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Mind and Body Approaches for Stress and Anxiety: What the Science Says

Clinical Guidelines, Scientific Literature, Info for Patients:  Mind and Body Approaches for Stress and Anxiety

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Relaxation techniques may be helpful in managing a variety of stress-related health conditions, including anxiety associated with ongoing health problems and in those who are having medical procedures. Evidence suggests that relaxation techniques may also provide some benefit for symptoms of post-traumatic stress disorder (PTSD) and may help reduce occupational stress in health care workers. For some of these conditions, relaxation techniques are used as an adjunct to other forms of treatment.

What Does the Research Show?

• Biofeedback for anxiety and depression in children. A 2018 systematic review included 9 studies—278 participants total—on biofeedback for anxiety and depression in children and adolescents with long-term physical conditions such as chronic pain, asthma, cancer, and headache. The review found that, although biofeedback appears promising, at this point it can’t be recommended for clinical use in place of or in addition to current treatments. 
• Heart rate variability biofeedback. A 2017 meta-analysis looked at 24 studies—484 participants total—on heart rate variability (HRV) biofeedback and general stress and anxiety. The meta-analysis found that HRV biofeedback is helpful for reducing self-reported stress and anxiety, and the researchers saw it as a promising approach with further development of wearable devices such as a fitness tracker.
• Progressive muscle relaxation. A 2015 systematic review , which included two studies on progressive muscle relaxation in adults older than 60 years of age, with a total of 275 participants, found that progressive muscle relaxation was promising for reducing anxiety and depression. The positive effects for depression were maintained 14 weeks after treatment.
• PTSD. A 2018 meta-analysis of 50 studies involving 2,801 participants found that relaxation therapy seemed to be less effective than cognitive behavioral therapy for PTSD and obsessive-compulsive disorder. No difference was found between relaxation therapy and cognitive behavioral therapy for other anxiety disorders, including generalized anxiety disorder, panic disorder, social anxiety disorder, and specific phobias. The review noted, however, that most studies had a high risk of bias, and there was a small number of studies for some of the individual disorders.
• Anxiety in people with cancer. In the 2023 joint guideline issued by the Society for Integrative Oncology and the American Society for Clinical Oncology on integrative oncology care of symptoms of anxiety and depression in adults with cancer, relaxation therapies may be offered to people with cancer to improve anxiety symptoms during active treatment (Type: Evidence based; Quality of evidence: Intermediate; benefits outweigh harms; Strength of recommendation: Moderate). 
• Relaxation techniques are generally considered safe for healthy people. In most research studies, there have been no reported negative side effects. However, occasionally, people report negative experiences such as increased anxiety, intrusive thoughts, or fear of losing control. 
• There have been rare reports that certain relaxation techniques might cause or worsen symptoms in people with epilepsy or certain psychiatric conditions, or with a history of abuse or trauma. 

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A range of research has examined the relationship between exercise and depression. Results from a much smaller body of research suggest that exercise may also affect stress and anxiety symptoms. Even less certain is the role of yoga, tai chi, and qigong—for these and other psychological factors. But there is some limited evidence that yoga, as an adjunctive therapy, may be helpful for people with anxiety symptoms.

• Yoga for children and adolescents. Findings from a 2021 meta-analysis and systematic review of 10 trials involving a total of 1,244 adolescents suggest a potential beneficial effect of tai chi and qigong on reducing anxiety and depression symptoms, and reducing cortisol level in adolescents. However, nonsignificant effects were found for stress, mood, and self-esteem. A  2020 systematic review  of 27 studies involving the effects of yoga on children and adolescents with varying health statuses, and with varying intervention characteristics, found that in studies assessing anxiety and depression, 58 percent showed reductions in both symptoms, while 25 percent showed reductions in anxiety only. Additionally, 70 percent of studies included in the review that assessed anxiety alone showed improvements. However, the reviewers noted that the studies included in the review were of weak-to-moderate methodological quality. 
• Yoga, tai chi, and qigong for anxiety. A  2019 review  concluded that yoga as an adjunctive therapy facilitates treatment of anxiety disorders, particularly panic disorder. The review also found that tai chi and qigong may be helpful as adjunctive therapies for depression, but effects are inconsistent.
• Yoga for anxiety. A  2021 randomized controlled trial examined whether Kundalini yoga and cognitive behavioral therapy (CBT) for generalized anxiety disorder (GAD) were each more effective than a control condition (stress education) and whether yoga was inferior to CBT for the treatment GAD. The trial found that Kundalini yoga was more efficacious for generalized anxiety disorder than the control, but the results support CBT remaining first-line treatment. A  2018 systematic review and meta-analysis  of 8 studies of yoga for anxiety (involving 319 participants with anxiety disorders or elevated levels of anxiety) found evidence that yoga might have short-term benefits in reducing the intensity of anxiety. However, when only people with diagnosed anxiety disorders were included in the analysis, there was no benefit. 
• Yoga is generally considered a safe form of physical activity for healthy people when performed properly, under the guidance of a qualified instructor. However, as with other forms of physical activity, injuries can occur. The most common injuries are sprains and strains. Serious injuries are rare. The risk of injury associated with yoga is lower than that for higher impact physical activities.
• Older people may need to be particularly cautious when practicing yoga. The rate of yoga-related injuries treated in emergency departments is higher in people age 65 and older than in younger adults.

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Some research suggests that practicing meditation may reduce blood pressure, anxiety and depression, and insomnia.

• Mindfulness-based stress reduction. A  2023 randomized controlled trial involving 208 participants found that mindfulness-based stress reduction (MBSR) is noninferior to escitalopram, a commonly used first-line psychopharmacologic treatment for anxiety disorders. A  2021 randomized controlled trial of 108 adults with generalized social anxiety disorder found that cognitive behavioral group therapy and MBSR may be effective treatments with long-term benefits for patients with social anxiety networks that recruit cognitive and attention-regulation brain networks. The researchers noted that cognitive behavioral therapy and MBSR may both enhance reappraisal and acceptance emotion regulation strategies.
• Mindfulness-based meditation. A  2019 review  concluded that as monotherapy or an adjunctive therapy, mindfulness-based meditation has positive effects on depression, and its effects can last for 6 months or more. Although positive findings are less common in people with anxiety disorders, the evidence supports adjunctive use. A 2019 analysis of 29 studies (3,274 total participants) showed that use of mindfulness-based practices among people with cancer significantly reduced psychological distress, fatigue, sleep disturbance, pain, and symptoms of anxiety and depression. However, most of the participants were women with breast cancer, so the effects may not be similar for other populations or other types of cancer. A  2014 meta-analysis  of 47 trials in 3,515 participants suggests that mindfulness meditation programs show moderate evidence of improving anxiety and depression. But the researchers found no evidence that meditation changed health-related behaviors affected by stress, such as substance abuse and sleep.
• Mindfulness-based programs for workplace stress. A  2018 systematic review and meta-analysis  of nine studies examined mindfulness-based programs with an employee sample, which targeted workplace stress or work engagement, and measured a physiological outcome. The review found that mindfulness-based interventions may be a promising avenue for improving physiological indices of stress. 
• Meditation is generally considered to be safe for healthy people.
• A 2019 review found no apparent negative effects of mindfulness-based interventions and concluded that their general health benefits justify their use as adjunctive therapy for patients with anxiety disorders.

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Hypnosis has been studied for anxiety related to medical or dental procedures. Some studies have had promising results, but the overall evidence is not conclusive.

• A  2022 systematic review and meta-analysis of 19 trials found positive effects of hypnotherapy for reducing dental anxiety and fear during dental treatment. However, the reviewers noted that despite positive effects of hypnotic interventions in the systematic review, the results of the meta-analysis are very heterogeneous. 
• The 2023 joint guideline issued by the Society for Integrative Oncology and the American Society for Clinical Oncology recommends that hypnosis may be offered to people with cancer to improve anxiety symptoms during cancer-related diagnostic and treatment procedures (Type: Evidence based; Quality of evidence: Intermediate; benefits outweigh harms; Strength of recommendation: Moderate).
• Hypnosis is a safe technique when practiced by a trained, experienced, licensed health care provider.

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• Carlson LE, Ismaila N, Addington EL, et al.  Integrative oncology care of symptoms of anxiety and depression in adults with cancer: Society for Integrative Oncology-ASCO guideline .  Journal of Clinical Oncology.  2023;41(28):4562-4591. 
• Chugh-Gupta N, Baldassarre FG, Vrkljan BH.  A systematic review of yoga for state anxiety: considerations for occupational therapy . C anadian Journal of Occupational Therapy . 2013;80(3):150-170.
• Cillessen L, Johannsen M, Speckens AEM, et al . Mindfulness-based interventions for psychological and physical health outcomes in cancer patients and survivors: a systematic review and meta-analysis of randomized controlled trials .  Psychooncology . 2019;28(12):2257-2269. 
• Cramer H, Lauche R, Anheyer D, et al.  Yoga for anxiety: a systematic review and meta-analysis of randomized controlled trials .  Depress Anxiety . 2018;35(9):830-843.
• Goessl VC, Curtiss JE, Hofmann SG.  The effect of heart rate variability of biofeedback training on stress and anxiety: a meta-analysis .  Psychological Medicine . 2017;47(15):2578-2586.
• Goldin PR, Thurston M, Allende S, et al . Evaluation of cognitive behavioral therapy vs mindfulness meditation in brain changes during reappraisal and acceptance among patients with social anxiety disorder: a randomized clinical trial .  JAMA Psychiatry . 2021;78(10):1134-1142.
• Goyal M, Singh S, Sibinga EMS, et al.  Meditation programs for psychological stress and well-being: a systematic review and meta-analysis.   JAMA Internal Medicine . 2014;174(3):357-368.
• Greenlee H, Balneaves LG, Carlson LE, et al.  Clinical practice guidelines on the use of integrative therapies as supportive care in patients treated for breast cancer .  Journal of the National Cancer Institute Monographs.  2014;50:346-358.
• Heckenberg RA, Eddy P, Kent S, et al.  Do workplace-based mindfulness meditation programs improve physiological indices of stress? A systematic review and meta-analysis .  Journal of Psychosomatic Research.  2018;114:62-71.
• Hoge EA, Bui E, Mete M, et al.  Mindfulness-based stress reduction vs escitalopram for the treatment of adults with anxiety disorders: a randomized clinical trial .  JAMA Psychiatry . 2023;80(1):13-21.
• James-Palmer A, Anderson EZ, Zucker L, et al. Yoga as an intervention for the reduction of symptoms of anxiety and depression in children and adolescents: a systematic review .  Frontiers in Pediatrics . 2020;8:78.
• Liu X, Li R, Cui J, et al.  The effects of tai chi and qigong exercise on psychological status in adolescents: a systematic review and meta-analysis .  Frontiers in Psychology . 2021;12:746975.
• Klainin-Yobas P, Oo WN, Suzanne Yew PY, et al.  Effects of relaxation interventions on depression and anxiety among older adults: a systematic review .  Aging and Mental Health . 2015;19(12):1043-1055.
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• Saeed SA, Cunningham K, Bloch RM.  Depression and anxiety disorders: benefits of exercise, yoga, and meditation .  American Family Physician . 2019;99(10):620-627.
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• Published: 27 November 2021

Psychological and biological resilience modulates the effects of stress on epigenetic aging

• Zachary M. Harvanek   ORCID: orcid.org/0000-0003-3702-1051 1 ,
• Nia Fogelman 2 ,
• Ke Xu   ORCID: orcid.org/0000-0002-6472-7052 1 , 3 &
• Rajita Sinha   ORCID: orcid.org/0000-0003-3012-4349 1 , 2 , 4 , 5  

Translational Psychiatry volume  11 , Article number:  601 ( 2021 ) Cite this article

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Our society is experiencing more stress than ever before, leading to both negative psychiatric and physical outcomes. Chronic stress is linked to negative long-term health consequences, raising the possibility that stress is related to accelerated aging. In this study, we examine whether resilience factors affect stress-associated biological age acceleration. Recently developed “epigenetic clocks” such as GrimAge have shown utility in predicting biological age and mortality. Here, we assessed the impact of cumulative stress, stress physiology, and resilience on accelerated aging in a community sample ( N  = 444). Cumulative stress was associated with accelerated GrimAge ( P  = 0.0388) and stress-related physiologic measures of adrenal sensitivity (Cortisol/ACTH ratio) and insulin resistance (HOMA). After controlling for demographic and behavioral factors, HOMA correlated with accelerated GrimAge ( P  = 0.0186). Remarkably, psychological resilience factors of emotion regulation and self-control moderated these relationships. Emotion regulation moderated the association between stress and aging ( P  = 8.82e−4) such that with worse emotion regulation, there was greater stress-related age acceleration, while stronger emotion regulation prevented any significant effect of stress on GrimAge. Self-control moderated the relationship between stress and insulin resistance ( P  = 0.00732), with high self-control blunting this relationship. In the final model, in those with poor emotion regulation, cumulative stress continued to predict additional GrimAge Acceleration even while accounting for demographic, physiologic, and behavioral covariates. These results demonstrate that cumulative stress is associated with epigenetic aging in a healthy population, and these associations are modified by biobehavioral resilience factors.

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Introduction.

Cumulative stress can have adverse psychiatric and physical effects, increasing risk for cardiometabolic diseases, mood disorders, post-traumatic stress disorder and addiction [ 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 ]. There are several potential psychological and biological mechanisms through which these effects may occur. For example, stress may reduce psychological resilience measures such as emotion regulation and self-control that are known to protect against psychiatric and physical health outcomes [ 1 , 12 , 13 , 14 ]. Notably, emotional stress exposure decreases cognitive and emotion regulation abilities [ 15 , 16 , 17 , 18 ], and this effect may be modulated by cortisol [ 15 ]. Furthermore, stress decreases self-control abilities [ 19 , 20 , 21 ] and impacts the likelihood of individuals engaging in healthy behaviors such as exercise or maintaining a healthy diet, and is associated with unhealthy behaviors such as smoking, alcohol, and drug use [ 22 , 23 , 24 , 25 ]. Recent evidence also suggests that stress effects on metabolic health may be affected by BMI-related changes in insulin resistance and other gut hormones [ 26 , 27 ]. Indeed, stress’s effects on physiology resulting in alterations in neuro-hormonal signaling pathways as well as increased inflammation are well documented [ 26 , 28 , 29 , 30 ]. Both stress and these physiologic changes may increase the risk of multiple physical and psychiatric illnesses, which in turn increase morbidity and mortality risk. This has often been described as an increased allostatic load, and notably many of these processes, such as metabolic and cardiovascular dysfunction, have been associated with human aging [ 31 ]. For example, insulin signaling might be linked to aging and aging-related diseases in humans [ 32 ], with recent data on metformin (a treatment for insulin resistance) suggesting it may be useful as an anti-aging drug [ 33 ].

There is growing evidence that cumulative stress may adversely impact health via accelerating the cellular aging process. For example, stress shortens telomere length and alters telomerase activity, and this interaction is modified by behavioral and psychological resilience factors [ 34 , 35 , 36 , 37 ]. However, recent studies have demonstrated mixed results on whether characteristics that contribute to resilience improve or worsen the impact of stress on health [ 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 ]. These data suggest that resiliency factors may modulate the relationship between chronic stress and aging, but to our knowledge this has not been tested in a healthy community sample. While there are many aspects of resilience, including cultural/societal, environmental, and personal which can decrease the negative consequences of stressors on individuals, herein we will focus on personal-level, psychological skills, including self-control and emotion regulation.

Recently developed DNA methylation-based epigenetic “clocks” appear to provide a more accurate measure of biological age than telomere length [ 48 , 49 , 50 , 51 ]. These clocks are built from a set of DNA methylation markers that correlate with chronologic age and serve as molecular estimators of biological age in cells, tissues, and individuals [ 52 ]. Epigenetic clocks have a significantly higher predictive value than previously used measures such as telomere length for frailty, [ 53 ] mortality risk [ 54 , 55 ], hazard ratios [ 56 ], and chronologic age [ 57 ]. The development of these biological aging markers promises to not only aid in identifying individuals at higher risk for aging-related illnesses, but potentially also developing interventions to prevent accelerated aging.

Previous studies (reviewed by Palma-Gudiel et al [ 58 ]) have utilized epigenetic clocks to demonstrate associations between trauma, early life adversity, or low socioeconomic status and accelerated epigenetic aging. Studies have often been focused upon selected populations, such military veterans [ 45 ], individuals with significant trauma histories [ 59 ], or specific cohorts at higher risk [ 60 , 61 , 62 ]. Notably, these studies did not exclude, and often explicitly included, individuals with significant mental and physical illnesses, including PTSD, MDD, and other disabilities [ 59 , 63 ]. These studies also primarily utilized epigenetic clocks trained upon chronologic age. However, a recently developed epigenetic clock, GrimAge, was trained using biomarkers of mortality and indicators of health, and has superior performance in predicting health outcomes when compared with other epigenetic clocks [ 51 , 64 ].

We utilized GrimAge Acceleration (“GAA”, defined as the residual of the regression of GrimAge to chronologic age, with a positive number indicating biological age greater than chronologic age) to conduct a cross-sectional study to answer three questions. First, is cumulative stress related to epigenetic markers of biological aging in a healthy young-to-middle-aged community population? Second, if stress is associated with epigenetic aging, does stress-related physiology contribute to stress-associated biological aging? And finally, how do psychological factors that contribute to resilience modulate these relationships? Based on previous research, we hypothesized that cumulative stress will be positively associated with GrimAge Acceleration (GAA), that stress effects on GrimAge will be related to changes in the hypothalamic-pituitary-adrenal axis (HPA) and insulin sensitivity, and that strong emotion regulation as measured by the Difficulties in Emotion Regulation Scale (DERS, [ 65 ]) and high self-control as measured by the Self Control Scale-Brief (SCS-B, [ 66 ]) will moderate the relationships between stress, physiology, and accelerated aging (See Fig. 1 for a model summarizing our hypotheses).

We hypothesize that stress is positively associated with accelerated biological aging, which we measure via GrimAge Acceleration (GAA), and that this relationship will be mediated by stress-related physiologic changes such as insulin and HPA signaling. We also hypothesize that strong psychological resilience factors will be protective against the negative consequences of stress on aging. Note that these relationships are predictive, not causative, as this study is cross-sectional and thus directionality of relationships cannot be conclusively examined.

Materials and methods

Cohort recruitment.

The participant cohort included 444 community adults between the ages of 18–50 in the greater New Haven, CT area who volunteered to participate in a study examining the role of stress and self-control at the Yale Stress Center as previously described [ 67 ]. Briefly, participants were recruited via advertisements online, in local newspapers, and at a community center between 2008 and 2012. Participants were excluded if they had a substance use disorder (not including nicotine) as assessed via the Structured Clinical Interview for Diagnostic and Statistical Manual of Mental Disorders, 4th Edition (SCID-I for DSM-IVTR), were pregnant, had a chronic medical condition (e.g, hypertension, diabetes, hypothyroidism), or were unable to read English at or above the 6th grade level. Participants were also excluded if they had a concussion with loss of consciousness greater than 30 minutes, another head injury such as documented traumatic brain injury or another injury with documented lasting deficits, or were using any prescribed medications for any psychiatric or medical disorders. Breathalyzer and urine toxicology screens were conducted at each appointment to ensure the participants were drug-free. Of a total of 1000 potential participants who underwent initial screening for eligibility, epigenetic data combined with physiologic and behavioral data were available on 444, who comprised the current sample. All participants provided written and verbal informed consent to participate, and the research protocol was reviewed and approved by the Yale IRB.

Initial assessment and measurement of physiologic parameters

All eligible subjects met with a research assistant for two intake sessions to complete a physical health review with the Cornell Medical Index (CMI, [ 68 ]), structured clinical interview for diagnoses (SCID) of DSM-IVTR psychiatric illnesses, cumulative stress interview, self-report assessments and a separate morning biochemical evaluation after fasting overnight. The structured clinical interview was performed by masters’ or doctoral level clinical research staff. Fasting insulin and glucose were obtained and Cortisol was assessed at four time-points, spaced 15 min apart beginning at 7:30 AM after overnight fasting and collected while participants were in a quiet and comfortable laboratory setting at the Yale Stress Center. Participants were financially compensated for participating in the study.

Psychological measures

Cumulative stress was assessed using the Cumulative Adversity Inventory (CAI, [ 69 ]), a 140-item multifaceted interview-based assessment of life events and subjective stress through which trained interviewers asked participants about specific stressful events that occurred during their lifetime, which comprised the subscales of major life events, life trauma events and recent life events. For purposes of scoring, a “yes” to the specific stressful event occurring led to a “1” and a sum of all the “yes” endorsements comprised the subscale score for these events subscale. The final subscale of chronic stress was the participant’s own sense of feeling overwhelmed and unable to manage the events for the other subscales listed. This was rated on a “not true”, “somewhat true”, or “very true” scale, with assigned scores of 0, 1, and 2, respectively. The final score is a sum of these values for the chronic stress subscale. The CAI-total score was a sum of each of the subscale score with a higher score indicating a higher overall level of lifetime cumulative stress. The CAI has been demonstrated to have excellent overall reliability as reported in previous research [ 12 , 26 , 70 , 71 , 72 ]. In our population for this study, the alpha reliability is 0.86. It has been previously shown to predict cumulative stress related brain volume reductions and sensitized stress functional responses as well as prediction of physical, metabolic and behavioral responses [ 26 , 70 , 71 , 72 ].

Emotion regulation was assessed using the Difficulties with Emotion Regulation Scale (DERS, [ 65 ]), which is a 41-item trait-level measure that assesses across domains of lack of emotional awareness, goals, clarity, strategies, acceptance, and impulse control in managing emotions. Higher scores on the DERS correspond to lower ability to regulate emotion. Alpha reliability has been reported to be >0.90 for the total score, and ≥0.80 for the sub-scores [ 65 ]. In this population, the alpha reliability is 0.92.

Self-control was assessed using the Self-Control Survey-Brief (SCS-B, [ 66 ]), which is a 13-item scale that assesses overall self-control. A higher score on the SCS-B suggests a stronger level of self-control. There are no sub-scores provided by the SCS-B, and the overall SCS-B has been reported to have an alpha reliability >0.80 [ 66 ]. The alpha reliability in this study is 0.85.

The Cornell Medical Index (CMI) was used to assess for participants’ current health. It is a 195-question interview that captures both physical and psychological health symptoms, and has been validated as an indicator for current general health in many studies [ 68 , 73 , 74 ]. A higher score on the CMI suggests more symptoms and worse overall health. The alpha reliability of the total CMI is 0.94. The psychological subscore has an alpha reliability of 0.92, and the biological subscore has a reliability of 0.90.

Cronbach alpha reliabilities for each of the scales described above were obtained using the alpha function in the R psych package [ 75 ].

DNA methylation and epigenetic clock analysis

DNA for epigenetic analysis was collected from whole blood samples as previously described [ 67 ]. Briefly, all samples were profiled using Illumina Infinium HumanMethylation450 Beadchips, which covers 96% of CpG islands and 99% of RefSeq genes. Quality control on these data are as previously published [ 67 ]. They are described in brief below:

Probe QC : To ensure high-quality data, we set a more stringent threshold of P  < 10 –12 . Intensity values showing P  > 10 −12 were set as zero. Additionally, we removed 11,648 probes on sex chromosomes and 36,535 probes within 10 base pairs of single-nucleotide polymorphisms. Finally, a total of 47,791 probes were removed and the remaining 437,722 probes were used for further analysis.

Sample QC : Using a detection P value < 10 –12 , one sample showing a call rate < 98% was excluded from analysis. Five samples showing sex discrepancy between the methylation predicted sex and self-reported sex were also excluded from analysis.

