The human physiology of well-being: A systematic review on the association between neurotransmitters, hormones, inflammatory markers, the microbiome and well-being

The human physiology of well-being: A systematic review on the association between neurotransmitters, hormones, inflammatory markers, the microbiome and well-being. Lianne P.de Vries et al. Neuroscience & Biobehavioral Reviews, June 11 2022, 104733. https://doi.org/10.1016/j.neubiorev.2022.104733

Highlights

• Higher blood levels of serotonin could be related to higher well-being.

• Faster decrease of cortisol levels over the day is associated with higher well-being.

• The levels of different inflammatory markers are negatively related to well-being.

• An association between the microbiome composition and well-being is suggested.

• More research to the physiological factors underlying well-being is needed.

Abstract

To understand the pathways through which well-being contributes to health, we performed a systematic review according to the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) guidelines on the association between well-being and physiological markers in four categories, neurotransmitters, hormones, inflammatory markers, and microbiome.

We identified 91 studies. Neurotransmitter studies (knumber of studies=9) reported only a possible positive association between serotonin and well-being. For the hormone studies (k=48), a lower momentary cortisol level was related to higher well-being (meta-analytic r=-.06), and a steeper diurnal slope of cortisol levels. Inflammatory marker studies (k=36) reported negative or non-significant relations with well-being, with meta-analytic estimates of respectively r=-.07 and r=-.05 for C-reactive protein and interleukin-6. Microbiome studies (k=4) reported inconsistent associations between different bacteria abundance and well-being.

The results indicate possible but small roles of serotonin, cortisol, and inflammatory markers in explaining differences in well-being. The inconsistent and limited results for other markers and microbiome require further research. Future directions for a complete picture of the physiological factors underlying well-being are proposed.

Keywords: well-beingphysiologyneurotransmittershormonesinflammatory markersmicrobiome

4. Discussion

To understand observed differences in well-being between people in more detail, and in order to enhance the development of future mental health prevention and intervention strategies, it is essential to identify physiological markers related to well-being. Therefore, the goal of this systematic review was to bring together the available literature on physiological markers related to well-being in four categories, namely neurotransmitters, hormones, inflammatory markers, and the microbiome. The systematic review resulted in respectively 48 and 36 studies on the association of hormones or inflammatory markers and well-being, whereas only 9 and 4 studies examined the relation between neurotransmitters or the microbiome and well-being. We first summarize and discuss the findings per category. Next, we propose directions for future research based on our current results.

4.1. Neurotransmitters

Nine studies investigated the association between levels of different neurotransmitters and well-being, mainly focusing on (nor)epinephrine and serotonin. In contrast to our expectations, we did not find studies that related dopamine levels to well-being and only a few studies related to (nor)epinephrine and serotonin. Levels of epinephrine and norepinephrine were mostly unrelated to measures of psychological well-being and positive affect. Only in a sample of older women (mean age=74), there was a moderate positive correlation between (nor)epinephrine and subscales of Ryff’s psychological well-being scale. More research on the moderating effects of well-being measure, age and sex is needed to confirm these findings.

Serotonin levels were more consistently positively related to the hedonic well-being measure positive affect, but the effect sizes were small. The relation between serotonin and other measures of hedonic well-being, e.g., life satisfaction, or eudaimonic well-being has not been investigated so far. In studies with larger sample sizes the moderation by age and sex should also be investigated.

The results should be interpreted in light of the difficulties of measuring neurotransmitters levels in humans due to their short term effects, low levels in the brain, and their mixture with other molecules (Niyonambaza et al., 2019). Furthermore there is an ongoing discussion whether urine or blood plasma measures of neurotransmitters reflect brain activity (Ailts et al., 2007, Marc et al., 2011). The suggested positive correlation between neurotransmitter levels in the brain and the rest of the body, i.e., urine or blood (Marc et al., 2011) does suggest that the detected association between serotonin in the blood plasma and well-being indicates the involvement of serotonin resulting from brain activity in well-being.

Applying positron emission tomography (PET) and labeling neurotransmitters can help to identify the regional specificity in the brain of neurotransmitters associated with well-being. For example, in the field of anxiety, it has been found that neurotransmission in social anxiety disorder is characterized by an overactive serotonin system in the amygdala, caudate nucleus, putamen, hippocampus and anterior cingulate cortex (Frick et al., 2015). Similarly, PET studies can directly give insight in the association of well-being and functioning of neurotransmitters in specific brain regions.

