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. Author manuscript; available in PMC: 2026 Jul 3.
Published in final edited form as: J Affect Disord. 2024 Oct 29;370:159–167. doi: 10.1016/j.jad.2024.10.115

Seasonal mood variation in youth and young adults with bipolar spectrum disorder: A longitudinal prospective analysis

Xavier Estrada-Prat a, Soledad Romero b,*, Roger Borras b, John Merranko c, Tina Goldstein c, Danella Hafeman c, Heather Hower d,e,f, Shirley Yen g, Jeffrey Hunt h, Benjamin Goldstein i,j, Neal Ryan c, Rasim Diler c, Michael Strober k, MaryKay Gill c, Boris Birmaher c
PMCID: PMC13324871  NIHMSID: NIHMS2153486  PMID: 39481686

Abstract

Objective:

To determine whether there are latitude and seasonal differences in the prevalence of mood episodes (depression and mania) in youth and young adults with Bipolar Spectrum Disorder (BD).

Methods:

Mood polarity was prospectively evaluated in 413 participants with BD. Participants were enrolled in the Course and Outcome of Bipolar Youth (COBY) study at three sites (University of California Los Angeles-UCLA, Brown University, and the University of Pittsburgh Medical Center-UPMC) and interviewed on average every 7 months for an average of 91.9 months (range: 6–228 months), with a total of 274,123 weekly mood ratings. Associations between light exposure and mood polarity were estimated using generalized linear mixed models with time-varying covariates, considering the latitude and seasonality of the study sites and other potential confounders.

Results:

Average age at intake and at last assessment was 12.6 ± 3.3 and 27.2 ± 4.8 years-old, respectively. There were significantly more depressive episodes during winter than during summer, spring, and autumn. Considering latitude, UCLA showed significantly lower prevalence of depressive episodes, and an absence of seasonal pattern of depression, compared to the Brown/UPMC sites. For the entire sample, there were more manic/hypomanic episodes during summer than during winter. However, there were no significant between site seasonal differences in the prevalence of manic/hypomanic episodes.

Conclusions:

Depressive episodes are more prevalent during the winter and although less significant, manic/ hypomanic episodes during the summer. Awareness and interventions to prevent or ameliorate the effects of seasonal variations in mood changes in BD are warranted.

Keywords: Mood polarity, Seasonal variation, Bipolar disorder, Child and adolescent

1. Introduction

Bipolar Disorder (BD) is a severe illness that usually onsets during adolescence (Birmaher et al., 2006). Exposure to light and other factors that may alter circadian rhythms (e.g., sleep deprivation and stress), with corresponding changes in neurotransmitters and hormones (e.g., melatonin and cortisol), may trigger a mood episode and influence episode polarity of the episode in people with BD (Rosenthal et al., 2020, 2021). The effects of exposure to light on polarity of mood episodes have been inferred by the differences in the prevalence of manic and depressive episodes in countries located at different latitudes. Specifically, studies evaluating the course of BD conducted in temperate and continental climate countries farther away from the equator (Peel et al., 2007) report that most of the recurrences in BD youth and adults are depressive episodes (Birmaher et al., 2009; Judd et al., 2002). In contrast, in countries closer to the equator with more tropical and dry climates (Peel et al., 2007) most recurrences seem to be manic episodes (Jairam et al., 2004; Narayanaswamy et al., 2014). A cross-sectional study conducted in a sample of 439 Indian adults with BD reported an average of 3.29 episodes of mania versus 1.08 episodes of depression (Narayanaswamy et al., 2014).

In addition to the effects of latitude, changes in seasons (Trenberth, 1983), especially in temperate and continental climate countries, may also influence mood episode polarity (Akhter et al., 2013; Geoffroy et al., 2014; Rosenthal et al., 2020). Seasonality is reported to occur in about 20 % of adults with BD (Goikolea et al., 2007). Numerous cohort studies in adults with BD that evaluated admissions to psychiatric hospitals concluded that manic episodes clustered in spring-summer and depressive episodes in early winter (Aguglia et al., 2018; Fellinger et al., 2019; Geoffroy et al., 2014). These findings are documented around the world and have been widely replicated (Geoffroy et al., 2014; Rosenthal et al., 2020). However, other studies have not reported significant associations between seasons and the polarity of mood episodes (Bauer et al., 2009; Goikolea et al., 2007; Murray et al., 2011).