Data processing and normalization : Data processing and normalization were performed using the recently published protocol (Lehne et al., 2015). We first perform background correction and within-array normalization to the original green/red channel intensity data using the preprocessIllumina function in the minfi R package. The processed data were transformed to M/U methylation categories. Next, we separately performed between-array-normalization with the quantile method using the normalizeBetweenArrays function in the limma R package (version 3.26.2) after dividing the data matrix into 6 independent parts: Type I M Green, Type I M Red, Type I U Red, Type I U Green, Type II Red, Type II Green. The normalized data were merged and the beta value at each CpG site was determined.

After obtaining beta values, epigenetic clock analysis was performed as described in Lu et al. using the New Methylation Age Calculator at https://dnamage.genetics.ucla.edu/new [ 51 ]. Data were normalized as per their protocol, and the advanced analysis option was used. We focus on GrimAge acceleration (GAA), which is defined as the residuals of a linear correlation of GrimAge to chronologic age. No effects of array batch on GAA were observed (Supplementary Fig. 1 ).

The analyses herein were performed without accounting for individual variations in cell types. The Houseman method was used to determine cell type proportion [ 76 ], and the inclusion of cell fractions as covariates in a linear model does not impact the primary conclusions of this paper (see Supplementary material).

Statistical analysis

Data organization and analysis were conducted using R 3.6.3 [ 77 ] and RStudio. Linear regressions were first implemented to examine univariate associations between independent and dependent variables. Multivariable linear regressions adjust for demographic (sex, race, years of education, marital status, income) and behavioral (smoking, alcohol use, and BMI) covariates unless otherwise stated. These covariates were selected due to prior work demonstrating a relationship to epigenetic aging. Chronologic age is incorporated into the model as part of the calculation of GAA (the residual of GrimAge regressed upon chronologic age). There was no significant correlation between chronologic age and GAA. Analyses of the relationship between CAI, GAA, psychological and physiologic variables were performed in both the univariate unadjusted model and the multivariate adjusted model accounting for demographic and behavioral measures, but except when the conclusions differ, statistical values in the text represent the multivariate models for simplicity. CAI, DERS, and SCS were mean-centered to address issues of collinearity (particularly regarding individual regression coefficients) when assessing for moderation.

All tests were two-tailed with alpha set at 0.05. Statistical significance in both standard linear regressions and moderation analyses were assessed from t values. R 2 reported on plots represent the simple relationship between the stated variables, while adjusted R 2 values in the text represent the model. Partial η 2 values represent the effect size for the specific variable, with a value >= 0.01 typically indicating a small effect, >= 0.06 a medium effect, and >= 0.14 a large effect [ 78 ]. Wilcoxon signed-rank test was used to compare data between sexes. Mediation analysis was performed to determine if stress impacts GAA via behavioral and physiologic factors. Simple mediation effects were calculated via R using 10,000 simulations without bootstrapping using the mediation package [ 79 ]. Mediation was considered significant if the proportion mediated was greater than 0 with an alpha of 0.05. Serial mediation was calculated via R using the Lavaan package [ 71 ], with an indirect effect defined as the product of the coefficients of the effect of stress on BMI, of BMI on HOMA, and of HOMA on GAA. Assessment of the individual variables’ attributable GrimAge acceleration as well as confidence intervals were calculated using the Emmeans package using unadjusted pairwise comparisons.

Demographics and clinical characteristics

As shown in Table 1 , study participants were healthy and without evidence of medical or psychiatric diseases. The majority were non-smokers (79.6%), social drinkers with low risky alcohol intake screening scores (72.7% of participants have Alcohol Use Disorders Identification Test (AUDIT) < 8, and 91.7% < 15), and were not obese (74.5% of participants have a BMI < 30, 89.2% < 35). Both physical and psychological symptoms assessed on the Cornell Medical Index (CMI, [ 68 ]) were low, with 86% of participants scoring below the typical screening threshold of 30.

Cumulative stress predicts accelerated biological aging as measured by GrimAge

As expected, there was a high association between individuals’ chronologic age and GrimAge (Age: t  = 51.4, P  < 2e−16, adjusted R 2  = 0.856, Fig. 2A ). This relationship is not altered by inclusion of the covariates of smoking, alcohol use, BMI, race, sex, income, and years of education (Age: t  = 49.1, P  < 2e−16, partial η 2  = 0.848; model (GrimAge ~ Age + covariates) adjusted R 2  = 0.912), and this relationship remained significant accounting for cellular fractions (Supplementary Table 1 ). Also, using a univariate linear regression, greater cumulative stress as measured by the total Cumulative Adversity Index (CAI) score significantly predicted higher GAA (CAI: t  = 4.82  P  = 2.00e−6, η 2  = 0.050, adjusted R 2  = 0.0478, Fig. 2B ). While there were significant differences in GAA based on sex ( P  = 1.33e−7), both males (CAI: P  = 3.35e−4, adjusted R 2  = 0.0586) and females (CAI: P  = 3.12e−5, adjusted R 2  = 0.0652) demonstrated similar correlations between stress and GAA. Further analysis showed these results are consistent across CAI subscales, as well as with the Childhood Trauma Questionnaire and several of its subscales (Supplementary Table 2 ).

A Chronologic age significantly predicts GrimAge ( P  < 2e−16). B Cumulative stress total as measured by the CAI (CAI-Total) significantly predicts GAA before ( P  = 2.00e−6) and after accounting for covariates. C Higher insulin resistance (as measured by HOMA) shows a significant positive correlation with GAA before ( P  = 1.11e−8) and after accounting for covariates. D The Cortisol/ACTH ratio is negatively correlated with GAA before accounting for covariates ( P  = 2.39e−6), but not afterward. P and R 2 values in the figure represent simple univariate models (Y ~ X). In the main text, models are adjusted for covariates as stated.

After accounting for the covariates of smoking, alcohol use, BMI, race, sex, income, and years of education, the relationship between GAA and CAI remains significant (CAI: t  = 2.073, P  = 0.0388, partial η 2  = 0.010; model (GAA ~ CAI-total + covariates): adjusted R 2  = 0.3869); individual covariate effects shown in Supplementary Table 3 ). When considered as potential mediators of the relationship between stress and GAA, BMI (proportion mediated = 0.288, P  = 0.0042) and smoking (proportion mediated = 0.443, P  = 0.0030), but not alcohol use (proportion mediated = 0.001, P  = 0.931), show partial mediating effects (Supplementary Table 4 ).

Consistent with the underlying assumption that GAA is related to measures of health, GAA also predicted psychological and physical health symptoms as measured by the CMI (Supplementary Fig. 2A ; total CMI: t  = 3.449, P  = 6.18e−4, adjusted R 2  = 0.024).

Stress-related physiology is associated with GrimAge acceleration

Given the known relationship between cumulative stress and physiology, we assessed the relationship between the stress-related physiologic factors of insulin resistance and HPA-axis signaling and GAA. We found that higher HOMA (a measure of insulin resistance) significantly predicted GAA (Fig. 2C , HOMA: t  = 2.362, P  = 0.0186, partial η 2  = 0.013; model (GAA ~ HOMA + Covariates): adjusted R 2  = 0.389).

We then assessed whether cortisol/ACTH ratio changes impacted GAA. Indeed, low cortisol/ACTH ratio, a measure of adrenal sensitivity, was associated with GAA in a simple univariate model, (Fig. 2D , Cort/ACTH ratio: t  = −4.78, P  = 2.39e−6, η 2  = 0.049, adjusted R 2  = 0.0470), though this becomes non-significant when accounting for covariates (Cort/ACTH ratio: t  = −0.721, P  = 0.471, partial η 2  = 0.001; model (GAA ~ Cort/ACTH + Covariates): adjusted R 2  = 0.3816). We also find a significant association between stress and Cortisol/ACTH ratio (Supplementary Fig. 2B , CAI: t  = −2.146  P  = 0.0324; model (Cort/ACTH ratio ~ CAI + covariates): adjusted R 2  = 0.2197).

Emotion regulation moderates the relationship between stress and GrimAge acceleration directly

We then asked whether the relationship between cumulative stress and epigenetic aging was modulated by characteristics that contribute to an individual’s psychological resilience. We hypothesized that strong emotion regulation abilities would be protective against stress-related accelerated aging. We found that emotion regulation as assessed by the Difficulties in Emotion Regulation Scale (DERS, [ 65 ]) significantly moderated the relationship between GAA and CAI (Fig. 3A , CAI:DERS: F  = 11.22, P  = 8.82e−4, partial η 2  = 0.025; model (GAA ~ CAI X DERS + covariates): adjusted R 2  = 0.4004), such that poor emotion regulation significantly increased the effects of CAI on GAA. There was not a significant difference between males and females in emotion regulation ( P  = 0.0949).

A Individuals with stronger emotion regulation (as measured by lower DERS scores) suffer less GAA at high stress than individuals with poor emotion regulation before (GAA ~ CAI X DERS P  = 9.51e−5; GAA ~ CAI X DERS + Covariates: P  = 8.82e−4) and after accounting for covariates. For panel A, “good” represents the slope at the 25th percentile of DERS, “fair” at the 50th percentile, and “poor” the 75th percentile. B Better self-control (as measured by higher B-SCS scores) is protective against the effects of stress on GAA before accounting for covariates (GAA ~ CAI X SCS P  = 0.00226; GAA ~ CAI X SCS + Covariates: P  = 0.130), but not after including them in the model. C Stronger self-control moderates the relationship between stress and insulin resistance before (HOMA ~ CAI X SCS P  = 0.0115; HOMA ~ CAI X SCS + Covariates P  = 0.00732) and after accounting for covariates. For panels (B) and (C), “good” represents the slope at the 75th percentile of B-SCS, “fair” at the 50th percentile, and “poor” the 25th percentile.

Self-control moderates the association between stress and insulin resistance, which is associated with GrimAge acceleration

We next assessed whether psychological resilience in the form of self-control (as measured via the SCS-B, [ 66 ]) alters the association between cumulative stress and GAA. We found higher self-control is protective against the effects of stress on GAA before accounting for covariates, but the interaction became non-significant when covariates were accounted for (Fig. 3B , CAI:SCS: F  = 2.303, P  = 0.130, partial η 2  = 0.005; model (GAA ~ CAI X SCS + Covariates: adjusted R 2  = 0.3874).

Given the potential interplay between self-control, insulin resistance, and stress, we next asked whether self-control moderated the relationship between stress and HOMA. We observed that, even when covariates are accounted for, self-control moderates the positive relationship between stress and HOMA, with stronger self-control blunting their relationship (Fig. 3C , CAI:SCS: F = 7.263, P  = 0.00732, partial η 2  = 0.017; model (HOMA ~ CAI X SCS + Covariates: adjusted R 2  = 0.2871). Notably, self-control does not moderate the relationship between CAI and BMI (CAI:SCS: F  = 0.679, P  = 0.41). Self-control did not significantly differ between males and females ( P  = 0.0550).

Exploratory mediation analyses suggest stress influences GrimAge via BMI and HOMA

While our ability to draw causative inferences are limited by the cross-sectional nature of our data, we used mediation analyses to explore potential relationships between weight, insulin resistance, and GAA. We hypothesized that the effects of BMI on GAA may be mediated through insulin resistance. Indeed, mediation analysis suggested that a significant portion of the effect of BMI on GAA may be mediated through HOMA (Supplementary Fig. 3A , proportion mediated = 0.247, P  = 0.02). Given these findings, we next asked whether BMI and insulin resistance act sequentially to mediate the effects of stress on GAA. We identified a significant indirect effect, suggesting that stress may affect GAA through increased BMI and elevated insulin resistance (Supplementary Fig. 3B , indirect effect = 0.003; P  = 0.030), though there continues to be a significant direct effect of stress on GAA as well (direct effect = 0.034, P  = 0.009).

Cumulative stress and estimated change in GrimAge

Finally, we sought to identify the comparative contributions of our significant variables to GAA. To do this, we constructed a linear regression model using all demographic covariates (sex, race, marital status, education, income), behavioral covariates (smoking, alcohol, BMI), physiologic factors (HOMA, Cortisol/ACTH ratio), and psychological factors. In this model, we continue to see a significant interaction between stress and emotion regulation in relation to GAA (CAI:DERS t  = 3.424, P  = 0.000677, partial η 2  = 0.027; model (GAA ~ CAI-total X DERS + HOMA + Cort/ACTH ratio + SCS + Covariates): adjusted R 2  = 0.4056). Notably in this model, HOMA ( t  = 2.308, P  = 0.0215, partial η 2  = 0.012), BMI ( t  = 2.641, P  = 0.00857, partial η 2  = 0.016), and smoking ( t  = 10.47, P  < 2e−16, partial η 2  = 0.204) also demonstrate significant effects on GAA. The impact of the cortisol/ACTH ratio on GAA is not significant ( t  = −0.668, P  = 0.504, partial η 2  = 0.001), and its removal from the model does not impact any of the above conclusions.

Using this final linear model, we estimated the changes in GrimAge for each significant variable (Table 2 ) using estimated marginal means [ 80 ]. When comparing the effects of high stress (CAI-total: 75th percentile) versus low stress (CAI-total: 25th percentile) in those with poor emotion regulation (DERS: 75th percentile), stress was associated with half a year of aging independent of all other covariates and physiologic factors. However, when emotion regulation was strong (DERS: 25th percentile), stress did not independently predict GAA. Again comparing 75th versus 25th percentiles, BMI independently was related to an increase of 0.46 years of GrimAge, and HOMA for ¼ of a year. We also identified daily smoking (3.8 years), male sex (1.2 years), self-identifying as Black (1 year), and never having married (0.71 years) as covariates that significantly predicted accelerated GrimAge. When accounting for cellular fractions we see similar results regarding the relationships between stress, emotion regulation, and GAA. However, when accounting for cellular fractions, the associations between GAA and both HOMA and marital status become non-significant (Supplementary Table 5 ). Prior literature [ 51 ] suggests that GrimAge predicts the hazard ratio exponentially (HR = 1.1 GAA ). Thus, each additional year of GAA would be expected to increase the relative risk of death by approximately 10%.

In this study, we report novel findings that cumulative stress is associated with accelerated epigenetic aging in a healthy, young-to-middle-aged community sample, even after adjusting for sex, race, BMI, smoking, alcohol use, income, marital status, and education. Epigenetic aging was measured by GrimAge, a marker which has previously been associated with increased morbidity and mortality and correlates with physical and psychological health symptoms in our study. The relationship between stress and age acceleration is most prominent in those with poor emotion regulation and was related to behavioral factors such as smoking and BMI. Both stress and GAA were associated with changes in insulin resistance, which was moderated via self-control. These results suggest a relationship between stress, physiology, and accelerated aging that is moderated by emotion regulation and self-control. Overall, these findings point to multiple potentially modifiable biobehavioral targets of intervention that may reduce or prevent the deleterious effects of stress on aging and long-term health outcomes.

This study included a generally healthy, young-to-middle-aged community population, yet we still identified a significant relationship between cumulative stress and age acceleration. The population was taking no prescription medications for any medical conditions, nor were they suffering from current mental illnesses, including major depressive disorder or generalized anxiety disorder. The study includes individuals with obesity, as well as a small number of individuals with risky drinking levels as determined by the AUDIT scores. The frequency of these individuals in the sample is generally in line with those in a community population, and thus we included alcohol use and BMI as covariates to account for the impact of these variables on the results. Prior work has demonstrated that GrimAge better predicts mortality than other epigenetic clocks, and GrimAge predicts lifespan more accurately than self-reporting smoking history, demonstrating that GrimAge is a biologically meaningful and potentially clinically useful biomarker for health [ 51 , 64 ]. Our findings are consistent with recent work showing that those with significant trauma histories [ 59 , 81 ] or with diagnoses of mental illnesses, such as Bipolar disorder or MDD, may experience accelerated aging as measured by epigenetic clocks [ 57 , 81 , 82 , 83 , 84 ]. In particular, this study builds on previous findings by Zannas et al that demonstrated a relationship between trauma and epigenetic aging using the Horvath clock. However, to the best of our knowledge this is the first study to investigate the impact of cumulative stress on epigenetic aging in a healthy community sample without significant physical or mental illness. Also it is the first to our knowledge to identify factors that contribute to psychological resilience as potential modulators of such an effect. This opens the possibility that the distinction between the effects of stress on pathologic and non-pathologic samples may be along a continuum. It would be interesting to examine resilience characteristics in the population studied by Zannas et al to determine if there is a limit to the protective effects of psychological resilience. Thus, preventive interventions that decrease stress and improve resilience may be useful for maintaining long-term mental and physical health.

The relationship between stress and epigenetic aging appears to be modulated via specific psychological traits, including emotion regulation and self-control. Those with better emotion regulation and higher levels of self-control were observed to have less age acceleration even at similar levels of stress. Indeed, based on their GAA, our estimates indicate that the relationship between stress and GrimAge is as powerful as BMI, but only for those with poor emotion regulation. As these are skills that may be developed through specific psychological interventions [ 85 ], these results raise the possibility that building emotion regulation skills could result in improvements in epigenetic aging, morbidity, and mortality [ 86 ] for these populations. As this is a cross-sectional study, we are not able to address whether these relationships are causal. These novel cross-sectional findings provide support for potential future research that may assess whether such an intervention could positively impact epigenetic aging and other indices of long-term health outcomes. Other studies could also examine different aspects of resilience, such as cultural or environmental factors that contribute to resilience to determine if they also are protective against the effects of stress on epigenetic age acceleration. Future studies could also explore other physiologic mechanisms through which psychological resilience may influence epigenetic aging. Based on prior work, inflammation could be particularly important for this relationship. In particular, prior studies have found C-reactive protein [ 87 ] and IL-6 [ 88 ] to be related to emotion regulation and measures of health. The work by Gianaros et al suggests that neurologic activity of the dorsal anterior cingulate cortex may be involved as well.

The relationship between cumulative stress, epigenetic aging, and insulin resistance is of particular note given the prominence of insulin signaling in aging-related pathways [ 89 , 90 ], as well as current trials investigating metformin as a potential anti-aging drug [ 33 ]. In association with this body of work, our study suggests insulin resistance as at least one factor through which stress is associated with accelerated aging, even in a healthy population not suffering from diabetes. As this study is limited by its cross-sectional nature, any causal hypotheses regarding interactions between stress, BMI, insulin resistance, and aging will require longitudinal data to draw specific inferences beyond correlative relationships. Longitudinal studies would also enable prospective assessments of stress, which may be less subject to recall bias based on their current context. This study also identifies the cortisol/ACTH ratio as a potential point of connection between stress and epigenetic aging. However, this measure is somewhat limited in that it reflects an acute measure of the HPA axis, and this relationship becomes non-significant with the inclusion of our covariates. Future studies could utilize other, longer-term measures of HPA axis function such as hair cortisol to better characterize the relationship between stress, epigenetic aging, and the HPA axis.

Nonetheless, this study is the first to identify a clear relationship between cumulative stress and GrimAge acceleration in a healthy population, which suggests stress may play a role in accelerated aging even prior to the onset of chronic diseases. Notably, this relationship was strongly moderated by resilience factors, including self-control and emotion regulation. We also identified smoking, BMI, insulin signaling, and potentially HPA signaling as mediators of this response. However, even when accounting for all these factors as well as demographic covariates such as race, cumulative stress continues to demonstrate a significant impact on GAA, suggesting other mechanisms relating stress to aging not identified herein are also present.

Code availability

R scripts utilized for data analysis are available by contacting the authors directly.

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Acknowledgements

The authors would like to acknowledge the Yale Center of Genome Analysis for DNA methylation profiling. Funding for this study is from NIH Common Fund UL1-DE019586 (R.S.), PL1-DA24859 (R.S.), R01-AA013892 (R.S.), NIH R01DA047063 (K.X.), NIH T32MH019961 (Z.M.H.), NIH R25MH071584 (Z.M.H.). These data were presented at the SOBP virtual conference in April 2021 as a poster.

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Zachary M. Harvanek, Ke Xu & Rajita Sinha

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Department of Psychiatry, Connecticut Veteran Healthcare System, West Haven, CT, USA

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Rajita Sinha

Child Study Center, Yale University, New Haven, CT, USA

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Z.M.H., K.X., and R.S. conceptualized the project. Z.M.H. and N.F. performed the data analysis, with recommendations from K.X. and R.S. Z.M.H. produced the figures and tables. Z.M.H. wrote the manuscript, and all authors contributed to and edited the manuscript.

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Harvanek, Z.M., Fogelman, N., Xu, K. et al. Psychological and biological resilience modulates the effects of stress on epigenetic aging. Transl Psychiatry 11 , 601 (2021). https://doi.org/10.1038/s41398-021-01735-7

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Annual Review of Psychology

Volume 72, 2021, review article, stress and health: a review of psychobiological processes.

• Daryl B. O'Connor 1 , Julian F. Thayer 2 , and Kavita Vedhara 3
• View Affiliations Hide Affiliations Affiliations: 1 School of Psychology, University of Leeds, Leeds LS2 9JT, United Kingdom; email: [email protected] 2 Department of Psychological Science, School of Social Ecology, University of California, Irvine, California 92697, USA; email: [email protected] 3 Division of Primary Care, School of Medicine, University of Nottingham, Nottingham NG7 2UH, United Kingdom; email: [email protected]
• Vol. 72:663-688 (Volume publication date January 2021) https://doi.org/10.1146/annurev-psych-062520-122331
• First published as a Review in Advance on September 04, 2020
• Copyright © 2021 by Annual Reviews. All rights reserved

The cumulative science linking stress to negative health outcomes is vast. Stress can affect health directly, through autonomic and neuroendocrine responses, but also indirectly, through changes in health behaviors. In this review, we present a brief overview of ( a ) why we should be interested in stress in the context of health; ( b ) the stress response and allostatic load; ( c ) some of the key biological mechanisms through which stress impacts health, such as by influencing hypothalamic-pituitary-adrenal axis regulation and cortisol dynamics, the autonomic nervous system, and gene expression; and ( d ) evidence of the clinical relevance of stress, exemplified through the risk of infectious diseases. The studies reviewed in this article confirm that stress has an impact on multiple biological systems. Future work ought to consider further the importance of early-life adversity and continue to explore how different biological systems interact in the context of stress and health processes.

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Does the perception that stress affects health matter? The association with health and mortality

Affiliation.

• 1 Department of Population Health Sciences, University of Wisconsin-Madison, Madison, WI 53726, USA.
• PMID: 22201278
• PMCID: PMC3374921
• DOI: 10.1037/a0026743

Objective: This study sought to examine the relationship among the amount of stress, the perception that stress affects health, and health and mortality outcomes in a nationally representative sample of U.S. adults.

Methods: Data from the 1998 National Health Interview Survey were linked to prospective National Death Index mortality data through 2006. Separate logistic regression models were used to examine the factors associated with current health status and psychological distress. Cox proportional hazard models were used to determine the impact of perceiving that stress affects health on all-cause mortality. Each model specifically examined the interaction between the amount of stress and the perception that stress affects health, controlling for sociodemographic, health behavior, and access to health care factors.

Results: 33.7% of nearly 186 million (unweighted n = 28,753) U.S. adults perceived that stress affected their health a lot or to some extent. Both higher levels of reported stress and the perception that stress affects health were independently associated with an increased likelihood of worse health and mental health outcomes. The amount of stress and the perception that stress affects health interacted such that those who reported a lot of stress and that stress impacted their health a lot had a 43% increased risk of premature death (HR = 1.43, 95% CI [1.2, 1.7]).

Conclusions: High amounts of stress and the perception that stress impacts health are each associated with poor health and mental health. Individuals who perceived that stress affects their health and reported a large amount of stress had an increased risk of premature death.

