Hormonal fluctuations under systemic stress: Investigating adolescent hair cortisol trends before and during the COVID-19 pandemic.

Hormonal fluctuations under systemic stress: Investigating adolescent hair cortisol trends before and during the COVID-19 pandemic. | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Hormonal fluctuations under systemic stress: Investigating adolescent hair cortisol trends before and during the COVID-19 pandemic. Luciana Tovo-Rodrigues, Marina Xavier Carpena, Jessica Mayumi Maruyama, and 8 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9436198/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 6 You are reading this latest preprint version Abstract Background Adolescence is a sensitive developmental period marked by increased reactivity of the HPA axis to environmental stressors. However, little is known about how chronic cortisol levels change during prolonged adversity. Using the COVID-19 pandemic as a natural experiment, this study examines how large-scale environmental disruption affects HPA-axis regulation during adolescence. We investigated hair cortisol concentration (HCC) pre- and during the pandemic in adolescents from Pelotas, Brazil, and identified predictors for change. Methods Data were drawn from the 2004 Pelotas Birth Cohort, a population-based longitudinal study. Hair samples (3 cm) were collected twice: pre-pandemic (T1) in 2019 through early 2020 (mean age 15.7 years) and mid-pandemic (T2) in 2021 (mean age 17.4 years). HCC was quantified via ELISA method. Socioeconomic, demographic, and pandemic-related experiences, as well as self-reported stress levels, were assessed as potential determinants of changes in HCC. A multivariate latent change score (LCS) model was used to analyze changes in HCC and their associations with predictors. Results A total of 1,509 individuals were included in the analyses. There was a significant mean increase in HCC of 1.89 pg/mg (p < 0.001) from T1 to T2. Pandemic-related experiences, such as maternal job loss (p = 0.005) and higher levels of perceived stress (p = 0.041), were associated with greater increases in HCC. Government cash transfer program participation (p < 0.001), having a black or mixed-race mother (p = 0.017), and being female (p < 0.001) were associated with lower increases in HCC. Conclusions Findings suggest significant physiological effects of the COVID-19 pandemic, highlighting the role of demographic, socioeconomic, and pandemic-related factors in shaping cortisol responses and advancing understanding of its impact on adolescent health, with implications for policy and support in low- and middle-income countries. These findings highlight the intricate relationship between cortisol changes and psychological outcomes across various populations during the pandemic. Health sciences/Biomarkers Health sciences/Diseases Health sciences/Health care Health sciences/Medical research Biological sciences/Psychology Social science/Psychology Health sciences/Risk factors Hair cortisol concentration Adolescence Chronic stress HPA axis COVID-19 pandemic Longitudinal cohort Socioeconomic factors Latent change score model Mental health Low- and middle-income countries (LMICs) Figures Figure 1 1 Introduction A critical window for both biological and psychological development, adolescence brings increased plasticity in neural circuits and heightened sensitivity of the hypothalamic-pituitary-adrenal (HPA) axis to environmental stressors. One of the most widely used biomarkers for assessing this physiological response to adversity is cortisol, a hormone released by the adrenal glands that profoundly influences health, including metabolism, immune function, cardiovascular health, and mental well-being. While the human body's acute stress response is adaptive, continual activation of the HPA axis during this developmental stage can lead to long-term dysregulation, which has been to various health conditions, including mental disorders (i.e., depression, anxiety, etc), and metabolic disorders (i.e., obesity and diabetes) [ 1 , 2 ]. Whereas most such studies focus on serum or saliva cortisol, hair cortisol concentrations (HCC) have emerged as a method for assessing cumulative stress exposure [ 3 ]. HCC provides a retrospective assessment of cortisol production over months and serves as a sort of “biological record” of a person’s stress history. Studies indicated that HCC levels were higher in depressed patients than in healthy controls [ 4 ]. Moreover, increased HCC has been associated with the incidence of cardiovascular disease, poorer recovery outcomes, and cardiometabolic risk factors such as hypertension and diabetes [ 5 ]. Hair cortisol analysis has significant advantages over acute measurement, as it can provide a more comprehensive overview of chronic physiological stress during key growth periods. The COVID-19 pandemic has been a significant global stressor, enabling us to explore how biological pathways are affected during large-scale environmental disruptions. More specifically, in triggering widespread psychological, social, and physiological challenges, large effects are reported on mental health [ 6 ]. Globally, the pandemic exacerbated existing inequalities, particularly in low- and middle-income countries (LMICs), where it strained already fragile healthcare systems [ 7 ]. LMICs faced unique challenges in managing the pandemic, including limited economic resources and healthcare capacity [ 8 ], which led to adverse effects on living standards, education, health, and gender equality [ 7 ], increasing stress-related effects. Research on HCC during the COVID-19 pandemic has yielded important information about stress levels, particularly among healthcare workers and children [ 9 , 10 ]. Significantly elevated HCC levels have been documented among nurses during pandemic peaks compared with pre-pandemic periods, with those working in high-risk environments showing particularly elevated levels [ 9 , 10 ]. Healthcare workers exhibited increased stress and burnout, with 40% showing HCCs outside the authors' healthy reference range. Those with burnout (12%) demonstrated higher HCC scores [ 10 ]. A direct correlation was observed between HCCs and perceived stress and emotional exhaustion in the same study [ 10 ]. For healthcare workers, HCCs increased by a median of 29% after the start of the pandemic, and changes in cortisol were associated with burnout status three months later [ 9 ]. These findings highlight the intricate relationship between cortisol changes and psychological outcomes across various populations during the pandemic. However, the limited number of population-based studies hinders a comprehensive understanding of the pandemic's impact on the general population and healthcare workers. In children and adolescents, the COVID-19 pandemic had a profound impact on their mental and physical health, but there is limited evidence involving cortisol from different sample sources. Fung et al. (2022) conducted a longitudinal analysis revealing that HCC in healthy children and adolescents increased significantly during the pandemic, reflecting heightened chronic stress [ 11 ]. Similarly, Bilodeau-Houle et al. [ 12 ] tracked HCC They found that an initial decrease in cortisol early in the pandemic was associated with increased post-traumatic stress symptoms later, suggesting a delayed stress response. Among adolescents, Taylor et al. used longitudinal data to demonstrate that elevated circulating cortisol concentrations predicted declines in emotional well-being, particularly following the implementation of local lockdowns[ 13 ]. Nevertheless, there is a critical need to understand not only if the pandemic functioned as a systemic biological stressor, but also to identify the "signatures of risk," i.e., the sociodemographic and behavioural factors that predict an individual's HPA-axis reactivity. To our knowledge, no longitudinal studies have examined pre- and post-pandemic HCC in population-based cohorts of adolescents from LMICs. By investigating these determinants, we can better understand the differential susceptibility of adolescents to chronic stress and inform public health interventions designed to mitigate the long-term mental and metabolic consequences of environmental crises on this vulnerable demographic. The aim of this study was to compare changes in HCC pre- to during the COVID-19 pandemic by identifying key determinants of change by demographic and socioeconomic factors, pandemic-related variables, and stress perceptions among adolescents, in the 2004 Pelotas Birth Cohort study. 2 Methods 2.1 Participants and Study Design This study utilized data from the 2004 Pelotas Birth Cohort, a population-based prospective study from Pelotas, Brazil. Detailed procedures were published elsewhere[ 14 ]. The cohort initially comprised 4,231 participants, representing 99% of all live births occurring between January 1 and December 31, 2004, among mothers residing in the urban area of Pelotas and in the Jardim América neighbourhood of the neighbouring municipality of Capão do Leão. Participants were subsequently followed at multiple time points across development, including at 3, 12, 24, and 48 months, and at 6, 11, 15, 17 (subsamples only, due to constraints imposed by the COVID-19 pandemic), and 18 years of age. Retention rates varied across waves, ranging from 50.4% to 95.7%, with approximately 85% of the original cohort assessed at 18 years, which constitutes the primary time point for the present analyses [ 14 – 16 ]. Data collection for the current analysis involved two time points: pre-pandemic (T1), conducted from November 2019 to March 2020 (mean age 15.7 years), and mid-pandemic (T2), conducted from August to December 2021 (mean age 17.4 years). Only participants with available cortisol data for both T1 and T2 were included in the analysis, resulting in an analytic sample of 1,509 adolescents. More detailed procedures were published elsewhere[ 14 ]. 2.2 Measures Hair samples were collected by trained field workers, as detailed in Martins et al. (2023) [ 17 ], at both T1 and T2 to measure cortisol concentration (pg/mg), which served as a biomarker for chronic stress exposure. The samples were taken from the posterior vertex of the scalp, a region commonly used in cortisol studies due to its consistent hair growth and reduced variability in cortisol deposition. Following the collection, a standardized protocol [ 18 , 19 ] was applied for washing, grinding, hormone extraction, and cortisol measurement in the laboratory. Hair cortisol was extracted from the 3 cm of hair closest to the scalp. Samples were suspended in 150 µl of diluent for 24 hours, and cortisol concentrations were measured in duplicate using the ELISA technique with the Salivary Cortisol High Sensitivity Immunoassay Kit (Cat# 1-3002, Salimetrics, Pennsylvania), following the manufacturer’s instructions. The ELISA plate reader (Spectramax 190) was used for quantification, and HCC were expressed in pg/mg. Outliers, defined as values exceeding four standard deviations (SD) from the HCC mean in the raw data, were removed from the analytical sample. Due to the right-skewed distribution, HCC values were log-transformed before inclusion in the regression models. The reported variance, skewness, and kurtosis values correspond to the log-transformed HCC data (Variance = 0.19; Skewness = 0.01; Kurtosis = 3.67), which showed an approximately normal distribution. 