure of cardiac sympathetic control (long PEP reflecting low cardiac sympathetic control) (Berntson et al., 1994); RSA reflects cardiac parasympathetic (vagal) control (high RSA reflecting high cardiac vagal control) (Yasuma and Hayano, 2004; Karagueuzian, 2008). Cardiac autonomic balance was also calculated [RSA − PEP(*−1)] (high scores indicate parallel high parasympathetic and low sympathetic activity).

It is expected that the various biological stress indicators within the same system (HPA axis, inflammation and ANS) will be closely correlated; this expectation was confirmed in our sample (see also Table 1). Therefore, in order to globally evaluate the HPA-axis functioning, the inflammatory status and ANS activity, the scores of the markers in each system were standardized and averaged. Three global scores were, therefore, obtained: inflammation (mean of standardized scores of CRP, IL-6 and TNF-α), HPA axis (mean of standardized scores of AUCg, AUCi, MEC and CSR) and ANS (mean of standardized scores of HR, PEP and RSA). Since low CSR indicates less efficient negative feedback of the HPA axis, high PEP reflects low sympathetic activity and, at the opposite, high RSA indicates high parasympathetic activity, the standardized scores were reversed (*−1) before calculating the mean with the other parameters in each system.

Table 1 - Correlations among biological measures of stress
Measures of stress HPA axis Inflammation ANS HPA-axis score a Inflammation score b ANS score c CAB d
AUCg AUCi MEC CSR CPR IL-6 TNF-( HR RSA PEP
HPA axis AUCg 0.48* 0.37* 0.21* −0.02 −0.03 0.03 0.01 −0.09* −0.02 0.64* −0.03 0.06 −0.07
AUCi 0.48* 0.07 −0.09* −0.04 0.06 <0.01 0.01 −0.05 −0.04 0.61* 0.01 0.05 −0.05
MEC 0.37* 0.07 −0.23* −0.02 <0.01 0.01 <0.01 −0.10* 0.02 0.75* 0.02 0.04 −0.04
CSR 0.21* −0.09* −0.23* 0.02 −0.04 −0.02 0.04 0.03 −0.04 −0.42* −0.04 0.03 ≤0.01
Inflammation CPR −0.02 −0.04 −0.02 0.02 0.34* 0.13* 0.20* −0.15* −0.12* <0.01 0.71* 0.23* −0.18*
IL-6 −0.03 0.06 <0.01 −0.04 0.34* 0.15* 0.12* −0.19* −0.06* ≤0.01 0.67* 0.16* −0.15*
TNF-( 0.03 0.01 0.01 −0.02 0.13* 0.15* 0.01 −0.07* <0.01 <0.01 0.48* 0.03 −0.02
ANS HR 0.01 <0.01 <0.01 0.20* 0.20* 0.12* 0.01 −0.33* −0.25* 0.01 0.17* 0.72* −0.38*
RSA −0.09* −0.10* −0.10* −0.15* −0.15* −0.19* −0.07* −0.33* 0.14* −0.11* −0.22* −0.65* 0.76*
PEP −0.02 0.02 0.02 −0.12* −0.12* −0.06* <0.01 −0.25* 0.14* −0.01 −0.10* −0.58* 0.77*
ANS, autonomous nervous system; AUCg, area under curve with respect to the ground; AUCi, area under the curve with respect to the increase; CAB, cardiac autonomic balance; CRP, C-reactive protein; CSR, cortisol suppression ratio (dexamethasone); HPA axis, hypothalamic–pituitary–adrenal axis; HR, heart rate; IL-6, interleukin-6; MEC, mean evening cortisol; PEP, pre-ejection period; RSA, respiratory sinus arrhythmia; TNF-α, tumor necrosis α.
aMean of AUCg, AUCi, MEC and CSR(*−1) standardized scores.
bMean of CPR, IL-6 and TNF-α standardized scores.
cMean of HR, RSA(*−1) and PEP(*−1) standardized scores.
dFormula: [RSA – PEP(*−1)] (high scores indicates parallel high parasympathetic and low sympathetic activity).
*Significant correlations at P < 0.001 (two-tailed).

Covariates

Socio-demographic factors included sex, age and years of attained education. As a health indicator, we considered the number of self-reported chronic diseases (including cardiovascular diseases, diabetes, lung disease, osteoarthritis, rheumatic disease, cancer, ulcer, intestinal problem, liver disease, epilepsy, and thyroid gland disease). Further, according to previous studies, apart from standard covariates, other additional factors were taken into account. For analyses on inflammation variables, we assessed the use of systemic anti-inflammatory medication (M01A, M01B, A07EB and A07EC) (Vogelzangs et al., 2012). For the HPA-axis analyses, awakening time, working status on the sampling day and season (categorized into dark months – October to February – and months with more daylight –March to September–) were considered (Vreeburg et al., 2009). For ANS analyses, additional adjustments were made for respiratory rate (for RSA), and for mean arterial pressure to account for potential between-subject differences in afterload in PEP analyses (Houtveen et al., 2005). Since participants with depression or anxiety disorder were oversampled in our cohort, and a recent episode of illness can influence biological stress measures, we evaluated recent episodes (in the last 6 months) of depressive (major depression, dysthymia) or anxiety disorder (panic disorder, agoraphobia, generalized anxiety disorder, and social phobia), as ascertained using the Composite Interview Diagnostic Instrument (CIDI version 2.1) (Andrews and Peters, 1998) (see section Statistical analyses for more details), as potential confounders of the association between biological stress measures and unhealthy lifestyle.