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The Social Psychology of Stress, Health, and Coping

• First Online: 01 January 2013

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• Deborah Carr Ph.D. 4 &
• Debra Umberson Ph.D. 5  

Part of the book series: Handbooks of Sociology and Social Research ((HSSR))

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The study of stress and health is one of the richest areas of research in the social and biomedical sciences. In this chapter, we first describe core concepts in the study of stress, coping, and health. Second, we summarize key theoretical perspectives that frame social psychological research on stress and health. Third, we review the methods and measures used, as well as limitations associated with these approaches. We draw on examples of empirical studies exploring stressors across multiple life domains, including early life adversity, work, family, and environmental strains, and show their impact on a range of physical and mental health outcomes. We also highlight gender, race, SES, and life course differences regarding the prevalence and nature of stress, coping resources, and stress outcomes. We conclude by suggesting directions for future research on stress, health and coping.

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Carr, D., Umberson, D. (2013). The Social Psychology of Stress, Health, and Coping. In: DeLamater, J., Ward, A. (eds) Handbook of Social Psychology. Handbooks of Sociology and Social Research. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-6772-0_16

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Stress Research

“The difficulty in science is often not so much how to make the discovery but rather to know that one has made it.”

– J.D. Bernal

Causes & Sources of Stress

Living conditions, the political climate, financial insecurity, and work issues are some stressors US adults cite as the cause of their stress. Ineffective communications increase work stress to the point of frustration that workers want to quit. These stressors, unfortunately, are not something people can just ignore. Quitting a job would result in debt and financial instability which, in turn, would be added stressors.

• 35% of workers say their boss is a cause of their workplace stress.
• 80% of US workers experience work stress because of ineffective company communications.
• 39% of North American employees report their workload the main source of the work stress.
• 49% of 18 – 24 year olds who report high levels of stress felt comparing themselves to others is a stressor.
• 71% of US adults with private health insurance say the cost of healthcare causes them stress while 53% with public insurance say the same.
• 54% of Americans want to stay informed about the news but following the news causes them stress.
• 42% of US adults cite personal debt as a source of significant stress.
• 1 in 4 American adults say discrimination is a significant source of stress.
• Mass shootings are a significant source of stress across all races; 84% of Hispanic report this, the highest among the races.

Stress Statistics

Two years after the World Health Organization declared COVID-19 a global pandemic, inflation, money issues and the war in Ukraine have pushed U.S. stress to alarming levels, according to polls conducted for the American Psychological Association.

A late-breaking poll, fielded March 1-3 by The Harris Poll on behalf of APA, revealed striking findings, with more adults rating inflation and issues related to the invasion of Ukraine as stressors than any other issue asked about in the 15-year history of the Stress in AmericaTM poll. This comes on top of money stress at the highest recorded level since 2015, according to a broader Stress in America poll fielded last month.

Top sources of stress were the rise in prices of everyday items due to inflation (e.g., gas prices, energy bills, grocery costs, etc.) (cited by 87%), followed by supply chain issues (81%), global uncertainty (81%), Russia’s invasion of Ukraine (80%) and potential retaliation from Russia (e.g., in the form of cyberattacks or nuclear threats) (80%).

Adults also reported separation and conflict as causes for straining and/or ending of relationships. Half of adults (51%, particularly essential workers at 61%) said they have loved ones they have not been able to see in person in the past two years as a result of the COVID-19 pandemic. Strikingly, more than half of all U.S. adults (58%) reported experiencing a relationship strain or end as a result of conflicts related to the COVID-19 pandemic, including canceling events or gatherings due to COVID-19 concerns (29%); difference of opinion over some aspect of vaccines (25%); different views of the pandemic overall (25%); and difference of opinion over mask-wearing (24%).

• 30% of Us adults eat comfort food “more than the usual” when faced with a challenging or stressful event.
• 51% of US adults engage in prayer—a routine activity—when faced with a challenge or stressful situation.
• Coping mechanisms of Gen Z and Millenials experiencing stress in the US 44% of Gen Z and 40% of Millenials sleep in while exercising counts for 14% and 20% respectively.
• 49% of US adults report enduring stressful situations as a coping behavior to handle stress.
• Less than 25% of those with depression worldwide have access to mental health treatments.

Sources: CompareCamp, American Psychological Association

Stress Management Statistics

A look at the stress management techniques employed by US adults to deal with their stress, an overwhelming majority are self-care practices. Though very helpful, it does not address the stressor at the root of the problem. Stress management programs would be beneficial not only for employees but for the company in the long run.

Stress Research from the National Library of Medicine

• Stress and Cardiovascular Disease
• Stress and Cancer
• Stress and Diabetes
• Post Traumatic Stress Disorder
• Stress and Aging
• Stress in Adolenscents
• Stress and Meditation
• Stress and Yoga
• Workplace Stress

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Alternative Therapies in Health and Medicine, A Peer-Reviewed Journal, 2010;16 (4):52-60. PDF version of the complete paper: Cardiac Coherence and PTSD in Combat Veterans

The Effect of a Biofeedback-based Stress Management Tool on Physician Stress: A Randomized Controlled Clinical Trial

Jane B. Lemaire, Jean E. Wallace, Adriane M. Lewin, Jill de Grood, Jeffrey P. Schaefer

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Woody Bedell; Mariette Kaszkin-Bettag, Ph.D.

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How stress affects your health

Stress can be brief, situational, and a positive force motivating performance, but if experienced over an extended period of time it can become chronic stress, which negatively impacts health and well-being.

• Chronic Illness

Stress : We’ve all felt it. Sometimes stress can be a positive force, motivating you to perform well at your piano recital or job interview. But often—like when you’re stuck in traffic—it’s a negative force. If you experience stress over a prolonged period of time, it could become chronic—unless you take action.

A natural reaction

Have you ever found yourself with sweaty hands on a first date or felt your heart pound during a scary movie? Then you know you can feel stress in both your mind and body.

This automatic response developed in our ancient ancestors as a way to protect them from predators and other threats. Faced with danger, the body kicks into gear, flooding the body with stress hormones such as adrenaline and cortisol that elevate your heart rate, increase your blood pressure, boost your energy, and prepare you to deal with the problem.

These days, you’re not likely to face the threat of being eaten. But you probably do confront multiple challenges every day, such as meeting deadlines, paying bills, and juggling childcare that make your body react the same way. As a result, your body’s natural alarm system—the “fight or flight” response—may be stuck in the on position. And that can have serious consequences for your health.

Pressure points

Even short-lived, minor stress can have an impact. You might get a stomachache before you have to give a presentation, for example. More major acute stress, whether caused by a fight with your spouse or an event like an earthquake or terrorist attack, can have an even bigger impact.

Repeated acute stress may also contribute to inflammation in the circulatory system , particularly in the coronary arteries, and this is one pathway that is thought to tie stress to a heart attack. It also appears that how a person responds to stress can affect cholesterol levels.

Chronic stress

When stress starts interfering with your ability to live a normal life for an extended period, it becomes even more dangerous. The longer the stress lasts, the worse it is for both your mind and body. You might feel fatigued, unable to concentrate, or irritable for no good reason, for example. But chronic stress causes wear and tear on your body, too.

The long-term activation of the stress response system and the overexposure to cortisol and other stress hormones that come with it can disrupt almost all of your body's processes. This can put you at increased risk for a variety of physical and mental health problems, including anxiety, depression, digestive issues, headaches, muscle tension and pain, heart disease, heart attack, high blood pressure, stroke, sleep problems, weight gain, and memory and concentration impairment.

Chronic stress may also cause disease, either because of changes in your body or the overeating, smoking, and other bad habits people use to cope with stress. Job strain—high demands coupled with low decision-making latitude—is associated with increased risk of coronary disease , for example. Other forms of chronic stress, such as depression and low levels of social support, have also been implicated in increased cardiovascular risk.

Chronic stress also  suppresses the body's immune system , making it harder to recover from illnesses.

What you can do

Reducing your stress levels can not only make you feel better right now, but may also protect your health long-term. Several research studies have demonstrated, for example, that interventions to improve psychological health can have a beneficial impact on cardiovascular health . As a result,  researchers recommend boosting your positive affect—feelings like happiness, joy, contentment, and enthusiasm—by making time for enjoyable activities every day.

Other strategies for reducing stress include:

• Identify what’s causing stress. Monitor your state of mind throughout the day. If you feel stressed, write down the cause, your thoughts, and your mood. Once you know what’s bothering you, develop a plan for addressing it. That might mean setting more reasonable expectations for yourself and others or asking for help with household responsibilities, job assignments, or other tasks. List all your commitments, assess your priorities, and then eliminate any tasks that are not absolutely essential.
• Build strong relationships. Relationships can be a source of stress. Research has found that negative, hostile reactions with your spouse cause immediate changes in stress-sensitive hormones, for example. But relationships can also serve as stress buffers. Reach out to family members or close friends and let them know you’re having a tough time. They may be able to offer practical assistance and support, useful ideas, or just a fresh perspective as you begin to tackle whatever’s causing your stress.
• Walk away when you’re angry. Before you react, take time to regroup by counting to 10. Then reconsider. Walking or other physical activities can also help you work off steam. Plus, exercise increases the production of endorphins, your body’s natural mood booster. Commit to a daily walk or other form of exercise—a small step that can make a big difference in reducing stress levels.
• Rest your mind. To help ensure you get the recommended seven or eight hours of shut-eye, cut back on caffeine, remove distractions such as television or computers from your bedroom, and go to bed at the same time each night. Research shows that activities like yoga and relaxation exercises not only help reduce stress, but also boost immune functioning .
• Get help. If you continue to feel overwhelmed, consult with a psychologist or other licensed mental health professional who can help you learn how to manage stress effectively. They can help you identify situations or behaviors that contribute to your chronic stress and then develop an action plan for changing them.

Recommended Reading

Related reading

• Stress effects on the body
• Stress in America

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• Open access
• Published: 15 July 2024

Enhancing psychological well-being in college students: the mediating role of perceived social support and resilience in coping styles

• Shihong Dong 1 ,
• Huaiju Ge 1 ,
• Wenyu Su 1 ,
• Weimin Guan 1 ,
• Xinquan Li 1 ,
• Yan Liu 2 ,
• Qing Yu 1 ,
• Yuantao Qi 2 ,
• Huiqing Zhang 3 &
• Guifeng Ma 1  

BMC Psychology volume  12 , Article number:  393 ( 2024 ) Cite this article

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Metrics details

The prevalence of depression among college students is higher than that of the general population. Although a growing body of research suggests that depression in college students and their potential risk factors, few studies have focused on the correlation between depression and risk factors. This study aims to explore the mediating role of perceived social support and resilience in the relationship between trait coping styles and depression among college students.

A total of 1262 college students completed questionnaires including the Trait Coping Styles Questionnaire (TCSQ), the Patient Health Questionnaire-9 (PHQ-9), the Perceived Social Support Scale (PSSS), and the Resilience Scale-14 (RS-14). Common method bias tests and spearman were conducted, then regressions and bootstrap tests were used to examine the mediating effects.

In college students, there was a negative correlation between perceived control PC and depression, with a significant direct predictive effect on depression ( β = -0.067, P  < 0.01); in contrast, negative control NC showed the opposite relationship ( β  = 0.057, P  < 0.01). PC significantly positively predicted perceived social support ( β  = 0.575, P  < 0.01) and psychological resilience ( β  = 1.363, P  < 0.01); conversely, NC exerted a significant negative impact. Perceived social support could positively predict psychological resilience ( β  = 0.303, P  < 0.01), and both factors had a significant negative predictive effect on depression. Additionally, Perceived social support and resilience played a significant mediating role in the relationship between trait coping styles and depression among college students, with three mediating paths: PC/NC → perceived social support → depression among college students (-0.049/0.033), PC/NC→ resilience → depression among college students (-0.122/-0.021), and PC/NC → perceived social support → resilience → depression among college students (-0.016/0.026).

The results indicate that trait coping styles among college students not only directly predict lower depression but also indirectly influence them through perceived social support and resilience. This suggests that guiding students to confront and solve problems can alleviate their depression.

Peer Review reports

Introduction

Depression is a complex mental disorder, characterized by cognitive, affective and psychosocial symptoms [ 1 , 2 ]. It is projected that by 2030, depression will rank first globally in terms of years lived with disability [ 3 , 4 ]. Depression is also one of the most common mental health issues among contemporary college students [ 5 , 6 ]. Studies have shown that the detection rate of depression among Chinese college students ranges from 23–34% [ 7 , 8 ]. Compared to non-student populations, college students have a higher prevalence of depression, and this rate seems to be increasing [ 9 ]. This vulnerable group of college students is in a unique developmental stage, facing pressures not only from life but also from the demands of academic coursework and complex interpersonal relationships, making the factors influencing depression among college students, particularly complex [ 9 , 10 ].

Exploring the mechanisms by which influencing factors affect the occurrence of depression in college students is of significant importance for early prevention [ 11 ]. Research has demonstrated that trait coping style is one of the risk factors for depression among college students. Trait coping refers to the strategies individuals employ in challenging situations, categorized into positive coping and negative coping [ 12 , 13 ]. Positive coping focuses on taking effective action and changing stressful situations, typically associated with problem-solving behaviors and regulation of positive emotions, which can help reduce the incidence of depression [ 14 ]. Conversely, negative coping is a passive approach centered around negative evaluations and emotional expression, often involving avoiding problems and social isolation, which is more likely to lead to the development of depression [ 14 ]. Research indicates that positive coping strategies are inversely correlated with depression, serving as protective factors against depression. Conversely, negative coping strategies are positively associated with depression, acting as risk factors for its onset [ 15 ].

Perceived social support refers to an individual’s subjective emotional state of feeling supported and understood by family, friends, and other sources [ 16 , 17 ]. Prior studies have shown that perceived social support can directly impact an individual’s level of depression and also have indirect effects [ 18 ]. The data indicate that social support can significantly influence coping mechanisms, with groups having higher levels of social support tended to respond more actively and positively to stress from various sources [ 19 ]. Social support is considered an important mediating factor in determining the relationship between psychological stress and health, representing an emotional experience where individuals feel supported, respected, and understood [ 16 ]. The relationship between individuals’ coping strategies and depression may be influenced by the mediating role of perceived social support [ 20 , 21 ]. In addition to this, resilience plays a role in all three.Resilience refers to the ability to adapt to stress and adversity, enhancing an individual’s psychological well-being [ 22 ]. Both coping styles and perceived social support significantly predict resilience positively [ 23 ]. For individuals with strong resilience, possessing a high level of adaptive capacity can mitigate the negative effects of stress on individuals, thereby enhancing their mental health.

In recent years, there has been a growing body of research on the prevalence of depression among college students. However, the rates of depression vary in different environments, and there is limited research on the mechanisms through which trait coping styles, perceived social support, and resilience impact depression. Therefore, this study aims to investigate the mechanisms through which positive coping styles(PC), negative coping styles(NC), perceived social support, and resilience influence depression among college students. Additionally, it seeks to analyze the mediating roles of perceived social support and resilience in this context. The goal is to provide insights into the reasons behind depression among college students under different coping strategies, aiding in timely psychological adjustment to promote the comprehensive development of the mental and physical well-being of college students.

The following assumptions were made:

Hypothesis 1

PC has a significant negative predictive effect on depression among college students. NC has a significant positive predictive effect on depression among college students.

Hypothesis 2

Perceived social support serves as a mediator between PC/NC and depression among college students.

Hypothesis 3

Resilience mediates the relationship between PC/NC and depression among college students.

Hypothesis 4

Perceived social support and psychological resilience mediate the relationship between PC/NC and depression among college students in a serial manner.

Data and methods

This is a cross-sectional study that was conducted from January through February 2024. Using the Questionnaire Star network platform, we presented the questionnaire online, which was openly accessible to college students at a university in Shandong. The average time to complete the survey was 15 min. Participation was voluntary and students were informed about the purpose of the study. Confidentiality was assured and questionnaires were submitted anonymously. A total of 1267 enrolled college students participated in the questionnaire survey. After excluding invalid questionnaires, 1262 valid questionnaires were included, resulting in an effective rate of 99.57%.

Trait coping style questionnaire

The Trait Coping Style Questionnaire (TCSQ) [ 24 ], developed by Qianjin Jiang, was utilized to assess the trait coping styles of college students. This questionnaire reflects the participants’ approaches to coping with situations, comprising a total of 20 items. It consists of two dimensions: negative coping style and positive coping style, each with 10 items. Using a 5-point Likert scale ranging from “definitely not” to “definitely yes,” scores were assigned from 1.00 to 5.00. The Cronbach’s α coefficient for negative coping style was 0.906 and for positive coping style was 0.786 in this study.

Depression scale

The Patient Health Questionnaire-9 (PHQ-9) [ 25 ] was used to assess depressive symptoms in the past two weeks. This scale consists of 9 items rated on a 4-point Likert scale ranging from “not at all” to “nearly every day,” with scores from 0 to 3. The total score ranges from 0 to 27, with higher scores indicating more severe depressive symptoms. The Cronbach’s α coefficient for this scale in the current study was 0.884.

Perceived Social Support Scale

The Perception Social Support Scale (PSSS) was compiled by James A.Blumenthal in 1987 and later translated and modified by Qianjin Jiang to form the Chinese version of the Zimetm Perception Social Support Scale (PSSS) [ 26 , 27 ]. PSSS comprises 12 self-assessment items rated on a 7-point Likert scale. The scale includes three dimensions: family support (items 3, 4, 8, 11), friend support (items 6, 7, 9, 12), and other support (items 1, 2, 5, 10), with a total score ranging from 12 to 84. Scores of 12–36 indicate low support, 37–60 indicate moderate support, and 61–84 indicate high support. The Cronbach’s α for this scale in the current survey was 0.968.

Resilience scale

The Resilience Scale (RS-14) [ 28 ] Chinese version consists of 14 items, each rated on a 7-point Likert scale from “not at all” to “completely,” with scores ranging from 1 to 7. The total score ranges from 14 to 98, with higher scores indicating better resilience. The Cronbach’s α for this scale in the current study was 0.925.

Statistical analysis

Data were organized and analyzed using SPSS 26.0 software. Confirmatory factor analysis was first conducted on the questionnaires. Descriptive analysis was then performed on the scores of each scale. Spearman was used to examine the relationships between trait coping styles, perceived social support, resilience, and depression. Mediation analysis was carried out using the SPSS PROCESS macro 3.4.1 software model 6 developed by Hayes, specifically designed for testing complex models. Model 6 was applied for two mediating variables, followed by the bias-corrected percentile Bootstrap method with 5000 resamples to estimate the 95% confidence interval of the mediation effect. A significant mediation effect was indicated if the 95% confidence interval (CI) did not include zero. A significance level of P  < 0.05 was considered statistically significant.

Examination of common method bias

Systematic errors in indicator data results caused by the same data collection method or measurement environment can typically be assessed through the Harman single-factor test on 55 items in the dataset to examine common method bias. The results indicated that there were 7 factors with eigenvalues greater than 1, and the variance explained by the first factor was 34.84%, which was below the critical threshold of 40%. Therefore, this study may not have a significant common method bias.

Descriptive statistics and correlation analysis

The mean scores, standard deviations, and correlations of each variable are presented in Table  1 . PC ( r = -0.326, P  < 0.01), resilience ( r =-0.445, P  < 0.01), and perceived social support ( r =-0.405, P  < 0.01) were negatively correlated with depression. PC ( r  = 0.336, P  < 0.01) and resilience ( r  = 0.469, P  < 0.01) were significantly positively correlated with perceived social support. PC was significantly positively correlated with resilience( r  = 0.635, P  < 0.01). NC was significantly positively correlated with depression( r  = 0.322, P  < 0.01) and PC( r  = 0.146, P  < 0.01). NC was significantly negatively correlated with perceived social support ( r =-0.325, P  < 0.01).

Analysis of chain mediation effects

The chain mediation model was validated using SPSS PROCESS Model 6. Trait coping styles were considered as the independent variable, while depression among college students was treated as the dependent variable. Perceived social support and resilience were included as the mediating variables, culminating in the path model depicted in Figs.  1 and 2 .

The results of the regression analysis, as shown in Table  2 , indicated that PC could significantly predict perceived social support in a positive direction ( β  = 0.575, P  < 0.01). Both PC ( β  = 1.363, P  < 0.01) and perceived social support ( β  = 0.303, P  < 0.01) had significant positive predictive effects on psychological resilience. When simultaneously predicting depression using PC, perceived social support, and psychological resilience, all three exhibited significant negative predictive effects ( β = -0.067, β = -0.085, β = -0.090, P  < 0.01). NC could significantly predict perceived social support in a negative direction ( β = -0.457, P  < 0.01). When NC ( β  = 0.191, P  < 0.01) and perceived social support ( β  = 0.508, P  < 0.01) jointly predict psychological resilience, they both had significant positive predictive effects. When simultaneously predicting depression using NC, perceived social support, and psychological resilience, NC ( β  = 0.057, P  < 0.01) showed a significant positive predictive effect, while perceived social support ( β = -0.072, P  < 0.01) and psychological resilience ( β = -0.112, P  < 0.01) demonstrated significant negative predictive effects.

Further employing the Bootstrap sampling method, with 5000 repetitions, the significance of the mediating effects and chain mediation effects between trait coping styles and depression among college students was examined. The results indicated that the direct effects of PC/NC on depression were significant, with direct impact values of -0.067/0.057 (26.38%/60.00%). Perceived social support and psychological resilience mediated the relationship between PC/NC and depression, with this mediation encompassing three pathways: the separate mediating effect of perceived social support, with effect values of -0.049 and 0.033 respectively; the separate mediating effect of resilience, with effect values of -0.122 and − 0.021 respectively; and the serial mediating effect from perceived social support to resilience, with effect values of -0.016, -0.021, and 0.026. The 95% confidence intervals for all pathways did not include 0, indicating significant indirect effects. Therefore, the total indirect effects were − 0.187 (73.62%) and 0.038 (40.00%), showing that PC had a weaker direct effect on depression compared to NC, but a stronger indirect effect. This was illustrated in Table  3 .

Chain mediation model of perceived social support and resilience between PC and depression. ** p  < 0.01

Chain mediation model of perceived social support and resilience between NC and depression. ** p  < 0.01

Previous research on the associations and specific pathways among depressive symptoms, trait coping styles, perceived social support, and resilience in college students has been limited. Therefore, this study utilized a chain mediation model to examine how trait coping styles, perceived social support, and resilience influence depressive symptoms in college students. The results indicate that perceived social support and resilience not only act as separate mediators between PC/NC and depression but also exhibit a chain mediation effect.

Mechanisms of the impact of PC/NC on depression in college students

This study found that trait coping styles can significantly and negatively predict depressive symptoms in college students directly, consistent with previous research [ 29 ]. In recent years, amidst the backdrop of the pandemic, numerous studies have emerged domestically and internationally focusing on college students’ mental health from the perspective of crisis event coping [ 30 ]. These studies have predominantly concentrated on trait coping styles as a mediating variable in predicting the occurrence of depressive symptoms, with fewer studies examining the direct impact of trait coping styles on depressive symptoms. College students, being in a unique developmental stage, face challenges from various aspects and bear the pressures of academic coursework, interpersonal relationships, and future employment. Research indicates that trait coping styles are a key factor influencing mental health [ 31 ]. Implementing healthy coping techniques and interventions can help individuals overcome negative emotions caused by stress, which is an adaptive coping mechanism that assists college students in facing stress and enhancing problem-solving abilities, thus preventing or reducing the occurrence of depression. Conversely, adopting passive or avoidant coping strategies, leading to inadequate resolution of stress events, can increase psychological stress [ 14 ], thereby exerting a negative impact on the mental health of college students [ 32 ]. Therefore, trait coping styles play a negative predictive role in depressive symptoms among college students. PC was a positive predictor of depression and NC was a negative predictor of depression. This is consistent with previous studies [ 24 , 29 ].