2.3 Predictors 2.3.1 Main Exposures: mid-pandemic variables The main exposures were assessed at the mid-pandemic wave (T2) and included the following variables: being a beneficiary of government pandemic-specific cash transfer program ( Programa Auxílio Emergencial ); fear of food shortage (assessed by the question “Did you ever feel worried or afraid about not having enough food during the pandemic?”, with possible answers yes/no), conflicts at home (assessed by the question “Did your family frequently experience fights and arguments during the lockdown”, with possible answers yes/no), and maternal job loss during the pandemic. Self-reported stress was measured using the stress subscale from the Depression Anxiety Stress Scales (DASS-21)​[ 20 , 21 ]. The questionnaire applied in this study was previously published in Matijasevich et al. (2025) [ 22 ] 2.3.2 Pre-pandemic (T1) variables The study included socioeconomic factors assessed at birth to describe our sample and adjust our analysis for potential confounders. This included family income measured in quintiles (first quintile are the poorest group), maternal schooling (categorized as 0–4 years, 5–8 years, and ≥ 9 years), maternal skin colour (categorized as White or Black/Brown), whether the mother was cohabiting with a partner at childbirth, and participant child sex (male and female). Household wealth index was determined using the National Wealth Index questionnaire [ 23 ]. Principal component analysis integrated factors such as the household head's education, the number of bedrooms and bathrooms, and ownership of assets like televisions, vehicles, refrigerators, washing machines, and computers. The resulting wealth index classified households into five wealth strata based on reference cut-off values for each municipality [ 24 ]. Using Pelotas-specific values, households were stratified into first strata (20–280), second (281–367), third (368–475), fourth (476–618), and fifth (619–1478). The highest values, the wealthiest were the families. The Tanner developmental stage variable at the pre-pandemic wave (T1) was constructed by combining four distinct subscales: two for boys (genital development and pubic hair development) and two for girls (genital development and pubic hair development). Each subscale was scored on a scale from 1 to 5, where 1 represents the prepubertal stage, 2 indicates the beginning of development, 3 represents the mid-stage of maturation, 4 corresponds to near-adult development, and 5 represents full adult maturation. The stages were combined into a two-digit code representing both genital and pubic hair development: the first digit corresponds to the genital stage, and the second digit corresponds to the pubic hair stage. In addition, we also included hair type, weekly hair wash frequency (both at T1 and T2) and use of corticosteroid medication in the past three months (both at T1 and T2) as confounders in the analysis. 2.4 Statistical analysis We used a complete-case analysis, i.e., we included only individuals with data on hair cortisol at both time points, resulting in N = 1,509. Initially, a paired t-test was employed to compare mean cortisol concentrations before and at the mid-pandemic waves. Next, we employed a multivariate latent change score (LCS) model to rigorously assess changes in adolescents' cortisol concentrations from before the pandemic (T1) to during the pandemic (T2). Figure 1 shows the LCS model, which captures changes by defining the differences between T1 and T2 as a latent change score, with the mean representing the average change in the sample, and the variance representing individual differences in change over time [ 25 , 26 ]. The proportional change reflects how much change depends on initial cortisol concentrations. Sociodemographic and peri-pandemic covariates were included to investigate predictors of cortisol concentrations at T1 and of latent change from T1 to T2. We correlated initial cortisol concentrations and latent change scores to explore interrelations. Analyses were conducted in Mplus 8.4 using maximum likelihood estimation with robust standard errors (MLR). The model fit in the multivariate latent change analysis was assessed using the χ² statistic, the Comparative Fit Index (CFI), the Tucker-Lewis Index (TLI), the Root Mean Square Error of Approximation (RMSEA), and the Standardized Root Mean Square Residual (SRMR). A good fit is considered when CFI/TLI ≥ 0.90, RMSEA < 0.08, and SRMR < 0.08 [ 27 ]. 2.5. Ethical aspects The study protocol and all follow-ups of the 2004 Pelotas Birth Cohort were approved by the Ethics Committee Board of the Medicine School at the Federal University of Pelotas and by the Ethics Committee Board of the University of São Paulo, in accordance with the Declaration of Helsinki and national ethical guidelines. At all follow-ups, all mothers or legal guardians received and signed written informed consent forms, and all adolescents received age-appropriate information and provided written informed assent at ages 15 and 17. 3 Results 3.1 Descriptive statistics Table 1 presents the sociodemographic characteristics of the original cohort and the analytic sample. The analytic sample consisted of 1,509 adolescents with available data on hair cortisol at both pre-pandemic and mid-pandemic time points. Compared to the original cohort (N = 4,321), the analytic sample had a slightly higher proportion of adolescents from families with higher maternal education (46.6% vs. 43.6%), greater household income (82% vs. 79.4%), and a higher wealth index (81.7% vs. 79.4%). Additionally, the analytic sample included more adolescents whose mothers were cohabiting with a partner at the time of childbirth (84.8% vs. 83.6%) and a slightly higher proportion of females (51.5% vs. 48.1%) and White individuals (74.8% vs. 73%). Table 1. Socioeconomic characteristics of original cohort and analytic sample Original cohort Analytic sample N = 4321 N = 1509 % (95% CI) % (95% CI) Maternal schooling (years) 0-4 5-8 ≥ 9 15.4 (14.4 – 16.6) 40.9 (39.4 – 42.4) 43.6 (42.1 – 45.1) 13.6 (11.9 – 15.4) 39.8 (37.4 – 42.3) 46.6 (44.1 – 49.1) Family income (quintiles) 1 st quintile (poorest) 2 nd to 5 th quintile 20.6 (19.4 – 21.8) 79.4 (78.1 – 80.6) 18.0 (16.2 – 20.04) 82.0 (79.9 – 83.8) Wealth index (quintiles) 1 st quintile (poorest) 2 nd to 5 th quintile 21.6 (20.3 – 23.1) 78.3 (76.9 – 79.7) 18.3 (16.2 – 20.5) 81.7 (79.5 – 83.8) Skin color White Black/Brown 73.0 (71.7 – 74.3) 27.0 (25.7 – 28.3) 74.8 (72.6 – 73.7) 25.2 (23.0 – 27.4) Mother cohabiting with a partner at childbirth Yes No 83.6 (82.5 – 84.7) 16.4 (15.3 – 17.5) 84.8 (81.5 – 86.5) 15.2 (13.4 – 17.1) Adolescent sex Male Female 51.9 (50.4 – 53.4) 48.1 (46.6 – 49.6) 48.4 (45.9 – 50.9) 51.5 (49.0 – 54.1) Note. CI = confidence interval Analytic sample is composed by the individuals with hair cortisol data on both pre-pandemic and mid-pandemic sample 3.2 Hair cortisol concentrations and latent change analysis The model fit indices indicate a good fit to the data. The chi-square test of model fit was not significant (χ²(9) = 10.340, p = 0.324), indicating that the observed data did not differ significantly from the model. The RMSEA value was 0.010 (90% CI: 0.000 to 0.032), suggesting a close approximate fit. The CFI and TLI values were 0.987 and 0.951, respectively, both above 0.90, supporting the model's adequacy. The SRMR value was 0.008, well below the recommended cutoff of 0.08, indicating minimal residuals. Overall, these indices suggest an excellent model fit. Table 2 presents the mean hair cortisol concentrations at both time points and the results for the latent change modelling. There was a significant increase in hair cortisol concentrations between T1 and T2. The mean cortisol concentration increased from 4.429 pg/mg at T1 (95% CI: 4.146–4.713) to 6.321 pg/mg at T2 (95% CI: 6.071–6.573), representing a mean difference of 1.892 pg/mg (95% CI: -2.248, -1.536), with t (1508) = -10.424, p < 0.001, indicating a statistically significant increase during the pandemic period​. To illustrate the variability in individual responses, a histogram of intraindividual changes in hair cortisol concentrations (T2 - T1) is presented in Figure S1 (Supplementary Material). The distribution is approximately symmetrical and centred around zero, indicating that while the mean cortisol concentration increased significantly at the group level, individual changes varied substantially, with most participants exhibiting modest shifts and a small number showing significant increases or decreases. Table 2 Mean hair cortisol levels at T1 and T2, latent change scores, individual variability and proportional change of hair cortisol levels from pre-pandemic wave (T1) to mid-pandemic wave (T2) Pre-pandemic wave (T1) Mid-pandemic wave (T2) Latent change scores (LCS; T 2 − T 1 ) Individual variance Proportional change Mean (95%CI) Mean (95%CI) M slope (SE) σ 2 b (SE) Cortisol levels (pg/mg) 4.429 (4.146–4.713) 6.321 (6.071–6.573) § 1.892 (0.181) * 0.984 (0.08) * -0.723 (0.092) * Note. Standardized coefficients are shown. Maximum likelihood robust estimator was used. Latent change scores show the mean increase or decrease of hair cortisol levels between pre-pandemic and mid-pandemic waves, modelled as a latent variable; Individual variance (σ2) capture the extent to which individuals differ in the change they manifest over time; Proportional change shows the extent to which the latent change scores are related to pre-pandemic scores. S.E. = standard error; § t(1508) = -10.424, p < 0.001. * p < 0.001 Similarly, the LCS modelling revealed a significant increase in cortisol concentrations, with a latent factor mean of 1.892 (SE = 0.181, p < 0.001). The variance (σ²) in the change was 0.984 (SE = 0.08, p < 0.001), indicating considerable variability in how cortisol concentrations changed across individuals. Additionally, the proportional change coefficient was negative (b = -0.723, SE = 0.092, p < 0.001), suggesting that increases in cortisol concentrations were smaller among those with higher cortisol concentrations at T1. 3.3 Predictors of hair cortisol concentration at T1 and latent changes We investigated several demographic and pandemic-related predictors of cortisol concentrations and changes from T1 to T2 (Table 3 ). We found that mother cohabiting with a partner at childbirth was associated with higher cortisol concentrations at T1 (β = 0.047, SE = 0.012, p < 0,001). Female adolescents showed significantly lower hair cortisol concentrations than males before the pandemic (β = -0.067, SE = 0.026, p = 0.009). Table 3 Pre-pandemic and mid-pandemic predictors of T1 hair cortisol and latent change scores from T1 to T2 Pre-pandemic (T 1 ) hair cortisol levels p Latent change score from T 1 to T 2 p β (SE) β (SE) Socioeconomic and pre-pandemic (T1) variables Maternal schooling (years) 0.009 (0.011) 0.405 -0.010 (0.020) 0.623 Family income (quintiles) -0.010 (0.019) 0.605 0.014 (0.022) 0.529 Wealth index (quintiles) -0.052 (0.033) 0.121 0.055 (0.029) 0.063 Mother cohabiting with a partner at childbirth 0.047 (0.012) < 0.001 -0.009 (0.017) 0.581 Black/mixed/other skin colour mothers 0.043 (0.033) 0.189 -0.064 (0.027) 0.017 Female adolescent -0.067 (0.026) 0.009 -0.084 (0.027) 0.002 Tanner developmental stage (at T1) - - 0.009 (0.011) 0.386 Pandemic-related predictors Beneficiary of pandemic-specific government cash transfer program a - - -0.040 (0.011) < 0.001 Fear of food shortage - - -0.041 (0.072) 0.566 Conflicts at home - - 0.025 (0.076) 0.744 Maternal job loss during the pandemic - - 0.045 (0.016) 0.005 Self-report stress levels b - - 0.043 (0.021) 0.041 Note. Standardized coefficients are shown. Maximum likelihood robust estimator was used. All the analyses were adjusted for hair type, weekly hair wash frequency (both at T 1 and T 2 ) and use of corticosteroid medication in the past three months (both at T 1 and T 2 ). S.E. = Standard errors. Statistically significant results are in bold. a Auxílio Emergencial; b Based on the stress subscale from the Depression Anxiety Stress Scales (DASS-21). Among pre-pandemic and pandemic-related predictors of the latent change, having a black or mixed mother (β = -0.064, SE = 0.027, p = 0.017), being a female adolescent (β = -0.084, SE = 0.027, p = 0.002) and being a beneficiary of the government pandemic-specific cash transfer program (β = -0.040, SE = 0.011, p < 0.001) emerged as negative predictors of change in cortisol concentrations from pre-pandemic to mid-pandemic waves. On the other hand, maternal job loss (β = 0.045, SE = 0.016, p = 0.005) and higher self-reported stress (β = 0.043, SE = 0.021, p = 0.041) were significant, positive predictors of changes in cortisol concentrations during the study period. 