Statistical analyses

Demographic and health-related characteristics were compared across each dichotomous lifestyle behavior as well as the unhealthy lifestyle patterns (healthy, intermediate and unhealthy, on the basis, respectively, of the absence, the presence of 1 or at least 2 self-reported unhealthy habits) using the Chi-square test, the Student’s t-test and the one-way analysis of variance.

Biological markers scores were not normally distributed. Therefore, natural logarithm-transformations were used in the analyses, and these values were presented back-transformed in the tables.

The associations between the lifestyle patterns (and dichotomous lifestyle behaviors) and each of the inflammatory, HPA axis, and ANS markers were tested in separate linear regression analyses. All analyses were adjusted for standard and additional covariates as described in the previous section.

The associations between the unhealthy continuous lifestyle index (and each dichotomous lifestyle factor) with HPA axis, ANS and inflammation global scores were tested in separate linear regression analyses adjusted for covariates. The effect of a recent episode of depression or anxiety as a moderating variable was quantified by regression analysis that is by regressing it, along with lifestyle index and their interaction with the outcome variables (HPA axis, ANS and inflammation index scores).

All statistical analyses were performed using SPSS v.23.0 (Statistics IS, 2015, Armonk, New York). A Bonferroni correction based on the number of the main dependent (three sets of correlated biological markers) and independent variables (two sets of correlated lifestyle factors) was applied (3 × 2 = 6 tests) and an alpha value of 0.008 was used to determine statistical significance. With these parameters, we had a sufficient power of more than 0.90 to detect small effect sizes (f2 < 0.01) in linear regression models with 5–10 predictors.

Results

Sample characteristics and lifestyle behaviors

The sample consisted of a total of 2783 participants; they were 947 (34%) males and 1836 (66%) females, aged 41.8 (±13.1) years.

The rates of unhealthy lifestyle factors in the whole sample were the following: 38.2% for smoking (n = 1064), 20.3% for excessive alcohol use (n = 566), 7.7% for drug use (n = 215), 23.2% for low physical activity (n = 645) and 24.8% for poor sleep (n = 690).

As expected, unhealthy factors tended to co-occur: smoking was significantly associated with more excessive alcohol use (Chi-sq = 98.88; P < 0.001), drugs use (Chi-sq = 139.36; P < 0.001) and low physical activity (Chi-sq = 23.47; P < 0.001). Excessive alcohol use was also associated with drug use (Chi-sq = 98.52; P < 0.001). Poor sleep was associated with low physical activity level (Chi-sq = 12.57; P < 0.001).

Participant characteristics, grouped into healthy (no unhealthy habits), intermediate (one unhealthy habit) and unhealthy lifestyle (two or more unhealthy habits) groups, for descriptive purposes only, are reported in Table 2. Subjects with a healthy and intermediate lifestyle were more likely to be females, more educated, with more favorable stress and inflammation profile and a lower number of chronic diseases than those with an unhealthy lifestyle. Those with a healthy lifestyle also made less use of anti-inflammatory medications. Finally, significantly lower rates of recent depressive and anxiety disorders were observed among subjects with a healthy lifestyle.