Separate mediating effects of perceived social support and resilience

After introducing perceived social support and resilience as two mediating variables, the predictive effect of PC/NC on depressive symptoms in college students remained significant. The results show that PC can positively predict perceived social support, and NC is the opposite, consistent with previous research [ 33 ]. Trait coping styles are an important predictive factor in altering college students’ perceptions of social support and the occurrence of depression. Individuals who adopt negative coping styles tend to perceive relatively less external support. Some argue that social support plays a reverse predictive role in trait coping styles; the more social support college students receive and feel, the more likely they are to actively adopt positive coping strategies to alleviate stress, potentially due to variations in study subjects and time [ 34 ]. In this pathway, perceived social support can significantly and negatively predict depressive symptoms, aligning with previous research findings [ 35 ]. Perceived social support is considered a crucial mediating factor influencing mental health, referring to an individual’s ability to perceive support and understanding from family, friends, and others. College students with lower levels of perceived social support often feel neglected and undervalued, leading to negative evaluations and self-doubt, making them more susceptible to depression. PC/NC and perceived social support can interact and influence the occurrence of depressive symptoms in college students [ 16 ].

Research indicates that PC can significantly and positively predict resilience, with an indirect effect value of 48.03%.In this pathway, the mediating effect of resilience is more pronounced, consistent with previous studies [ 36 ]. There is a close connection between resilience and coping styles; college students who adopt positive coping strategies often exhibit stronger psychological resilience, being more willing to confront issues and seek help from others to solve problems. When facing pressures such as academic challenges, they approach them with a positive mindset, overcoming adversity [ 37 ]. It is believed that adopting positive coping strategies to address problems can enhance college students’ levels of psychological resilience [ 10 , 38 ]. Resilience can significantly and negatively predict depressive symptoms. depressive symptoms, College students with higher levels of resilience tend to define the severity of events less severely when stress events occur, resulting in lower psychological burdens and reduced likelihood of experiencing depressive symptoms [ 10 ]. Additionally, when facing setbacks or stress, individuals who adopt positive coping strategies actively utilize internal and external protective factors to combat current difficulties and pressures, and employ effective emotional control to mitigate the impact, thereby enhancing their levels of psychological resilience and reducing the occurrence of depression.

Chain mediation effect of perceived social support and psychological resilience

This study elucidates that PC/NC perceived social support, and psychological resilience are independent factors influencing depressive symptoms in college students, with perceived social support and psychological resilience playing a mediating role between coping styles and depressive symptoms. The share of total indirect effect values is 73.62% and 40.00%, respectively, with the third chain path accounting for 6.30% and 27.37% of the total effect ratio, respectively. This confirms the existence of this chain mediation effect, although the chain mediation effect is not as pronounced as the individual mediation effects. Positive coping styles not only directly negatively predict depressive symptoms in college students but also exert an indirect influence on depressive symptoms through perceived social support and psychological resilience. Likewise, negative coping styles not only directly positively predict depressive symptoms in college students but also have an indirect impact on depressive symptoms through perceived social support and psychological resilience, thus demonstrating the value and significance of these two mediating variables in reducing the occurrence of depressive symptoms in college students.

Initially, adopting positive coping styles and being able to perceive social support are crucial factors influencing psychological resilience in college students. There exists a relatively stable systemic relationship between students’ social support and psychological resilience, confirming that social support can enhance individuals’ levels of psychological resilience [ 16 ]. Furthermore, coping styles can affect the occurrence of depressive symptoms from both internal and external perspectives. This is because the social support perceived by college students includes not only tangible social support resources but also their subjective perception of social support, with these two factors constituting external and internal protective factors of psychological resilience [ 39 ]. Positive coping and effective adaptation can enhance college students’ perception of social support, enabling them to mobilize personal, familial, and societal protective factors better when facing various life challenges, thereby mitigating or eliminating difficulties and suppressing the onset of depressive symptoms, whereas negative coping styles yield the opposite effect. The chain mediation proposed in this study integrates the research on perceived social support, psychological resilience, and depressive symptoms in college students, facilitating a more comprehensive understanding of the internal mechanisms through which coping styles influence depressive symptoms in college students. This holds significance in advocating for a proactive attitude in college students to confront and resolve difficulties and in increasing attention to the mental health of college students.

Limitations, strengths and future research

The findings of this study hold theoretical value and practical implications, offering a reference basis for improving the mental health of college students. However, there are certain limitations to consider. Firstly, the survey in this study was conducted through self-reporting, which may introduce certain biases. Future research could explore data collection through various methods. Secondly, this study employed a cross-sectional design to investigate the impact of trait coping styles, on depression among college students and its potential mechanisms. However, this research approach does not allow for causal inferences between variables, and further validation of the study’s conclusions could be achieved through longitudinal or experimental research.

In summary, this study aims to improve the mental health of college students by examining how their coping styles, along with their perceived social support and psychological resilience, affect depressive symptoms. The research analyzes the connections between these factors and suggests that positive coping styles may help prevent depression. However, the study has its limitations and future research should use long-term experiments to better understand these relationships. Since depression in college students can be influenced by many factors, future studies should also consider additional variables and use a mix of experimental and longitudinal approaches to more clearly understand how to reduce depression in this group.

Data availability

The datasets used and analysed during the current study are available from the corresponding author upon reasonable request.

Abbreviations

Patient Health Questionnaire-9

Trait Coping Style Questionnaire

Positive coping styles

Negative coping styles

Resilience Scale

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Acknowledgements

We would like to provide our extreme thanks and appreciation to all students who participated in our study.

This work was financially supported by the National Food Safety Risk Center Joint Research Program [grant number (LH2022GG06)] and the Weifang Medical College Teaching Reform Program (2023YBC008).

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SD and GM conceived and designed the study. HG, WS, WG, and YL undertook the data collection and analysis. SD, QY, YQ, XLand HZ drafted the manuscript. SD and GM reviewed the manuscript. The authors read and approved the final manuscript.

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Dong, S., Ge, H., Su, W. et al. Enhancing psychological well-being in college students: the mediating role of perceived social support and resilience in coping styles. BMC Psychol 12 , 393 (2024). https://doi.org/10.1186/s40359-024-01902-7

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ORIGINAL RESEARCH article

Optimism and mental health in college students: the mediating role of sleep quality and stress.

• 1 School of Nursing, Zuckerberg College of Health Sciences, University of Massachusetts Lowell, Lowell, MA, United States
• 2 School of Pharmacy, College of Medicine, National Taiwan University, Taipei, Taiwan
• 3 Institute of Biomedical Sciences, Academia Sinica, Taipei, Taiwan
• 4 Institute of Statistical Science, Academia Sinica, Taipei, Taiwan
• 5 Department of Psychology, College of Fine Arts, Humanities & Social Sciences, University of Massachusetts Lowell, Lowell, MA, United States

Objective: College students showed a high prevalence of stress, anxiety, and depression, with medical and nursing students experiencing particularly elevated levels of mental health challenges.

Optimism significantly influences overall well-being by promoting a healthy lifestyle and cognitive responses. However, the association of optimism with sleep quality, stress, and mental health in college students remains unexplored. This study aimed to (1) explore the associations of optimism with sleep quality, stress, and mental health and (2) ascertain whether sleep quality and stress mediate the association between optimism and mental health among college students.

Methods: A cross-sectional study was conducted using online surveys with students from health science majors at a public university in the northeast United States from September to December 2022. A total of 222 students participated in the study, providing data on sociodemographics, optimism, sleep quality, stress, anxiety, and depression. Parallel and serial mediation models were utilized to examine the potential mediating roles of sleep quality and stress in the association between optimism and mental health.

Results: The study found that optimism influences anxiety and depression through both direct and indirect pathways. In line with predictions, the parallel mediation analysis revealed that the impact of optimism on anxiety (β total  = −0.598, 95% confident interval [CI]: −0.778 to −0.392) and depression (β total  = −0.724, 95% CI: −0.919 to −0.519) was mediated by stress and sleep quality. Furthermore, the serial mediation models revealed that stress and sleep quality co-mediated the relationship betweenoptimism and anxiety (indirect effect [IE] = −0.074, 95% CI: −0.135 to −0.029) or depression (IE = −0.084, 95% CI: −0.142 to −0.036) in a sequential manner.

Conclusion: Optimism was negatively correlated with poor sleep quality, stress, anxiety, and depression. Enhanced optimism was linked to high sleep quality and less stress, anxiety, and depression. These insights emphasize the potential for school-based optimism interventions to improve sleep quality, ameliorate stress-related concerns, and alleviate mental health challenges in college students.

1 Introduction

Stress, anxiety, and depression are increasingly problematic in our society, with severe consequences for both physical and mental health ( Blanco et al., 2021 ). Beyond personal health implications, mental health problems impose a substantial economic burden on society, projected to reach around 16 trillion US dollars for the global economy by 2030 ( Trautmann et al., 2016 ; Patel et al., 2018 ). College students are a particularly vulnerable population, exhibiting a higher prevalence of mental health issues compared to the general population ( Mofatteh, 2021 ). According to the National College Health Assessment from the American College Health Association, over 76% of undergraduates reported moderate or severe psychological distress ( American College Health Association, 2022 ). Furthermore, in the National Healthy Minds Study, 41% of college students disclosed their experience of major and moderate depression and 36% of them reported moderate or severe anxiety ( Healthy Minds Network, 2023 ). Poor mental health in college students may be associated with a range of problems, such as impaired academic performance, heavy alcohol consumption, substance abuse, low self-esteem, and suicide attempts ( Hysenbegasi et al., 2005 ; Skidmore et al., 2016 ; Hiçdurmaz et al., 2017 ; Liu et al., 2019 ; Sæther et al., 2019 ). Given these costly outcomes for students, universities, and society, the mental health of undergraduate students is not only a crucial public health concern but also a pressing research priority.

College students’ poor mental health may stem from various challenges, including concerns regarding academic performance, pressure to succeed, and post-graduation plans ( Beiter et al., 2015 ). Particularly, students in health science fields such as medicine and nursing often grapple with heightened levels of anxiety and depression than other non-medical peers due to their heavy workload, including theoretical responsibilities and hands-on patient care ( Mofatteh, 2021 ). In addition, the quality of sleep has been closely linked to mental health issues, with poor sleep quality exacerbating the susceptibility of college nursing students to mental illnesses, including anxiety and depression ( Zhang et al., 2018 ). Students suffering from poor sleep quality often confronted high levels of perceived stress, which in turn precipitated anxiety or depression symptoms ( Doane et al., 2015 ; Zhang et al., 2018 ), impaired psychosocial functioning ( Tavernier and Willoughby, 2014 ), and negatively impacted academic performance ( Okano et al., 2019 ). Furthermore, individuals with heightened stress were prone to develop concurrent anxiety ( Ghorbani et al., 2008 ), exhibit compromised sleep quality ( Liu et al., 2017 ), employ less healthy coping strategies ( Evans et al., 2015 ), and thus manifest depressive symptoms.

Optimism, defined as harboring positive expectations for the future ( Scheier and Carver, 1985 ), is a positive personality trait contributing to positive psychology ( Seligman and Csikszentmihalyi, 2014 ). Optimism is particularly vital during periods of uncertainty ( Carver et al., 1989 ), as demonstrated by its role as a protective factor against fear, stress, anxiety, and depression during the COVID-19 pandemic ( Vos et al., 2021 ). Heightened levels of optimism are associated with lower levels of anxiety and improved academic achievement among college students ( Singh and Jha, 2013 ), as well as better coping skills in response to stress ( Solberg Nes et al., 2009 ). Recent studies have demonstrated that optimism can promote positive emotions and higher life satisfaction, particularly during the COVID-19 pandemic ( Martinez et al., 2022 ). On the contrary, some studies suggested that lower levels of optimism and hope are aligned with decreased subjective well-being among young adults facing high levels of stress due to the pandemic ( Genç and Arslan, 2021 ).

In the present, with the majority of studies focusing on the psychological health problems in college students, the mechanisms implicated in the links among optimism, sleep quality, stress, and mental health remain unclear. Therefore, this study aimed to Blanco et al. (2021) explore the associations of optimism with sleep quality, stress, and mental health; and Trautmann et al. (2016) ascertain whether sleep and stress mediate the connection between optimism and mental health, providing valuable insights into a promising intervention strategy regarding elevating optimism levels, thereby bolstering students’ mental well-being as they confront adversity.

2 Materials and methods

2.1 study design and setting.

This study was a quantitative cross-sectional study conducted among 222 undergraduate students in health science majors at a public university in the northeast United States in the fall of 2022. We employed a non-probability purposive sampling method to administer online surveys to all freshmen, sophomore, junior, and senior health science students.

2.2 Inclusion and exclusion criteria

We included undergraduate students who were: Blanco et al. (2021) aged 18 years or older; Trautmann et al. (2016) currently enrolled full-time; and Patel et al. (2018) having access to the internet and capacity in computer typing. Students who were unable to provide informed consent were excluded.

2.3 Sample size

A minimum correlation coefficient of 0.3 was assumed for variables including optimism, sleep quality, stress, and mental health, as supported by previous studies ( Cohen, 2013 ; Zhang et al., 2018 ). With an aim to achieve 80% statistical power and a type I error rate of 0.05, a power analysis suggested a sample size of at least 67 participants for this study.

2.4 Measurements

2.4.1 revised life orientation test.

The LOT-R was employed to measure the level of optimism ( Scheier et al., 1994 ). Consisting of 10 items, the LOT-R presents with 3 items, respectively, oriented in positive and negative directions, along with 4 filler items. Respondents are requested to indicate the extent to which they agree with each item on a 5-point Likert scale that ranges from strongly disagree to strongly agree. The total score is from 0 to 24, and the higher the LOT-R score, the higher the levels of optimism. The acceptable internal consistency of the LOT-R was reported as Cronbach’s α of 0.82 ( Shifren and Anzaldi, 2018 ) in the stroke population, and stability (test–retest reliability) over a 4-month period in college students was reported as r  = 0.79 ( Scheier et al., 1994 ). A scoring range of 0 to 13 points indicates a low level of optimism, whereas a range of 14 to 18 points suggests moderate optimism, and 19 to 24 points signifies a high level of optimism ( Przybyszowski et al., 2022 ). In this study, the scale demonstrated good reliability with a Cronbach’s alpha of 0.79.

2.4.2 Pittsburgh sleep quality index

The PSQI was used to assess the sleep quality in the previous month ( Buysse et al., 1989 ). Comprising 19 questions concerning sleep habits, the PSQI involves 7 components: sleep duration, sleep latency, sleep disturbance, daytime dysfunction, use of sleeping medications, habitual sleep efficiency, and overall sleep quality. This instrument has been previously validated for college students ( Lemma et al., 2014 ). Each component is scored on a scale of 0 to 3, yielding a global sleep quality score ranging from 0 to 21. Participants with PSQI scores exceeding 5 were identified as experiencing poor sleep quality ( Buysse et al., 1989 ). The PSQI in this study demonstrated good reliability with a Cronbach’s alpha coefficient of 0.63.

2.4.3 Perceived stress scale

The PSS is a 10-item questionnaire for evaluating the perception of stress during the past month ( Cohen et al., 1983 ). Participants were asked to rate the frequency of experiencing certain feelings and thoughts using a 5-point Likert scale. With 4 positively stated items reverse scored (e.g., 0 = 4, 1 = 3, 2 = 2, 3 = 1, and 4 = 0), the total PSS scores range from 0 to 40 and the higher PSS scores indicate the higher level of stress. The scale demonstrated good internal consistency (Cronbach’s alpha) previously in undergraduate students ( Lin et al., 2020 ). The Cronbach’s alpha in this study was 0.81.

2.4.4 General anxiety disorder-7

The GAD-7 is a self-administered 7-item questionnaire, designed to assess the symptom severity of anxiety ( Spitzer et al., 2006 ). The GAD-7 is an acceptable questionnaire, with Cronbach’s alpha of 0.89 in general populations ( Lowe et al., 2008 ). Each item is scored on a 4-point Likert-type scale, ranging from 0 to 3, and summed up with a final score ranging from 0 to 21. The scores of 5, 10, and 15 are cutoff points indicating mild, moderate, and severe levels of anxiety ( Spitzer et al., 2006 ). The GAD-7 scale demonstrated excellent reliability with a Cronbach’s alpha of 0.92 for this study sample.

2.4.5 Patient health questionnaire 9

The PHQ-9 is a self-administered questionnaire, employed to test the extent of depression ( Kroenke et al., 2001 ). The internal reliability of the PHQ-9 was demonstrated, with Cronbach’s alpha of 0.81 to 0.84 ( Kroenke et al., 2016 ). Composed of 9 items, each rated on a 4-point Likert-type scale ranging from 0 to 3, the total scores on the PHQ-9 range from 0 to 27. Cutoff scores of 5, 10, 15, and 20 represent mild, moderate, moderately severe, and severe depression ( Kroenke et al., 2016 ). In this study, the scale demonstrated excellent reliability with a Cronbach’s alpha of 0.90.

2.5 Data collection

Structured questionnaires comprising students’ sociodemographics (e.g., age, biological sex, race/ethnicity, study majors, grade levels, and history of psychotherapy, psychiatric medication, or psychiatric disorder), optimism (LOT-R), sleep quality (PSQI), stress (PSS), anxiety (GAD-7), and depression (PHQ-9), were administered through Qualtrics Online Surveys. Within the online survey, all the subjects were introduced to the study’s purpose and procedures, potential risks and benefits, and assurance of privacy and confidentiality before proceeding to the survey sections. Additionally, embedded consent in the online survey required participants to agree prior to the initiation of the survey.

2.6 Data analysis

Following the assessment of statistical normality, descriptive statistics, including the number of participants, age, biological sex, race/ethnicity, study majors, and grade levels, were reported as mean ± SD or frequency (%). The Pearson correlation was applied to the relationships between sleep quality, stress, and mental health among college students. All the analyses were performed using SPSS 28.0 for Windows (SPSS Inc., Chicago, IL). Values of p  < 0.05 were considered statistically significant. Mediation analysis, for both parallel and serial mediating effects of stress and sleep quality on the relationship between optimism and mental health, was performed using the package lavaan ( Rosseel, 2012 ), version 0.6.16, implemented in the R system for statistical computing ( Team RC, 2013 ). Mediation models were adjusted for the potential covariate, biological sex, to statistically control its effects and more accurately estimate the relationship between the predictors and the outcomes in the models.

3.1 Participant characteristics

A total of 222 students participated in the study, with a mean age of 20.3 (± 2.44) years. Predominately, respondents were female (81.5%), nursing (40.5%), sophomore (33.8%), and junior (31.1%) students. Almost 20% of the participants had a psychiatric history, with 59.5 and 50.0% of them experiencing anxiety and depression, respectively ( Table 1 ). Figure 1 provides an overview of variable measurements within the respondent population. The LOT-R scores across the study population indicated a range of low optimism with a mean score of 13.12 ± 3.99. It is noteworthy that 55.3% of the students were categorized as having low optimism, while 37.7% fell into the moderate optimism category ( Table 2 ). As outlined in Table 2 and Figure 1 , participants reported experiencing various psychological states, including poor sleep quality, moderate stress, mild anxiety, and mild depression.

Table 1 . Descriptive statistics for respondent demographic characteristics.

Figure 1 . The proportion of students across various severity levels in PHQ-9, GAD-7, PSS, PSQI, and LOT-R. The X-axis indicates the percentage of students, and the Y-axis denotes the variable measurements. LOT-R, revised Life Orientation Test; PSQI, Pittsburg Sleep Quality Index; PSS, Perceived Stress Scale; GAD-7, General Anxiety Disorder-7; PHQ-9, Patient Health Questionnaire-9.

Table 2 . Comparison of each variable measurements by biological sex.

Upon a biological sex-based analysis, it was observed that male students exhibited notably better sleep quality, lower levels of stress, anxiety, and depression in comparison to female counterparts ( Table 2 ). However, there was no statistical difference observed in the optimism levels between male (14.08 ± 3.99) and female students (12.90 ± 3.97). In addition, no statistically significant differences were identified in the mean scores for LOT-R, PSQI, PSS, GAD-7 or PHQ-9 based on respondents’ ethnicity, study major and grade levels (Supplementary Tables 1–3).

3.2 Correlations of optimism with sleep quality, stress, and mental health among college students

As Table 3 shows, the college students’ optimism (LOT-R) demonstrated a significant negative correlation with poor sleep quality ( r  = −0.281, p  < 0.001), stress ( r  = −0.486, p  < 0.001), anxiety ( r  = −0.423, p  < 0.001), and depression ( r  = −0.476, p  < 0.001). Notably, poor sleep quality (PSQI >5) showed a positive correlation with stress ( r  = 0.498, p  < 0.001), anxiety ( r  = 0.488, p  < 0.001), and depression ( r  = 0.499, p  < 0.001). Moreover, elevated stress levels were linked to higher anxiety ( r  = 0.675, p  < 0.001) and depression ( r  = 0.675, p  < 0.001).

Table 3 . The associations of optimism with sleep quality, stress, anxiety, and depression among college students ( n  = 222).

3.3 The mediating role of stress and sleep quality

3.3.1 parallel mediation models.

To understand how optimism, directly and indirectly, influenced anxiety or depression, parallel mediation models were conducted. In Figure 2A , as the direct effect of optimism toward anxiety became insignificant (Direct Effect [DE] = −0.174, p  = 0.060), stress and sleep quality fully mediated the association between optimism and anxiety after the adjustment for biological sex. Specifically, stress and sleep quality exhibited significant indirect effects on this association (Indirect Effect [IE] PSS  = −0.333, p  < 0.001; IE PSQI  = −0.091, p  = 0.005). These findings indicated that students with enhanced optimism may experience lower anxiety levels through reduced stress and improved sleep quality. Besides, compared to sleep quality, lower perceived stress was the more dominant route on the impact of optimism on anxiety.

Figure 2 . Parallel mediation models for the mediating effects of stress and sleep quality on the relationship between optimism and mental health. Direct and indirect effects of optimism on (A) anxiety or (B) depression through stress and sleep quality are illustrated. The models are adjusted for biological sex. All effects presented are unstandardized. The direct effect and the (total effect) are depicted on the path directly from optimism to anxiety or depression. *** p  < 0.001. LOT-R, revised Life Orientation Test; PSQI, Pittsburg Sleep Quality Index; PSS, Perceived Stress Scale; GAD-7, General Anxiety Disorder-7; PHQ-9, Patient Health Questionnaire-9.

Following the adjustment for biological sex and the introduction of stress and sleep quality into the Model 2 ( Figure 2B ), the attenuated path coefficient (DE = −0.303, p  = 0.001) of optimism on depression revealed the partial mediating roles of stress and sleep quality in the optimism-depression relationship. Both stress and sleep quality demonstrated significant mediating effects on optimism with depression (IE PSS  = −0.327, p  < 0.001; IE PSQI  = −0.095, p  = 0.015), reflecting the stronger mediating role of stress. Similar to the Model 1 ( Figure 2A ), students with higher optimism levels were less susceptible to depression through reduced stress and enhanced sleep quality. Additionally, the influence of optimism on depression appeared to be primarily channeled through lower perceived stress. Overall, the total effects derived from these two models revealed statistically significant negative associations between optimism and anxiety (β total  = −0.598, 95% CI: −0.778 to −0.392), and optimism and depression (β total  = −0.724, 95% CI: −0.919 to −0.519), which implied a more pronounced impact of optimism on depression compared to anxiety.