4 Discussion This study used the COVID-19 pandemic as a natural experiment to examine the extent to which systemic environmental disruption affects HPA-axis dynamics during the developmentally sensitive window of adolescence. We examined longitudinal changes in HCC within the 2004 Pelotas Birth Cohort and observed an overall upward trend in cortisol concentrations. This physiological shift, however, was non-linear and heterogeneous. Several key factors contributed to these changes, including maternal skin color, adolescent sex, participation in pandemic-specific government cash transfer program, participant’s self-reported stress, and maternal job loss. The increases in HCC from before to during the pandemic in this sample of adolescents are consistent with prior studies documenting significant rises in HCC among healthy children and adolescents during the COVID-19 pandemic [ 11 , 12 , 28 ]. For instance, a multicentric study observed a significant increase in HCC concentrations in female adolescents after the onset of the COVID-19 pandemic [ 29 ]. Additionally, research from the United States revealed that after local pandemic lockdowns, adolescents showed significant increases in circulating cortisol concentrations, which were associated with changes in perceived emotional well-being, indicating that physiological stress, as measured by cortisol, was correlated with emotional state changes in young individuals [ 13 ]. However, research on HCC during the COVID-19 pandemic has yielded mixed results. For example, a cross-cultural study comparing Dutch and US adolescents found no significant overall change in HCC from pre-pandemic to pandemic, except among Dutch girls, who showed a considerable increase [ 29 ]. Nevertheless, another study conducted in the United States found no significant changes in HCC concentrations in children and adolescents during the COVID-19 pandemic, suggesting that other factors, such as coping mechanisms or environmental influences, might have moderated the stress response in specific populations [ 11 ]. The significant rise observed in our population-based study may reflect increased environmental volatility and the vulnerability of social support structures typical of LMIC contexts, in which the pandemic's physiological impact may have been disproportionately severe. A key question in the literature is whether HCC is associated with self-perceived stressful events. The validity of HCC as a biomarker of chronic stress remains debated. While several studies support its use as a reliable indicator, others highlight inconsistencies and caution against interpreting findings [ 3 ]. In our cohort, higher levels of self-reported stress were associated with increased HCC concentrations, providing empirical support for its relevance as an indicator of chronic stress in this population. Although this perspective is consistent with other high-stress groups, including healthcare workers [ 10 ], it adds another piece to the already extensive debate on the relationship between subjective and biological measures of stress [ 11 , 30 , 31 ]. For example, while several studies have shown that pandemic-specific perceived stress did not correlate with cortisol in adolescence [ 11 ], or reported that adverse life events, rather than perceived stress, alone significantly increased the risk of HCC [ 30 ], our results demonstrate a more synchronized psychophysiological response. This coupling depends on the chronic nature of the stressor and the individual's personal coping system, both inside and outside the individual. These contrasting findings underscore the complex, multifaceted nature of stress responses during the pandemic, which can vary significantly across individuals and contexts. Our analysis suggested distinct sexual dimorphisms in cortisol response. Female adolescents had lower HCC before the pandemic and smaller increases in HCC during the evaluation period, suggesting consistently lower cortisol levels over time than their male counterparts. A meta-analysis reported that men generally exhibit higher HCC than women [ 32 ], which supports our findings. Other studies, including a cohort study involving older adults [ 31 ], a systematic review of children [ 32 ], and a more recent study [ 33 ], have also reported lower HCC in females. Therefore, our findings suggest that male adolescents in this cohort may have exhibited a heightened physiological vulnerability. Such sex-dependent action on the HPA axis is probably dependent on the unique pairing of pubertal maturation stages with the characteristics of the environmental challenge. This aligns with findings that sex hormones, in particular androgens and estrogens, modulate HPA-axis activation in response to adversity [ 33 ]. Our findings indicate that maternal skin color, a characteristic closely related to socioeconomic conditions in Brazil, is associated with distinct trajectories of HCC during adolescence. Adolescents whose mothers self-identified as Black or mixed-race showed a less pronounced increase in HCC over time compared to those with White mothers. In the Brazilian context, where Black and mixed-race populations are disproportionately exposed to socioeconomic disadvantage, discrimination, and other stressors, one might expect higher levels of chronic stress in these groups. However, this pattern does not fully align with expectations and should therefore be interpreted with caution. Although mechanisms underlying these divergent patterns have yet to be fully elucidated, they may not reflect lower stress exposure in that group. Rather, this attenuated path might be interpreted guardedly under the ‘weathering’ hypothesis and allostatic exhaustion theory [ 34 , 35 ]. Chronic or persistent exposure to systemic structural inequities and racialized stressors, according to this hypothesis, could result in the development of a ‘blunted’ HPA-axis phenotype. This maladaptive physiological downregulation occurs when a system is burdened by chronic adversity and thus experiences impaired plasticity or a reduced capacity to mount a vigorous hormonal response to novel acute stressors, such as the uncertainty of the pandemic [ 34 , 35 ]. As a result, the pronounced “spike” seen in White adolescents may reflect this more reactive physiological response in this group. Our results also suggest a potential role of community and governmental programs in mitigating stress, with possible indirect benefits for health during periods of widespread adversity such as the COVID-19 pandemic. Participation in the government’s Auxílio Emergencial (Emergency Aid) program appeared to mitigate the increase in HCC. This pandemic-specific cash transfer program provided critical financial support to vulnerable families, including informal workers, the unemployed, and low-income households. Beneficiaries of this program showed a less pronounced increase in HCC, suggesting that the financial support helped buffer some of the stress experienced during the pandemic. Therefore, it highlighted the importance of targeted social protection policies in mitigating the adverse effects of crises like the COVID-19 pandemic. Our findings also highlight the significant impact of maternal job loss during the pandemic on HCC changes. Adolescents whose mothers lost their jobs exhibited a more substantial increase in HCC during the pandemic, highlighting the impact of economic instability and the added caregiving responsibilities resulting from school closures and lockdown measures. Research shows that women, particularly those in informal or low-wage employment, faced higher unemployment rates during the pandemic, further intensifying social inequalities [ 36 ]. This economic strain contributed to elevated stress concentrations, which likely influenced both maternal and child health outcomes, including disruptions in the HPA axis and increased cortisol secretion [ 37 ]. Furthermore, maternal job loss has been linked to worsened mental health outcomes, such as anxiety and depression, particularly in low-income and racialized populations. [ 38 , 39 ] Our findings should be interpreted with limitations in mind. A key limitation is the use of complete case analysis, which resulted in a final sample of 1,509 adolescents. This analytical sample tended to include participants from slightly more advantaged backgrounds, which may have led to an underestimation of the associations. The latent change-score model captured individual variation in cortisol concentrations from before to during the pandemic. Unmeasured factors, such as clinical interventions or significant behavioral changes, may have influenced these fluctuations. We also accounted for Tanner’s developmental stages, which could influence changes in HCC. However, we found no significant associations with pubertal stage in our analysis. This study has several strengths, including the use of longitudinal data from a representative cohort of Brazilian adolescents, which allows examination of changes in cortisol concentrations over a critical period before and during the COVID-19 pandemic. The use of an LCS model provides a robust approach for investigating individual variations in cortisol change, capturing both average changes and individual differences in detail. Hair cortisol samples collected at two time points offer an objective, non-invasive measure of cortisol, a putative biomarker of chronic stress exposure. The sample was analyzed using the same experimental reagents, avoiding the batch effects on the experiments. Furthermore, the inclusion of a broad range of sociodemographic and pandemic-related variables helps identify potential predictors of cortisol concentrations, expanding knowledge of the social and psychological determinants of stress in adolescents. Conclusions Overall, this study supports hair cortisol as a marker of stress during widespread adversity, using the COVID-19 pandemic as a natural experiment. We observed increased hair cortisol concentrations among adolescents, indicating a physiological response to this prolonged period of environmental stress. The observation that this increase was positively associated with perceived stress levels reinforces the role of HCC as a reliable biomarker of psychological distress in this group. Nevertheless, the variability in cortisol profiles, shaped by maternal skin color, economic factors such as maternal job loss, and the moderating impact of government cash transfer programs, shows that the biological impact of the global crisis is inextricably linked to social and structural determinants. Finally, the results underpin a more fundamental call for targeted work on disparities, as structural inequalities and the promotion of social support are crucial measures to ensure the healthy and stable development and long-term health of future generations in the face of future social upheavals. Abbreviations HCC = hair cortisol concentration DASS-21 = Depression Anxiety Stress Scales ELISA = Enzyme-Linked Immunosorbent Assay LCS = Latent Change Score Model LMICs = Low- and Middle-Income Countries Declarations Ethics approval and consent to participate The study protocol and all follow-ups of the 2004 Pelotas Birth Cohort were approved by the Ethics Committee Board of the Medicine School at the Federal University of Pelotas and by the Ethics Committee Board of the University of São Paulo, in accordance with the Declaration of Helsinki and national ethical guidelines. At all follow-ups, all mothers or legal guardians received and signed written informed consent forms, and all adolescents received age-appropriate information and provided written informed assent at ages 15 and 17. Thus, all participants received the necessary information and provided explicit written informed consent to participate. Consent for publication Not applicable Availability of data and materials The data that supports the findings of this study are available from the corresponding author upon reasonable request Competing interests We declare no competing interests. Funding This work was supported by the Brazilian Association of Public Health (ABRASCO); the Children's Pastorate; the World Health Organization [Grant no. 03014HNI]; the National Support Program for Centers of Excellence (PRONEX) [Grant no. 04/0882.7]; the Brazilian National Research Council (CNPq) [Grant no. 481012-2009-5; 484077-2010-4; 470965-2010-0; 481141-2007-3; 426024/2016-8]; the Brazilian Ministry of Health [Grant no. 25000.105293/2004-83]; the São Paulo Research Foundation (FAPESP) [Grant no. 2014/13864-6; 2020/07730-8] and L’Oréal-Unesco-ABC Program for Women in Science in Brazil 2020. LTR, ISS, AJDB, and AM are supported by CNPq Research Scholarship. Individual grants: CNPq supports LTR (grant nº 308319/2021-4), ISS (grant nº 303042/2018-4), and AM (grant nº 312746/2021-0); Wellcome Trust supports MXC (grant nº 225019/Z/22/Z). This research was funded in whole or in part, by the Wellcome Trust [210735_A_18_Z] For the purpose of open access, the author has applied a CC BY public copyright license to any Author Accepted Manuscript version arising from this submission. Authors' contributions LTR, AM e JM were responsible for conceptualization. MXC, JMM, LTR and IO contributed to data curation, while JMM performed the formal analysis. Funding acquisition was secured by LTR, AJDB, ISS, AM and JM. Methodology was developed by LTR, AG, IO and JM. Project administration was carried out by LTR, AJDB and ISS. Supervision was provided by LTR and IO. The original draft was written by LTR, MXC, JBS, AT and JMM. All authors contributed to the review and editing of the manuscript. Acknowledgements This article uses data from the 2004 Pelotas Birth Cohort Study, conducted by the Postgraduate Program in Epidemiology at the Federal University of Pelotas, in collaboration with the Brazilian Public Health Association (ABRASCO). We thank all the adolescents and their families; this research would not have been possible without their ongoing participation. References Staufenbiel, S. M., Penninx, B. W. J. H., Spijker, A. T., Elzinga, B. M. & van Rossum, E. F. C. Hair cortisol, stress exposure, and mental health in humans: A systematic review. Psychoneuroendocrinology 38 , 1220–1235. https://doi.org/10.1016/j.psyneuen.2012.11.015 (2013). Adam, E. K. et al. Diurnal cortisol slopes and mental and physical health outcomes: A systematic review and meta-analysis. Psychoneuroendocrinology 83 , 25–41. https://doi.org/10.1016/j.psyneuen.2017.05.018 (2017). Martins-Silva, T. et al. 