Little is known about the effect of latitude and seasonality in the course of BD in youths. A 52-month prospective study of 25 adolescents with BD in India reported recurrence of mania in 64 % (Jairam et al., 2004). In a descriptive study in United State of America (USA), seven children with seasonal affective disorder were studied over a two-year follow-up period showing the presence of depressive symptoms during the winter months (Rosenthal et al., 1986). In American community samples, one study found that in 3.3 % of the adolescent population, late autumn and winter were associated with negative symptoms such as general “bad feelings”, lower energy level, increased irritability, lower grades, and decreased social activity (Swedo et al., 1995). However, another community study did not find any association between seasons and mood changes in youths (Nillni et al., 2009).

The above noted studies indicate that the amount of sunlight may affect the polarity of mood recurrences. This suggests that latitude and seasonality, as a measure of the amount of sunlight, may affect the course of illness of BD. However, most of the reported studies have one or more of the following limitations: cross-sectional research design, retrospective reports, inclusion of small samples, and not controlling for confounders such as demographic variables, geographic site, and comorbid disorders (Goikolea et al., 2007; Narayanaswamy et al., 2014).

The aim of this study was to evaluate the association between sunlight and polarity of mood episodes in a large longitudinal prospective study from childhood or adolescence to adulthood of subjects with BD, considering the latitude and the seasonality of the study sites, and potential confounders. Based on the existing literature, we hypothesized that, over follow-up, participants would experience more depressive episodes during winter and more manic/hypomanic episodes during the spring-summer seasons. As a secondary outcome, exploratory analyses were performed to study whether these associations may be moderated by sociodemographics, latitude (geographic site), clinical characteris-tics, or global psychosocial functioning.

2. Methods

2.1. Participants

The methods for the COBY study have been described in detail previously (Axelson et al., 2006). Briefly, 446 participants between 7 and 17 years were enrolled from October 2000 through July 2006. All participants fulfilled Diagnostic and Statistical Manual of Mental Disorders-IV (DSM-IV) criteria (Diagnostic and statistical manual of mental disorders: DSM-IV, n.d.) for BD-I, BD-II, or BD-Not Otherwise Specified (NOS) based on the COBY operationalized definition (Axelson et al., 2006). The sample was recruited from outpatient clinics (84.4 %), inpatient units (4.4 %), advertisements (6.7 %), and referrals from other physicians (4.4 %), at the 3 study sites: University of Pittsburgh Medical Center (UPMC), Brown University (Brown), and University of California at Los Angeles (UCLA).

For the current study, we included a subsample of 413 youths who had at least one prospective follow-up assessment. The assessments included were completed until March 2020 to avoid possible bias due to the COVID-19 pandemic. Participants were interviewed on average every 7 months for an average of 91.9 months (7.7 years) (range: 6 months–228 months), with a retention rate of 78 %.

2.2. Procedures

Each university’s institutional review board approved the study before enrollment, and informed assent/consent was obtained from participating youths and their parents at intake. Participants’ symptomatic and psychosocial course were assessed by trained research staff and reviewed by a study investigator, who was ultimately responsible for the clinical ratings.

2.2.1. Psychiatric diagnosis

At intake, youths and parents were interviewed for psychiatric disorders and treatment with the Schedule for Affective Disorders and Schizophrenia for School-Age Children–Present and Lifetime Version (K-SADS-PL) (Kaufman et al., 1997). Severity of mood symptoms was ascertained with the K-SADS Mania Rating Scale (K-SADS-MRS) (Axelson et al., 2003), and the depression section of the K-SADS-P (K-SADS-DEP-P), both derived from the mood disorders sections of the K-SADS-Present Episode (K-SADS-P, 4th revision) (Chambers et al., 1985). Diagnostic interrater agreement was high for the K-SADS-PL (range 93–100 %). The intraclass correlation coefficient (ICC) for the K-SADS-MRS and K-SADS-DEP-P were 0.83. Kendall’s concordance coefficient was 0.85 for a major depressive episode, and 0.78 for hypomania/mania episodes.

2.2.2. Psychiatric symptoms

Week-by-week longitudinal changes in mood and other psychiatric symptoms were assessed using the Adolescent-Longitudinal Interval Follow-Up Evaluation (A-LIFE) (Keller et al., 1987), and the corresponding Psychiatric Status Rating Scales (PSR) since the last follow-up. At each interview, there is a retrospective recall of weekly symptom-atology from the previous interview to the current interview utilizing a calendar and several memory aids (Axelson et al., 2011). The PSR uses numeric values linked to DSM-IV criteria, ranging from 1 to 6 for mood disorders. For mood disorders, PSR scores ≤2 indicate euthymia, scores of 3–4 indicate subsyndromal symptoms, and scores ≥5 indicate full syndromal symptoms (episode). The consensus scores obtained after interviewing parents (in ≤18 years old child, but not required in young adults) and their children were used for analyses. The A-LIFE and corresponding PSR show good psychometric properties (Warshaw et al., 2001). PSR reliability in COBY was good/very good (Axelson et al., 2011). ICC was 0.85 when assessing percent of time fulfilling DSM-IV criteria for a mood episode, and 0.82 for subthreshold mood symp-toms. More specifically, the ICCs and the Kendall’s coefficients of concordance for a major depressive episode were between 0.74 and 0.79, respectively, and for hypomania/mania, 0.6–0.67, respectively.