3.3.2 Serial mediation models

To explore the mediating roles of stress and sleep quality linked in casual chains, guided by a specific directional flow, serial mediation models were employed. Since two mediators of stress and sleep quality were used and with two different outcome variables (anxiety and depression), a total of four different causal order models were produced, with only two of which were presented in Figure 3 . Each distinct casual order of the mediators was examined to compare the significant paths generated by the four models. The total indirect effects derived from serial mediation models were all found to be statistically significant. Of note, the two mediators were shown to partially mediate in the relationship between optimism and anxiety, as well as in the optimism-depression link. This observation denotes a nuanced departure from the result derived from the parallel mediation models ( Figure 2 ).

Figure 3 . Serial mediation models for the mediating effects of stress and sleep quality on the relationship between optimism and mental health. Four serial mediating pathways are illustrated, respectively, in the serial mediation models of (A) optimism to anxiety and (B) optimism to depression. The models are adjusted for biological sex. All effects presented are unstandardized. The direct effect and the (total effect) are depicted on the path directly from optimism to anxiety or depression. * p  < 0.05, *** p  < 0.001. LOT-R, revised Life Orientation Test; PSQI, Pittsburg Sleep Quality Index; PSS, Perceived Stress Scale; GAD-7, General Anxiety Disorder-7; PHQ-9, Patient Health Questionnaire-9.

In the analysis of serial mediation models depicting the transition from optimism to anxiety, both the indicated indirect effect path (LOT-R/PSS/PSQI/GAD-7, IE = −0.074, 95% CI: −0.135 to −0.029) presented in Figure 3A and the alternative indirect effect path (LOT-R/PSQI/PSS/GAD-7, IE = −0.066, 95% CI: −0.114 to −0.032) demonstrated statistical significance. Additionally, within the serial mediation models that trace the impact of optimism on depression, the specified indirect pathway denoted as LOT-R/PSS/PSQI/PHQ-9 ( Figure 3B , IE = −0.084, 95% CI: −0.142 to −0.036) and the alternative pathway of LOT-R/PSQI/PSS/PHQ-9 (IE = −0.064, 95% CI: −0.114 to −0.026) similarly exhibited significant mediating effect. Collectively, these findings imply that increased optimism would lead to lower perceived stress, subsequently improving sleep quality and ultimately contributing to reduced levels of anxiety and depression.

4 Discussion

This study explored the inter-relations between optimism and mental health in college students, with a specific aim to examine the mediating effects of sleep quality and stress in the relationship. Our findings revealed a significant negative correlation between optimism and poor sleep quality, stress, anxiety as well as depression. The result was consistent with the previous finding indicating that college students with higher scores on optimism reported improved sleep quality and lower levels of stress, anxiety, and depression, which might be targeted to reduce mental health problems and improve academic success ( Cheuk Yan and Wong, 2011 ; Kim et al., 2022 ). Additionally, parallel mediation analysis demonstrated that stress and sleep quality fully mediated the relationship between optimism and anxiety, while partially mediating the optimism-depression link. Regarding serial mediation analysis, a significant mediating path sequentially followed by stress and sleep quality was demonstrated both in optimism-anxiety and optimism-depression models.

With the potential underlying mechanism inferred from parallel and serial mediation analyses, this study highlighted the importance of optimism as a mechanism through which reduced levels of stress and improved sleep quality can translate into anxiety and depression. Moreover, the findings also underlined the vulnerability of health science students, as they contend with a variety of academic and clinical stressors, including long hours of study, the demanding nature of examinations, and lack of free time ( Papazisis et al., 2008 ; Sakai et al., 2022 ). Therefore, school-based interventions may hold promise in ameliorating students’ stress, improving their sleep quality, and further reducing the levels of anxiety, and depression.

Aside from examining the association between optimism and mental health among college students, this study also serves to confirm the degree of optimism, sleep quality, stress, anxiety, and depression experienced by college students in the post-COVID era. The emergence of the coronavirus disease 2019 (COVID-19) has brought forth not only physical health problems but also psychological issues ( Tran et al., 2020 ; Xiong et al., 2020 ). College students, in particular, are adversely impacted owing to the uncertainty surrounding academic achievement, future career prospects, and social lives ( Aristovnik et al., 2021 ). The disruptions caused by school closures, cancelation of social events, remote online courses, and exam postponements during the COVID-19 pandemic heightened their emotional distress ( Cao et al., 2020 ; Lei et al., 2020 ; Xiong et al., 2020 ). However, the psychological challenges faced by students did not end with the remission of the pandemic and the easing of social restrictions. In this study, our participants reported low optimism, poor sleep quality, moderate stress, mild anxiety, and depression, reflecting the persisting psychological impact of the pandemic. Consistent with our findings, other study has also highlighted that students perceived intensified levels of stress and anxiety, as well as moderate depression after returning to campus ( Al-Rawi et al., 2022 ). Factors contributing to their psychological struggles include fear of infection and enduring social, family, and economic changes resulting from the COVID-19 pandemic ( Wang et al., 2022 ).

There are several limitations inherent in this study. First, the study sample was exclusively drawn from Health Sciences students at a single public university in the United States, thereby limiting the generalizability of the findings to broader populations or countries. Second, the biological sex composition of the participants was predominately female, and recent studies have shown biological sex-based differences in student mental health consequences of the COVID-19 pandemic ( Ausín et al., 2021 ; Prowse et al., 2021 ). Hence, this biological sex imbalance may limit the generalizability of the results to the male population. Third, the study lacked longitudinal follow-up and, therefore, did not demonstrate causal relationships, although the outcomes do imply that researchers, clinicians, and schools should take into account these variable interactions between optimism and mental health among college students.

5 Conclusion

This study documented the direct and indirect effects of stress and sleep quality and its sequential mediating pathway in the connection between optimism and mental health within health science college students. Findings from the study underscore the significance of fostering academic optimism to alleviate stress and improve sleep quality, ultimately expecting to ease the mental health burdens experienced by college students. Consequently, the development of diverse academic programs focused on enhancing the optimism of college students becomes imperative.

Data availability statement

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

Ethics statement

The study involving human participants was reviewed and approved by the Institutional Review Board (IRB) at the University of Massachusetts Lowell (IRB# 22-072-LAI-EXM). The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study.

Author contributions

Y-JL: Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing. E-YT: Resources, Visualization, Writing – review & editing. PJ: Formal analysis, Writing – original draft, Writing – review & editing. Y-SW: Formal analysis, Methodology, Writing – review & editing. Y-HC: Formal analysis, Methodology, Writing – review & editing. SO’L: Data curation, Writing – review & editing. SM: Data curation, Writing – review & editing. YZ: Conceptualization, Validation, Writing – review & editing. JS: Validation, Writing – review & editing. YW: Methodology, Validation, Writing – review & editing.

The author(s) declare that financial support was received for the research, authorship, and/or publication of this article. This study was funded by the Donna Manning Pilot Research Program and the University of Massachusetts Lowell (Faculty start-up D50210000000022 from Y-JL).

Acknowledgments

The authors express gratitude to all the participants in this study.

Conflict of interest

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

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpsyg.2024.1403146/full#supplementary-material .

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Keywords: optimism, sleep quality, stress, anxiety, depression, mediation analysis

Citation: Lai Y-J, Tsai E-Y, Jarustanaput P, Wu Y-S, Chen Y-H, O’Leary SE, Manachevakul S, Zhang Y, Shen J and Wang Y (2024) Optimism and mental health in college students: the mediating role of sleep quality and stress. Front. Psychol . 15:1403146. doi: 10.3389/fpsyg.2024.1403146

Received: 18 March 2024; Accepted: 25 June 2024; Published: 16 July 2024.

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Copyright © 2024 Lai, Tsai, Jarustanaput, Wu, Chen, O’Leary, Manachevakul, Zhang, Shen and Wang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Yun-Ju Lai, [email protected]

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

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Post-Traumatic Stress Disorder (PTSD)

Post-traumatic stress disorder (PTSD) can develop after exposure to a potentially traumatic event that is beyond a typical stressor. Events that may lead to PTSD include, but are not limited to, violent personal assaults, natural or human-caused disasters, accidents, combat, and other forms of violence. Exposure to events like these is common. About one half of all U.S. adults will experience at least one traumatic event in their lives, but most do not develop PTSD. People who experience PTSD may have persistent, frightening thoughts and memories of the event(s), experience sleep problems, feel detached or numb, or may be easily startled. In severe forms, PTSD can significantly impair a person's ability to function at work, at home, and socially.

Additional information about PTSD can be found on the NIMH Health Topics page on Post-Traumatic Stress Disorder .

Prevalence of Post-Traumatic Stress Disorder Among Adults

• An estimated 3.6% of U.S. adults had PTSD in the past year.
• Past year prevalence of PTSD among adults was higher for females (5.2%) than for males (1.8%).
• The lifetime prevalence of PTSD was 6.8%. 2

Post-Traumatic Stress Disorder with Impairment Among Adults

• Of adults with PTSD in the past year, degree of impairment ranged from mild to serious, as shown in Figure 2. Impairment was determined by scores on the Sheehan Disability Scale. 3
• Impairment was distributed evenly among adults with PTSD. An estimated 36.6% had serious impairment, 33.1% had moderate impairment, and 30.2% had mild impairment

Prevalence of Post-Traumatic Stress Disorder Among Adolescents

• An estimated 5.0% of adolescents had PTSD, and an estimated 1.5% had severe impairment. Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV) criteria were used to determine impairment.
• The prevalence of PTSD among adolescents was higher for females (8.0%) than for males (2.3%).

Data Sources

• Harvard Medical School, 2007. National Comorbidity Survey (NCS). (2017, August 21). Retrieved from https://www.hcp.med.harvard.edu/ncs/index.php   . Data Table 2: 12-month prevalence DSM-IV/WMH-CIDI disorders by sex and cohort   .
• Harvard Medical School, 2007. National Comorbidity Survey (NCS). (2017, August 21). Retrieved from https://www.hcp.med.harvard.edu/ncs/index.php   . Data Table 1: Lifetime prevalence DSM-IV/WMH-CIDI disorders by sex and cohort   .
• Kessler RC, Chiu WT, Demler O, Merikangas KR, Walters EE. Prevalence, severity, and comorbidity of 12-month DSM-IV disorders in the National Comorbidity Survey Replication. Arch Gen Psychiatry. 2005 Jun;62(6):617-27. PMID: 15939839 
• Merikangas KR, He JP, Burstein M, Swanson SA, Avenevoli S, Cui L, Benjet C, Georgiades K, Swendsen J. Lifetime prevalence of mental disorders in U.S. adolescents: results from the National Comorbidity Survey Replication--Adolescent Supplement (NCS-A). J Am Acad Child Adolesc Psychiatry. 2010 Oct;49(10):980-9. PMID: 20855043 

Statistical Methods and Measurement Caveats

National comorbidity survey replication (ncs-r).

Diagnostic Assessment and Population:

• The NCS-R is a nationally representative, face-to-face, household survey conducted between February 2001 and April 2003 with a response rate of 70.9%. DSM-IV mental disorders were assessed using a modified version of the fully structured World Health Organization Composite International Diagnostic Interview (WMH-CIDI), a fully structured lay-administered diagnostic interview that generates both International Classification of Diseases, 10 th Revision, and DSM-IV diagnoses. The DSM-IV criteria were used here. The Sheehan Disability Scale (SDS) assessed disability in work role performance, household maintenance, social life, and intimate relationships on a 0–10 scale. Participants for the main interview totaled 9,282 English-speaking, non-institutionalized, civilian respondents. Post-traumatic stress disorder (PTSD) was assessed in a subsample of 5,692 adults. The NCS-R was led by Harvard University.
• Unlike the DSM-IV criteria used in the NCS-R and NCS-A, the current DSM-5 no longer places PTSD in the anxiety disorder category. It is listed in a new DSM-5 category, Trauma- and Stressor-Related Disorders.

Survey Non-response:

• In 2001-2002, non-response was 29.1% of primary respondents and 19.6% of secondary respondents.
• Reasons for non-response to interviewing include: refusal to participate (7.3% of primary, 6.3% of secondary); respondent was reluctant- too busy but did not refuse (17.7% of primary, 11.6% of secondary); circumstantial, such as intellectual developmental disability or overseas work assignment (2.0% of primary, 1.7% of secondary); and household units that were never contacted (2.0).
• For more information, see PMID: 15297905  .

National Comorbidity Survey Adolescent Supplement (NCS-A)

• The NCS-A was carried out under a cooperative agreement sponsored by NIMH to meet a request from Congress to provide national data on the prevalence and correlates of mental disorders among U.S. youth. The NCS-A was a nationally representative, face-to-face survey of 10,123 adolescents aged 13 to 18 years in the continental United States. The survey was based on a dual-frame design that included 904 adolescent residents of the households that participated in the adult U.S. National Comorbidity Survey Replication and 9,244 adolescent students selected from a nationally representative sample of 320 schools. The survey was fielded between February 2001 and January 2004. DSM-IV mental disorders were assessed using a modified version of the fully structured World Health Organization Composite International Diagnostic Interview.
• The overall adolescent non-response rate was 24.4%. This is made up of non-response rates of 14.1% in the household sample, 18.2% in the un-blinded school sample, and 77.7% in the blinded school sample. Non-response was largely due to refusal (21.3%), which in the household and un-blinded school samples came largely from parents rather than adolescents (72.3% and 81.0%, respectively). The refusals in the blinded school sample, in comparison, came almost entirely (98.1%) from parents failing to return the signed consent postcard.
• For more information, see PMID: 19507169  .

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LINCOLN, Neb. (DTN) -- About one in four farmers in the United States turn to binge drinking in response to high stress levels, according to a new study conducted by researchers at the University of Georgia.

What's more, the study that included a survey of 1,045 farmers found them turning to alcohol because of stigma among the farming community and actual barriers to accessing mental health care in rural areas.

"Beyond cultural factors that act as a barrier to help-seeking, like resilience and the stigmatization of mental health in the farming community, farmers are also isolated from both social support and healthcare resources," the study published in the Journal of Agromedicine said.

"These factors may reinforce the use of alcohol to manage the high levels of stress associated with farming. The risk associated with farming, lack of access to healthcare resources, and a scarcity of healthcare providers who understand the farming population, are compounded by stigma associated with seeking treatment for mental health and substance use disorders creating a vicious cycle that promotes unhealthy coping strategies in farming populations."

The survey was distributed to the ag community in a number of ways including at the 2023 Georgia and American Farm Bureau Federation conferences between Nov. 1, 2022, and Feb. 1, 2023.

A whopping 96% of survey respondents reported consuming alcohol. That includes about 27% of farmers reporting drinking alcohol two to three times or four or more times a week. About 35% of farmers surveyed reported consuming alcohol roughly two to four times per month. About 70% of respondents were male while 28% of respondents were female, with an average age of about 33.

"When participants consumed alcohol, 34% indicated that they consumed three or four drinks in one sitting, 22.5% reported drinking five or six drinks in one sitting, with 18.2% reported having seven or more drinks in one sitting over the last three months," the paper said.

"Binge drinking behavior was reported on a weekly or daily basis by 23.4% of respondents, with 19.6% reporting binge drinking on a weekly basis and 3.8% reporting binge drinking on a daily basis."

FARMER STRESS LEVELS

The study authors also surveyed producers about their perceived stress and mental health stigma. The farmers surveyed reported overall high stress levels, as well as concerns about social stigma and financial costs connected with seeking mental health services.

"Participants most strongly endorsed concerns about others taking them less seriously, treating them differently and talking about them behind their back, as well as personal feelings of shame as markers of stigma associated with help-seeking for mental health support," the study authors said in the published paper.

"Participants endorsed a fair amount of formal healthcare challenges. Participants most strongly endorsed concerns about paying out of pocket for specialty care, lack of insurance, and lack of awareness of available resources for substance use disorders as primary challenges associated with accessing healthcare."

The farmers surveyed reported being either second- or third-generation producers and 80% of them are married. Most of them, about 48%, reported being farm owners or managers. About 78% of the farmers surveyed produce beef cattle, wheat and corn.

STUDY LIMITATIONS

The study authors outline a number of limitations with their work including that survey respondents were much younger than the average age of farmers across the industry.

For instance, the younger demographic surveyed could be attributed to the use of digital recruitment strategies as well as the content of the survey itself, the authors said.

"Participants in prior research conducted in the farming community indicated that older farmers would be unlikely to engage in discussions of stressors and mental health, and this generational distinction may have contributed to a younger respondent pool," according to the paper.

"Within the farming population, it has been observed that younger farmers are more likely to report not only higher stress but higher rates of alcohol consumption as well which might have skewed the results of this study. In addition, a majority of responses were from farm owners and managers with limited responses from farm workers and spouses.

The study authors said farm workers are more likely to be foreign-born, "experience poverty," lack access to health insurance and have lower education levels which could influence behavior and health outcomes.

Christina Proctor, lead author of the study and a clinical associate professor at UGA's College of Public Health, said in a news release that alcohol is the "most acceptable way" for farmers to deal with stress rather than seeking out help.

Statistics from the Centers for Disease Control and Prevention show suicide rates in rural areas are higher than anywhere else in the country.

"Knowing the stigma that exists within rural farming populations about seeking care and then looking at death by suicide numbers, it really is a public health issue because there are drastic, traumatic outcomes associated with not being able to ask for that care, using alcohol to cope and then feeling hopeless," she said.

Read DTN's special issue about mental health, "Rays of Hope Shedding Light on Rural Mental Health Challenges," at https://www.dtnpf.com/…

Todd Neeley can be reached at [email protected]

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News and features

Smell of human stress affects dogs’ emotions leading them to make more pessimistic choices.

Press release issued: 22 July 2024

Dogs experience emotional contagion from the smell of human stress, leading them to make more ‘pessimistic’ choices, new research finds. The University of Bristol-led study, published in Scientific Reports today [22 July], is the first to test how human stress odours affect dogs' learning and emotional state.

Evidence in humans suggests that the smell of a stressed person subconsciously affects the emotions and choices made by others around them. Bristol Veterinary School researchers wanted to find out whether dogs also experience changes in their learning and emotional state in response to human stress or relaxation odours.

The team used a test of ‘optimism’ or ‘pessimism’ in animals, which is based on findings that ‘optimistic’ or ‘pessimistic’ choices by people indicate positive or negative emotions, respectively.

The researchers recruited 18 dog-owner partnerships to take part in a series of trials with different human smells present. During the trials, dogs were trained that when a food bowl was placed in one location, it contained a treat, but when placed in another location, it was empty.  Once a dog learned the difference between these bowl locations, they were faster to approach the location with a treat than the empty location.  Researchers then tested how quickly the dog would approach new, ambiguous bowl locations positioned between the original two.

A quick approach reflected ‘optimism’ about food being present in these ambiguous locations – a marker of a positive emotional state – whilst a slow approach indicated ‘pessimism’ and negative emotion. These trials were repeated whilst each dog was exposed to either no odour or the odours of sweat and breath samples from humans in either a stressed (arithmetic test) or relaxed (listening to soundscapes) state.

Researchers discovered that the stress smell made dogs slower to approach the ambiguous bowl location nearest the trained location of the empty bowl. An effect that was not seen with the relaxed smell.  These findings suggest that the stress smell may have increased the dogs’ expectations that this new location contained no food, similar to the nearby empty bowl location.

Researchers suggest this ‘pessimistic’ response reflects a negative emotional state and could possibly be a way for the dog to conserve energy and avoid disappointment.

The team also found that dogs continued to improve their learning about the presence or absence of food in the two trained bowl locations and that they improved faster when the stress smell was present.

Dr Nicola Rooney,  Senior Lecturer in Wildlife and Conservation at Bristol Veterinary School and the paper’s lead author explained: “Understanding how human stress affects dogs' wellbeing is an important consideration for dogs in kennels and when training companion dogs and dogs for working roles such as assistance dogs.

“Dog owners know how attuned their pets are to their emotions, but here we show that even the odour of a stressed, unfamiliar human affects a dog’s emotional state, perception of rewards, and ability to learn. Working dog handlers often describe stress travelling down the lead, but we’ve also shown it can also travel through the air.”

Dr Zoe Parr-Cortes, PhD student at Bristol Veterinary School and primary researcher on the project expressed her thanks to everyone involved in the study, especially all the participants and dog owners who took part in the research.

‘Does the odour of human stress or relaxation affect dogs’ cognitive bias? By Z. Parr-Cortes, N. J. Rooney et al. in Scientific Reports [open access].

Further information

Study information

The 18 dogs included in this study comprised ranged from eight months to ten years old.  Breeds consisted of two Springer spaniels; two Cocker spaniels; two Labrador Retrievers; two Braque d’Auvergne; one Whippet; one Golden Retriever; one Miniature Poodle and seven mixed breed dogs.  Eight dogs were registered as teaching dogs at the University of Bristol.

Study at the Bristol Veterinary School The University of Bristol offer a number of postgraduate courses including the MSc Global Wildlife Health and Conservation and a range of undergraduate degrees .

Based at Bristol's  Langford Campus , Bristol Veterinary School boasts first-class clinical facilities and encompasses a small animal hospital, a dairy farm, diagnostic laboratories, and farm animal, small animal and equine practices.

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Best practices for stress measurement: How to measure psychological stress in health research

Despite the strong evidence linking psychological stress to disease risk, health researchers often fail to include psychological stress in models of health. One reason for this is the incorrect perception that the construct of psychological stress is too vague and broad to accurately measure. This article describes best practices in stress measurement, detailing which dimensions of stressor exposures and stress responses to capture, and how. We describe when to use psychological versus physiological indicators of stress. It is crucial that researchers across disciplines utilize the latest methods for measuring and describing psychological stress in order to build a cumulative science.

Introduction

Epidemiological studies confirm that both experiencing a greater number of stressful events and reporting high perceived stress over long periods of time are associated with worse mental and physical health, and mortality ( Epel et al., 2018 ). The association between greater stressor exposure and increased disease risk has been replicated with many different types of stressor exposures (e.g. discrimination, caregiving, work stress) and a range of aging-related health outcomes (e.g. cardiovascular disease, metabolic syndrome, mortality). The mechanistic pathways underlying these associations have also been detailed ( Boyce, 2015 ; McEwen, 2015 ; Miller et al., 2009 ). Despite this compelling evidence, however, health researchers often measure stress using unvalidated measures or select a single type of stress to measure, thus either missing entirely or underestimating the role stress plays in predicting disease onset or progression.

One of the main reasons for the lack of sophisticated measurement and inclusion of psychological stress in health models may be the incorrect assumption that stress is too broad and nebulous of a construct to accurately measure. It is true that psychological scientists too often fail to specify what they mean when using the term “stress” or other variants such as “stressor,” “acute stress,” “stress response,” and “stress biomarker.” Social and behavioral scientists tend to use the term loosely, often failing to define it clearly in a manuscript and using it to refer to a range of experiences, from living in poverty to giving a public speech to current negative mood. Kagan (2006) pointed out this lack of specificity, providing a fair critique of the state of the literature. The lack of specificity in language, however, does not represent a true lack of specificity in theoretical or methodological approaches. Although psychological stress researchers have made great strides in differentiating different forms of stress in recent decades, the problem is rather that the language used in journal articles has not always accurately reflected these advancements—and these advancements have been kept within a small, specialized subset of researchers. Thus, the purpose of this article is to provide health researchers across disciplines with a useful update on best practices for measuring stress and offer suggested language for how to describe stress-related constructs with more granular language.

Fundamentals of stress measurement

The term “stress” is an umbrella term representing experiences in which the environmental demands of a situation outweigh the individual’s perceived psychological and physiological ability to cope with it effectively ( Cohen et al., 2016 ). One important distinction in studying stress is to differentiate between exposures to stressful events and the responses to these events. Stressful events or “stressors” are discrete events that can be objectively rated as having the potential to alter or disrupt typical psychological functioning, such as losing your job or getting divorced. Stress responses are the cognitive, emotional, and biological reactions that these stressful events evoke.