Furthermore, there is a lot of development in new ways to assess serotonin in different tissues and with new techniques, such as real-time continuous monitoring (Si and Song, 2018, Su et al., 2020). This might enable researchers to assess the level of different neurotransmitters more easily in the future and replicate the possible involvement of serotonin in complex traits like well-being.

4.2. Hormones

The association of different hormones with well-being has been investigated more often compared to the neurotransmitter research, as hormones are currently easier to assess via, for example, saliva samples. Of the 48 hormone studies, 39 studies included one or more measures of cortisol. The meta-analysis on the association between the level of momentary cortisol and well-being resulted in a small negative effect, r=-.06, indicating that lower cortisol levels are related to higher levels of well-being. In addition, although a meta-analysis could not be performed, another relatively consistent finding was the association of a faster decrease of cortisol levels over the day (i.e., steeper slope) with higher well-being. The results of the relation between the cortisol awakening response and total cortisol secretion and well-being were less consistent. However, as reported by Smyth et al. (2015), the timing of cortisol sampling is important. In their study, only when the participants strictly adhered to the sampling protocol, lower cortisol awakening response was associated with higher well-being. Furthermore, as indicated by Booij et al. (2016), large individual differences in the relation between different measures of cortisol and well-being were present in their sample. This makes it difficult, if not impossible, to find consistent associations when averaging the relation within a large sample. In an earlier review, the inconsistency of findings regarding hormones and positive affects is also suggested to be due to the variability in samples, age, measures of well-being and timing (Dockray and Steptoe, 2010). Furthermore, as cortisol is “the stress hormone” and there is a clear negative association between stress and well-being (e.g., Schiffrin et al., 2009), stress might mediate the relation between diurnal cortisol and well-being and controlling for stress is needed in future studies.

Cortisol can be sampled in saliva, urine, or hair and the levels in the different samples reflect different processes. Whereas salivary and urinary cortisol reflect the real-time levels of cortisol, hair cortisol reflects the cortisol exposure over longer periods of time and is related to chronic stress (Russell et al., 2012). Cortisol measured in cortisol and urine versus hair is therefore not directly comparable. We identified two studies using a hair sample of cortisol and only one (Smyth et al., 2016) reported a small negative association with well-being in elderly participants. Research in larger samples is needed to examine the relation of hair cortisol (i.e., long-term cortisol exposure) and well-being.

To summarize, most measures of cortisol were not consistently related to well-being and individual differences could play a large role in the association. However, the small associations between momentary levels of cortisol and the slope of the cortisol decrease over the day and well-being were consistent. This effect was not different for hedonic and eudaimonic well-being. In future research, researchers need to be stricter on the timing of the cortisol sample and avoid variability, e.g., by using tube caps with time recording and strict instructions to the participants. In addition, focusing on the individual patterns instead of the average cortisol response or level across individuals is necessary to understand the relation between cortisol and well-being in more detail.

The association of other hormones with well-being were investigated in only a few studies and most of these studies did not report a (consistent) significant association, limiting the ability to draw conclusions. DHEA-S and testosterone were not related to different measures of well-being in respectively 5 of the 6 studies and 3 of the 4 studies. This might reflect a power issue, as most sample sizes of the discussed studies are small (n<100) or the absence of a detectable association between the levels of these hormones and well-being. More promising is the positive relation between vitamin-D in the blood and well-being. However, since this is based on only two studies, more research is needed to confirm this association.

Whereas oxytocin has mainly been investigated in relation to positive social behaviour, oxytocin is also suggested to play a role in different behaviors and traits related to well-being, such as emotional processing, trust and depressive behaviors (IsHak et al., 2011). However, surprisingly, the direct relation between oxytocin and well-being has only been investigated in a single study (Barraza et al., 2013). In a small sample (noxytocin=21) of older adults (Mage=80) no association could be reported. Future direct and powerful studies should shed more light on the hypothesized association between well-being and oxytocin.

Finally, most studies on the different hormone levels included relatively older samples (average age: 53.1, and in 6 of the 14 studies the average age is above 65). Since hormone production and levels are affected by age (Sternbach, 1998, Van Cauter et al., 1996), more research is needed to study the effects of age on the association between hormones and well-being in age diverse samples.