Full recovery was defined as 8 consecutive weeks with a PSR score ≤ 2. Recurrence was defined as a PSR score ≥ 5 for one week for hypomania/mania, and two weeks for a major depressive episode. Similar to the existing literature (Judd et al., 2002), “change in mood polarity” was defined as a switch between a major depressive episode (PSR rating ≥ 3) and hypomania/mania (PSR rating ≥ 3), or vice versa, with or without intervening weeks at the asymptomatic status.

Socio-economic status (SES) was ascertained using the 4-factor Hollingshead Scale (Hollingshead, 1975).

2.2.3. Psychosocial functioning

Participants’ current and most severe past global functioning were assessed using the Children’s Global Assessment Scale (CGAS) for participants under age 22, and the Global Assessment of Functioning (GAF) for participants ages 22 and older at follow-up (Diagnostic and statistical manual of mental disorders: DSM-IV, n.d.; Shaffer et al., 1983).

2.2.4. Latitude

As reported in the literature, latitude and seasons were used to assess the amount of light received. For studying the effects of latitude on the prevalence of mood episodes, we followed the Köppen climate classification, the most widely used system to classify climate (Peel et al., 2007).The Brown (n = 135) and UPMC (n = 204) sites have cold continental climate and similar latitudes (41.82 for Providence, and 40.44 for Pittsburgh), thus, they were grouped as a single site for the analyses. The UCLA site (n = 74) was kept separate because it has a temperate climate and it is located in a southern latitude (34.05 for Los Angeles).

2.2.5. Seasonality

For studying the effects of seasonality, we followed the Trenberth season classification, which has similarly been widely used to classify seasons (Trenberth, 1983). For the analyses, seasons were classified as follows: Winter (December, January, February); Spring (March, April, May); Summer (June, July, August); and Autumn (September, October, November).

2.3. Statistical analysis

Comparisons between groups were described with mean and standard deviation for quantitative variables and counts with frequencies for the categorical ones. Comparisons were done using standard statistical tests (e.g., Student’s t and Chi-square tests) as appropriate.

Longitudinal associations between demographic and clinical variables and the event of interest at each follow-up were estimated by fitting Generalized Linear Mixed Models (GLMM) with an unstructured covariance matrix. This approach can accommodate non-Gaussian outcomes and dependence among correlated observations arising from longitudinal or repeated measures studies and a different number of subjects per subgroup. The primary outcome was defined as the occur-rence of a major depressive episode, manic, subsyndromal depression, and hypomanic episode. To assess differential seasonal effects across sites due to geographical differences, we used GLMM with time (month or season), or time (month or season) x site interaction. Odds ratios (OR) and 95 % confidence intervals (CI) were computed for relative comparisons.

Typically, the standard school calendar in the USA goes from autumn (beginning in September) through spring (ending in June). Given that several conditions can impact school performance (Pagerols et al., 2022), and as a consequence associate with more depression during the school year (Franke et al., 2018), the following variables were evaluated between sites at intake: Attention Deficit Hyperactivity Disorder (ADHD), Anxiety Disorders, Disruptive Behavior Disorders (DBD), Substance Use Disorder (SUD) (Franke et al., 2018), and history of physical/sexual abuse (Andreu Pascual et al., 2020). The interaction effect of seasonal patterns in participants younger and older than 18 years old and BD subtypes was explored because around the age of 18, many adolescents leave the family home to go to college or to work and live independently.

All models were adjusted for the next potential confounders; age, sex, SES as demographic variables, variables that showed group differences between Brown/UPMC and UCLA and presence or absence of any type of pharmacological treatment.

All analyses were two-tailed, with statistical significance set at p-value < 0.05, and post hoc comparisons were assessed with the Tukey method. To fit the models, we used the libraries lme4 (v. 1.1–35.1) and emmeans (v. 1.8.8) (https://cran.r-project.org/web/packages/emmeans/index.html). All analyses were performed using R package version 4.3.1 for Windows.

3. Results

3.1. Demographics

The results were based on the prospective evaluation of 413 youths 7–17 years 11 months of age at intake (mean age = 12.6 ± 3.3 years old) with BD diagnoses (BD-I, n = 244; BD-II, n = 28; BD-NOS, n = 141). The average age during the follow-up was 20.6 years (7.6 to 35.2 years old), and 27.2 ± 4.8 at last assessment, with a total of 274,123 weekly mood ratings.