Measuring stressor exposures versus stress responses

Stressor exposures can be measured with self-report questionnaires such as a life events checklist, assessed by an interviewer, or objectively determined based on proximity to an event (e.g. living in NYC during the September 11 terrorist attacks). The Life Events and Difficulties Schedule (LEDS; Brown and Harris, 1978 ) is a structured interview protocol that is considered the gold standard for assessing stressor exposure across someone’s lifetime. This interview protocol is time intensive in both the data collection and data processing stages. To streamline the process of capturing stressor exposures across the life span, a computer-assisted methodology was developed (e.g. The Stress and Adversity Inventory [STRAIN]; Slavich and Shields, 2018 ). In both the LEDS and the STRAIN, participants are asked whether they have experienced a range of stressful life events at any point in their life. For each endorsed stressor, they are asked follow-up questions to provide greater context about the experience (e.g. how old were you when it happened, how long did it go on for, how stressful or threatening was it). The LEDS requires a trained interviewer to administer the measure, while the STRAIN can be completed either by an interviewer or by participants themselves. The LEDS also relies on blind raters to score the severity of a stressor using this contextual information, while the STRAIN relies on the participants reporting of event severity. The STRAIN’s automated structure of follow-up questions allows the respondent to complete the interview much more quickly than the LEDS and reduces data processing time. Both measures provide a comprehensive assessment of stressor exposures across the lifespan, and use different methods to determine the severity of these experiences.

An individual’s response to the stressor sometimes matters more than mere exposure to it, particularly when it comes to the impact of the stressor on physical health. For example, caregiving for a family member with a debilitating illness is often considered a chronic stressor because of the constant physical and emotional demands. There is a significant amount of research examining the impact of being a dementia caregiver, in particular, given the large increase in the number of family dementia caregivers as the population ages in the United States. In fact, the Alzheimer’s Association estimated in 2018 that there were over 16 million family caregivers providing an estimated 18.5 billion hours of care to people with Alzheimer’s or other dementias ( Alzheimer’s Association, 2019 ). Empirical evidence has shown that family caregivers of Alzheimer’s patients have worse physical and mental health compared to age-matched non-caregivers ( Kiecolt-Glaser et al., 1987 ; Vitaliano et al., 2003 ). However, not every caregiver’s health is damaged by their caregiving role ( Roth et al., 2015 ). This may be because the negative impact of caregiving is caused by individuals’ subjective response to the caregiving situation, not from the mere exposure of being a caregiver. Thus, a better predictor of health decline would be the degree to which caregivers report high levels of psychological burden from their caregiving role. Empirical evidence supports this perspective; for example, Alzheimer’s caregivers who reported emotional distress or physical strain from caregiving had 63 percent greater mortality than caregivers who reported no distress ( Schulz et al., 1999 ).

Stress responses can be measured with self-report measures, behavioral coding, or via physiological measurements. These responses include emotions, cognitions, behaviors, and physiological responses instigated by the stressful stimuli. One of the simplest ways to measure stress responses is through self-reports of perceived stress related to a specific stressor or to one’s life circumstances ( Cohen et al., 1983 ). For example, the Perceived Stress Scale is a 10-item self-report measure that captures an individual’s perception of how overwhelmed they are by their current life circumstances. Responses to acute stressors have traditionally been studied in controlled laboratory settings in order to capture responses that unfold within minutes of stressor exposure (e.g. emotional and physiological reactivity to an acute stress task). A commonly used acute stress paradigm is the Trier Social Stress Test (TSST), a standardized laboratory stress task in which participants give a speech and perform mental arithmetic in front of judges ( Kirschbaum et al., 1993 ). The TSST reliably evokes an acute stress response for the majority of participants. Outside of the laboratory, new technology has enhanced our ability to capture real-time stress responses in daily life using mobile phones and wearables, which many researchers are now doing. Considering the impact of both stressor exposure and stress responses on health may improve the prediction of health outcomes, as many models of stress propose that the stress response mediates the effect of stress exposures on health outcomes ( McEwen, 1998 ; Wheaton et al., 2013 ).

Selecting stress measures

Due to constraints on participant burden and other considerations, difficult choices about which type of stress to measure need to be made by researchers. Common types of psychological stress measured using self-report questionnaires in adult samples are major life events, traumatic events, early life stress exposure, and current chronic or perceived stress in various domains (i.e. loneliness, marital discord, experiences of discrimination, work stress, financial strain, neighborhood safety and cohesion, and current perceived stress). The choice of which type of stressor exposure to measure depends on what is most relevant to the study population, the specific research question, and the hypothesized mechanisms linking that stress type to the outcome of interest. To begin the selection, consider first what is the most relevant stress type(s), given the sample’s demographic makeup. For example, measures that capture religious persecution or combat exposure would be particularly important for a sample living in a conflict zone, while the amount of overwhelm related to being a parent (parenting stress) may be most relevant for a sample of mothers caring for their child who has an autism spectrum disorder. In both cases, it would also be important to measure types of stressors that may not be directly related to the circumstances—such as levels of loneliness and financial strain. Capturing a range of stressor types reduces the likelihood that the individual’s psychological and social distress is underestimated.

Stressor and stress response characteristics

In addition to identifying stressor type(s) of interest, there are several key measurement considerations when choosing specific measures of stress to include in studies or analyzing existing stress measure data. These considerations include characteristics of the stressor or response (e.g. timescale, types of stressor response) as well as measurement characteristics (e.g. life stage of exposure and measurement assessment window). We briefly describe these aspects below (see Epel et al., 2018 for further discussion).

Timescale of the stressor

Stressors generally take place along the following timescales: chronic stressors, life events, daily events/hassles, and acute stress. Table 1 provides definitions for each of these timescales. It is important to note that naturalistic experiences of stress rarely fall neatly into one category. For example, death of a loved one is often considered a major life event but, depending on the cause of death, may also be considered a chronic stressor, such as if the family member was sick for years or months before the death. Similarly, arguments with a spouse may be considered an acute stressor, but if they happen every day they may be considered chronic. There is a significant amount of gray area between categories. Researchers should first make a thoughtful attempt to pick the category that best aligns with the stressor and with the way that stressor type has been described in past research, and then describe the exposure with as much specificity as possible.

Types of stress by timescale.

Types of stress response

Responses to stressor exposures provide additional useful information beyond measuring stressor exposure alone. Stress responses include psychological, behavioral, cognitive, and physiological reactions related to the stressor exposure that can occur before, during, or after the exposure. Psychological stress responses include specific emotions triggered by the stressor, as well as efforts to regulate that emotion ( Gross, 2002 ). Behavioral responses include coping behaviors such as smoking or seeking social support. Cognitive responses include appraisals of the exposure (e.g. as a threat versus challenge; Blascovich and Mendes, 2010 ) and perseverative cognitions (e.g. rumination Brosschot et al., 2005 ). Physiological responses include immune, autonomic, neuroendocrine, and neural changes related to stressor exposure. Further details about the various stress responses deserve more attention than can be described here ( Epel et al., 2018 ). As a part of selecting stress measures, researchers should identify the type of stress response that is most relevant for their research question and sample. Often, studies will assess multiple types of stress responses simultaneously.

Additional characteristics of the stressor

There are additional stressor exposure attributes that can be described and captured to thoroughly assess the exposure. These include, but are not limited to, duration, severity, controllability, life domain, the target of the stressor (e.g. self, close other), and the potential of the stressor to elicit specific harmful emotional responses (e.g. social status threat). Lack of control, social status threat, and stressor severity have been identified as potent attributes that predict worse outcomes across a range of stressor types and scenarios.

Characteristics of stress measurement

Life stage during stressor exposure.

In addition to the timescale of the stressor, another important characteristic of stressor exposure is the developmental or life stage during which the stressor occurs. Knowing the person’s age during the exposure informs hypotheses about which psychological and biological processes the stressor may have impacted. This is because developing systems are more open to environmental cues and are thus more likely to be impacted by stress exposure. “Sensitive periods” are specific time points in the life course during which physiological systems are maximally influenced by external environmental factors, and thus stressor exposure can have a particularly strong influence on development ( Knudsen, 2004 ). Sensitive periods during which stress may have the greatest effect are likely: prenatal ( Van Den Bergh et al., 2005 ; Weinstock, 2001 ), before age 5 ( Zeanah et al., 2011 ), during puberty ( Fuhrmann et al., 2015 ), entry into parenthood ( Saxbe et al., 2018 ), and during menopause ( Gordon et al., 2015 ). Identifying and measuring stress during sensitive periods could greatly increase our understanding of who is at risk for the negative effects of stress, the mechanistic pathways linking stress exposure to health decline, and where and how to focus intervention efforts.

Measurement assessment window

The window of measurement is also essential to consider to avoid measurement error and improve specificity in hypotheses. Measures can ask about stressors and stress responses across a wide range of time frames, such as in the present moment, over the course of that day, the past week, the past month, the past year, in childhood, or across the entire lifespan. For example, there are fundamental differences in a measure that ask participants to report on stress exposure in the past month versus across their lifespan. The latency between stressor exposure and measurement is crucial, as retrospective autobiographical reports are prone to bias and error, especially when there have been years or decades since the exposure in question ( Bradburn et al., 1987 ; Hardt and Rutter, 2004 ). In addition to the latency between exposure and measurement, several other factors can impact the accuracy of retrospective reports, such as mental state at the time of recall and the emotional salience of a given memory ( Shiffman et al., 2008 ). This may lead to overestimating the frequency of emotionally salient stressors and underestimating the frequency of more mundane, daily stressors ( Bradburn et al., 1987 ; Shiffman et al., 2008 ). For these reasons, it can be beneficial to measure stressor exposure and responses in close proximity to their occurrence whenever possible.

The experimental studies examining acute stressor exposure and responses, there are additional considerations with the measurement assessment window. Because the timing of stressor exposure is controlled, researchers can begin measuring psychological, behavioral, and physiological states prior to the stressor exposure and continue measuring throughout and after exposure. By measuring responses before, during, and after exposure, researchers can access (and predict) anticipation of and recovery from the stressor exposure.

Summary of steps for selecting stress measures

There are of course numerous considerations for selecting the appropriate stress measure for your study. In sum, researchers should identify the type or types of stress that are most relevant to their research question and sample. Stress measure selection should then be refined based on characteristics of the stressor and/or stress response that the researcher intends to measure, such as the timescale, the type(s) of stress responses the researcher is interested in, and other attributes of the stressor (e.g. duration, severity, controllability). Selection of stress measures should also account for measurement characteristics, such as the life stage during stressor exposure and the measurement assessment window (e.g. framing of questions, timing of assessment relative to occurrence of the stressor).

Beyond these stress-specific considerations, researchers should also follow general best practices for measure selection. For example, validated scales should be used when available. The Stress Measurement Network Toolbox provides a resource for validated measures of different types of stress that has beeen curated by experts ( https://stressmeasurement.org ). Measures should also be selected based on the uniqueness of the sample, and hile validated scales are preferred, some samples or exposures may require researchers to develop a new scale or modify an existing scale to fit their needs. These practical steps for selecting a stress measure are summarized in Table 2 .

Summary of steps for choosing appropriate stress measures.

Compelling evidence linking stress to physical health

The types of stress that have the most consistent and compelling relationships with disease risk and mortality are acute stress reactivity, early life stress, work or occupational stress, and social isolation/loneliness. A comprehensive review of these literatures is outside the scope of the present article; however, the following section highlights foundational studies linking these stress types physical health, with a particular emphasis on cardiovascular disease (because it is the leading cause of death in developed countries) and mortality. Effect sizes are included where possible, as are references to reviews and meta-analyses for further reading. Of note, we do not review the literature here on the impact of cumulative life stress (aggregate number of stressor exposures and/or intensity of stress responses over one’s life course). Despite initial compelling work on the impact of cumulative life stress on cardiovascular disease outcomes, this area of research is still in its infancy, with a need for measurement approaches to be unified across research studies to allow for building of a collective science ( Albert et al., 2013 ; Slopen et al., 2018 ).

Research on acute stress reactivity and physical health

Decades of research have shown that heightened cardiovascular reactivity and delayed recovery to acute stressors are prospectively associated with increased cardiovascular disease risk ( Brosschot et al., 2005 ; Chida and Steptoe, 2010 ; Steptoe and Marmot, 2005 ). One of the earliest studies in this area was a longitudinal study of healthy adult men (age 45–55; n  = 279) in which those classified as “hyper-reactors” (defined as > 20 mmHg increase in diastolic blood pressure to the cold pressor acute stress task) were 2.4 times more likely to have a myocardial infarction or die from cardiovascular disease in the following 20 years than men who showed a rise of < 20 mmHg ( Keys et al., 1971 ). Cortisol and inflammatory responses to acute stressors have also been shown to prospectively predict incident hypertension ( Hamer and Steptoe, 2012 ; Steptoe et al., 2016 ). Heightened reactions and prolonged recovery time periods may be driven by perseverative cognitions before (worrying) and after (rumination) stressor exposure ( Brosschot et al., 2005 , 2006 ). Despite the evidence linking reactivity to disease outcomes, the clinical meaningfulness of these associations is still debated ( Treiber et al., 2003 ). Importantly, a blunted response to an acutely stressful situation (sometimes termed a “hyporeactive response”), is also linked to worse health ( Carroll et al., 2017 ). For example, in a sample of 725 healthy adults from the Dutch Famine Birth Cohort Study, decreased cardiovascular and/or cortisol response to the acute stressor was associated with obesity, risk of becoming obese, depressive symptoms, anxiety, and poor self-rated and functional health ( De Rooij, 2013 ). In addition, there are several other reactivity patterns that have been hypothesized to represent maladaptive response profiles such as lack of habituation when exposed to repeated stressors of the same kind (see McEwen, 1998 ). Thus, the clinical meaningfulness of different stress reactivity profiles is largely debated.

Research on early life stress and physical health

The evidence linking early life stress to increased adult disease risk and mortality is strong. A foundational study in this area, the Adverse Childhood Experiences (ACE) Study, included nearly 10,000 adults and demonstrated that a greater number of self-reported retrospective adverse childhood experiences (e.g. physical abuse, living with an alcohol-dependent adult, witnessing violence) was positively associated in a graded relationship with the presence of ischemic heart disease, cancer, chronic lung disease, skeletal fractures, and liver disease, after controlling for demographic factors ( Felitti et al., 1998 ). Convincingly, reporting seven or more ACE was associated with three times the likelihood of heart disease compared to reporting no ACE ( Dong et al., 2004 ). These findings have been so compelling that significant changes in clinical and educational settings have been undertaken in recent years to recognize the role that early trauma has on current and future cognitive, socio-emotional, and behavioral outcomes for both children and adults.

Research on work stress and physical health

Epidemiological studies consistently demonstrate associations between high work stress and worse physical and mental health. One of the most widely studied models of work stress is job strain, which is a combination of high demands (workload and intensity) and low control ( Karasek, 1979 ). Decades of research has linked high job strain to anxiety and depression, increased blood pressure (BP), cardiovascular events, and metabolic syndrome ( Chandola et al., 2006 ; Landsbergis et al., 2013 ; Madsen et al., 2017 ; Nyberg et al., 2013 ). An analysis of the Whitehall II study cohort found that chronic work stress was associated with coronary heart disease (CHD) risk, with the associations strongest in participants under 50 (RR = 1.68, 95% CI 1.17–2.42). Other components of work stress, such as effort-reward imbalance, also predict cardiovascular disease risk ( Dragano et al., 2017 ).

Research on social isolation, loneliness, and physical health

A meta-analysis of decades of work on social isolation and loneliness found that being socially isolated, lonely, and/or living alone corresponded to an average of 29 percent, 26 percent, and 32 percent increased likelihood of mortality ( Holt-Lunstad et al., 2015 ). The mortality risk for the most socially isolated adults in the National Health and Nutritional Examination Survey (hazard ratio (HZ) = 1.62 for men, HZ = 1.75) was found to be comparable to the risk of smoking (HR = 1.72 for men, HZ = 1.86) and having high BP (HR = 1.16 for men, HR = 1.32 for women) ( Pantell et al., 2013 ). These strong relationships suggest that meaningful connection with others is an essential component of health and well-being. Several short measures have been created to capture this important social determinant of health, including a validated three-item measure of loneliness ( Hughes et al., 2004 ).

Biological pathways from stress to disease

There are numerous plausible biological pathways linking stress to cardiovascular disease, with most of the current evidence pointing to stress-related alterations in the immune, autonomic, and neuroendocrine systems. The brain networks that orchestrate stress-induced changes in these peripheral systems have also been identified ( Gianaros and Wager, 2015 ; Gianaros and Jennings, 2018 ), and can be described as the systems related to threat processing, safety processing, and social cognition ( Muscatell and Eisenberger, 2012 ). One widely accepted stress-disease model is the “wear and tear” hypothesis ( Charles et al., 2013 ; McEwen, 1998 ; Selye, 1956 ). This hypothesis is centered on the postulation that prolonged or repeated stress prematurely depletes the finite amount of “adaptational energy” of the organism, decreasing the body’s ability to successfully adapt to environmental challenges ( Selye, 1956 ). In this model, stressful events cause stress responses that involve activation of physiologic systems. In the short term, mobilizing physiological resources to respond to a discrete event or threat is adaptive. In the long term, however, frequent and repeated mobilization of these resources wears down these response systems and maladaptive patterns appear ( McEwen, 1998 ). The “wear and tear” hypothesis is theoretically compelling, but currently lacks definitive empirical support. This is because we do not currently have data that demonstrates the slow degradation of multiple physiological systems over decades in humans, an effort that requires tremendous investment. Instead, most studies have chosen one or maybe two physiological systems to measure to try to capture degradation or maladaptive responses to stressors, thus providing support, but not direct evidence for the “wear and tear” hypothesis. Other potential pathways include stress-related changes in endothelial function, elevated chronic inflammation, metabolic dysfunction, changes in DNA repair, changes in gene expression, and telomere shortening. These are all exciting areas of research, some of which fit in to the “wear and tear” hypothesis (e.g. telomere shortening; Epel et al., 2004 ) and others that suggest alternate processes (e.g. biological embedding of early experiences; Miller et al., 2011 ). These pathways are relevant for numerous chronic diseases beyond cardiovascular disease.

Associations between stress and immune system functioning are especially relevant given that the major diseases of aging in the United States are mediated, in part, through the immune system. The top three leading causes of death in the United States—cardiovascular disease, cancer, and chronic lower respiratory disease—all share the common thread of being characterized by elevated chronic inflammation ( Aghasafari et al., 2019 ; Golia et al., 2014 ; Grivennikov et al., 2010 ). Because of this common thread, chronic systemic inflammation has become a recent focus of health research. Stress exposure has been examined extensively as a predictor of increased systemic inflammation. Indeed, elevated systemic inflammation has been found in those experiencing chronic stress like caregivers ( Gouin et al., 2008 ), immediately after a stressful life event like death of a loved one ( Cohen et al., 2015 ), historical stress like childhood adversity ( Slopen et al., 2010 , 2012 ), daily stress ( Chiang et al., 2012 ), and in response to lab-based stress tasks ( Marsland et al., 2017 ). A short-term inflammatory response to stress is thought to be adaptive because it involves recruiting immune cells to the site of a real or potential injury in order to heal wounds resulting from stressor exposure. However, when there is no wound to heal, as is the case with psychosocial stressor exposure, repeated or exaggerated inflammatory responses may cause long-term damage and contribute to disease processes ( Black and Garbutt, 2002 ; Miller et al., 2002 ; Rohleder, 2014 ).

Is there an “objective” way to measure stress?

Stress and health researchers have searched for many years for a single biological indicator that someone is “under stress.” However, there is no single stress-specific biomarker. This is likely because acute stress is not the only state that evokes reliable biological changes (e.g. increased heart rate and BP). Other non-acute stress states, such as feeling excited, focusing attention on non-negative affect inducing stimuli, or exercising, also trigger biological responses that are similar to those evoked by negative affect inducing acute stressors like increased heart rate and blood pressure. This is even true for what is often termed the “stress hormone,” cortisol—not all cortisol increases are triggered by increases in psychological stress responses, nor does every experience that people perceive as “stressful” cause cortisol to rise ( Dickerson and Kemeny, 2004 ).

While measuring stress-related biomarkers may not provide a perfect indicator of whether someone is under stress or not, there are still compelling reasons to include these biomarkers in research studies of stress and health. Stress-related biomarkers are objectively measured biological indicators of physiological processes that are either implicated in the pathway from stress to disease or serve as a marker of that process. In typical models of the stress-health relationship, the stressful event (X) leads to a biological change (Y) that then leads to the disease state or related outcome (Z). Stress-related biomarkers can be the variable inserted in any component (X, Y, or Z) of this model; examples of the stress-related biomarker in each part of this basic model are shown in Figure 1 . In example A, the biomarker serves as a mediator, or a part of the causal pathway between a stressor and a health outcome. In example B, the biomarker serves as a predictor of stress-related psychosocial and behavioral processes that ultimately impact health outcomes. In example C, the biomarker serves as an outcome of psychological and physiological responses to a traumatic stressor. The way a biomarker is conceptualized (e.g. as a mediator, predictor, or outcome) depends on the research question and study methods. As such, choosing a stress-related biomarker to include in a study depends on the design of the study and the outcomes of interest. Table 3 provides further tools for how to choose the appropriate biomarker. It is also important to keep in mind that a biomarker may not be needed to answer a research question, despite the desire for a seemingly “objective” indicator of stress or stress reduction.

Examples of how stress-related biomarkers can be modeled as either the predictor, the mediator, or the outcome in research studies.

Essential questions for following best practices in choosing an appropriate stress-related biomarker.

One area of research that requires particularly careful consideration of biomarker selection is when biomarkers are used as an outcome in psychosocial intervention trials. The scientific community is often eager to find an objective biological indicator that a psychosocial intervention can improve health; this is typically done by measuring improvement in a biomarker from pre- to post-intervention. There has been a trend in recent years toward using changes in biomarkers as an indicator of an intervention’s success, rather than relying on subjective psychological reports of well-being. This approach is problematic for several reasons, including variability in baseline biomarker profiles, unknown reliability of biomarker assessment over time, unknown stability of these changes, and lack of evidence for the long-term impact of small changes in stress-related biomarkers on disease risk. Therefore, biomarkers should not replace self-report, behavioral, and cognitive outcomes as primary outcomes in psychosocial intervention trials aimed at reducing stress or related goals.

Variability in exposures and responses

Despite stress exposure being an inevitable part of life, not everyone develops stress-related illnesses at the same speed. One primary reason for this is that stress exposures are not distributed evenly across social groups. Women, young adults, members of racial-ethnic minority groups, divorced and widowed persons, and poor and working-class individuals report greater chronic stress and cumulative stress exposure across their lives ( Thoits, 2010 ). In addition, recent research has demonstrated that both psychological and physiological stress responses vary remarkably within and between people. While the physiological systems that are activated in response to a stressor are generally universal and non-specific as initially proposed by one of the founders of the field of stress, Hans Selye (1956) , the pattern of these responses vary considerably in terms of the degree of the system’s activation and how long the systems are activated for. Individual-level differences and environmental contexts interact to influence the psychological and physiological stress response trajectories. These include socioeconomic and cultural factors, genetic and developmental factors, historical and current stressors, stable protective factors, and health behaviors. A model integrating these different levels of experience is presented by our group in detail in Epel et al. (2018) and reprinted here with permission ( Figure 2 ).

Transdisciplinary model of psychological stress: Integrating contextual, historical, habitual, and acute stress processes.