4.3. Inflammatory markers

The results of the 36 studies on the inflammatory markers and well-being showed more consistent results compared to the previous categories. CRP was negatively associated with well-being in 14 of the 26 studies and IL-6 was negatively associated with well-being in 11 of the 25 studies, whereas the other studies did not find a significant effect. Additionally, both CRP (r=-.07) and IL-6 (r=-.05) showed small but significant negative relations with well-being in a meta-analysis. Based on the available studies, the well-being measure was not a significant moderator, suggesting that the inflammatory markers have an influence on overall well-being and not on specific aspects of hedonic or eudaimonic well-being.

Besides CRP and IL-6, fibrinogen was negatively related to well-being in three of the seven studies, and other inflammatory markers such as other interleukins or white blood cell count were either negatively related with well-being or non-significantly. Based on these results, a consistent pattern of negative associations between different inflammatory markers and well-being emerges. Lower levels of baseline inflammatory markers, i.e., reflecting less activation of the immune system, is linked to higher well-being. The non-significant findings can either be due to weaker designs or smaller samples, leading to lower power.

Similar to the hormone studies, the reviewed inflammatory marker studies included relatively older samples. The average age of the samples is 52.6 (SD=13.7) and in 17 of the 36 studies the average age is above 50, while only two studies the average age is below 30 years. As some studies suggested moderation by age (e.g., Fancourt and Steptoe, 2020), more research is needed into the effects of age on the association between inflammation and well-being in younger and age diverse samples.

A next step in the research on inflammation and well-being is the direction of effect. The direction of effect between inflammation and mental ill-being, i.e., depression, appears to be bidirectional. Patients with inflammatory diseases have a higher likelihood to develop major depressive disorder and often individuals with major depression show increased inflammatory markers, and the levels decrease with the recovery from depression (e.g., Amodeo et al., 2018; Dahl et al., 2014). As well-being and mental ill-being are related but have independent effects on health and other outcomes, the direction of effect between inflammation and well-being should be investigated. Some longitudinal studies in this review showed significant associations between inflammatory markers and well-being a few years later, indicating a possible causal effect from inflammation to well-being.

4.4. Microbiome

Lastly, the composition and diversity of the gut microbiome in relation to well-being is a relatively new and fast developing research field. We could only identify four studies that related the gut microbiome diversity or composition to well-being. All studies reported significant results with the abundance of different bacteria or the diversity of the microbiome associated with higher hedonic well-being, i.e., positive affect or quality of life, indicating that it is likely that the microbiome plays a role in well-being. However, more research is needed to be confident about the specific associations between the microbiome composition and well-being, because one study only included 3 participants, different effects of different bacteria have been studied, and there might be a publication bias in that only studies with significant effects are published in this upcoming field.

Microbiome research is further complicated by the possible effects of variation in dietary habits and geography on the composition of the gut microbiota. Ideally, when investigating the microbiome, participants should be in a stable environment, keep a constant diet and living habit, and maintain a certain activity level. As this can be difficult in daily life, Li et al. (2016) minimized the possible confounding by other factors by investigating three participants that stayed 105 days in a closed human life support system with minimal interference, i.e., a laboratory that simulates a lunar-like environment. This study gave the first insights in the unconfounded relation between the gut microbiome and well-being. In future studies outside such a system, the possible confounding by diet, environment and activity should be taken into account.

Another point of discussion is the current sampling methods for gut microbiome. Tang et al. (2020) reviewed the methods and concluded that more precise sampling methods for the composition and diversity of the gut microbiome are needed. Current measures from fecal samples (and other non-invasive methods) are just a proxy for the composition of the gut microbiome. More precise sampling methods are needed to increase the reliability of the microbiome research and to replicate findings.

4.5. Future directions

In different categories consistent relationships between physiological markers and well-being (e.g., the hormone cortisol, and inflammatory markers CRP and IL-6) were reported. With respect to these effects, further research should be conducted to investigate the direction of the effect or possible moderators or confounders on the effect, as suggested above. In other categories, such as neurotransmitters and the microbiome, additional research is needed to get a complete picture of the role of these physiological markers in relation to well-being. Besides further research into the association of physiological markers related to well-being in the single categories, promising fields for future research include the integration or combination of multiple physiological categories in relation to well-being, the direction of causality, and innovative ways to measure and analyze physiological data.