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Body composition, mental health and genetic assessment at the 6 years follow-up. Int. J. Epidemiol. [Internet] . 43 , 1437–1437. https://doi.org/10.1093/ije/dyu144 (2014). Martins, R. C. et al. Determinants of hair cortisol in preschool children and their mothers: A Brazilian birth cohort study. Psychoneuroendocrinology 150 , 106027. https://doi.org/10.1016/j.psyneuen.2023.106027 (2023). Ferro, M. A. & Gonzalez, A. Hair cortisol concentration mediates the association between parent and child psychopathology. Psychoneuroendocrinology 114 , 104613. https://doi.org/10.1016/j.psyneuen.2020.104613 (2020). Claire Buchan, M. et al. Hair Cortisol and Health-Related Quality of Life in Children with Mental Disorder. Chronic Stress . 5. https://doi.org/10.1177/24705470211047885 (2021). Vignola, R. C. B. & Tucci, A. M. Adaptation and validation of the depression, anxiety and stress scale (DASS) to Brazilian Portuguese. J. Affect. Disord . 155 , 104–109. https://doi.org/10.1016/j.jad.2013.10.031 (2014). Henry, J. D. & Crawford, J. R. The short-form version of the Depression Anxiety Stress Scales (DASS‐21): Construct validity and normative data in a large non‐clinical sample. Br. J. Clin. Psychol. 44 , 227–239. https://doi.org/10.1348/014466505X29657 (2005). Matijasevich, A. et al. The impact of the COVID-19 pandemic on the lives of the 2004 Pelotas (Brazil) birth cohort adolescents. Cad Saude Publica . 41. https://doi.org/10.1590/0102-311xen063724 (2025). Barros, A. J. D. & Victora, C. G. Indicador econômico para o Brasil baseado no censo demográfico de 2000. Rev. Saude Publica . 39 , 523–529. https://doi.org/10.1590/S0034-89102005000400002 (2005). Ewerling, F. & Barros, A. J. D. After 10 years, how do changes in asset ownership affect the Indicador Econômico. Nacional? Rev. Saude Publica . 51. https://doi.org/10.1590/s1518-8787.2017051006517 (2017). Klopack, E. T., Wickrama, K. & K. AS. Modeling Latent Change Score Analysis and Extensions in Mplus: A Practical Guide for Researchers. Struct. Equ Model. 27 , 97–110. https://doi.org/10.1080/10705511.2018.1562929 (2020). Kievit, R. A. et al. Developmental cognitive neuroscience using latent change score models: A tutorial and applications. Dev. Cogn. Neurosci. 33 , 99–117. https://doi.org/10.1016/j.dcn.2017.11.007 (2018). Kline, R. B. Principles and practice of structural equation modeling (Guilford, 2023). Raymond, C. et al. Pre-pandemic socio-emotional vulnerability, internalizing and externalizing symptoms predict changes in hair cortisol concentrations in reaction to the COVID-19 pandemic in children. Psychoneuroendocrinology 144 , 105888. https://doi.org/10.1016/j.psyneuen.2022.105888 (2022). Vacaru, S. V. et al. Adolescents’ hair cortisol concentrations during COVID-19: Evidence from two longitudinal studies in the Netherlands and the United States. Dev. Psychobiol. 65. https://doi.org/10.1002/dev.22438 (2023). Broadbent, E. et al. Changes in hair cortisol in a New Zealand community sample during the Covid-19 pandemic. Compr. Psychoneuroendocrinol . 17 , 100228. https://doi.org/10.1016/j.cpnec.2024.100228 (2024). Wells, S. et al. Associations of hair cortisol concentration with self-reported measures of stress and mental health-related factors in a pooled database of diverse community samples. Stress 17 , 334–342. https://doi.org/10.3109/10253890.2014.930432 (2014). Stalder, T. et al. Stress-related and basic determinants of hair cortisol in humans: A meta-analysis. Psychoneuroendocrinology 77 , 261–274. https://doi.org/10.1016/j.psyneuen.2016.12.017 (2017). Zuloaga, D. G., Lafrican, J. J. & Zuloaga, K. L. Androgen regulation of behavioral stress responses and the hypothalamic-pituitary-adrenal axis. Horm. Behav. 162 , 105528. https://doi.org/10.1016/j.yhbeh.2024.105528 (2024). Adam, E. K. et al. Developmental histories of perceived racial discrimination and diurnal cortisol profiles in adulthood: A 20-year prospective study. Psychoneuroendocrinology 62 , 279–291. https://doi.org/10.1016/j.psyneuen.2015.08.018 (2015). Gee, G. C., Hing, A., Mohammed, S., Tabor, D. C. & Williams, D. R. Racism and the Life Course: Taking Time Seriously. Am. J. Public. Health . 109 , S43–S47. https://doi.org/10.2105/AJPH.2018.304766 (2019). Enciso-Alfaro, S., Marhroub, S., Martínez‐Córdoba, P. & García‐Sánchez, I. The effect of COVID ‐19 on employment: A bibliometric review of a she‐cession. Corp. Soc. Responsib. Environ. Manag . 31 , 3444–3467. https://doi.org/10.1002/csr.2756 (2024). Marques, E. S., de Moraes, C. L., Hasselmann, M. H., Deslandes, S. F. & Reichenheim, M. E. A violência contra mulheres, crianças e adolescentes em tempos de pandemia pela COVID-19: panorama, motivações e formas de enfrentamento. Cad Saude Publica . 36. https://doi.org/10.1590/0102-311x00074420 (2020). Nguyen, L. H. et al. The mental health burden of racial and ethnic minorities during the COVID-19 pandemic. PLoS One . 17 , e0271661. https://doi.org/10.1371/journal.pone.0271661 (2022). Perry, N. B., Donzella, B., Troy, M. F. & Barnes, A. J. Mother and child hair cortisol during the COVID-19 pandemic: Associations among physiological stress, pandemic-related behaviors, and child emotional-behavioral health. Psychoneuroendocrinology 137 , 105656. https://doi.org/10.1016/j.psyneuen.2021.105656 (2022). Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9436198","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":638462326,"identity":"31df7d55-9205-419f-81fc-36d69d457d76","order_by":0,"name":"Luciana Tovo-Rodrigues","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5klEQVRIiWNgGAWjYBACPiA+AOd9AGI2dgJa2GBaeICYcQZIhJkILQwwLcwggoGgFonkhwd+7rBjsGfvMXxs82ubPB8zA+OHjzn4tKQZHOw9k8zAw3PG2Di377ZhGzMDs+TMbfi05DAc4AUq45FIS5PO7bnNCGSzMfMS0HLwb1s9RItlz217orQc5m07DNSSfEya4cftRMJaeJ4ZHJZtOw70yuHDhr0Nt5PbmBmb8fqFnz358ce3bdVy7O2NjQ9+/LltO7+9+eCHj3i0wAA4RhgY28BkA2H1CPCHFMWjYBSMglEwUgAAw+ZHBnvaswcAAAAASUVORK5CYII=","orcid":"","institution":"Federal University of Pelotas","correspondingAuthor":true,"prefix":"","firstName":"Luciana","middleName":"","lastName":"Tovo-Rodrigues","suffix":""},{"id":638462327,"identity":"a1d44415-dc76-4a65-a885-14f457ab0dc0","order_by":1,"name":"Marina Xavier Carpena","email":"","orcid":"","institution":"Federal University of Pelotas","correspondingAuthor":false,"prefix":"","firstName":"Marina","middleName":"Xavier","lastName":"Carpena","suffix":""},{"id":638462328,"identity":"b3c4bb9e-e0be-4835-b094-f1a186ef135f","order_by":2,"name":"Jessica Mayumi Maruyama","email":"","orcid":"","institution":"Universidade Presbiteriana Mackenzie","correspondingAuthor":false,"prefix":"","firstName":"Jessica","middleName":"Mayumi","lastName":"Maruyama","suffix":""},{"id":638462329,"identity":"4a7ee605-b6c2-414b-a628-8e1faf213fa5","order_by":3,"name":"Andrea Gonzalez","email":"","orcid":"","institution":"McMaster University","correspondingAuthor":false,"prefix":"","firstName":"Andrea","middleName":"","lastName":"Gonzalez","suffix":""},{"id":638462330,"identity":"670165b8-4977-45cb-ad00-b17be9bb1be4","order_by":4,"name":"Aluisio JD Barros","email":"","orcid":"","institution":"Federal University of Pelotas","correspondingAuthor":false,"prefix":"","firstName":"Aluisio","middleName":"JD","lastName":"Bar","suffix":"JD"},{"id":638462331,"identity":"4e6cbdac-6b92-47b6-b48d-faf6e3e8113b","order_by":5,"name":"Iná S. Santos","email":"","orcid":"","institution":"Federal University of Pelotas","correspondingAuthor":false,"prefix":"","firstName":"Iná","middleName":"S.","lastName":"Santos","suffix":""},{"id":638462332,"identity":"8b15e251-ce30-4183-b941-b88d1da828e3","order_by":6,"name":"Isabel Oliveira","email":"","orcid":"","institution":"Federal University of Pelotas","correspondingAuthor":false,"prefix":"","firstName":"Isabel","middleName":"","lastName":"Oliveira","suffix":""},{"id":638462333,"identity":"ce7d048c-bb8a-4857-a48e-649c880b3b03","order_by":7,"name":"Alexia Tormen","email":"","orcid":"","institution":"Federal University of Pelotas","correspondingAuthor":false,"prefix":"","firstName":"Alexia","middleName":"","lastName":"Tormen","suffix":""},{"id":638462334,"identity":"8ff83f2f-b508-4cac-a23b-a661a64041e6","order_by":8,"name":"Jaqueline Bohrer Schuch","email":"","orcid":"","institution":"Hospital Moinhos de Vento","correspondingAuthor":false,"prefix":"","firstName":"Jaqueline","middleName":"Bohrer","lastName":"Schuch","suffix":""},{"id":638462335,"identity":"00b32f23-f378-42f1-91ec-184ebe66a350","order_by":9,"name":"Alicia Matijasevich","email":"","orcid":"","institution":"Universidade de São Paulo","correspondingAuthor":false,"prefix":"","firstName":"Alicia","middleName":"","lastName":"Matijasevich","suffix":""},{"id":638462336,"identity":"18227fe0-6464-462a-8bbb-8fbb973ff38b","order_by":10,"name":"Joseph Murray","email":"","orcid":"","institution":"Federal University of Pelotas","correspondingAuthor":false,"prefix":"","firstName":"Joseph","middleName":"","lastName":"Murray","suffix":""}],"badges":[],"createdAt":"2026-04-16 09:38:30","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9436198/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9436198/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":109213207,"identity":"3acc1e25-96a3-4b14-8f7f-c10680a16288","added_by":"auto","created_at":"2026-05-13 17:04:43","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":117668,"visible":true,"origin":"","legend":"\u003cp\u003eLatent change score (LCS) model for changes in hair cortisol levels (pg/mg) between the pre- (T\u003csub\u003e1\u003c/sub\u003e) and mid-pandemic (T\u003csub\u003e2\u003c/sub\u003e) periods. \u003csup\u003ea\u003c/sup\u003eSocioeconomic predictors included family income, maternal schooling, skin colour, cohabiting with a partner, wealth index and maternal age at birth. \u003csup\u003eb\u003c/sup\u003eMid-pandemic predictors included beneficiary of pandemic-specific government cash transfer program, fear of food shortage, conflicts at home, maternal job loss during pandemic, Tanner developmental stage, and self-report stress level.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-9436198/v1/e48a146847f1d7ace0c11655.png"},{"id":109296684,"identity":"346da4e3-3a8c-4f38-997e-c110ce1b0a7a","added_by":"auto","created_at":"2026-05-15 08:50:09","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":419231,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9436198/v1/f5da5b40-fd39-4ff0-9bcf-c999f621aca0.pdf"},{"id":109249132,"identity":"8809fa9b-93a6-4076-850e-54666d9b6214","added_by":"auto","created_at":"2026-05-14 08:42:47","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":18160,"visible":true,"origin":"","legend":"","description":"","filename":"FigureS1.docx","url":"https://assets-eu.researchsquare.com/files/rs-9436198/v1/82819b4a62609301b5ed6e7c.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Hormonal fluctuations under systemic stress: Investigating adolescent hair cortisol trends before and during the COVID-19 pandemic.","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eA critical window for both biological and psychological development, adolescence brings increased plasticity in neural circuits and heightened sensitivity of the hypothalamic-pituitary-adrenal (HPA) axis to environmental stressors. One of the most widely used biomarkers for assessing this physiological response to adversity is cortisol, a hormone released by the adrenal glands that profoundly influences health, including metabolism, immune function, cardiovascular health, and mental well-being. While the human body's acute stress response is adaptive, continual activation of the HPA axis during this developmental stage can lead to long-term dysregulation, which has been to various health conditions, including mental disorders (i.e., depression, anxiety, etc), and metabolic disorders (i.e., obesity and diabetes) [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWhereas most such studies focus on serum or saliva cortisol, hair cortisol concentrations (HCC) have emerged as a method for assessing cumulative stress exposure [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. HCC provides a retrospective assessment of cortisol production over months and serves as a sort of \u0026ldquo;biological record\u0026rdquo; of a person\u0026rsquo;s stress history. Studies indicated that HCC levels were higher in depressed patients than in healthy controls [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Moreover, increased HCC has been associated with the incidence of cardiovascular disease, poorer recovery outcomes, and cardiometabolic risk factors such as hypertension and diabetes [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Hair cortisol analysis has significant advantages over acute measurement, as it can provide a more comprehensive overview of chronic physiological stress during key growth periods.