The association between psychological resilience and hair cortisol concentration in adults: A systematic review and meta-analysis

Abstract

Background

Resilience measures are typically based on subjective self-assessment, which is prone to bias. Objective biological/physiological measures of resilience are therefore needed. Hair cortisol concentration is a particularly promising candidate as a biomarker for resilience.

Methods

We conducted a meta-analytic review from inception to April 2023 in PubMed, EMBASE, Cochrane Library, and Psych Info databases. All data were analyzed using a random-effects model.

Results

Eight studies were identified that included a total of 1,064 adults. The random effects model demonstrated that resilience and hair cortisol concentration were inversely correlated (r = −0.18, 95% confidence interval [CI] = −0.27 to −0.09) with substantial heterogeneity (I² = 54.2%, p = 0.03). The inverse association was stronger in those who were age 40 years or younger compared to those who were over 40 years. The correlation coefficients between psychological resilience and hair cortisol concentration among adults assessed by different resilience measures were r = −0.29 (95% CI = −0.49 to −0.08) for the CD-RISC-10; r = −0.21 (95% CI = −0.31 to −0.11) for the CDRISC- 25, and r = −0.08 (95% CI = −0.22 to 0.06) for the BRS. Six of eight studies examined the connection between resilience and perceived stress, where the weighted mean correlation coefficient was r = −0.45 (95% CI = −0.56 to −0.33), with considerable heterogeneity (I² = 76.2%, p = 0.001).

Conclusions

There is a negative association between psychological resilience and hair cortisol concentration based on these eight studies. Additional research, particularly prospective studies, is needed to determine whether hair cortisol concentration can be used as a biomarker for psychological resilience.

Keywords

Resilience hair cortisol concentration systematic review meta-analysis

Introduction

Mental health is an important topic that has been explored in different countries in the world. According to the World Health Organization’s data, in 2019, approximately 970 million people worldwide, or one in every eight individuals, were experiencing a mental disorder.¹ However, due to the COVID-19 pandemic, the number of people experiencing mental health symptoms increased considerably in 2020.²

Psychological resilience (hereafter, termed simply “resilience”) refers to a person’s capacity to deal with adverse events and a dynamic process of positive recovery from stressful circumstances.³ Previous studies have shown that resilience positively impacts mental health, while individuals with lower levels of resilience are more susceptible to developing affective psychotic disorders.^4,5 For instance, refugees and war victims represent someone with an increased likelihood of experiencing mental health problems.⁶ However, not all of them would manifest symptoms of psychological disorder.⁷ This underscores the fact that resilience varies across different individuals.

Numerous instruments have been established to assess resilience, with the Connor-Davidson Resilience Scale (CD-RISC) commonly used.⁸ Other instruments include the Brief Resilient Scale (BRS),⁹ the Resilience Scale for Adults (RSA),¹⁰ and the ego-Resilience Scale (ERS).¹¹ At present, psychological scales are dominant in the assessing resilience landscape.¹² However, these scales are based on subjective reports, susceptible to self-report bias. Some scholars suggest that resilience should be assessed by combing bio-physiological features with traditional psychometric properties.¹³ Therefore, it is crucial to introduce objective biological biomarkers of resilience.

A well-established notion is that the hypothalamic-pituitary-adrenal (HPA) system reactivity could be a sensitive indicator of resilience.¹⁴ The HPA axis in the human body is a crucial stress response system that reacts to both acute and chronic stressors. When the body experiences a stressor, the hypothalamus releases corticotrophin releasing factor, a peptide hormone, into the corticotrophin-releasing portal system. This prompts the production of adrenocorticotropic hormone, another polypeptide tropic hormone, in the anterior lobe of the pituitary gland. As a result of this process, cortisol is synthesized and released as part of the stress response, leading to behavioral reactions such as increased blood pressure, rapid heart rate, and sleep disturbances. The release of cortisol, one of the end products of the HPA axis, has been strongly associated with psychological distress.¹⁵ Moreover, it has also been found that oxidative stress and inflammation may closely interact with the cortisol secretion in the stress response system.¹⁶ For example, a negative correlation was observed between salivary cortisol and proinflammatory cytokines among post-traumatic stress disorder patients.¹⁷