4.5.1. Integration

A first observation based on the reviewed studies is that the findings of the different studies are diverse and not connected. Most studies investigated the relation between one physiological marker and well-being. Similar to the criticized candidate gene literature (i.e., investigating the association of a single or a few candidate genes with well-being, depression or other genetically complex phenotypes) in which results are mixed and do not seem to replicate (e.g., Border et al., 2019; Johnson et al., 2017; van de Weijer, in press), the pick-and-choose strategy for physiological markers might have led to similar inconsistent results. Where the genome-wide association approach has been introduced to systematically search for genetic variants for complex traits, a similar data-driven approach should be used for future research into the physiology of well-being. Combining multiple physiological markers across the different categories, aka an multi-omics approach, could result in a more complete picture of the physiology underlying well-being.

Combining multiple physiological markers across the different categories could result in a more complete picture of the physiology underlying well-being. An example of combining data is multi-omics approaches, that combine and integrate multiple types of omics data, such as genomics, proteomics, transcriptomics, epigenomics, metabolomics, and microbiomics (Hasin et al., 2017). All the different processes influence each other and by combining these data, researchers can get a broader picture and a more comprehensive insight in the physiological markers and human biology underlying traits or diseases. To learn more about multi-omics, Wörheide et al. (2021) and Subramanian et al. (2020) provide helpful overviews and different applications of this approach within the domain of mental ill-being, e.g., for aggressive behavior and psychiatric disorders, can be found (Hagenbeek et al., 2021, Korologou-Linden et al., 2021).

To understand the physiology underlying well-being, multi-omics approaches can also be applied to the combination of hormones, neurotransmitters, inflammatory markers, and the microbiome. For example, the stress hormone cortisol, and inflammation, the reaction of the immune system, are strongly linked (e.g., Adam et al., 2017; Morey et al., 2015). Furthermore, recent research reported an influence of the gut microbiome on mental health via the level of neurotransmitters (Liu et al., 2020). The gut microbiome can alter the levels of different neurotransmitter and this alteration of neurotransmitters influences mental health. Similarly, an interaction between three categories, namely the gut microbiome, the stress response, including cortisol, and immune system is suggested to play a role in depression, and anxiety (Peirce and Alviña, 2019). As we have shown that cortisol, different immune factors and possibly the microbiome are associated with well-being, investigating these factors at the same time might lead to a clearer picture about the relation between the human physiology and well-being. To conclude, for a complete overview of the physiological markers underlying well-being, combining measures of multiple physiological markers into a large well-being study is needed.

4.5.2. Direction of effect

As we reported consistent associations of (diurnal) cortisol and different inflammatory markers with well-being, a next step is to investigate the direction of the effect between the physiological marker and well-being. Can the association be explained by a causal relationship from the physiological marker to well-being, vice versa, in both directions or is the association explained by another factor? If the direction of causation is known, this can help to design interventions to enhance well-being or prevent poorer mental health. The reported associations in this review are only correlational and it is impossible to determine causality in cross-sectional observational studies. Causality analyses, such as longitudinal (intervention) studies and Mendelian Randomization can enable future researchers to investigate the direction of causality in this field.

Longitudinal studies in which either well-being or the level of physiological factors, such as hormones or neurotransmitters are observed over time, or manipulated (e.g., by triggering their response or substitution) can allow for causal interferences to be made. For example, in the experimental design of Barraza et al. (2013) half of the participants received oxytocin for 10 days and the other half a placebo. The levels of well-being were compared before and after the treatment. There was no effect of the treatment on well-being in both groups. However, if an increase in well-being the oxytocin group, but not the placebo group had been reported, this would be evidence for a causal relation between oxytocin and well-being. Similarly, the other way around, interventions that increase well-being can be used to investigate if well-being has a causal effect on various physiological factors. For example, a meta-analysis across 20 randomized control trials (RCT) reported that mindfulness mediation is associated with immune system processes involved in inflammation, and biological aging, i.e., meditation resulted in a decrease in CRP levels (Black and Slavich, 2016). Similarly, a recent meta-analysis on the effects of meditation interventions on cortisol levels reported that such interventions resulted in reduced cortisol levels, but only when assessed in blood compared to saliva and in people at risk for somatic illnesses (Koncz et al., 2021). As mindfulness and meditation have also been linked to increased well-being, these findings could indicate a causal link between well-being and different physiological factors. Future randomized control studies specific to well-being interventions or physiological manipulations are needed to confirm these hypotheses and investigate the direction of causation.