\u003c/p\u003e \u003cp\u003eThe COVID-19 pandemic has been a significant global stressor, enabling us to explore how biological pathways are affected during large-scale environmental disruptions. More specifically, in triggering widespread psychological, social, and physiological challenges, large effects are reported on mental health [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Globally, the pandemic exacerbated existing inequalities, particularly in low- and middle-income countries (LMICs), where it strained already fragile healthcare systems [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. LMICs faced unique challenges in managing the pandemic, including limited economic resources and healthcare capacity [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], which led to adverse effects on living standards, education, health, and gender equality [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], increasing stress-related effects.\u003c/p\u003e \u003cp\u003eResearch on HCC during the COVID-19 pandemic has yielded important information about stress levels, particularly among healthcare workers and children [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Significantly elevated HCC levels have been documented among nurses during pandemic peaks compared with pre-pandemic periods, with those working in high-risk environments showing particularly elevated levels [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Healthcare workers exhibited increased stress and burnout, with 40% showing HCCs outside the authors' healthy reference range. Those with burnout (12%) demonstrated higher HCC scores [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. A direct correlation was observed between HCCs and perceived stress and emotional exhaustion in the same study [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. For healthcare workers, HCCs increased by a median of 29% after the start of the pandemic, and changes in cortisol were associated with burnout status three months later [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. These findings highlight the intricate relationship between cortisol changes and psychological outcomes across various populations during the pandemic. However, the limited number of population-based studies hinders a comprehensive understanding of the pandemic's impact on the general population and healthcare workers.\u003c/p\u003e \u003cp\u003eIn children and adolescents, the COVID-19 pandemic had a profound impact on their mental and physical health, but there is limited evidence involving cortisol from different sample sources. Fung et al. (2022) conducted a longitudinal analysis revealing that HCC in healthy children and adolescents increased significantly during the pandemic, reflecting heightened chronic stress [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Similarly, Bilodeau-Houle et al. [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] tracked HCC They found that an initial decrease in cortisol early in the pandemic was associated with increased post-traumatic stress symptoms later, suggesting a delayed stress response. Among adolescents, Taylor et al. used longitudinal data to demonstrate that elevated circulating cortisol concentrations predicted declines in emotional well-being, particularly following the implementation of local lockdowns[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eNevertheless, there is a critical need to understand not only if the pandemic functioned as a systemic biological stressor, but also to identify the \"signatures of risk,\" i.e., the sociodemographic and behavioural factors that predict an individual's HPA-axis reactivity. To our knowledge, no longitudinal studies have examined pre- and post-pandemic HCC in population-based cohorts of adolescents from LMICs. By investigating these determinants, we can better understand the differential susceptibility of adolescents to chronic stress and inform public health interventions designed to mitigate the long-term mental and metabolic consequences of environmental crises on this vulnerable demographic.\u003c/p\u003e \u003cp\u003eThe aim of this study was to compare changes in HCC pre- to during the COVID-19 pandemic by identifying key determinants of change by demographic and socioeconomic factors, pandemic-related variables, and stress perceptions among adolescents, in the 2004 Pelotas Birth Cohort study.\u003c/p\u003e"},{"header":"2 Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Participants and Study Design\u003c/h2\u003e \u003cp\u003eThis study utilized data from the 2004 Pelotas Birth Cohort, a population-based prospective study from Pelotas, Brazil. Detailed procedures were published elsewhere[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. The cohort initially comprised 4,231 participants, representing 99% of all live births occurring between January 1 and December 31, 2004, among mothers residing in the urban area of Pelotas and in the Jardim Am\u0026eacute;rica neighbourhood of the neighbouring municipality of Cap\u0026atilde;o do Le\u0026atilde;o. Participants were subsequently followed at multiple time points across development, including at 3, 12, 24, and 48 months, and at 6, 11, 15, 17 (subsamples only, due to constraints imposed by the COVID-19 pandemic), and 18 years of age. Retention rates varied across waves, ranging from 50.4% to 95.7%, with approximately 85% of the original cohort assessed at 18 years, which constitutes the primary time point for the present analyses [\u003cspan additionalcitationids=\"CR15\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eData collection for the current analysis involved two time points: pre-pandemic (T1), conducted from November 2019 to March 2020 (mean age 15.7 years), and mid-pandemic (T2), conducted from August to December 2021 (mean age 17.4 years). Only participants with available cortisol data for both T1 and T2 were included in the analysis, resulting in an analytic sample of 1,509 adolescents. More detailed procedures were published elsewhere[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Measures\u003c/h2\u003e \u003cp\u003eHair samples were collected by trained field workers, as detailed in Martins et al. (2023) [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], at both T1 and T2 to measure cortisol concentration (pg/mg), which served as a biomarker for chronic stress exposure. The samples were taken from the posterior vertex of the scalp, a region commonly used in cortisol studies due to its consistent hair growth and reduced variability in cortisol deposition. Following the collection, a standardized protocol [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] was applied for washing, grinding, hormone extraction, and cortisol measurement in the laboratory.\u003c/p\u003e \u003cp\u003eHair cortisol was extracted from the 3 cm of hair closest to the scalp. Samples were suspended in 150 \u0026micro;l of diluent for 24 hours, and cortisol concentrations were measured in duplicate using the ELISA technique with the Salivary Cortisol High Sensitivity Immunoassay Kit (Cat# 1-3002, Salimetrics, Pennsylvania), following the manufacturer\u0026rsquo;s instructions. The ELISA plate reader (Spectramax 190) was used for quantification, and HCC were expressed in pg/mg.\u003c/p\u003e \u003cp\u003eOutliers, defined as values exceeding four standard deviations (SD) from the HCC mean in the raw data, were removed from the analytical sample. Due to the right-skewed distribution, HCC values were log-transformed before inclusion in the regression models. The reported variance, skewness, and kurtosis values correspond to the log-transformed HCC data (Variance\u0026thinsp;=\u0026thinsp;0.19; Skewness\u0026thinsp;=\u0026thinsp;0.01; Kurtosis\u0026thinsp;=\u0026thinsp;3.67), which showed an approximately normal distribution.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Predictors\u003c/h2\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e2.3.1 Main Exposures: mid-pandemic variables\u003c/h2\u003e \u003cp\u003eThe main exposures were assessed at the mid-pandemic wave (T2) and included the following variables: being a beneficiary of government pandemic-specific cash transfer program (\u003cem\u003ePrograma Aux\u0026iacute;lio Emergencial\u003c/em\u003e); fear of food shortage (assessed by the question \u0026ldquo;Did you ever feel worried or afraid about not having enough food during the pandemic?\u0026rdquo;, with possible answers yes/no), conflicts at home (assessed by the question \u0026ldquo;Did your family frequently experience fights and arguments during the lockdown\u0026rdquo;, with possible answers yes/no), and maternal job loss during the pandemic. Self-reported stress was measured using the stress subscale from the Depression Anxiety Stress Scales (DASS-21)​[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. The questionnaire applied in this study was previously published in Matijasevich et al. (2025) [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e2.3.2 Pre-pandemic (T1) variables\u003c/h2\u003e \u003cp\u003eThe study included socioeconomic factors assessed at birth to describe our sample and adjust our analysis for potential confounders. This included family income measured in quintiles (first quintile are the poorest group), maternal schooling (categorized as 0\u0026ndash;4 years, 5\u0026ndash;8 years, and \u0026ge;\u0026thinsp;9 years), maternal skin colour (categorized as White or Black/Brown), whether the mother was cohabiting with a partner at childbirth, and participant child sex (male and female). Household wealth index was determined using the National Wealth Index questionnaire [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Principal component analysis integrated factors such as the household head's education, the number of bedrooms and bathrooms, and ownership of assets like televisions, vehicles, refrigerators, washing machines, and computers. The resulting wealth index classified households into five wealth strata based on reference cut-off values for each municipality [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Using Pelotas-specific values, households were stratified into first strata (20\u0026ndash;280), second (281\u0026ndash;367), third (368\u0026ndash;475), fourth (476\u0026ndash;618), and fifth (619\u0026ndash;1478). The highest values, the wealthiest were the families.\u003c/p\u003e \u003cp\u003eThe Tanner developmental stage variable at the pre-pandemic wave (T1) was constructed by combining four distinct subscales: two for boys (genital development and pubic hair development) and two for girls (genital development and pubic hair development). Each subscale was scored on a scale from 1 to 5, where 1 represents the prepubertal stage, 2 indicates the beginning of development, 3 represents the mid-stage of maturation, 4 corresponds to near-adult development, and 5 represents full adult maturation. The stages were combined into a two-digit code representing both genital and pubic hair development: the first digit corresponds to the genital stage, and the second digit corresponds to the pubic hair stage.\u003c/p\u003e \u003cp\u003eIn addition, we also included hair type, weekly hair wash frequency (both at T1 and T2) and use of corticosteroid medication in the past three months (both at T1 and T2) as confounders in the analysis.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Statistical analysis\u003c/h2\u003e \u003cp\u003eWe used a complete-case analysis, i.e., we included only individuals with data on hair cortisol at both time points, resulting in N\u0026thinsp;=\u0026thinsp;1,509. Initially, a paired t-test was employed to compare mean cortisol concentrations before and at the mid-pandemic waves. Next, we employed a multivariate latent change score (LCS) model to rigorously assess changes in adolescents' cortisol concentrations from before the pandemic (T1) to during the pandemic (T2). Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the LCS model, which captures changes by defining the differences between T1 and T2 as a latent change score, with the mean representing the average change in the sample, and the variance representing individual differences in change over time [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. The proportional change reflects how much change depends on initial cortisol concentrations. Sociodemographic and peri-pandemic covariates were included to investigate predictors of cortisol concentrations at T1 and of latent change from T1 to T2. We correlated initial cortisol concentrations and latent change scores to explore interrelations. Analyses were conducted in Mplus 8.4 using maximum likelihood estimation with robust standard errors (MLR). The model fit in the multivariate latent change analysis was assessed using the χ\u0026sup2; statistic, the Comparative Fit Index (CFI), the Tucker-Lewis Index (TLI), the Root Mean Square Error of Approximation (RMSEA), and the Standardized Root Mean Square Residual (SRMR). A good fit is considered when CFI/TLI\u0026thinsp;\u0026ge;\u0026thinsp;0.90, RMSEA\u0026thinsp;\u0026lt;\u0026thinsp;0.08, and SRMR\u0026thinsp;\u0026lt;\u0026thinsp;0.08 [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.5. Ethical aspects\u003c/h2\u003e \u003cp\u003e The study protocol and all follow-ups of the 2004 Pelotas Birth Cohort were approved by the Ethics Committee Board of the Medicine School at the Federal University of Pelotas and by the Ethics Committee Board of the University of S\u0026atilde;o Paulo, in accordance with the Declaration of Helsinki and national ethical guidelines. At all follow-ups, all mothers or legal guardians received and signed written informed consent forms, and all adolescents received age-appropriate information and provided written informed assent at ages 15 and 17.