Figure 2 presents a transdisciplinary model that describes psychological stress as encompassing as a set of interrelated processes. The figure illustrates that stressors are experienced within the context of a person’s life, represented by the contextual factors in the blue triangle. These contextual factors include individual-level characteristics such as personality and demographics, the environment in which one lives, current and past stressor exposures, and protective factors—all of which combine to determine the baseline allostatic state of physiological regulation, and the lens through which stressors are perceived and assigned meaning. Contextual factors and habitual processes together influence psychological and physiological responses to acute and daily stressors. These responses, if dysregulated, are thought to lead to allostatic load and ultimately biological aging and early disease. Reprinted from Frontiers in Neuroendocrinology ( Epel et al., 2018 ).

Advanced statistical models can be used to examine variability in stress responses (both psychological and physiological) within and between people ( Bryk and Raudenbush, 1987 ; McArdle and Epstein, 1987 ). Within-person variability in stress responses means that a person’s response to a stressor within one life domain (e.g. work) does not necessarily predict how they will respond to a stressor within another life domain (e.g. family). Between-person variability means that different people respond to the same stressor in a variety of ways. As an example of variability in psychological stress responses, in a sample of 1,532 healthy adults from the Changing Lives of Older Couples prospective study, psychological responses to the death of one’s spouse took on four discrete trajectories (e.g. chronic grief, chronic depression, temporary depression, resilient), suggesting that there is not one universal pattern for spousal grief ( Galatzer-Levy and Bonanno, 2012 ). Cortisol can be used as an example of variability in physiological stress responses Cortisol generally increases in response to laboratory-based acute stress tasks if they are uncontrollable and characterized by social-evaluative threat ( Dickerson and Kemeny, 2004 ), such as the TSST described earlier ( Kirschbaum et al., 1993 ). However, around 30 percent of people do not mount a cortisol response, and there is tremendous variability in the size of the response. Individual-level predictors of this variability include age, gender, sex steroid levels, smoking, coffee, and alcohol consumption ( Kudielka et al., 2009 ). Interestingly, these differences are not driven by differences in the emotional responses to the task as acute stressors are not strongly correlated to the physiological responses. In a review of 49 acute stress studies, only 25 percent reported a significant correlation between the two emotional and physiological responses ( Campbell and Ehlert, 2012 ).

Empirical evidence supports a strong relationship between psychological stress and disease development. These studies may be underestimating the impact of stressor exposure and the stress response on health, given that measuring these constructs has been challenging and limited. Recent work in the stress field has identified important aspects of psychological stress to capture in order to fully test the role that psychological stress plays in predicting disease; these include capturing the specific type(s) of stressor exposure, a wide range of psychological, cognitive, behavioral, and physiological responses to the exposure, and contextual and individual-level factors that moderate the impact of the exposure and response. In this article, we identified ways for researchers to improve the language specificity when describing stress measures and offered guidance on how to choose the appropriate stress measure. We encourage the adoption of more precise language when writing about stress in academic papers, more careful selection of stress measures, with a focus on validated measures when possible, and theoretically driven integration of mechanistic pathways linking stress to health outcomes. The ultimate goal of having sophisticated research on the relationship between stress, health, and well-being is to develop evidence-based ways to help people thrive in our stress-filled world.

Acknowledgments

Members of the Stress Measurement Network provided essential input on the thoughts presented here, and we thank them for their contribution.

Conflict of 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 funded by the National Institute on Aging of the National Institutes of Health [R24AG048024; K01AG057859].

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• Published: 15 July 2024

Endoplasmic reticulum stress promotes hepatocellular carcinoma by modulating immunity: a study based on artificial neural networks and single-cell sequencing

• Zhaorui Cheng   ORCID: orcid.org/0009-0008-3474-1161 1 ,
• Shuangmei Li 1   na1 ,
• Shujun Yang 1 ,
• Huibao Long 1 ,
• Haidong Wu 1 ,
• Xuxiang Chen 1 ,
• Xiaoping Cheng 2 &
• Tong Wang   ORCID: orcid.org/0000-0003-4644-4179 1  

Journal of Translational Medicine volume  22 , Article number:  658 ( 2024 ) Cite this article

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Introduction

Hepatocellular carcinoma (HCC) is characterized by the complex pathogenesis, limited therapeutic methods, and poor prognosis. Endoplasmic reticulum stress (ERS) plays an important role in the development of HCC, therefore, we still need further study of molecular mechanism of HCC and ERS for early diagnosis and promising treatment targets.

The GEO datasets (GSE25097, GSE62232, and GSE65372) were integrated to identify differentially expressed genes related to HCC (ERSRGs). Random Forest (RF) and Support Vector Machine (SVM) machine learning techniques were applied to screen ERSRGs associated with endoplasmic reticulum stress, and an artificial neural network (ANN) diagnostic prediction model was constructed. The ESTIMATE algorithm was utilized to analyze the correlation between ERSRGs and the immune microenvironment. The potential therapeutic agents for ERSRGs were explored using the Drug Signature Database (DSigDB). The immunological landscape of the ERSRGs central gene PPP1R16A was assessed through single-cell sequencing and cell communication, and its biological function was validated using cytological experiments.

An ANN related to the ERS model was constructed based on SRPX, THBS4, CTH, PPP1R16A, CLGN, and THBS1. The area under the curve (AUC) of the model in the training set was 0.979, and the AUC values in three validation sets were 0.958, 0.936, and 0.970, respectively, indicating high reliability and effectiveness. Spearman correlation analysis suggests that the expression levels of ERSRGs are significantly correlated with immune cell infiltration and immune-related pathways, indicating their potential as important targets for immunotherapy. Mometasone was predicted to be the most promising treatment drug based on its highest binding score. Among the six ERSRGs, PPP1R16A had the highest mutation rate, predominantly copy number mutations, which may be the core gene of the ERSRGs model. Single-cell analysis and cell communication indicated that PPP1R16A is predominantly distributed in liver malignant parenchymal cells and may reshape the tumor microenvironment by enhancing macrophage migration inhibitory factor (MIF)/CD74 + CXCR4 signaling pathways. Functional experiments revealed that after siRNA knockdown, the expression of PPP1R16A was downregulated, which inhibited the proliferation, migration, and invasion capabilities of HCCLM3 and Hep3B cells in vitro.

The consensus of various machine learning algorithms and artificial intelligence neural networks has established a novel predictive model for the diagnosis of liver cancer associated with ERS. This study offers a new direction for the diagnosis and treatment of HCC.

As one of the most commonly primary malignancies, liver cancer has become one of the top five causes of cancer-related death around the world according to the World Health Organization [ 1 ]. Approximately 90% of liver cancer patients die from hepatocellular carcinoma (HCC) which is the most common pathological type [ 2 ]. Surgical treatment remains the most effective way to HCC. However, due to the insidious onset and rapid progression of HCC, patients frequently fail to avail themselves of the surgical option because of delayed medical consultation [ 3 ]. Moreover, HCC patients often face the daunting challenge of high prevalence in chemotherapy drug resistance, distant metastasis and recurrences, consequently resulting in an unfavorable prognosis [ 3 , 4 ]. Therefore, it is vital to deeply investigate the underlying mechanism of HCC occurrence and development, so that we can find new and promising targets for diagnosis and treatment of HCC patients.

Endoplasmic reticulum (ER) involved in lipid and carbohydrate metabolism and calcium strorage [ 5 , 6 ]. Moreover, as the largest and the most powerful organelle in eukaryotic cells, ER is also mainly responsible for the synthesis, transportation and folding of protein [ 5 , 6 ]. Endoplasmic reticulum stress (ERS) refers to the protein folding disorder in ER under pathological or physiological stimuli, such as activation of oncogenes, oxidative stress, hypoxia, and infection [ 7 , 8 ]. ERS regulate three main pathway of unfolded protein response (UPR), including PRKR-like ER kinase, activated transcription factor 6, and inositol requirement Enzyme 1 which alleviate the load of unfolded proteins load, and maintain cell homeostasis and function [ 9 ]. UPR pathways are activated in most cancer types because protein synthesis increases dramatically during the rapid proliferation of tumor cells [ 10 , 11 ]. As the initiating factor of UPR, ERS plays a crucial role in the therapy response and prognosis of cancer. At the beginning of chemotherapy, drugs cause deficiencies in nutrients and hypoxia of tumor cells, which lead to the ERS followed by UPR [ 12 , 13 ]. Once the UPR is activated, tumor cells release pro-survival components including cytokines, growth factors, and other factors, which induce cancer cell growth and proliferation and suppressing anti-tumor immune response [ 14 , 15 ], It is reported that when HCC mice were treated with the IRE1α-inhibitor, alleviation of tumor load and collagen accumulation were observed, which indicate that regulating ERS and UPR is an effective way to inhibit drug resistance to HCC.

In our study, machine learning techniques such as Random Forest (RF) and Support Vector Machine (SVM) algorithms were applied to screen for key genes associated with hepatocellular carcinoma (HCC). Subsequently, by integrating these genes, an artificial neural network was utilized to construct an ERS-related HCC diagnostic model. On the training set and three validation sets, the diagnostic model exhibited satisfactory predictive performance. We also conducted a comprehensive analysis of the expression levels, immune infiltration, methylation, and mutation status of ERSRGs. Our research offers a novel perspective on understanding the molecular mechanisms of HCC and identifies potential targets for developing new diagnostic and therapeutic strategies for HCC.

Data sources used for analysis

The author first integrated gene expression matrices from GSE25097, GSE62232, and GSE65372, analyzed the gene expression differences between normal and liver cancer tissues, and conducted functional enrichment analysis. By comparing the intersection of differentially expressed genes with genes related to endoplasmic reticulum stress, and employing two machine learning methods, six candidate biomarkers were identified, including SRPX, THBS4, CTH, PPP1R16A, CLGN, and THBS1. Based on these genes, an artificial neural network (ANN) algorithm was utilized to construct a diagnostic model. Subsequently, the diagnostic performance of these candidate genes was validated in three independent validation sets (GSE121248, GSE45267, and GSE84005). Moreover, molecular docking was employed to screen potential target drugs, and the immune cell infiltration rate, methylation level, and mutation rate of the marker genes were assessed. It was found that PPP1R16A exhibited a high copy mutation rate and was significantly correlated with the level of immune cell infiltration. To further identify PPP1R16A as a core gene in the endoplasmic reticulum stress model, single-cell sequencing and cell communication analyses were conducted to study its expression and distribution patterns in the tumor microenvironment. Finally, the biological function of the PPP1R16A gene was validated through in vitro experiments. The overall design of this study is illustrated in Fig.  1 .

The overall flow of this study

Data collection and preprocessing

Transcriptome data and clinical information of HCC patients and normal tissue donors were from the GEO database ( https://www.ncbi.nlm.nih.gov/geo/ ). Three datasets were obtained, including GSE25097 (249 normal tissues and 268 tumor tissues), GSE62232 (10 normal tissues and 81 tumor tissues), and GSE65372 (15 normal tissues and 39 tumor tissues). These datasets were combined and removed repeating tissues using “sva” R package. A total of 662 samples were obtained, and the expression matrix of 14,738 genes was used as the training set. Besides, validation sets consisted of three datasets including GSE121248 (37 normal tissues and 70 tumor tissues), GSE45267 (39 normal tissues and 48 tumor tissues), and GSE84005 (38 normal tissues and 38 tumor tissues). All data is standardized and log-transformed by using the R “limma” software package for subsequent analysis [ 16 ].

The identification of differential expressed genes and ERSRGs

The “limma” R package was used to detect differential expressed genes (DEGs) between HCC and normal tissues in the training set with |log2 fold change (FC)|> 1.5 and adjusted p  < 0.05 as cutoff value. The volcano plot and heat map showing the differential expression of genes between HCC and normal tissue were made using the “ggplot2” and “heatmap” R packages. The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) functional enrichment analysis were conducted among DEGs using “clustersProfiler”, “enrichplot”, “limma”, “ggplot2” and “org.Hs.eg.db” R package. The hallmark gene set “h.all.v7.4.symbols.gmt” was downloaded from MSigDB datasets ( https://www.gsea-msigdb.org/) 17 and used for gene set variance analysis (GSVA) analysis with the p  < 0.05 and false discovery rate (FDR) < 0.25. In addition, 15 hallmark gene sets including 312 ERSRGs were also downloaded from the MSigDB database.

The construction and validation of artificial neural network (ANN) prediction model using artificial intelligence algorithms

With “random Forest” R package, we established the RF model using fivefold cross-validation method to iterate on the variables’ number at each split and tree. When the number of branches was 125, we got the minimum residual error. We ranked genes according to Gini coefficient score and those genes with score > 20 were finally selected [ 18 , 19 ]. Using the “e1071” and “caret” R package, the SVM algorithm is applied to delete the SVM-generated feature vectors and identify the optimal variables. We fitted a linear SVM model, sorted the variables by their weights and eliminated the variables with low weights. Through the cross validation, the number of selected genes was determined when root mean square error is minimal [ 20 ]. ERSRG with RF and SVM algorithms, the ANN predictive model was established. The ANN model was constructed based on a multilayer perceptron network using R package “neuralnet” and “NeuralNetTools”. This ANN model include input layers, hidden layers, and output layers, and was tested using back-propagation algorithms. The first layer through input layers, neurons transmit the weighted data to neural groups, and then the hidden layers was applied to randomly select bias. Once the hidden nodes’ net sums is validated, the output responses were provided through transfer function [ 21 ]. In our study, six ERSRG were selected as the input nodes, as well as HCC and normal tissues were used as the output nodes. Besides, the predictive performance of ANN prognostic models was evaluated through the area under curve (AUC) of time-dependent receiver operating characteristics (ROC) curves using R package “pROC”.

Survival and methylation analysis of HCC patients

Survival analysis for six ERSRG was performed using the gene expression profiling interactive analysis (GEPIA) website ( http://gepia.cancer-pku.cn/index.html ). The Kaplan–Meier (K-M) survival analysis was performed to compare patient differences between high and low expression of ERSRG group in conjunction with the log-rank test. Obtain the methylation levels of the six ERSRGs from the UALCAN website ( https://ualcan.path.uab.edu/ )

Immune, co-mutation and genetic alterations analysis of six ERSRG in HCC patients

The infiltration of 29 type of immune cells and immune-related pathways were analyzed in HCC patients through ssGSEA analysis using the “GSVA” and “GSEABase” R package [ 22 , 23 ]. The relationship of ERSRG expression with immune cell infiltration and pathway enrichment was identified through Spearman analysis for coefficient calculation. Genomic data of six ERSRG including somatic mutations and DNA copy-number alterations was obtained from cBioPortal website ( https://www.cbioportal.org/ ). Besides, co-mutation analysis of six ERSRG was applied with “corplot” R package to explore the expression correlation among them.

Single-cell RNA-seq analysis

GSE149614, liver cancer single cell data set, which includes 10 liver cancer samples. Seurat package (version 4) is used for single cell data processing and analysis. Specifically, 3,4411 cells are obtained by preliminary screening according to the data of genes expressed in at least 3 cells and cells expressing at least 200 genes. Further, according to the secondary screening conditions of 500 < nfeature < 5000 and mitochondrial gene ratio < 10%, cell populations retained in each sample is shown in Table  1 .

Normalize data + find variable features + scale data function pipeline was used to standardize and normalize the data, and runpca function was used to calculate the top 50 principal components. According to the results of jackstraw and elbow plot, the top 20 principal components are the most appropriate. The find clusters function identifies 31 clusters with a resolution of 0.4 to 30, and umap performs dimensionality reduction clustering.

Cell-cell interaction analysis

The cell communication was analyzed using the R package ‘cell chat’. First, according to the expression of the protein phosphatase 1 regulatory subunit 16 A (PPP1R16A), the presence or absence of hepatocytes was divided into PPP1R16A positive and negative groups, and they were analyzed together with other cells. Additionally, the netAnalysis_compute Centrality function compared the outgoing signals and incoming signals among different cells to determine the core pathways mediating cell-cell interactions, and the hierarchy plot visualization was performed for some selected pathways. The netAnalysis_contribution function calculated the contribution of the receptor-ligand pairs in specific pathways and displayed the ligand-receptor pairs with the highest contribution.

Cell culture and transfection

The liver cancer cell lines HCCLM3, HepG2, Hep3B, and the normal liver cell line LO2 were all purchased from Shanghai Fuheng Cell Biology Co., Ltd. and cultured according to standard procedures. Lipid-mediated siRNA transfection was performed using the lipo3000 reagent (Invitrogen, USA) according to the siRNA product instructions (Ribio, China). The siRNA sequences targeting PPP1R16A are as follows: siRNA#1 - TGCCCGAAATGACCTGGAA; siRNA#2 - TGCGGCATCTATACTCCAA; siRNA#3 - CCAACATCAATGCCTGTGA.

Real-time quantitative qRT-PCR

Total RNA from HCC cells was extracted using the TRIzol reagent (Invitrogen) and reverse transcribed into cDNA using PrimeScript Reverse Transcriptase (Takara, Japan) before qRT-PCR. Quantitative PCR was performed using SYBR Premix EX TaqTM II (Takara, Japan) and the LightCycler 480 real-time PCR system (Roche, Shanghai, China). GAPDH was used as an endogenous control gene to normalize the expression of the target gene. Each sample was analyzed in triplicate. The thermal cycling program included holding for 10 s at 95 °C, 30 s at 60 °C, and 60 s at 72 °C. Then, melt curve data was collected. The primer sequences are shown in Table  2 .

Cell proliferation, migration and invasion assay

After transfecting cells with siRNA for 48 h, 3,000 cells per well were seeded into 96-well plates. Each well was contained 100 µl complete growth medium and 10µL CCK-8 was added and mixed in each well. After 2 h, the absorbance at 450 nm was measured. Cell migration and invasion were determined using a Transwell chamber. In the migration assay, cells (5*10 3 ) after 48 h of siRNA transfection were seeded into the upper chamber (Corning) of serum-free medium, and the lower chamber was filled with medium containing 20% FBS. After approximately 24 h, they were fixed with 4% paraformaldehyde for 20 min and stained with 0.1% crystal violet for 15 min. The invasion assay followed the same procedure as the migration assay, except that the lower chamber of the invasion experiment was coated with Matrigel (BD Biosciences).

Drug prediction and molecular docking

The drug prediction for the core genes of the endoplasmic reticulum stress model was conducted using the DSigDB database from Enrichr ( https://maayanlab.cloud/Enrichr/ ). Subsequently, the small molecules were docked with the aforementioned central targets using AutoDock Vina (Scripps Research, San Diego, CA). The docking results were evaluated and analyzed using the PLIP system ( https://plip-tool.biotec.tu-dresden.de/plip-web/plip/index ). Finally, the molecular docking (MD) results in two-dimensional structure were visualized using LIGPLOT software version 4.5.3, and the MD diagrams were generated using PyMOL. Protein structures were obtained from the PDB ( https://www.pdb.org/ ) or AlphaFold ( https://alphafold.com/ ), and drug-related data were retrieved from PubChem ( https://pubchem.ncbi.nlm.nih.gov/ ).

Cell scratch

Cells were seeded equably in 6-wll plate, and then transfected for 48 h. Approximately 5*10 5 cells were added to each well, and 10uL needles were used to scrape three horizontal lines on the surface of plate. Following this, the cells were cultured in a 2% serum medium for 24 h. Optical microscopy was used to collect images of cells at after 0 and 24 hours.

Statistical analysis

Version R 4.1.3 was used for all statistical studies. Differences between two groups were compared through Mann–Whitney test for non-normally distributed variables and unpaired t-test for normally distributed variables. Spearman analysis was used for correlation analysis and coefficient calculation. We analyzed the RT-qPCR results using paired t-tests and drew a Venn diagram using funrich software. P value < 0.05 was defined as statistically significant.

The identification of DEGs between HCC and normal tissues

We performed principal component analysis (PCA) of genes of datasets GSE25097, GSE62232 and GSE65372, under both non-batch remove and batch remove condition respectively, and results were presented in Fig.  2 A and B. The results show that we successfully removed batch effects from different data sets. The volcano plot in Fig.  2 C showed the 763 DEGs between HCC and normal tissues, of which 215 genes were up-regulated and 548 genes were down-regulated. The heatmap in Fig.  2 D also presented the expression level of DEGs between HCC and normal tissues.

Differential gene expression analysis between liver cancer and normal tissues.( A ) Principal component analysis (PCA) of genes without batch removal for datasets including GSE25097, GSE62232, and GSE65372. ( B ) PCA of genes with batch removal for datasets including GSE25097, GSE62232, and GSE65372. ( C ) A volcano plot representing 763 differentially expressed genes (DEGs) between liver cancer tissues and normal tissues. ( D ) A heatmap showing the 763 DEGs between HCC and normal tissues

GO and KEGG as well as GESA enrichment analysis

The functional enrichment of DEGs between HCC and normal tissues was analyzed. The biological process in GO analysis of DEGs mainly included the alpha-amino acid metabolism, small molecule catabolism, carboxylic acid catabolism, organic acid catabolism, cellular amino acid metabolism. The cellular component in GO analysis was enriched in collagen-containing extracellular matrix, collagen trimers, blood microparticles, mitotic spindle and chromosomal regions. The molecular function in GO analysis was mainly involved in monooxygenase activity, iron ion binding, oxidoreductase activity acting on paired donors, with incorporation or reduction of molecular oxygen, heme binding, and steroid hydroxylase activity (Fig.  3 A-C). Besides, the KEGG analysis showed that the DEGs mainly contributed to tryptophan metabolism, complement and coagulation cascades, PPAR signaling pathway, cell cycle, and tyrosine metabolism in Fig.  3 D. Furthermore, the GSVA analysis of DEGs was conducted to explore the molecular pathway enrichment of DEGs. The results showed that DEGs were mainly abundant in hallmark MYC targets V1, hallmark E2F targets, hallmark inflammatory response, hallmark TNF-α signal via NF-κB and hallmark interferon-γ response (Fig.  3 E).

Functional enrichment of DEGs between liver cancer tissues and normal tissues.( A - C ) Gene ontology (GO) analysis of DEGs, including molecular function (MF), cellular component (CC), and biological process. ( D ) Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis of DEGs. ( E ) Gene Set Variation Analysis (GSVA) of DEGs.

The identification of ERS-related DEGs

The 11 ERS-related DEGs were obtained through the intersection of 312 ERSRGs and 763 DEGs in Fig.  4 A.A RF analysis further screened 11 ERSRGs. The residual graph in Fig.  4 B showed that the residual was the smallest when the number of branches was 125. As presented in Figs.  4 C and 11 ERSRGs were scored in this RF tree, and 7 of them with a score of > 20 were finally selected according to the descending Gini coefficient. Additionally, SVM analysis was also applied to screening the 11 ERSRGs. As depicted in Fig.  4 D, the root mean square error was the smallest when 6 of 11 ERSRGs were retained. Through the intersection of 7 ERSRGs in RF analysis and 6 ERSRGs in SVM analysis, 6 ERSRGs including SRPX, THBS4, CTH, PPP1R16A, CLGN, and THBS1 were finally determined for the construction of the ANN below (Fig.  4 E).

Identification of ERS-related differentially expressed genes. (A) ERS-related genes ( n  = 312) were cross-referenced with DEGs ( n  = 763) to identify ERS-related differentially expressed genes (ERSRGs) ( n  = 11). ( B ) Residual plot for the selection of ERSRGs using the Random Forest (RF) algorithm. ( C ) The 11 selected ERSRGs are arranged in descending order according to the Gini coefficient. ( D ) Selection of ERSRGs using the Support Vector Machine (SVM) algorithm. ( E ) Identification of 6 ERSRGs through the intersection of genes selected by RF and SVM.