Until recently, cortisol concentrations could be extracted in blood serum, saliva, or urine samples.¹⁸ Unfortunately, it is likely that traditional measures of cortisol concentration are influenced by various factors such as the time of collection, individual arousal levels, circadian rhythmicity, and acute exposures to daily stressors.¹⁹ For instance, measuring cortisol levels in serum, saliva, or urine only provides information on short-term or acute cortisol fluctuations.²⁰ However, the psychological state measured by the psychological scale generally reflects throughout the psychological state (such as a month), and the time reflected on saliva, serum, or urine cortisol levels are inconsistent. The hair cortisol concentration (HCC), which reflects long-term cortisol levels, is, therefore, one promising approach to assess HPA axis activity retrospectively over several months. Hair grows approximately one cm per month; thus, three centimeters is equivalent to three months of hair cortisol accumulation.²¹

Furthermore, unlike saliva, blood serum, and urine cortisol, HCC is not influenced by the factors abovementioned factors. HCC serves as a retrospective marker of the HPA axis and offers the following advantages: (1) it is a non-invasive measure;¹⁹ (2) short-term fluctuations or rhythms do not impact it; and (3) hair sampling is simple to store at room temperature and transport.²²

HCC as a biomarker of stress system has become prevalent in recent research as above superiority. For instance, a large sample study including 1279 participants revealed that individuals who self-reported the highest levels of stress also exhibited significantly elevated levels of HCC.²³ Similarly, a systematic review found that chronic stress was associated with increased HCC.²⁴ Silvia’s study, which used HCC as a biomarker to assess stress levels, found that participants who underwent stress reduction interventions exhibited decreased HCC level.²⁵ These studies indicate there was a correlation between HCC and chronic stress. Similarly, a few studies have reported a possible link between resilience and HCC. A cross-sectional study, found that students with low resilience showed higher levels of perceive stress and HCC in response to adverse events.²⁶ Therefore, HCC may serve as a biomarker of resilience.

Although numerous studies have investigated the association between resilience and hair cortisol, there is still also some controversy about the conclusion. One study, which used the RS-13 (the resilience scale of 13 items), showed that resilience and sense of coherence were not correlated with the activation of the HPA axis among young students.²⁷ Lehrer’s study, which included adults with a mean age of 45.29, and used the BRS scale, found that resilience can regulate the correlation between perceived stress and HCC, but there was no significant relationship between resilience and HCC.²⁸ However, another study conducted on 151 pregnant and puerperium women, who were divided into high and low resilience groups using the CD-RISC scale, found resilient people displayed lower HCC, higher mental health, and lower mental illness symptoms than those who were less resilient.²⁹ Further, in mixed ancestry females, using CD-RISC scale, lower HCC predicted a higher level of resilience.³⁰ In summary, using different self-reported resilience measurement scales could produce discrepant results. Furthermore, the results were skewed across age groups. Therefore, to fill in the knowledge gaps, it would be helpful and meaningful to perform a quantitative synthesis to confirm their relationship, and examine whether study characteristics, including participant age and the self-reported measurement scales, modify it.

To our knowledge, there has been no systematic review and meta-analysis of the correlation between resilience and hair cortisol concentration in adults. Therefore, we conducted a meta-analysis to comprehensively summarize and quantify the strength of the association between resilience and hair cortisol concentration.

Methods

Search strategy

From the inception to April 15, 2023, we thoroughly searched various databases, including PubMed, EMBASE, the Cochrane Library, Psych Info, CNKI, CBM disc, Wan Fang, and VIP. We utilized relevant keywords or subject headings during the search process. This study has been registered in PROSPERO (CRD42023418631).

Inclusion and exclusion criteria

The inclusion criteria for studies were as follows: (1) they were observational studies, including cross-sectional, case-control, and cohort studies; (2) the age of the participants was 18 years old or older; (3) they included a measure of hair cortisol and resilience; (4) they examined the association between resilience and hair cortisol concentration, and (5) they provided outcome data that could be utilized to determine effect size.

The exclusion criteria were: (1) studies using small samples (N <10) or case studies; (2) studies conducted on patients with specific pathogens like HIV,³¹ and receiving hormonal treatment; and (3) no full text available.

Data extraction and synthesis

Two reviewers independently extracted the data and then verified it by a third party. A Microsoft Excel spreadsheet was used to extract data items from the studies that met the inclusion criteria, including details such as first author, publication year, country of origin, study design, sample size, age and gender of participants, the instrument for assessing resilience and stress, the hair sample analysis method, r-value of association, and the main conclusion.