\u003c/p\u003e \u003c/div\u003e"},{"header":"3 Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Descriptive statistics\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents the sociodemographic characteristics of the original cohort and the analytic sample. The analytic sample consisted of 1,509 adolescents with available data on hair cortisol at both pre-pandemic and mid-pandemic time points. Compared to the original cohort (N\u0026thinsp;=\u0026thinsp;4,321), the analytic sample had a slightly higher proportion of adolescents from families with higher maternal education (46.6% vs. 43.6%), greater household income (82% vs. 79.4%), and a higher wealth index (81.7% vs. 79.4%). Additionally, the analytic sample included more adolescents whose mothers were cohabiting with a partner at the time of childbirth (84.8% vs. 83.6%) and a slightly higher proportion of females (51.5% vs. 48.1%) and White individuals (74.8% vs. 73%).\u003c/p\u003e \u003cp\u003eTable 1. Socioeconomic characteristics of original cohort and analytic sample\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"655\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 361px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 147px;\"\u003e\n \u003cp\u003e\u0026nbsp;Original cohort\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 147px;\"\u003e\n \u003cp\u003eAnalytic sample\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 361px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 147px;\"\u003e\n \u003cp\u003eN = 4321\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 147px;\"\u003e\n \u003cp\u003eN = 1509\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 361px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 147px;\"\u003e\n \u003cp\u003e% (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 147px;\"\u003e\n \u003cp\u003e% (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 361px;\"\u003e\n \u003cp\u003eMaternal schooling (years)\u003c/p\u003e\n \u003cp\u003e0-4\u003c/p\u003e\n \u003cp\u003e5-8\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;\u0026ge; 9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e15.4 (14.4 \u0026ndash; 16.6)\u003c/p\u003e\n \u003cp\u003e40.9 (39.4 \u0026ndash; 42.4)\u003c/p\u003e\n \u003cp\u003e43.6 (42.1 \u0026ndash; 45.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e13.6 (11.9 \u0026ndash; 15.4)\u003c/p\u003e\n \u003cp\u003e39.8 (37.4 \u0026ndash; 42.3)\u003c/p\u003e\n \u003cp\u003e46.6 (44.1 \u0026ndash; 49.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 361px;\"\u003e\n \u003cp\u003eFamily income (quintiles)\u003c/p\u003e\n \u003cp\u003e1\u003csup\u003est\u003c/sup\u003e quintile (poorest)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; 2\u003csup\u003end\u003c/sup\u003e to 5\u003csup\u003eth\u003c/sup\u003e quintile\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e20.6 (19.4 \u0026ndash; 21.8)\u003c/p\u003e\n \u003cp\u003e79.4 (78.1 \u0026ndash; 80.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e18.0 (16.2 \u0026ndash; 20.04)\u003c/p\u003e\n \u003cp\u003e82.0 (79.9 \u0026ndash; 83.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 361px;\"\u003e\n \u003cp\u003eWealth index (quintiles)\u003c/p\u003e\n \u003cp\u003e1\u003csup\u003est\u003c/sup\u003e quintile (poorest)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; 2\u003csup\u003end\u003c/sup\u003e to 5\u003csup\u003eth\u003c/sup\u003e quintile\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e21.6 (20.3 \u0026ndash; 23.1)\u003cbr\u003e\u0026nbsp;78.3 (76.9 \u0026ndash; 79.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e18.3 (16.2 \u0026ndash; 20.5)\u003cbr\u003e\u0026nbsp;81.7 (79.5 \u0026ndash; 83.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 361px;\"\u003e\n \u003cp\u003eSkin color\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;White\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;Black/Brown \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e73.0 (71.7 \u0026ndash; 74.3)\u003c/p\u003e\n \u003cp\u003e27.0 (25.7 \u0026ndash; 28.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e74.8 (72.6 \u0026ndash; 73.7)\u003c/p\u003e\n \u003cp\u003e25.2 (23.0 \u0026ndash; 27.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 361px;\"\u003e\n \u003cp\u003eMother cohabiting with a partner at childbirth\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;No\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e83.6 (82.5 \u0026ndash; 84.7)\u003c/p\u003e\n \u003cp\u003e16.4 (15.3 \u0026ndash; 17.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e84.8 (81.5 \u0026ndash; 86.5)\u003c/p\u003e\n \u003cp\u003e15.2 (13.4 \u0026ndash; 17.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 361px;\"\u003e\n \u003cp\u003eAdolescent sex\u003c/p\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Female\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 147px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e51.9 (50.4 \u0026ndash; 53.4)\u003c/p\u003e\n \u003cp\u003e48.1 (46.6 \u0026ndash; 49.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e48.4 (45.9 \u0026ndash; 50.9)\u003c/p\u003e\n \u003cp\u003e51.5 (49.0 \u0026ndash; 54.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 655px;\"\u003e\n \u003cp\u003eNote. \u0026nbsp;CI = confidence interval\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;Analytic sample is composed by the individuals with hair cortisol data on both pre-pandemic and mid-pandemic sample\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\u003c/br\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Hair cortisol concentrations and latent change analysis\u003c/h2\u003e \u003cp\u003eThe model fit indices indicate a good fit to the data. The chi-square test of model fit was not significant (χ\u0026sup2;(9)\u0026thinsp;=\u0026thinsp;10.340, p\u0026thinsp;=\u0026thinsp;0.324), indicating that the observed data did not differ significantly from the model. The RMSEA value was 0.010 (90% CI: 0.000 to 0.032), suggesting a close approximate fit. The CFI and TLI values were 0.987 and 0.951, respectively, both above 0.90, supporting the model's adequacy. The SRMR value was 0.008, well below the recommended cutoff of 0.08, indicating minimal residuals. Overall, these indices suggest an excellent model fit.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents the mean hair cortisol concentrations at both time points and the results for the latent change modelling. There was a significant increase in hair cortisol concentrations between T1 and T2. The mean cortisol concentration increased from 4.429 pg/mg at T1 (95% CI: 4.146\u0026ndash;4.713) to 6.321 pg/mg at T2 (95% CI: 6.071\u0026ndash;6.573), representing a mean difference of 1.892 pg/mg (95% CI: -2.248, -1.536), with t (1508) = -10.424, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, indicating a statistically significant increase during the pandemic period​. To illustrate the variability in individual responses, a histogram of intraindividual changes in hair cortisol concentrations (T2 - T1) is presented in Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e (Supplementary Material). The distribution is approximately symmetrical and centred around zero, indicating that while the mean cortisol concentration increased significantly at the group level, individual changes varied substantially, with most participants exhibiting modest shifts and a small number showing significant increases or decreases.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e Mean hair cortisol levels at T1 and T2, latent change scores, individual variability and proportional change of hair cortisol levels from pre-pandemic wave (T1) to mid-pandemic wave (T2)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePre-pandemic wave\u003c/p\u003e \u003cp\u003e(T1)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMid-pandemic wave (T2)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLatent change scores (LCS; T\u003csub\u003e2 \u0026minus;\u003c/sub\u003e T\u003csub\u003e1\u003c/sub\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIndividual variance\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eProportional change\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMean (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eM\u003c/em\u003e\u003csub\u003eslope\u003c/sub\u003e (SE)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eσ\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eb\u003c/em\u003e (SE)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCortisol levels (pg/mg)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.429 (4.146\u0026ndash;4.713)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.321 (6.071\u0026ndash;6.573)\u003csup\u003e\u0026sect;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.892 (0.181)\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.984 (0.08)\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.723 (0.092)\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003eNote. Standardized coefficients are shown. Maximum likelihood robust estimator was used. Latent change scores show the mean increase or decrease of hair cortisol levels between pre-pandemic and mid-pandemic waves, modelled as a latent variable; Individual variance (σ2) capture the extent to which individuals differ in the change they manifest over time; Proportional change shows the extent to which the latent change scores are related to pre-pandemic scores.\u003c/p\u003e \u003cp\u003eS.E. = standard error; \u003csup\u003e\u0026sect;\u003c/sup\u003e t(1508) = -10.424, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001. \u003csup\u003e*\u003c/sup\u003e p\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eSimilarly, the LCS modelling revealed a significant increase in cortisol concentrations, with a latent factor mean of 1.892 (SE\u0026thinsp;=\u0026thinsp;0.181, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The variance (σ\u0026sup2;) in the change was 0.984 (SE\u0026thinsp;=\u0026thinsp;0.08, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), indicating considerable variability in how cortisol concentrations changed across individuals. Additionally, the proportional change coefficient was negative (b = -0.723, SE\u0026thinsp;=\u0026thinsp;0.092, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), suggesting that increases in cortisol concentrations were smaller among those with higher cortisol concentrations at T1.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Predictors of hair cortisol concentration at T1 and latent changes\u003c/h2\u003e \u003cp\u003eWe investigated several demographic and pandemic-related predictors of cortisol concentrations and changes from T1 to T2 (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). We found that mother cohabiting with a partner at childbirth was associated with higher cortisol concentrations at T1 (β\u0026thinsp;=\u0026thinsp;0.047, SE\u0026thinsp;=\u0026thinsp;0.012, p\u0026thinsp;\u0026lt;\u0026thinsp;0,001). Female adolescents showed significantly lower hair cortisol concentrations than males before the pandemic (β = -0.067, SE\u0026thinsp;=\u0026thinsp;0.026, p\u0026thinsp;=\u0026thinsp;0.009).