Another approach to study the direction of causation, that does not need longitudinal data or any intervention, is Mendelian Randomization (MR), which uses genetic variants to test the causal relationships between an exposure variable and outcome. MR relies on the natural, random assortment of genetic variants resulting in a random distribution of genetic variants in a population (Smith and Ebrahim, 2003). In short, if the assumptions are met and a genetic variant is associated both with the exposure (e.g., inflammatory marker levels) and the outcome (e.g., well-being), this would provide supportive evidence for a causal effect of the immune response on well-being. To learn more about Mendelian Randomization, see Gagliano Taliun and Evans, (2021) and Smith and Ebrahim (2003) for an overview and guidelines. Different applications of this approach within the domain of mental ill-being with physiological factors can be found as well (for example (Kappelmann et al., 2021, Perry et al., 2021).

Finally, results of animal studies can indicate possible causal effects of well-being and physiological factors. Although there are limitations in generalizing results from animal studies to human well-being, these results can be the starting point for research in humans and provide clues about the mechanisms and causality. Animal research has been helpful in health-related research areas, but is rare in the well-being field, largely because of the subjective nature of well-being. In the field of depression and stress, animal research on physiological factors has reported different causal mechanisms. For example, in rats, a microbiome transplantation from severely depressed patients to the rats induced depression-like behaviors, like anhedonia and anxiety-like behaviours (Kelly et al., 2016). Similarly, rodents that experienced more induced stress showed higher levels of inflammatory markers (Powell et al., 2013). These results could indicate a possible causal effect between well-being and different physiological factors and future animal research to well-being can be used to investigate causality and confirm these hypotheses.

4.5.3. Innovations and data-driven research

Related to innovations in the methods to measure physiological markers, e.g., real-time continuous monitoring (Si and Song, 2018, Su et al., 2020), there are also rapid developments in the approaches to collect and analyse (big) data. Using the developments in the artificial intelligence and machine learning fields, patterns can be detected in physiological data that we would not predict. These approaches enable us to focus more on data-driven research instead of hypothesis driven research (Scheel et al., 2020). For example, using a data driving approach, and applying machine learning, Poletti et al. (2021) could distinguish between unipolar and bipolar depression based on the plasma levels of 54 cytokines, chemokines and growth factors (i.e., the immune-inflammatory signature) of the participants. For more information about artificial intelligence and machine learning, see overview articles, e.g., Jordan and Mitchell, 2015; Yann LeCun, Yoshua Bengio, 2015). Different applications of this approach within the domain of mental ill-being with physiological factors can be found as well (for example (Poletti et al., 2021, Wardenaar et al., 2021).

4.6. Limitations

The low number of studies in some categories of this systematic review limits our ability to draw more firm conclusions about the association between the physiological factors and well-being and this highlights the need for more studies investigating the physiology of well-being. Furthermore, the low number of studies could indicate a possible publication bias, especially in the newer fields, if studies with non-significant findings are not published.

Another limitation, touched upon briefly in the results of the different categories, is that only a limited number of studies controlled for negative affect and depressive symptoms when investigating physiological factors in relation to well-being. Since well-being and ill-being are related (Baselmans et al., 2018, Okbay et al., 2016), controlling for ill-being when investigating the relation between physiological factors and well-being can help to disentangle the independent associations of physiological factors with well-being and ill-being.

A similar approach of controlling for confounding effects could be interesting for hedonic and eudaimonic well-being measures. Although hedonic and eudaimonic well-being measures are strongly correlated, they also capture slightly different parts of well-being. As proposed by Ryff et al. (2004) hedonic and eudaimonic well-being could have partly different neurobiological and physiological correlates. To learn more about the distinction between hedonic and eudaimonic well-being, future studies should include both measures and when examining the effects of hedonic well-being control for eudaimonic well-being and vice versa.

A quantitative meta-analysis on the association between cortisol levels, two inflammatory markers (CRP and IL-6), and well-being was possible due to a substantial number of homogenous study designs and reported effects. We only included studies that reported bivariate correlations, not including standardized regression coefficients and other effect sizes, since in many regression different covariates are added, leading to biased estimates when including the partial correlations between the markers and well-being. As a result, for the other categories and factors, a meta-analysis was not possible, since the studies were too heterogeneous in study methods, analysis techniques, and reported statistics. Furthermore, even though we performed a detailed literature search according to the PRISMA guidelines, it is possible we missed some papers.

Finally, in all included studies, well-being is measured with self-report measures. While self-report has its limitations, i.e., recall and reporting biases, well-being is conceptually a subjective experience and currently it is not possible to reliably measure well-being objectively.

4.7. Conclusion

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