Quality assessment

The quality of studies that satisfied the inclusion criteria was evaluated by two reviewers independently, and a third reviewer resolved disputes. The AHRQ Quality Assessment Tool was utilized to evaluate cross-sectional studies,³² whereas the Newcastle-Ottawa-Scale (NOS) was performed to evaluate the quality of case-control and cohort studies.³³

Statistical analysis

We used STATA Version 15.1 to conduct statistical analyses and to assess heterogeneity with chi-squared and the I² statistic. If the p > 0.10 or I²<50%, the results of related studies are considered homogeneous and acceptable, and a fixed-effects model is applied. However, if p ≤ 0.1 or I²≥50% and heterogeneity are detected, a subgroup analysis is conducted to identify the source of heterogeneity. If it is impossible to remove the heterogeneity, a random-effects model is used instead. The r-values and 95% confidence intervals (CI) represented the effect size. The Summary r value comprehensively assessed the correlation between resilience and hair cortisol concentration according to the range of absolute value of Summary r. Consequently, a correlation below 0.10 is deemed a minor effect, 0.30 is categorized as moderate, and above 0.50 is considered substantial.³⁴

Results

Literature search and selection

The process of selecting literature was illustrated in the flow diagram (Figure 1). Initially, 1483 published papers were obtained from eight databases. Finally, eight studies fulfilled the eligibility criteria and were incorporated into the meta-analysis.

Characteristics and quality of included studies

The meta-analysis was based on 1064 participants from eight studies. Three kinds of statistical approaches were conducted in the eight studies, including Pearson correlation analyses,^26,28,29,35 Spearman correlation analyses,^36,37 and standardized regression analyses.^30,38 Regarding the quality of included cross-sectional studies, only one study³⁰ scored 8 points, regarded as high quality, and the remaining four studies^28,32,35,36 scored 4 and 7 points and were assessed with moderate quality, respectively. Of the three prospective longitudinal studies, two^37,38 scored 7 points and were considered to be of high quality, while the rest of one study,²⁹ was of medium quality, scoring 5 points (Table 1).

Table 1.

Characteristic of the Included Studies and Correlations of the Resilience and HCC.

First author	Country	Study design	Participants				Instrument		Analysis method of HCC	Statistical analysis	r-value	Conclusion
First author	Country	Study design	Population	Sample size	Age (years)	Female (%)	Resilience	Stress	Analysis method of HCC	Statistical analysis	r-value	Conclusion
Engert³⁸	Germany	prospective, longitudinal study	adults	80	35.65 ± 11.49	90	BRS	PSS	LC-MS/MS	standardized regression	−0.14	1) There were no significant correlations found between HCC and the PSS. 2) No associations were found between HCC and resilience.
Heuvel³⁰	South Africa	cross-sectional study	females	164	46.50 ± 15.0	100	CD-RISC-25	PSS	LC-MS/MS	standardized regression	−0.21	1) HCC was inversely associated with resilience. 2) PSS was not linked to HCC. 3) There was a negative correlation found between PSS and CD-RISC.
Lehrer²⁸	America	cross-sectional study	adults	228	45.29 ± 14	68	BRS	PSS	EIA kit	Pearson correlation	0.03	1) Resilience had a negative association with PSS. 2) No associations were found between HCC and resilience.
Alhalal³⁶	Saudi Arabia	cross-sectional study	womean	156	32.20 ± 7.9	100	CD-RISC-25	—	ELISA	Spearman correlation	−0.17	1) There was a significant negative correlation between HCC and resilience.
García²⁶	Spain	cross-sectional study	students	80	21.14 ± 4.17	65	CD-RISC-10	—	LC-MS/MS	Pearson correlation	−0.39	1) The mean scores of resilience were statistically associated with HCC.
García²⁹	Spain	prospective, longitudinal study	pregnant women	151	33.01 ± 4.42	100	CD-RISC-10	PSS	ELISA	Pearson correlation	−0.20	1) A statistically significant correlation between resilience and PSS was found. 2) A weak relationship between resilience and HCC was found.
Mayer³⁶	America	prospective, longitudinal study	Medicine internship	74	27.41 ± 2.36	55	CD-RISC-25	PSS	EIA kit	Spearman correlation	−0.30	1) Lower HCC were linked with higher resilience. 2) There were no significant correlations observed between PSS and HCC.
Robin³⁵	Australia	cross-sectional study	students	131	21.71 ± 4.94	71	BRS	PSS	ELISA	Pearson correlation	−0.17	1)No significant correlations were observed among resilience, PSS, and HCC.