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePre-pandemic and mid-pandemic predictors of T1 hair cortisol and latent change scores from T1 to T2\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePre-pandemic (T\u003csub\u003e1\u003c/sub\u003e) hair cortisol levels\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLatent change score from T\u003csub\u003e1\u003c/sub\u003e to T\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eβ (SE)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eβ (SE)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eSocioeconomic and pre-pandemic (T1) variables\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaternal schooling (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.009 (0.011)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.405\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.010 (0.020)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.623\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFamily income (quintiles)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.010 (0.019)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.605\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.014 (0.022)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.529\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWealth index (quintiles)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.052 (0.033)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.055 (0.029)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.063\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMother cohabiting with a partner at childbirth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.047 (0.012)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.009 (0.017)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.581\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlack/mixed/other skin colour mothers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.043 (0.033)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.189\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e-0.064 (0.027)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.017\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale adolescent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e-0.067 (0.026)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.009\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e-0.084 (0.027)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTanner developmental stage (at T1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.009 (0.011)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.386\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePandemic-related predictors\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBeneficiary of pandemic-specific government cash transfer program\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e-0.040 (0.011)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFear of food shortage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.041 (0.072)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.566\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConflicts at home\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.025 (0.076)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.744\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaternal job loss during the pandemic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.045 (0.016)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.005\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSelf-report stress levels\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.043 (0.021)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.041\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eNote.\u003c/em\u003e Standardized coefficients are shown. Maximum likelihood robust estimator was used. All the analyses were adjusted for hair type, weekly hair wash frequency (both at T\u003csub\u003e1\u003c/sub\u003e and T\u003csub\u003e2\u003c/sub\u003e) and use of corticosteroid medication in the past three months (both at T\u003csub\u003e1\u003c/sub\u003e and T\u003csub\u003e2\u003c/sub\u003e). S.E. = Standard errors. Statistically significant results are in bold.\u003c/p\u003e \u003cp\u003e\u003csup\u003ea\u003c/sup\u003eAux\u0026iacute;lio Emergencial; \u003csup\u003eb\u003c/sup\u003eBased on the stress subscale from the Depression Anxiety Stress Scales (DASS-21).\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAmong pre-pandemic and pandemic-related predictors of the latent change, having a black or mixed mother (β = -0.064, SE\u0026thinsp;=\u0026thinsp;0.027, p\u0026thinsp;=\u0026thinsp;0.017), being a female adolescent (β = -0.084, SE\u0026thinsp;=\u0026thinsp;0.027, p\u0026thinsp;=\u0026thinsp;0.002) and being a beneficiary of the government pandemic-specific cash transfer program (β = -0.040, SE\u0026thinsp;=\u0026thinsp;0.011, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) emerged as negative predictors of change in cortisol concentrations from pre-pandemic to mid-pandemic waves. On the other hand, maternal job loss (β\u0026thinsp;=\u0026thinsp;0.045, SE\u0026thinsp;=\u0026thinsp;0.016, p\u0026thinsp;=\u0026thinsp;0.005) and higher self-reported stress (β\u0026thinsp;=\u0026thinsp;0.043, SE\u0026thinsp;=\u0026thinsp;0.021, p\u0026thinsp;=\u0026thinsp;0.041) were significant, positive predictors of changes in cortisol concentrations during the study period.\u003c/p\u003e \u003c/div\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eThis study used the COVID-19 pandemic as a natural experiment to examine the extent to which systemic environmental disruption affects HPA-axis dynamics during the developmentally sensitive window of adolescence. We examined longitudinal changes in HCC within the 2004 Pelotas Birth Cohort and observed an overall upward trend in cortisol concentrations. This physiological shift, however, was non-linear and heterogeneous. Several key factors contributed to these changes, including maternal skin color, adolescent sex, participation in pandemic-specific government cash transfer program, participant\u0026rsquo;s self-reported stress, and maternal job loss.\u003c/p\u003e \u003cp\u003eThe increases in HCC from before to during the pandemic in this sample of adolescents are consistent with prior studies documenting significant rises in HCC among healthy children and adolescents during the COVID-19 pandemic [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. For instance, a multicentric study observed a significant increase in HCC concentrations in female adolescents after the onset of the COVID-19 pandemic [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Additionally, research from the United States revealed that after local pandemic lockdowns, adolescents showed significant increases in circulating cortisol concentrations, which were associated with changes in perceived emotional well-being, indicating that physiological stress, as measured by cortisol, was correlated with emotional state changes in young individuals [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. However, research on HCC during the COVID-19 pandemic has yielded mixed results. For example, a cross-cultural study comparing Dutch and US adolescents found no significant overall change in HCC from pre-pandemic to pandemic, except among Dutch girls, who showed a considerable increase [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Nevertheless, another study conducted in the United States found no significant changes in HCC concentrations in children and adolescents during the COVID-19 pandemic, suggesting that other factors, such as coping mechanisms or environmental influences, might have moderated the stress response in specific populations [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. The significant rise observed in our population-based study may reflect increased environmental volatility and the vulnerability of social support structures typical of LMIC contexts, in which the pandemic's physiological impact may have been disproportionately severe.\u003c/p\u003e \u003cp\u003eA key question in the literature is whether HCC is associated with self-perceived stressful events. The validity of HCC as a biomarker of chronic stress remains debated. While several studies support its use as a reliable indicator, others highlight inconsistencies and caution against interpreting findings [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. In our cohort, higher levels of self-reported stress were associated with increased HCC concentrations, providing empirical support for its relevance as an indicator of chronic stress in this population. Although this perspective is consistent with other high-stress groups, including healthcare workers [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], it adds another piece to the already extensive debate on the relationship between subjective and biological measures of stress [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. For example, while several studies have shown that pandemic-specific perceived stress did not correlate with cortisol in adolescence [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], or reported that adverse life events, rather than perceived stress, alone significantly increased the risk of HCC [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], our results demonstrate a more synchronized psychophysiological response. This coupling depends on the chronic nature of the stressor and the individual's personal coping system, both inside and outside the individual. These contrasting findings underscore the complex, multifaceted nature of stress responses during the pandemic, which can vary significantly across individuals and contexts.\u003c/p\u003e \u003cp\u003eOur analysis suggested distinct sexual dimorphisms in cortisol response. Female adolescents had lower HCC before the pandemic and smaller increases in HCC during the evaluation period, suggesting consistently lower cortisol levels over time than their male counterparts. A meta-analysis reported that men generally exhibit higher HCC than women [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e], which supports our findings. Other studies, including a cohort study involving older adults [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], a systematic review of children [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e], and a more recent study [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e], have also reported lower HCC in females. Therefore, our findings suggest that male adolescents in this cohort may have exhibited a heightened physiological vulnerability. Such sex-dependent action on the HPA axis is probably dependent on the unique pairing of pubertal maturation stages with the characteristics of the environmental challenge. This aligns with findings that sex hormones, in particular androgens and estrogens, modulate HPA-axis activation in response to adversity [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOur findings indicate that maternal skin color, a characteristic closely related to socioeconomic conditions in Brazil, is associated with distinct trajectories of HCC during adolescence. Adolescents whose mothers self-identified as Black or mixed-race showed a less pronounced increase in HCC over time compared to those with White mothers. In the Brazilian context, where Black and mixed-race populations are disproportionately exposed to socioeconomic disadvantage, discrimination, and other stressors, one might expect higher levels of chronic stress in these groups. However, this pattern does not fully align with expectations and should therefore be interpreted with caution. Although mechanisms underlying these divergent patterns have yet to be fully elucidated, they may not reflect lower stress exposure in that group. Rather, this attenuated path might be interpreted guardedly under the \u0026lsquo;weathering\u0026rsquo; hypothesis and allostatic exhaustion theory [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Chronic or persistent exposure to systemic structural inequities and racialized stressors, according to this hypothesis, could result in the development of a \u0026lsquo;blunted\u0026rsquo; HPA-axis phenotype. This maladaptive physiological downregulation occurs when a system is burdened by chronic adversity and thus experiences impaired plasticity or a reduced capacity to mount a vigorous hormonal response to novel acute stressors, such as the uncertainty of the pandemic [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. As a result, the pronounced \u0026ldquo;spike\u0026rdquo; seen in White adolescents may reflect this more reactive physiological response in this group.\u003c/p\u003e \u003cp\u003eOur results also suggest a potential role of community and governmental programs in mitigating stress, with possible indirect benefits for health during periods of widespread adversity such as the COVID-19 pandemic. Participation in the government\u0026rsquo;s Aux\u0026iacute;lio Emergencial (Emergency Aid) program appeared to mitigate the increase in HCC. This pandemic-specific cash transfer program provided critical financial support to vulnerable families, including informal workers, the unemployed, and low-income households. Beneficiaries of this program showed a less pronounced increase in HCC, suggesting that the financial support helped buffer some of the stress experienced during the pandemic. Therefore, it highlighted the importance of targeted social protection policies in mitigating the adverse effects of crises like the COVID-19 pandemic.