Table 2 - Sample characteristics stratified for lifestyle categories
Sample characteristics Lifestyle Categories (n = 2783) P-value
Healthy (n = 883) Medium (n = 978) Unhealthy (n = 922)
Demographics
Sex (female), n (%) 668 (36.4) 645 (35.1) 523 (28.5) <0.001
Age (years), mean (SD) 41.1 (13.3) 41.7 (13.1) 42.4 (12.7) 0.14
Education level attained 12.9 (3.1) 12.1 (3.2) 11.7 (3.3) <0.001
Health factors
Number of chronic diseases, mean (SD) 0.8 (1.0) 0.9 (1.1) 1.0 (1.1) 0.001
Anti-inflammatory drugs (frequent use), n (%) 49 (19.4) 87 (34.4) 117 (46.2) <0.001
Cardiac medication (yes), n (%) 112 (27.1) 159 (38.4) 143 (34.5) 0.08
Recent depressive/anxiety episode (positive), n (%) a 412 (25.6) 569 (35.4) 628 (39.0) <0.001
Physiological stress systems b,c
HPA-axis function, mean (SD)
AUCg (nmol/l/h) 1737 16.71 (1.43) 17.55 (1.47) 19.05 (1.45) <0.001
AUCi (nmol/l/h) 1077 3.03 (3.43) 3.72 (3.24) 4.06 (3.13) 0.01
Mean evening cortisol (nmol/l) 1892 4.01 (1.65) 4.63 (1.76) 5.55 (1.68) <0.001
Cortisol suppression ratio 1789 2.59 (1.67) 2.38 (1.66) 2.26 (1.68) 0.001
Autonomic nervous system (ANS), mean (SD)
Heart rate (bpm) 2669 71.63 (1.14) 71.12 (1.14) 71.35 (1.15) 0.59
Pre-ejection period (ms) 2645 117.57 (1.16) 118.54 (1.17) 119.93 (1.17) 0.002
Respiratory sinus arrhythmia (ms) 2669 39.79 (1.71) 37.97 (1.73) 37.18 (1.80) 0.19
Inflammation, mean (SD)
C-reactive protein (mg/l) 2742 1.13 (3.49) 1.27 (3.32) 1.45 (3.54) 0.001
Interleukin-6 (pg/ml) 2743 0.80 (2.16) 0.84 (2.13) 0.90 (2.01) 0.02
Tumor necrosis factor-alpha (pg/ml) 2726 0.84 (1.90) 0.81 (1.90) 0.86 (1.84) 0.86
Bold fonts indicate significant associations.
AUCg, area under the curve with respect to the ground; AUCi, area under the curve with respect to the increase.
aRecent depression/anxiety diagnoses (past 6 months).
bNatural logarithm-transformed factors presented back-transformed.
cControlled for covariates as explained in methods.

Biological stress systems

Correlations among measures of the different biological stress systems are reported in Table 1. In the whole sample, strong correlations were observed for HPA-axis markers: AUCg, AUCi and MEC were all positively correlated, whereas CSR was negatively correlated with AUCi and MEC, though positively with AUCg. Inflammatory markers were all highly and positively correlated. Regarding the ANS, HR correlated negatively with RSA (high parasympathetic activity) and PEP (low sympathetic activity). RSA and PEP were positively associated, indicating an inverse correlation between sympathetic and the parasympathetic activation.

Further, correlations among markers within different biological systems were observed. Overall, inflammation markers were associated with high HR, high sympathetic (low PEP) and low parasympathetic activity (low RSA). A high basal and ‘output’ activity of the HPA axis (high MEC and high AUCg) were also correlated with a low parasympathetic activity (low RSA).

Correlations were similar in healthy subjects and those with a recent episode of major depression or anxiety disorder (data not shown).

Lifestyle and biological stress systems

Associations between single lifestyle factors and each biological measure are shown in Table 3. All lifestyle factors were significantly associated at least with one biological marker, and in several cases with biological markers related to different systems. Use of all substances (smoking, alcohol and drugs) was associated with at least some HPA-axis measure; cigarette smoking and poor physical activity were associated with increased CRP and IL-6 levels. Finally, the ANS system was associated with all lifestyle factors, though nonsignificantly in some cases. Of interest, smoking was associated with increased PEP (lower sympathetic activity); drug use, sleep and, as a trend, excessive alcohol use, with increased RSA (higher parasympathetic activity); poor physical activity and, as a trend, poor sleep, with increased HR, whereas drugs use was associated with decreased HR.