HCC: hair cortisol concentration; CD-RISC-25: 25-item Connor-Davidson Resilience Scale; CD-RISC-10: 10-item Connor-Davidson Resilience Scale; BRS: Brief Resilient Scale; PSS: Perceived Stress Scale; r -value: Pearson correlation coefficients; LC-MS/MS: liquid chromatography-tandem mass spectrometry; EIA kit: enzyme-linked immunoassay kit; ELISA: enzyme-linked immunosorbent assay.

Association between resilience and hair cortisol concentration

During the meta-analysis, the effect sizes of the connection between resilience and hair cortisol concentration ranged from −0.09 to −0.26 and displayed considerable heterogeneity (X² = 15.27, p = 0.03, I² = 54.2%). As a result, a random-effects model was used to analyze the data. We found that the pooled Fisher's Z between resilience and HCC was −0.18 (95% confidence interval = −0.27 to −0.09; p < 0.00,001). In the eight studies, a minor effect was observed. The Summary r value across all studies was −0.18 (95% confidence interval = −0.26 to −0.09), indicating that greater resilience scores are connected to lower hair cortisol concentration (Figure 2).

Subgroup analysis

To identify the heterogeneity and potential confounding factors, two kinds of subgroup analyses were conducted based on the age of participants and the instruments used to assess resilience. We examined the 95% confidence interval of the results presented in Table 2 and Figure 3. In samples including people with a mean age of younger than 40 years, larger correlation coefficients were found (r = −0.21; 95% CI = −0.28 to −0.14), compared with samples of people with an average age older than 40 years (r = −0.09; 95% CI = −0.32 to 0.15). The association between resilience and hair cortisol concentration varied depending on the scale used. The correlation coefficients for CD-RISC-10, CD-RISC-25, and BRS were (r = −0.29; 95% CI = −0.49 to −0.08), (r = −0.21; 95% CI = −0.31 to −0.11), (r = −0.08; 95% CI = −0.22 to 0.06), respectively.

Table 2.

Pooled and subgroup results for the relationship between resilience and HCC.

characteristics	including studies	heterogeneity			effects model	meta-analysis
characteristics	including studies	Q	P	I²(%)	effects model	r-value	95%CI	P
Total studies	8	15.27	0.03	54.20	random-model	−0.18	−0.27, −0.09	<0.01
Mean age(years)
<40	6	4.59	0.47	0.00	random-model	−0.21	−0.28, −0.14	<0.01
≥40	2	5.69	0.02	82.40	random-model	−0.09	−0.32, 0.15	0.47
Used scale
CD-RISC-25	3	1.04	0.60	0.00	random-model	−0.21	−0.31, −0.11	<0.01
CD-RISC-10	2	2.22	0.14	55.00	random-model	−0.29	−0.49, −0.08	<0.01
BRS	3	3.92	0.14	49.00	random-model	−0.08	−0.22, 0.06	0.27

Association between resilience and perceived stress

We found six of eight studies examining resilience concerning perceived stress. The relationship between resilience and perceived stress had significant variability across different studies (X² = 21.04, p = 0.001, I² = 76.2%), justifying using the random effects model in our meta-analysis. Across all of the findings reported in the meta-analysis, a negative correlation was found (pooled Fisher's Z = −0.49; 95% CI = −0.63 to −0.35; p < 0.001). We converted the pooled Fisher's Z value to a Summary r-value, calculated as −0.45 (95% CI = −0.56 to −0.33), indicating an association between more excellent resilience scores and lower perceived stress (Figure 4).

Discussion

All of the included eight studies analyzed reported a negative relationship between resilience and hair cortisol concentration, except for one study.³⁸ Our current research found that the higher the resilience score, the lower the hair cortisol concentration in adults. Moreover, evidence also suggests that the higher resilience in adults, the lower they perceived their stress across the included six studies.