\u003c/p\u003e \u003cp\u003eOur findings also highlight the significant impact of maternal job loss during the pandemic on HCC changes. Adolescents whose mothers lost their jobs exhibited a more substantial increase in HCC during the pandemic, highlighting the impact of economic instability and the added caregiving responsibilities resulting from school closures and lockdown measures. Research shows that women, particularly those in informal or low-wage employment, faced higher unemployment rates during the pandemic, further intensifying social inequalities [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. This economic strain contributed to elevated stress concentrations, which likely influenced both maternal and child health outcomes, including disruptions in the HPA axis and increased cortisol secretion [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Furthermore, maternal job loss has been linked to worsened mental health outcomes, such as anxiety and depression, particularly in low-income and racialized populations. [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eOur findings should be interpreted with limitations in mind. A key limitation is the use of complete case analysis, which resulted in a final sample of 1,509 adolescents. This analytical sample tended to include participants from slightly more advantaged backgrounds, which may have led to an underestimation of the associations. The latent change-score model captured individual variation in cortisol concentrations from before to during the pandemic. Unmeasured factors, such as clinical interventions or significant behavioral changes, may have influenced these fluctuations. We also accounted for Tanner\u0026rsquo;s developmental stages, which could influence changes in HCC. However, we found no significant associations with pubertal stage in our analysis.\u003c/p\u003e \u003cp\u003eThis study has several strengths, including the use of longitudinal data from a representative cohort of Brazilian adolescents, which allows examination of changes in cortisol concentrations over a critical period before and during the COVID-19 pandemic. The use of an LCS model provides a robust approach for investigating individual variations in cortisol change, capturing both average changes and individual differences in detail. Hair cortisol samples collected at two time points offer an objective, non-invasive measure of cortisol, a putative biomarker of chronic stress exposure. The sample was analyzed using the same experimental reagents, avoiding the batch effects on the experiments. Furthermore, the inclusion of a broad range of sociodemographic and pandemic-related variables helps identify potential predictors of cortisol concentrations, expanding knowledge of the social and psychological determinants of stress in adolescents.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eOverall, this study supports hair cortisol as a marker of stress during widespread adversity, using the COVID-19 pandemic as a natural experiment. We observed increased hair cortisol concentrations among adolescents, indicating a physiological response to this prolonged period of environmental stress. The observation that this increase was positively associated with perceived stress levels reinforces the role of HCC as a reliable biomarker of psychological distress in this group. Nevertheless, the variability in cortisol profiles, shaped by maternal skin color, economic factors such as maternal job loss, and the moderating impact of government cash transfer programs, shows that the biological impact of the global crisis is inextricably linked to social and structural determinants. Finally, the results underpin a more fundamental call for targeted work on disparities, as structural inequalities and the promotion of social support are crucial measures to ensure the healthy and stable development and long-term health of future generations in the face of future social upheavals.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eHCC = hair cortisol concentration\u003c/p\u003e\n\u003cp\u003eDASS-21 = Depression Anxiety Stress Scales\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eELISA = Enzyme-Linked Immunosorbent Assay\u003c/p\u003e\n\u003cp\u003eLCS = Latent Change Score Model\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eLMICs = Low- and Middle-Income Countries\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study protocol and all follow-ups of the 2004 Pelotas Birth Cohort were approved by the Ethics Committee Board of the Medicine School at the Federal University of Pelotas and by the Ethics Committee Board of the University of S\u0026atilde;o Paulo, in accordance with the Declaration of Helsinki and national ethical guidelines. At all follow-ups, all mothers or legal guardians received and signed written informed consent forms, and all adolescents received age-appropriate information and provided written informed assent at ages 15 and 17. Thus, all participants received the necessary information and provided explicit written informed consent to participate.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that supports the findings of this study are available from the corresponding author upon reasonable request\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Brazilian Association of Public Health (ABRASCO); the Children\u0026apos;s Pastorate; the World Health Organization [Grant no. 03014HNI]; the National Support Program for Centers of Excellence (PRONEX) [Grant no. 04/0882.7]; the Brazilian National Research Council (CNPq) [Grant no. 481012-2009-5; 484077-2010-4; 470965-2010-0; 481141-2007-3; 426024/2016-8]; the Brazilian Ministry of Health [Grant no. 25000.105293/2004-83]; the S\u0026atilde;o Paulo Research Foundation (FAPESP) [Grant no. 2014/13864-6; 2020/07730-8] and L\u0026rsquo;Or\u0026eacute;al-Unesco-ABC Program for Women in Science in Brazil 2020. LTR, ISS, AJDB, and AM are supported by CNPq Research Scholarship. Individual grants: CNPq supports LTR (grant n\u0026ordm; 308319/2021-4), ISS (grant n\u0026ordm; 303042/2018-4), and AM (grant n\u0026ordm; 312746/2021-0); Wellcome Trust supports MXC (grant n\u0026ordm; 225019/Z/22/Z). This research was funded in whole or in part, by the Wellcome Trust [210735_A_18_Z] For the purpose of open access, the author has applied a CC BY public copyright license to any Author Accepted Manuscript version arising from this submission.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLTR, AM e JM were responsible for conceptualization. MXC, JMM, LTR and IO contributed to data curation, while JMM performed the formal analysis. Funding acquisition was secured by LTR, AJDB, ISS, AM and JM. Methodology was developed by LTR, AG, IO and JM. Project administration was carried out by LTR, AJDB and ISS. Supervision was provided by LTR and IO. The original draft was written by LTR, MXC, JBS, AT and JMM. All authors contributed to the review and editing of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis article uses data from the 2004 Pelotas Birth Cohort Study, conducted by the Postgraduate Program in Epidemiology at the Federal University of Pelotas, in collaboration with the Brazilian Public Health Association (ABRASCO). We thank all the adolescents and their families; this research would not have been possible without their ongoing participation.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eStaufenbiel, S. M., Penninx, B. W. J. H., Spijker, A. T., Elzinga, B. M. \u0026amp; van Rossum, E. F. C. 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A viol\u0026ecirc;ncia contra mulheres, crian\u0026ccedil;as e adolescentes em tempos de pandemia pela COVID-19: panorama, motiva\u0026ccedil;\u0026otilde;es e formas de enfrentamento. \u003cem\u003eCad Saude Publica\u003c/em\u003e. 36. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1590/0102-311x00074420\u003c/span\u003e\u003cspan address=\"10.1590/0102-311x00074420\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNguyen, L. H. et al. 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Mother and child hair cortisol during the COVID-19 pandemic: Associations among physiological stress, pandemic-related behaviors, and child emotional-behavioral health. \u003cem\u003ePsychoneuroendocrinology\u003c/em\u003e \u003cb\u003e137\u003c/b\u003e, 105656. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.psyneuen.2021.105656\u003c/span\u003e\u003cspan address=\"10.1016/j.psyneuen.2021.105656\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2022).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Hair cortisol concentration, Adolescence, Chronic stress, HPA axis, COVID-19 pandemic, Longitudinal cohort, Socioeconomic factors, Latent change score model, Mental health, Low- and middle-income countries (LMICs)","lastPublishedDoi":"10.21203/rs.3.rs-9436198/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9436198/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eAdolescence is a sensitive developmental period marked by increased reactivity of the HPA axis to environmental stressors. However, little is known about how chronic cortisol levels change during prolonged adversity. Using the COVID-19 pandemic as a natural experiment, this study examines how large-scale environmental disruption affects HPA-axis regulation during adolescence. We investigated hair cortisol concentration (HCC) pre- and during the pandemic in adolescents from Pelotas, Brazil, and identified predictors for change.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eData were drawn from the 2004 Pelotas Birth Cohort, a population-based longitudinal study. Hair samples (3 cm) were collected twice: pre-pandemic (T1) in 2019 through early 2020 (mean age 15.7 years) and mid-pandemic (T2) in 2021 (mean age 17.4 years). HCC was quantified via ELISA method. Socioeconomic, demographic, and pandemic-related experiences, as well as self-reported stress levels, were assessed as potential determinants of changes in HCC. A multivariate latent change score (LCS) model was used to analyze changes in HCC and their associations with predictors.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eA total of 1,509 individuals were included in the analyses. There was a significant mean increase in HCC of 1.89 pg/mg (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) from T1 to T2. Pandemic-related experiences, such as maternal job loss (p\u0026thinsp;=\u0026thinsp;0.005) and higher levels of perceived stress (p\u0026thinsp;=\u0026thinsp;0.041), were associated with greater increases in HCC. Government cash transfer program participation (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), having a black or mixed-race mother (p\u0026thinsp;=\u0026thinsp;0.017), and being female (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) were associated with lower increases in HCC.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eFindings suggest significant physiological effects of the COVID-19 pandemic, highlighting the role of demographic, socioeconomic, and pandemic-related factors in shaping cortisol responses and advancing understanding of its impact on adolescent health, with implications for policy and support in low- and middle-income countries. These findings highlight the intricate relationship between cortisol changes and psychological outcomes across various populations during the pandemic.\u003c/p\u003e","manuscriptTitle":"Hormonal fluctuations under systemic stress: Investigating adolescent hair cortisol trends before and during the COVID-19 pandemic.","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-13 17:04:39","doi":"10.21203/rs.3.rs-9436198/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"233444533502534324029551988248707293056","date":"2026-05-11T15:10:55+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-05-05T13:23:04+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-04-24T09:27:27+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-17T05:23:59+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-17T05:23:01+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2026-04-16T09:25:57+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"8026bf8e-6c42-484a-b688-43b4e1dcddb8","owner":[],"postedDate":"May 13th, 2026","published":true,"recentEditorialEvents":[{"type":"reviewerAgreed","content":"233444533502534324029551988248707293056","date":"2026-05-11T15:10:55+00:00","index":56,"fulltext":""},{"type":"reviewersInvited","content":"18","date":"2026-05-05T13:23:04+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":67951698,"name":"Health sciences/Biomarkers"},{"id":67951699,"name":"Health sciences/Diseases"},{"id":67951700,"name":"Health sciences/Health care"},{"id":67951701,"name":"Health sciences/Medical research"},{"id":67951702,"name":"Biological sciences/Psychology"},{"id":67951703,"name":"Social science/Psychology"},{"id":67951704,"name":"Health sciences/Risk factors"}],"tags":[],"updatedAt":"2026-05-13T17:04:39+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-13 17:04:39","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9436198","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9436198","identity":"rs-9436198","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}