Table 3 - Associations between unhealthy lifestyle behaviors and single biological stress measures
Lifestyle factors HPA axis Inflammation ANS
AUCg AUCi MEC CSR CRP IL-6 TNF-α HR PEP RSA
B (95% CI), P-value B (95% CI), P-value B (95% CI), P-value B (95% CI), P-value B (95% CI), P-value B (95% CI), P-value B (95% CI), P-value B (95% CI), P-value B (95% CI), P-value B (95% CI), P-value
Unhealthy lifestyle (index) 0.06 (0.04–0.08), <0.001 0.09 (0.02–0.16), 0.01 a 0.14 (0.11–0.16), <0.001 0.05 (−0.07 to0.02), 0.001 0.08 (0.03–0.12), 0.001 0.03 (<01–0.06), 0.02 a −0.01 (−0.03–0.02), .86 −0.01 (−0.01 to 0.01), 0.72 0.01 (0.01–0.02), 0.006 0.02 (−0.01 to 0.03), 0.06
Smoking 0.17 (0.13–0.21)
<0.001
0.25 (0.10–0.39), 0.001 0.41 (0.36–0.46), <0.001 0.12 (−0.17 to0.06), <0.001 0.15 (0.06–0.24), 0.001 0.12 (0.07–0.18), <0.001 −0.01 (−0.05 to 0.04), 0.79 0.01 (−0.01 to 0.01), 0.75 0.02 (0.01–0.04), <0.001 0.02 (−0.02 to 0.05), 0.35
Excessive Alcohol 0.07 (0.03–0.12), 0.002 0.12 (−0.06 to 0.30), 0.20 0.10 (0.03–0.16), 0.004 −0.02 (−0.08 to 0.05), 0.58 0.01 (−0.11 to 0.12), 0.90 0.01 (−0.08 to 0.06), 0.80 −0.01 (−0.07 to 0.05), 0.65 −0.01 (−0.02 to 0.01), 0.09 0.01 (−0.01 to 0.02), 0.30 0.04 (0.01–0.08), 0.04 a
Drug use 0.05 (−0.02 to 0.13), 0.18 −0.11 (−0.39 to 0.16), 0.42 0.22 (0.12–0.33), <0.001 −0.03 (−0.13 to 0.08), 0.64 −0.03 (−0.19 to 0.14), 0.76 −0.01 (−0.10 to 0.09), 0.86 −0.09 (−0.17 to 0.01), 0.06 −0.02 (−0.04 to −0.01), 0.02 a 0.01 (−0.02 to 0.03), 0.56 0.10 (0.04–0.17), 0.001
Low physical activity 0.02 (−0.03 to 0.06), 0.64 −0.05 (−0.23 to 0.13), 0.56 0.05 (−0.01 to 0.11), 0.11 −0.03 (−0.09 to 0.03), 0.37 0.29 (0.18–0.40), <0.001 0.05 (−0.01 to 0.12), 0.11 0.04 (−0.02 to 0.10), 0.17 0.02 (0.01–0.03), 0.001 −0.01 (−0.02 to 0.01), 0.79 −0.04 (−0.08 to 0.01), 0.07
Poor sleep −0.03 (−0.06 to 0.01), 0.16 −0.06 (−0.20 to 0.09), 0.43 0.01 (−0.05 to 0.05), 0.98 −0.03 (−0.08 to 0.02), 0.28 0.07 (−0.01 to 0.16), 0.10 0.02 (−0.04 to 0.07), 0.56 0.02 (−0.02 to 0.07),.34 0.01 (0.01–0.02), 0.01 a 0.01 (−0.01 to 0.02), 0.36 0.05 (−0.08 to0.02), 0.004
Bold fonts indicate significant associations.
ANS, autonomous nervous system; AUCg, area under curve with respect to the ground; AUCi, area under the curve with respect to the increase; CRP, C-reactive protein; CSR, cortisol suppression ratio (dexamethasone); HPA axis, hypothalamic–pituitary–adrenal axis; HR, heart rate; IL-6, interleukin-6; MEC, mean evening cortisol; PEP, Pre-EJECTION period; RSA, respiratory sinus arrhythmia; TNF-α, tumor necrosis α.
aTrends of association.

When considering the cumulative biological stress scores, the unhealthy lifestyle index (continuous total number of unhealthy behaviors) was positively associated with the HPA axis and the inflammation systems, but not with the ANS factor (Table 4). As shown in Fig. 1, the HPA-axis (hyperactivity) index and the inflammation index increased with the increase in the number of unhealthy lifestyle factors.

Table 4 - Association between unhealthy lifestyle and lifestyle factors with biological systems involved in stress response
Lifestyle factors HPA-axis score a ANS score b Inflammation score c
B (95% CI), P-value B (95% CI), P-value B (95% CI), P-value
Unhealthy lifestyle (index) 0.16 (0.12–0.19), <0.001 −0.003 (−0.05 to <0.01), 0.09 0.04 (<0.01–0.06), 0.008
Tobacco smoking 0.45 (0.38–0.51), <0.001 −0.03 (−0.09 to 0.02), 0.26 0.09 (0.04–0.15), <0.001
Excessive alcohol intake 0.14 (0.05–0.22), 0.002 −0.04 (−0.11 to 0.03), 0.24 <0.01 (−0.06 to 0.07), 0.90
Drug use 0.20 (0.06–0.33), 0.007 0.14 (−0.25 to 0.04), 0.009 d −0.03 (−0.13 to 0.06), 0.51
Low physical activity 0.04 (−0.04 to 0.12), 0.31 0.08 (0.01−0.15), 0.02 d 0.11 (0.05–0.17), <0.001
Poor sleep −0.02 (−0.09 to 0.04), 0.47 0.05 (<0.01–0.11), 0.08 0.04 (<−0.01 to 0.09), 0.10
Bold fonts indicate significant associations.
ANS, autonomous nervous system; AUCg, area under curve with respect to the ground; AUCi, area under the curve with respect to the increase; CRP, C-reactive protein; CSR, cortisol suppression ratio (dexamethasone); HPA axis, hypothalamic–pituitary–adrenal axis; HR, heart rate; IL-6, interleukin-6; MEC, mean evening cortisol; PEP, pre-ejection period; RSA, respiratory sinus arrhythmia; TNF-α, tumor necrosis α.
aMean of AUCg, AUCi, MEC and CSR(*−1) standardized scores.
bMean of HR, RSA(*−1) and PEP(*−1) standardized scores.
cMean of CPR, IL-6 and TNF-α standardized scores.
dTrends of association.

F1
Fig. 1:
Mean standardized biological systems scores stratified for the number of unhealthy lifestyle factors. HPA axis, hypothalamic–pituitary–adrenal axis; ANS, autonomous nervous system.