Hair cortisol may be an indicator of an adult’s psychological resilience. Previous study in young adults revealed that those who possess resilience would exhibit better control over their cortisol levels than those who do not.³⁹ In a recent study, resilience scores were found to be increased as the HCC decreased after receiving cognitive behavioral therapy in pregnant women.⁴⁰ Similarly, Baliyan⁸ and Ruiz-Robledillo⁴¹ found that resilient people had low cortisol levels. The inverse relationship between resilience and hair cortisol concentration may be attributed to the fact that individuals with lower resilience tend to experience tremendous perceived stress, activating the HPA axis and causing an elevation in cortisol secretion.⁴² Conversely, resilient individuals do not increase cortisol secretion. Consistent with our results, prior research has shown an inverse correlation between resilience and self-perceived stress.⁴³ The developer of the CD-RISC scale asserts that the degree of resilience measured by this scale may reflect the biological aspect of resilience, which may attenuate cortisol response and evaluate an individual’s capacity to manage stress.⁹ Since HCC is a marker assessing the HPA axis activation, it is reasonable to find a significant correlation between the CD-RISC score and HCC when they are assessed concurrently. Therefore, HCC, which reflects the retrospective activity of the HPA axis, can be a highly promising psychological resilience biomarker. It may also act as an “early warning sign” of less resilience that can help monitor physiological status, identify those non-resilient individuals, and intervene early.

Our subgroup analysis for the connection between HCC and resilience found that the correlation coefficients varied through age groups and resilience scales. First, the effect size of the group with an average age of less than 40 years was higher than those over 40 years, possibly because HCC increased with age. According to a meta-analysis, there is a positive association between age and HCC,⁴⁴ probably due to decreased sensitivity of the HPA axis feedback. Secondly, the studies using the CD-RISC-10 scale to measure resilience showed the most prominent effect size, while the smallest effect size was found in studies using the BRS. The CD-RISC-25 scale included five dimensions, including ability, instinct, acceptance of change, control, and mental influence, with 25 items. The CD-RISC-10 is the shorter version of the CD-RISC-25, and it assesses an individual’s capacity to tolerate adverse experiences, such as failure, pressure, and change in circumstances. On the other hand, the BRS is a unidimensional tool consisting of six items that measure an individual’s ability to recover or bounce back from stress. Overall, the dimensions and items varied considerably for the three scales. In future studies examining the connection between hair cortisol and resilience, it is necessary to explore the correlation between different dimensions of the resilience scale and hair cortisol concentration. This will help to clarify the distinctions among various resilience scales.

Hair cortisol, as a biomarker of resilience, may pose particular challenges. Specifically, individuals with alopecia or who have undergone chemotherapy may need help providing long enough hair samples, which can lead to incomplete data. In addition, this meta-analysis only focused on hair cortisol as a biomarker of resilience, whereas it is uncommon for a single type of biomarker to possess enough discriminating ability.^13,45 Hence, an integrative measure may be better at predicting an individual’s resilience level.

Limitations

The meta-analysis fell short in several aspects: (1) The sample size of included studies was insufficient, and the female participants predominated (83.1%), which may influence the conclusion. Nevertheless, the limited sample size precluded us from conducting subgroup analyses with gender effects. (2) The meta-analysis did not account for the connection between different dimensions of resilience and hair cortisol concentration as the included studies did not explore their relationship; (3) The meta-analysis was restricted to a select group of countries, carefully considering cultural variations. Therefore, the applicability of the results to populations in other countries should be approached with caution.

Conclusion

A negative association between psychological resilience and hair cortisol concentration suggests that HCC may be an objective, biological measurement of an individual’s resilience level. The findings of this study may allow for earlier identification and intervention in those who may be less resilient. However, it is essential to note that hair cortisol concentration should not be used as the sole indicator of psychological resilience, as various factors influence it. Additionally, further prospective research, in large samples, gender groups, and different regions or countries, is needed to determine whether hair cortisol concentration can be used as a biomarker for psychological resilience.

Footnotes

Author contributions

XLW and MXR formulated the research question, drafted the manuscript, and led the study's design. XLW and LYX worked together to generate the search strategy, determine selection criteria, evaluate literature quality, extract and synthesize data, and perform statistical analysis. TSF, WF, and ZX conceived the study and contributed to the conception and design of the meta-analysis. MXR reviews the manuscript, advises on data analysis, suggests changes, and ensures the overall quality of the manuscript.

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by Department of Human Resources and Social Security of Sichuan Province (30320200061) and Science & Technology Department of Sichuan Province (23ZDYF2075).

ORCID iDs

LiWen Xiang

XiaoRong Mao

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