Construction and validation of ANN prediction model in HCC

The ANN was established including 6 neurons (SRPX, THBS4, CTH, PPP1R16A, CLGN, and THBS1) as the input layer and 2 neurons (HCC and normal tissues, respectively) as the output layer (Fig.  5 A). When the hidden layer included 5 neurons, the predictive performance of the model was the highest with the AUC value of 0.979 (95% CI: 0.968–0.989) in the training group (Fig.  5 B). The efficiency of this ANN predictive model was also assessed in the other 3 validation groups. The AUC value of the ANN model was 0.958 (95% CI: 0.914–0.992), 0.936 (95% CI: 0.852-1.000) and 0.970 (95% CI: 0.920-1.000) in GSE121248, GSE45267 and GSE84005, respectively (Fig.  5 C-E). Above all, this ANN prediction model based on the ER is a potentially powerful tool to predict HCC diagnosis.

Construction and Validation of the Artificial Neural Network (ANN) Prediction Model for Liver Cancer. ( A ) The ANN comprises 6 neurons as the input layer, 2 neurons as the output layer, and 5 neurons as the hidden layer. ( B ) The AUC value of the ANN prediction model in the training set. ( C - E ) The AUC values of the ANN prediction model in the validation groups

The impact of endoplasmic reticulum stress core genes on immune infiltration deserves attention

The expression of six ERSRGs between HCC and normal tissues was deeply investigated in Fig.  6 A. The results showed that there were significantly higher expression levels of CLGN, PPP1R16A and THBS4 and lower expression levels of CTH, SRPX and THBS1 in HCC tissues compared with normal tissues, implying that CLGN, PPP1R16A and THBS4 as oncogenic genes played an important role in HCC development and progression while CTH, SRPX and THBS1 as onco-suppressor genes may play an inhibitory role in HCC. The interaction of 6 ERSRGs were further analyzed to identify the co-occurrence or mutually exclusive relationship of them in Fig.  6 B. There was an intense co-occurrence relationship between THBS1 and SRPX with a correlation coefficient of 0.67 as well as a mutually exclusive relationship between SRPX and THBS4 with a correlation coefficient of -0.53. In order to explore the underlying mechanism that these ERSRGs contributed to HCC occurrence and progression, the relationship of 6 ERSRGs expression levels with the immune cell infiltration and immune-related pathways was analyzed as presented in Fig.  6 C. We can conclude that THBS1 and SRPX were significantly positively associated with the infiltration of most immune cells and immune-related pathways such as neutrophils, T helper cells, tumor-infiltrating lymphocytes (TILs), cytokines and chemokines receptors (CCR), and para-inflammation etc. In contrast, THBS4, PPP1R16A and CLGN were significantly negatively associated with the infiltration of most of immune cells and immune related pathways such as neutrophils, T helper cells, TILs, CCR, checkpoint, T cell co-inhibition. Above findings further demonstrated that the ERSRGs can regulate tumor immune micro-environment to expose effects on HCC prognosis.Genes related to the TNF family molecules and chemotactic factors have been collected from previous literature. The ggcor and ggpplot2 packages were utilized to analyze and visualize the expression correlations between core genes and two types of immune activity molecules. The results show green squares representing positive correlations, purple squares indicating negative correlations, solid lines representing positive correlations, and dashed lines depicting negative correlations. Notably, CLGN, SRPX, and THBS1 exhibit positive correlations with most immune activity molecules, whereas PPP1R16A, CTH, and THBS4 show negative correlations with most immune activity molecules (Fig.  6 D). This conclusion is largely consistent with the results of the immune cells and processes depicted in the preceding figure.

Expression and Immune infiltration Analysis of Six ERSRGs. ( A ) The expression of six ERSRGs between HCC and normal tissues. ( B ) The Co-mutation analysis of six ERSRGs. ( C ) The relationship of six ERSRGs expression levels with the immune cell infiltration and immune-related pathway. ( D )Correlation between the expression levels of 6 ERSRGs and immune-related molecules

Drug screening and molecular docking of characteristic genes

The results showed the top 20 drugs with the highest score values, among which L-cysteine had the highest binding score, while Mometasone had the strongest binding significance (Fig.  7 A). Therefore, we conducted further molecular docking model simulations of the target genes bound by these two drugs, and the results showed that THBS1 had the lowest binding energy with Mometasone compared to CTH, which was − 6.917 kcal/mol. Therefore, Mometasone may be the most suitable therapeutic drug for THBS1 (Fig.  7 B-D).

Predict the top 20 candidate drugs for endoplasmic reticulum stress-related genes based on PubChem. ( A ) Predict the top 20 most significant candidate compounds for ERSRGs using the DSigDB database. ( B - G ) Molecular docking between THBS1 and Mometasone

The six ERSRGs-related survival, mutation and methylation analysis

It was depicted from the Kaplan–Meier (KM) curve that the HCC patients with low expression levels of CLGN and PPP1R16A had significantly longer overall survival than those with high expression levels of two genes (Fig.  8 A and C). By contrast, HCC patients with high expression levels of CTH statistically lived longer compared with those with low expression level of CTH (Fig.  8 B). However, there was no significant difference in the prognosis of HCC patients between high and low expression of SRPX, THBS1 and THBS4 which needed further validation (Fig.  8 D-F). In addition, gene mutation and copy number variation of 6 ERSRGs in 379 HCC patients from the cBioportal website was compared in Fig.  7 G. There was the highest mutation rate of PPP1R16A among 6 ERSRGs which was characterized by gene amplification. In addition to describing the survival and mutation status of core ERS genes, we also analyzed the methylation status of the promoters corresponding to these core genes. According to the calculation method on the UALCAN website ( https://ualcan.path.uab.edu/ ), we found that there were significant differences in the degree of promoter methylation between the control group and the liver cancer cell group for CLGN, CTH, PPP1R16A, and THBS1. Among them, the promoter methylation level of CLGN was stronger in the tumor group, while the methylation levels of the other three genes were significantly reduced in the tumor group, especially PPP1R16A (Fig.  8 H-M).

Survival, mutation and methylation Analysis Related to ERSRGs. ( A - F ) Kaplan-Meier curves representing the differences in overall survival between groups with high and low expression levels of six ERSRGs. ( G ) Comparison of gene mutations and copy number variations among the six ERSRGs. ( H - M ) Calculation of methylation levels of six ERSRGs genes

Single-cell sequencing analysis of the immune landscape of the PPP1R16A gene

The UMAP diagram shows that all cells can be annotated into seven types of cells, including liver parenchymal cells, macrophages and so on (Fig.  9 A). The bubble chart shows the marker genes used in seven different annotation cell groups. Such as liver Parenchymal cells (CD24, MDK), Macrophages (CD68, CD163), Fibroblasts (PDGFRb), Endothelial cells (PECAM1), T/NK cells (CD3D, CD3E), Plasma cells (JSRP1), B cells (MS4A1) (Fig.  9 B). Single cell analysis showed that PPP1R16A was mainly expressed in malignant liver parenchymal cells (Fig.  9 C). Go enrichment analysis showed that the cells with high expression of PPP1R16A were mainly related to the process of lipid metabolism, such as cholesterol metabolism, fatty acid metabolism and so on (Fig.  9 D). KEGG enrichment analysis showed that PPP1R16A was related to glucose metabolism, lipid metabolism and PPAR signaling pathway. These results suggest that PPP1R16A is highly enriched in liver parenchyma, which may aggravate the progression of liver cancer by affecting metabolism related pathways (Fig.  9 E).To further explore the downstream pathways of PPP1R16A, we conducted a GSEA analysis of KEGG at the single-cell level. The differential analysis results of all genes were sorted by their logFC, and GSEA analysis was performed using the clusterProfiler package and GSVA package. The resulting KEGG analysis results were further clustered and visualized using the aPEAR package. The results showed that among all significant signaling pathways, seven pathways had clustered modular characteristics and were more core signaling pathways (Fig.  9 F).

Single-cell RNA-seq Data Analysis (GSE149613). (A) UMAP plot of annotated cell types. ( B ) Bubble chart showing markers corresponding to different cell types. ( C ) UMAP diagram shows the expression of ppp1R16A ( D ) Go enrichment analysis, including BP, CC, MF. ( E )KEGG enrichment analysis; The color close to blue indicates a smaller p value, and the larger bubble table indicates that more differential genes are enriched in this pathway.( F )Analysis of downstream signaling pathways of GSEA at the single-cell level

Exploration of the impact of the PPP1R16A gene in the tumor microenvironment through cellular communication

Based on the expression of PPP1R16A in malignant liver parenchymal cells, we can divide the liver parenchymal cells into PPP1R16A_pos and PPP1R16A_neg. Using the Cellchat package to calculate the cell communication between these two types of cells and other cell types, we found that fibroblasts, endothelial cells, and PPP1R16A_pos cells are the core cells in the communication network, with fibroblasts being the cell with the strongest output signals. From the perspective of PPP1R16A_pos as the signal sender, it has more communication numbers and intensities with endothelial cells and macrophages (Fig.  10 A-B). PPP1R16A_pos cells had more communication with VTN, PARs, complement, CD46, PROS, CADM, GDF, CDH, and OCLN compared to other cells. When looking only at PPP1R16A_pos cells themselves, the strongest output signaling pathway was the macrophage migration inhibitory factor (MIF) pathway. In a lateral comparison, PPP1R16A_pos cells had more communication with MK, FN1, ANGPTL, THBS, PTN, CADM, EGF, CDH, and OCLN. When looking only at PPP1R16A_pos cells themselves, the strongest received signal was the COLLAGEN pathway (Fig.  10 C). We further studied the specific interactions between MIF and COLLAGE pathways. Hierarchical diagram shows that PPP1R16A_pos cells, as MIF signal senders, mainly send signals to T/NK cells, macrophages, B cells, and plasma cells (Fig.  10 D). As a COLLAGEN signal receiver, it mainly receives signals from endothelial cells and fibroblasts, of which fibroblasts received the most signals (Fig.  10 E). Further mining of the ligands and receptors that are most likely to play a role in the pathway, the highest pairing probability in the MIF pathway was between the MIF ligand and the CD74 + CXCR4 receptor (Fig.  10 F), and the highest probability of pairing in the COLLAGEN pathway was between the COL4A1 ligand and the SDC1 receptor (Fig.  10 G).

Inference of cell–cell communications in TME. ( A - B ) A cell–cell communications between the identified cell types. ( C ) The incoming and outgoing signaling pathways of each cell type. ( D - G ) The hierarchical diagram displays the specific interaction between MIF and COLLAGE pathways

Upregulation of PPP1R16A in liver cancer and knockdown of PPP1R16A significantly suppresses the proliferative, invasive, and migratory abilities of HCC cells

By summarizing all the aforementioned findings, we have established that PPP1R16A was a pivotal gene associated with ERS and exhibiting a high copy number mutation. QRT-PCR experiments, showed that human HCC cell lines exhibited a higher expression of PPP1R16A compared to normal liver cells (Fig.  11 A). We simultaneously transfected siRNA into Hep3B and HCCLM3 cells, selecting the highly most effective siRNA-PPP1R16A#1 for subsequent experiments (Fig.  11 B and C). CCK8 experiments indicated that knockdown of PPP1R16A suppressed the proliferation capacity ability of Hep3B and HCCLM3 cells (Fig.  11 D). Meanwhile, scratch test and Transwell experiments demonstrated that knockdown of PPP1R16A inhibited the migration and invasion abilities of HCCLM3 and Hep3B cells. These observations suggested that PPP1R16A is a positive regulator of HCC cells (Fig.  11 E and F).

Validation of PPP1R16A Expression Levels and Knockout of PPP1R16A Significantly Inhibits Proliferation, Invasion, and Migration Capabilities of HCC ( A )Expression levels of PPP1R16A mRNA in HCC cell lines. ( B - C ) Knockout efficiency of PPP1R16A mRNA in Hep3B and HCCLM3 cells. ( D ) CCK8 assays indicate that the knockdown of PPP1R16A inhibits the proliferation abilities of Hep3B and HCCLM3 cells. ( E - F ) Knockout of PPP1R16A inhibits the migration and invasion capabilities of Hep3B and HCCLM3 cells (* p  < 0.05, ** p  < 0.01, *** p  < 0.001, ns: not significant)

Hepatocellular carcinoma (HCC) is a type of tumor with poor prognosis, exhibiting a high incidence rate and mortality rate globally. It is a highly refractory disease, despite advancements in surgical and systemic treatments, the prognosis for HCC remains unsatisfactory. Early diagnosis and treatment can improve the survival rate of HCC patients. It is imperative to detect and identify the progression characteristics of the tumor at an early stage. Therefore, developing new treatment techniques and identifying new therapeutic targets is of critical importance.Currently, endoplasmic reticulum stress has garnered widespread attention from various cancer researchers. During endoplasmic reticulum stress, when cells are overly stimulated, the unfolded protein response (UPR) is triggered. This response influences the regulation of the endoplasmic reticulum (ER) balance through three distinct receptors: IRE1α, PERK, and ATF6. Research has shown that the activation of ERS affects tumor cell proliferation, invasion, metastasis, and promotes rapid tumor progression [ 24 , 25 ].However, the biological mechanisms underlying ERSRGs remain unclear, and their impact on HCC warrants further exploration。.

In this study, we conducted a comprehensive analysis of transcriptomic data between liver cancer and normal tissues. We identified differentially expressed genes (DEGs) between liver cancer and normal tissues, as well as the functions and molecular pathway enrichment of these DEGs. Based on the expression profile analysis of the training set, six endoplasmic reticulum stress-related genes (ERSRGs) were identified using the Random Forest (RF) and Support Vector Machine (SVM) algorithms. Subsequently, an artificial neural network (ANN) prediction model was constructed and demonstrated effective predictive performance. This model was further validated on three independent test sets, confirming its superior predictive capability. We also conducted an in-depth study on the association and function of these genes in tumorigenesis and immunomodulation.

The current (ERSRGs) encompass six potential genes (SRPX, THBS4, CTH, PPP1R16A, CLGN, and THBS1). In fact, previous studies have elucidated the significant roles of some of these genes in various tumors. Cystathionine γ-lyase, encoded by the CTH gene, plays a crucial role in the cysteine sulfur metabolism pathway. It catalyzes the generation of hydrogen sulfide (H 2 S), L-cysteine, α-ketobutyrate, and ammonia [ 26 ]. Several studies have indicated that aberrant activation of the CTH/H 2 S signaling pathway is closely linked to the occurrence and progression of HCC [ 27 ]. X-rays activate the p38 mitogen-activated protein kinase, which in turn activates the CTH/H 2 S signaling pathway, inducing epithelial-mesenchymal transition and promoting invasion of liver cancer cells [ 28 ]. Recent research has also reported that FOXC1, by regulating CTH, inhibits cysteine metabolism, increases reactive oxygen species levels, and promotes tumorigenesis. Overexpression of CTH significantly inhibits the proliferation, invasion, and metastasis of liver cancer cells induced by FOXC1 [ 29 ]. In contrast, CTH presents a potential therapeutic target when normally regulated in contrast to FOXC1. Furthermore, Sushi repeat-containing protein X-linked (SRPX) has been identified as a potential therapeutic target in HCC treatment. SRPX has been identified through mRNA expression network analysis, and has been shown to suppress cancer cell stemness [ 30 ]. SRPX also regulates the migration and invasion of ovarian cancer through the Ras homolog family member A signaling pathway [ 31 ]. Thrombospondin-1 (THBS1), known for inhibiting angiogenesis, has been studied for its potential as a therapeutic target [ 32 ]. THBS1 promotes the progression and development of various cancers by regulating angiogenesis and tumor vascular perfusion [ 33 ]. Additionally, THBS1 modulates innate and adaptive immune cells through the CD47 signaling molecules, thereby restricting anti-tumor immunity [ 34 ]. Overexpression of THBS4 promotes the proliferation and migration of liver cancer cells, participates in the regulation of epithelial-mesenchymal transition progression and interacts with members of the integrin family to modulate the FAK/PI3K/AKT pathway [ 35 ]. The miR-142 is highly correlated with THBS4 overexpression in HCC tissue samples, by regulating THBS4 expression in HCC cells [ 36 ], PPP1R16A encoded the membrane-associated subunit of protein phosphatase 1 which is located on the plasma membrane as a CAR-binding protein [ 37 ]. The area under the ROC curve for PPP1R6A in global and initial-stage tumors was 0.82 and 0.76, respectively, showing excellent sensitivity and specificity to define the diagnosis likelihood of endometrial carcinoma [ 38 ]. However, the role of PPP1R6A in HCC diagnosis and prognosis is rarely known and requires further exploration. A recent study reported that upregulation of CLGN in HCC is significantly related to poor prognosis, especially in advanced stages which might be regulated by miR-194-3p, thus providing a potentially therapeutic target and prognosis predictor in HCC [ 39 ].

In cellular communication analysis, we found that the expression levels of these ERSRGs were closely associated with immune cell infiltration and the activity of immune-related pathways. Single-cell sequencing revealed that the high expression of PPP1R16A in the liver parenchyma may be a trigger for high-copy mutations. Given that MIF acted as a macrophage stimulator, we speculate from cell communication results that PPP1R16A cells may promote macrophage aggregation through the MIF pathway, which inducing M2 polarization of liver cancer cells. Recent study suggests that novel ERSRGs signature could an independent prognostic factor for HCC [ 40 ]. ERS regulate immune levels by regulating myeloid cells, mainly macrophages which is related to tumor evasion of the immune response, and chemoresistance. ER-stressed HCC cells release exosomes, upregulate the expression of PD-L1 in macrophages, and consequently suppresses T-cell function. A higher density of infiltrated macrophages in the liver has been observed to be associated with enhanced tumor aggressiveness and unfavorable prognosis among patients with HCC [ 11 ].

In recent years, immune checkpoint inhibitors (ICIs) as a new therapeutic approach targeting T cells regulatory pathways, have much attention [ 41 , 42 ], and have great prospects in the field of anti-tumor therapy. Through the analysis of ssGSEA results, we found that THBS1 and SRPX showed significant positive correlations with immune cell infiltration, neutrophils, helper T cells, TILs, CCR, and inflammatory response-related pathways. In contrast, THBS4, PPP1R16A, and CLGN exhibited significant negative correlations with immune cell infiltration, neutrophils, helper T cells, TILs, CCR, immune checkpoints, T cell co-inhibition, and other immune-related pathways. These findings suggest that ERSRGs is closely related to the immune status of liver cancer, and offered a new research direction for the combination targeting of ERSRGs and ICIs in the treatment of liver cancer. Through combination therapy, there is potential to enhance anti-tumor immune responses and improve the prognosis of liver cancer.

We also found in cellular communication analysis that, simultaneously, fibroblasts, as essential factors promoting tumor metastasis, may reshape the tumor microenvironment by enhancing the collagen pathway and promoting collagen deposition to affect the function of PPP1R16A cells. These results further indicate that PPP1R16A may influence the prognosis of HCC by regulating the tumor immune microenvironment. Additionally, our experimental results suggest that knocking out PPP1R16A can inhibit the proliferation, invasion, and migration capabilities of HCC cells, indicating that PPP1R16A may be a crucial tumor-promoting factor. Cancer-associated fibroblasts produce collagen and change the extracellular matrix, which is an important mechanism of tumor metastasis. Modulating targeting specific signaling molecules responsible for crosstalk between Cancer‐associated fibroblasts and tumor cells is considered a promising approach to modulating HCC metastasis [ 43 , 44 ]. Our study indicated that PPP1R16A may be one of such potential targets.

However, it is important to note that there are some limitations that need further addressing and in-depth exploration. Firstly, Considering the bioinformatics analysis based on public cancer databases, it is crucial to further validate the diagnostic and predictive performance of ERSRG markers in large-scale and prospective clinical trials and assess their potential clinical applications. This will contribute to ensuring the reliability and reproducibility of the analysis results and provide a more solid foundation for the clinical application of ERSRGs in liver cancer patients. Secondly, Despite some cell experimental validation was involved, providing support for preliminary findings, further in vivo and in vitro experiments are needed to thoroughly investigate the functions of ERSRGs in HCC. This expanded experimental research will contribute to a more comprehensive and in-depth understanding of the exact mechanisms of action of these genes in the development and progression of HCC, and contribute to a more comprehensive understanding of the potential efficacy and mechanisms of ERSRGs in combination with ICIs in liver cancer. Therefore, future research directions should include broader experimental designs to more comprehensively and systematically reveal the role of ERSRGs in the biology of HCC biology.

Conclusions

In this study, the researchers integrated the six identified ERSRGs into an ANN prediction model based on RF and SVM algorithms. Furthermore, we further investigated the biological mechanisms, immune regulation, and genomic mutations associated of these six ERSRGs in the diagnosis of liver cancer. The comprehensive analysis of ERSRGs provides a powerful tool for the prognosis prediction and personalized treatment of liver cancer patients. The feature model based on ERSRGs holds promise as a crucial prediction and therapeutic decision support system in the field of liver cancer research.

Data availability

All data generated or analyzed during this study are included in this published article.

Abbreviations

Hepatocellular Carcinoma

Artificial Neural Network

Endoplasmic Reticulum Stress

Protein Phosphatase 1 Regulatory Subunit 16 A

Area Under the Curve

Endoplasmic Reticulum Stress Related Gene

Unfolded Protein Respons

Differential Expressed Genes

Gene Expression Omnibus

Gene Ontology

Kyoto Encyclopedia of Genes and Genomes

Gene Set Variance Analysis

Receiver Operating Characteristic

Uniform Manifold Approximation and Projection

Random Forest

Support Vector Machine

Gene Expression Profiling Interactive Analysis

Principal Components

Sushi Repeat-Containing Protein X-linked

Thrombospondin-1

Macrophage Migration Inhibitory Factor

Immune Checkpoint Inhibitors

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This work was supported by grants provided by the National Natural Science Foundation of China (No. 81070125, 81270213, 81670306); the Science and Technology Foundation in Guangdong Province (No. 2010B031600032, 2014A020211002); the National Natural Science Foundation of Guangdong Province (No. 2017A030313503); the Science and Technology Foundation in Guangzhou City (No. 201806020084); the Fundamental Research Funds for the Central Universities (No. 13ykzd16, 17ykjc18); the Futian District Health and Public Welfare Research Project of Shenzhen City (No. FTWS2019001, FTWS2021016, FTWS2022018, FTWS2023064), the Shenzhen Fundamental Research Program (No. JCYJ20190808101405466, JCYJ20210324115003008, JCYJ20220530144404009).

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Zhaorui Cheng, Shuangmei Li and Shujun Yang contributed equally to this work.

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Department of Emergency, The Eighth Affiliated Hospital of Sun Yat- sen University, Shenzhen, 518003, Guangdong, P. R. China

Zhaorui Cheng, Shuangmei Li, Shujun Yang, Huibao Long, Haidong Wu, Xuxiang Chen & Tong Wang

The First Affiliated Hospital of Jiangxi Medical College, Nanchang University, Nanchang, 330000, Jiangxi, P. R. China

Xiaoping Cheng

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ZC, TW – designed study protocol, writing the manuscript; SL – construction and validation of ANN prediction model in HCC; SY – cell study in vitro; ZC – statistical data analysis; HL, HW, XC, XC – searching of world literature and discussion of the obtained results with the results of previous research; All the authors approved the final version of the article to be published.

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Cheng, Z., Li, S., Yang, S. et al. Endoplasmic reticulum stress promotes hepatocellular carcinoma by modulating immunity: a study based on artificial neural networks and single-cell sequencing. J Transl Med 22 , 658 (2024). https://doi.org/10.1186/s12967-024-05460-9

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DOI : https://doi.org/10.1186/s12967-024-05460-9

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