Moderation analysis

Post hoc regression models were performed in order to control the potential moderator effect of a recent episode of depression or anxiety disorder on identified associations. According to these analyses, a recent episode (1609 subjects, 57.8% of the sample, which was associated with lifestyle) did not influence the association between the unhealthy lifestyle index and the HPA axis (unhealthy lifestyle × recent episode: P = 0.59), the ANS (P = 0.93) and the inflammation system (P = 0.72).

Discussion

According to previous large evidence of an impact of individual lifestyle factors (smoking, alcohol, etc.) on the biological systems involved in stress response, we hypothesized that a growing involvement in multiple risky habits would be associated with increasing dysregulations at the level of these systems. Our data support this hypothesis. Indeed, an increasing unhealthy lifestyle was associated with more HPA-axis dysregulations and higher systemic inflammation. Further, the use of drugs of abuse significantly impacted autonomic cardiac activity, but the association with a number of unfavorable lifestyle indicators did not show a linear trend with ANS dysregulation.

HPA-axis dysregulations were mainly associated with tobacco smoking and, to a lower extent, with excessive alcohol intake and recent drug use. These findings are consistent with the known effect of nicotine, alcohol and virtually all substances of abuse, on glucocorticoid signaling, leading to stress-related neuroadaptations of the HPA axis (Rohleder and Kirschbaum, 2006; Edwards et al., 2015; Fosnocht and Briand, 2016). Substance-induced stress reactivity is probably a mechanism contributing to addictive phenotypes (Lovallo, 2006; Fosnocht and Briand, 2016).

Consistently with previous analyses on this same sample of individuals (Hu et al., 2017), smoking and drugs were associated with a predominant parasympathetic activation. Cigarette smoking, as well as acute administration of common drugs that activate brain reward pathways, are expected to activate the ANS. However, regular and chronic uses of these drugs are associated with autonomic adaptations leading to reduced sympathetic response and blunted autonomic responses. These adaptations are thought to be involved in the maintenance of substance use (Sinha, 2008).

In line with a recent analysis on this same sample (Hu et al., 2017), poor physical activity increased HR, confirming the negative effects of sedentary behaviors (vs. active life) on autonomic cardiac regulation (Besnier et al., 2017). Moreover, we also found nonsignificant trends of autonomic activation associated with poor sleep (high HR and low RSA), in agreement with findings supporting sympathetic hyperarousal in insomnia (Roth et al., 2007).

The unhealthy lifestyle, especially tobacco smoking and a low level of physical activity, were significantly associated with higher inflammation levels. Accordingly, smoking and physical inactivity, along with a range of other lifestyle factors such as psychosocial stress, poor diet and sleep, have been associated with systemic inflammation, especially the one related to depressive conditions (Berk et al., 2013).

Anxiety disorders and depression are strongly characterized by alterations of biological systems of stress response (Maes et al., 1998; Raison et al., 2006; Sgoifo et al., 2015). Our sample was composed of more than half of patients with a recent or current depressive or anxious episode. However, recent episodes did not moderate the associations between unhealthy lifestyle and biological dysregulations, further supporting the significance of lifestyle factors in disorders characterized by dysregulation of stress response systems.

Medications may have a complex effect on lifestyle and inflammation, antidepressants may decrease inflammation and increase activity in depression but also potentially induce weight gain; therefore, a concomitant psychoeducation treatment is strongly suggested (El-Khoury et al., 2020; Hidalgo-Mazzei et al., 2020; Benedetti et al., 2022; Dörks et al., 2022).

Limitations and strengths of the study

Among limitations, this cross-sectional study was unable to capture the high variability and reciprocal interactions of physiological stress markers, and to suggest possible causal paths linking lifestyle and biological dysregulations. The compliance with saliva sampling might have been inaccurate, and although hardly feasible in large cohorts, multiple sampling days would have enhanced the HPA function assays. Self-report of lifestyle is also prone to inaccuracy and bias. Unfortunately, no data about dietary patterns, a relevant lifestyle factor [e.g. (Quirk et al., 2013)], was collected at the time of biological evaluation.

Strengths of the present study are represented by its large sample size, the collection of a range of unhealthy lifestyle factors, enabling us to derive a global unhealthy lifestyle index, and the collection of relevant socio-demographic, health-related and psychiatric variables in a large adult age range. This is, to our knowledge, the first study to link a range of unhealthy lifestyle factors with the major physiological stress systems and to shed light on the cumulative effect of multiple lifestyle factors on stress markers.

Conclusion

Present findings confirm an association between the unhealthy lifestyle and dysregulations of the biological systems involved in stress response. The biological effects mediated by lifestyle may account for a significant proportion of the environmental risk for several noncommunicable and chronic disorders, and, therefore, be susceptible to preventive as well as therapeutic interventions.

Acknowledgements

Conflicts of interest

There are no conflicts of interest.

References

Andrews G, Peters L (1998). The psychometric properties of the composite international diagnostic interview. Soc Psychiatry Psychiatr Epidemiol 33:80–88.
Arena R, McNeil A, Sagner M, Hills AP (2017). The current global state of key lifestyle characteristics: health and economic implications. Prog Cardiovasc Dis 59:422–429.
Badini I, Coleman JRI, Hagenaars SP, Hotopf M, Breen G, Lewis CM, Fabbri C (2022). Depression with atypical neurovegetative symptoms shares genetic predisposition with immuno-metabolic traits and alcohol consumption. Psychol Med 52:726–736.
Barakat A, Vogelzangs N, Licht CM, Geenen R, MacFarlane GJ, de Geus EJ, et al. (2012). Dysregulation of the autonomic nervous system and its association with the presence and intensity of chronic widespread pain. Arthritis Care Res (Hoboken) 64:1209–1216.
Benedetti F, Zanardi R, Mazza MG (2022). Antidepressant psychopharmacology: is inflammation a future target? Int Clin Psychopharmacol 37:79–81.
Berk M, Williams LJ, Jacka FN, O’Neil A, Pasco JA, Moylan S, et al. (2013). So depression is an inflammatory disease, but where does the inflammation come from? BMC Med 11:200.
Berntson GG, Cacioppo JT, Binkley PF, Uchino BN, Quigley KS, Fieldstone A (1994). Autonomic cardiac control. III. Psychological stress and cardiac response in autonomic space as revealed by pharmacological blockades. Psychophysiology 31:599–608.
Besnier F, Labrunée M, Pathak A, Pavy-Le Traon A, Galès C, Sénard JM, Guiraud T (2017). Exercise training-induced modification in autonomic nervous system: an update for cardiac patients. Ann Phys Rehabil Med 60:27–35.
Blaine SK, Sinha R (2017). Alcohol, stress, and glucocorticoids: from risk to dependence and relapse in alcohol use disorders. Neuropharmacology 122:136–147.
Boschloo L, Vogelzangs N, Licht CM, Vreeburg SA, Smit JH, van den Brink W, et al. (2011). Heavy alcohol use, rather than alcohol dependence, is associated with dysregulation of the hypothalamic-pituitary-adrenal axis and the autonomic nervous system. Drug Alcohol Depend 116:170–176.
Carroll BJ (1984). Dexamethasone suppression test, 1984. Clin Neuropharmacol 7: S146.
Chen C, Nakagawa S, An Y, Ito K, Kitaichi Y, Kusumi I (2017). The exercise-glucocorticoid paradox: how exercise is beneficial to cognition, mood, and the brain while increasing glucocorticoid levels. Front Neuroendocrinol 44:83–102.
Coups EJ, Gaba A, Orleans CT (2004). Physician screening for multiple behavioral health risk factors. Am J Prev Med 27:34–41.
Cui C, Shurtleff D, Harris RA (2014). Neuroimmune mechanisms of alcohol and drug addiction. Int Rev Neurobiol 118:1–12.
Dörks M, Hoffmann F, Jobski K (2022). Antidepressant drug use and regional prescribing patterns in Germany: results from a large population-based study. Int Clin Psychopharmacol 37:185–192.
Duivis HE, Vogelzangs N, Kupper N, de Jonge P, Penninx BW (2013). Differential association of somatic and cognitive symptoms of depression and anxiety with inflammation: findings from the Netherlands Study of Depression and Anxiety (NESDA). Psychoneuroendocrinology 38:1573–1585.
Edwards S, Little HJ, Richardson HN, Vendruscolo LF (2015). Divergent regulation of distinct glucocorticoid systems in alcohol dependence. Alcohol 49:811–816.
El-Khoury J, Beayno A, Elbejjani M, Abed Al Ahad M, Majari G, Ahmad A, Noufi P (2020). The association of antidepressant monotherapy and weight change in a Middle Eastern psychiatric population. Int Clin Psychopharmacol 35:201–207.
Elnazer HY, Sampson AP, Baldwin DS (2021). Effects of celecoxib augmentation of antidepressant or anxiolytic treatment on affective symptoms and inflammatory markers in patients with anxiety disorders: exploratory study. Int Clin Psychopharmacol 36:126–132.
Fosnocht AQ, Briand LA (2016). Substance use modulates stress reactivity: behavioral and physiological outcomes. Physiol Behav 166:32–42.
Gardner JD, Mouton AJ (2015). Alcohol effects on cardiac function. Compr Physiol 5:791–802.
Grandner MA, Seixas A, Shetty S, Shenoy S (2016). Sleep duration and diabetes risk: population trends and potential mechanisms. Curr Diab Rep 16:106.
Heller RF, O’Connell DL, Roberts DC, Allen JR, Knapp JC, Steele PL, Silove D (1988). Lifestyle factors in monozygotic and dizygotic twins. Genet Epidemiol 5:311–321.
Hidalgo-Mazzei D, Llach C, Vieta E (2020). mHealth in affective disorders: hype or hope? A focused narrative review. Int Clin Psychopharmacol 35:61–68.
Hiles S, Lamers F, Penninx B (2017). An unhealthy lifestyle index predicting longitudinal course of depression and anxiety. Psychosom Med 79:A141–A141.
Houtveen JH, Groot PF, Geus EJ (2005). Effects of variation in posture and respiration on RSA and pre-ejection period. Psychophysiology 42:713–719.
Hu MX, Lamers F, de Geus EJ, Penninx BW (2017). Influences of lifestyle factors on cardiac autonomic nervous system activity over time. Prev Med 94:12–19.
Javaheri S, Redline S (2017). Insomnia and risk of cardiovascular disease. Chest 152:435–444.
Karagueuzian HS (2008). Consensus statement from the Cardiac Nomenclature Study Group of Arrhythmias of the European Society of Cardiology, and the Task Force on Cardiac Nomenclature from the North American Society of Pacing and Electrophysiology on Living Anatomy of the Atriove. J Cardiovasc Electrophysiol 11:1298–1298.
King K, Meader N, Wright K, Graham H, Power C, Petticrew M, et al. (2015). Characteristics of interventions targeting multiple lifestyle risk behaviours in adult populations: a systematic scoping review. PLoS One 10:e0117015.
Kishioka S, Kiguchi N, Kobayashi Y, Saika F (2014). Nicotine effects and the endogenous opioid system. J Pharmacol Sci 125:117–124.
Kuzminskaite E, Vinkers CH, Elzinga BM, Wardenaar KJ, Giltay EJ, Penninx BWJH (2020). Childhood trauma and dysregulation of multiple biological stress systems in adulthood: results from the Netherlands Study of Depression and Anxiety (NESDA). Psychoneuroendocrinology 121:104835.
Lamers F, Vogelzangs N, Merikangas KR, de Jonge P, Beekman AT, Penninx BW (2013). Evidence for a differential role of HPA-axis function, inflammation and metabolic syndrome in melancholic versus atypical depression. Mol Psychiatry 18:692–699.
Lanfranchi PA, Somers VK (2002). Arterial baroreflex function and cardiovascular variability: interactions and implications. Am J Physiol Regul Integr Comp Physiol 283:R815–R826.
Licht CM, Penninx BW, de Geus EJ (2012). Effects of antidepressants, but not psychopathology, on cardiac sympathetic control: a longitudinal study. Neuropsychopharmacology 37:2487–2495.
Lopresti AL, Hood SD, Drummond PD (2013). A review of lifestyle factors that contribute to important pathways associated with major depression: diet, sleep and exercise. J Affect Disord 148:12–27.
Lovallo WR (2006). Cortisol secretion patterns in addiction and addiction risk. Int J Psychophysiol 59:195–202.
Maes M, Song C, Lin A, De Jongh R, Van Gastel A, Kenis G, et al. (1998). The effects of psychological stress on humans: increased production of pro-inflammatory cytokines and a Th1-like response in stress-induced anxiety. Cytokine 10:313–318.
McEwen BS (1998). Protective and damaging effects of stress mediators. N Engl J Med 338:171–179.
Middlekauff HR, Park J, Moheimani RS (2014). Adverse effects of cigarette and noncigarette smoke exposure on the autonomic nervous system: mechanisms and implications for cardiovascular risk. J Am Coll Cardiol 64:1740–1750.
Möller-Leimkühler AM (2010). Higher comorbidity of depression and cardiovascular disease in women: a biopsychosocial perspective. World J Biol Psychiatry 11:922–933.
Nater UM, Skoluda N, Strahler J (2013). Biomarkers of stress in behavioural medicine. Curr Opin Psychiatry 26:440–445.
Peake JM, Della Gatta P, Suzuki K, Nieman DC (2015). Cytokine expression and secretion by skeletal muscle cells: regulatory mechanisms and exercise effects. Exerc Immunol Rev 21:8–25.
Penninx BW, Beekman AT, Smit JH, Zitman FG, Nolen WA, Spinhoven P, et al.; NESDA Research Consortium (2008). The Netherlands Study of Depression and Anxiety (NESDA): rationale, objectives and methods. Int J Methods Psychiatr Res 17:121–140.
Phillips C, Fahimi A (2018). Immune and neuroprotective effects of physical activity on the brain in depression. Front Neurosci 12:498.
Poortinga W (2007). The prevalence and clustering of four major lifestyle risk factors in an English adult population. Prev Med 44:124–128.
Prather AA, Vogelzangs N, Penninx BW (2015). Sleep duration, insomnia, and markers of systemic inflammation: results from the Netherlands Study of Depression and Anxiety (NESDA). J Psychiatr Res 60:95–102.
Pruessner JC, Kirschbaum C, Meinlschmid G, Hellhammer DH (2003). Two formulas for computation of the area under the curve represent measures of total hormone concentration versus time-dependent change. Psychoneuroendocrinology 28:916–931.
Quirk SE, Williams LJ, O’Neil A, Pasco JA, Jacka FN, Housden S, et al. (2013). The association between diet quality, dietary patterns and depression in adults: a systematic review. BMC Psychiatry 13:175.
Raison CL, Capuron L, Miller AH (2006). Cytokines sing the blues: inflammation and the pathogenesis of depression. Trends Immunol 27:24–31.
Rohleder N, Kirschbaum C (2006). The hypothalamic–pituitary–adrenal (HPA) axis in habitual smokers. Int J Psychophysiol 59:236–243.
Rom O, Avezov K, Aizenbud D, Reznick AZ (2013). Cigarette smoking and inflammation revisited. Respir Physiol Neurobiol 187:5–10.
Roth T, Roehrs T, Pies R (2007). Insomnia: pathophysiology and implications for treatment. Sleep Med Rev 11:71–79.
Sarnyai Z, Shaham Y, Heinrichs SC (2001). The role of corticotropin-releasing factor in drug addiction. Pharmacol Rev 53:209–243.
Sarris J, O’Neil A, Coulson CE, Schweitzer I, Berk M (2014). Lifestyle medicine for depression. BMC Psychiatry 14:107.
Sarris J, Thomson R, Hargraves F, Eaton M, de Manincor M, Veronese N, et al. (2020). Multiple lifestyle factors and depressed mood: a cross-sectional and longitudinal analysis of the UK Biobank (N = 84,860). BMC Med 18:354.
Schuit AJ, van Loon AJ, Tijhuis M, Ocké M (2002). Clustering of lifestyle risk factors in a general adult population. Prev Med 35:219–224.
Sgoifo A, Carnevali L, Pico Alfonso MDLA, Amore M (2015). Autonomic dysfunction and heart rate variability in depression. Stress 18:343–352.
Silva DA, Peres KG, Boing AF, González-Chica DA, Peres MA (2013). Clustering of risk behaviors for chronic noncommunicable diseases: a population-based study in southern Brazil. Prev Med 56:20–24.
Sinha R (2008). Chronic stress, drug use, and vulnerability to addiction. Ann N Y Acad Sci 1141:105–130.
Späth-Schwalbe E, Uthgenannt D, Voget G, Kern W, Born J, Fehm HL (1993). Corticotropin-releasing hormone-induced adrenocorticotropin and cortisol secretion depends on sleep and wakefulness. J Clin Endocrinol Metab 77:1170–1173.
Spiegel K, Leproult R, Van Cauter E (1999). Impact of sleep debt on metabolic and endocrine function. Lancet 354:1435–1439.
Statistics IS (2015). v. 23.0, IBM Corp. IBM SPSS Statistics for Windows, Version 23.0. Armonk: NY.
Saunders JB, Aasland OG, Babor TF, de la Fuente JR, Grant M (1993). Development of the Alcohol Use Disorders Identification Test (AUDIT): WHO Collaborative Project on Early Detection of Persons with Harmful Alcohol Consumption--II. Addiction 88:791–804.
van Aken MO, Romijn JA, Miltenburg JA, Lentjes EG (2003). Automated measurement of salivary cortisol. Clin Chem 49:1408–1409.
van Reedt Dortland AK, Vreeburg SA, Giltay EJ, Licht CM, Vogelzangs N, van Veen T, et al. (2013). The impact of stress systems and lifestyle on dyslipidemia and obesity in anxiety and depression. Psychoneuroendocrinology 38:209–218.
Vgontzas AN, Bixler EO, Lin HM, Prolo P, Mastorakos G, Vela-Bueno A, et al. (2001). Chronic insomnia is associated with nyctohemeral activation of the hypothalamic-pituitary-adrenal axis: clinical implications. J Clin Endocrinol Metab 86:3787–3794.
Vogelzangs N, Duivis HE, Beekman AT, Kluft C, Neuteboom J, Hoogendijk W, et al. (2012). Association of depressive disorders, depression characteristics and antidepressant medication with inflammation. Transl Psychiatry 2:e79.
Vreeburg SA, Hoogendijk WJ, van Pelt J, Derijk RH, Verhagen JC, van Dyck R, et al. (2009). Major depressive disorder and hypothalamic-pituitary-adrenal axis activity: results from a large cohort study. Arch Gen Psychiatry 66:617–626.
Vreeburg SA, Hoogendijk WJ, DeRijk RH, van Dyck R, Smit JH, Zitman FG, Penninx BW (2013). Salivary cortisol levels and the 2-year course of depressive and anxiety disorders. Psychoneuroendocrinology 38:1494–1502.
Walsh R (2011). Lifestyle and mental health. Am Psychol 66:579–592.
Yasuma F, Hayano J-I (2004). Heartbeat synchronizes with respiratory rhythm only under specific circumstances. Chest 126:1386–1387.
Zhou Z, Chen P, Peng H (2016). Are healthy smokers really healthy? Tob Induc Dis 14:35.
Keywords:

autonomic nervous system; HPA axis; inflammation; lifestyle; stress response

Copyright © 2022 The Author(s). Published by Wolters Kluwer Health, Inc.