Demographic and clinical characteristics at intake are described in Table 1. Participants at the Brown/UPMC were younger, had lower SES, and were less likely to be female, and to live with both biological par-ents, compared to participants at UCLA. Participants at Brown/UPMC were more likely to have a BD-NOS subtype, lifetime comorbid ADHD, Anxiety Disorders, and history of abuse, compared to UCLA participants. Finally, Brown/UPMC participants had higher C-GAS scores than UCLA participants.

Table 1.

Demographic and clinical characteristics of COBY youth at intake (N = 413).

Brown/UPMC
 UCLA
 F/c2 p
(n = 339)
 (n = 74)
Mean (SD)/n, (%) Mean (SD)/ n, (%)
Demographics
Age
12.3 (3.2)
14 (3.2)
17.1
<0.001
Sex assigned at birth (female)
Self-reported ethnicity
(non-Hispanic white)		 147 (43.4 %)

280 (82.6 %)		 45 (60.8 %)

59 (79.7 %)		 7.4

0.33		 0.006

0.56
SES 3.2 (1.2) 4.3 (0.8) 59.2 <0.001
Living with both natural parents
Duration of follow-up
(months)		 130 (38.3 %)

85.27 (50.67)		 44 (59.5 %)

84.78 (50.64)		 11.1

1.76		 0.001

0.08
Clinical characteristics
Bipolar disorder
BD-I: 190 (56%)
BD-II: 23 (6.8%)
BD-NOS: 126
(37.2 %)
BD-I: 54 (73.3 %)
BD-II: 5 (6.8%)
BD-NOS: 15
(20.3 %)
8.02
0.02
Lifetime ADHD (yes) 211 (62.2 %) 34 (45.9 %) 6.68 0.01
Lifetime physical or sexual
abuse (yes)		 76 (22.4 %) 5 (6.8 %) 9.45 0.002
Lifetime any anxiety disorder (yes)
Lifetime any substance
abuse disorder (yes)		 148 (43.7 %)

14 (4.1 %)		 14 (18.9 %)

2 (2.7 %)		 15.59

0.3		 <0.001

0.6
Lifetime any disruptive
behavioral disorder (yes)		 165 (48.7 %) 30 (40.5 %) 1.6 0.2
General functioning
C-GAS
56 (11.1)
49.2 (15.2)
19.3
<0.001
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Abbreviations: UPMC: University of Pittsburgh Medical Center; Brown: Brown University; UCLA: University of California at Los Angeles; SD: standard devia-tion; BD-I: Bipolar Disorder type I; BD-II: Bipolar Disorder type II; BD-NOS: Bi-polar Disorder Not Otherwise Specified; SES: socio-economic status; ADHD: attention deficit hyperactivity disorder; C-GAS: children’s global assessment scale.

3.2. Monthly and seasonal prevalence of mood episodes

3.2.1. Depression

By Month: As shown in Fig. 1a, for the group as a whole (UPMC, Brown, and UCLA sites), comparisons by month showed that there were significantly more major depressive episodes from November to February, compared to March through September. The most significant difference was between the months of December and August (OR = 1.39; 95 % CI: 1.25–1.54 p < 0.0001). By season: As shown in Fig. 1b, for the group as a whole, major depressive episodes were significantly higher during winter (5.8 %, 95 % CI 4.8 %–7.1 %), than during summer (4.5 %, 95 % CI: 3.7 %–5.5 %), spring (4.9 %, 95 % CI 4 %–6 %), and autumn (5.2 %, 95 % CI: 4.3 %–6.3 %).

Fig. 1.

Fig. 1.

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Monthly (a) and seasonal (b) prevalence of depressive episodes.

(a) There were significantly more major depressive episodes in November (5.6 %, CI: 4.6 %–7 %), December (6.1 %, CI: 4.9 %–7.5 %), January (5.8 %, CI: 4.7 %–7.2%), and February (5.6 %, CI: 4.5 %–6.9 %), when compared to April (4.8 %, CI: 3.9 %–5.9 %), May (4.7 %, CI: 3.8 %–5.8 %), June (4.7 %, CI: 3.8 %–5.8 %), July (4.5 %, CI: 3.6 %–5.6 %), August (4.5 %, CI: 3.6 %–5.5 %), and September (4.9 %, CI: 3.9 %–6 %) (all p-values < 0.0001). (b) The risk of having a depressive episode was significantly higher in winter (mean: 5.8 %, CI 4.8 %–7.1 %) when compared with summer (mean 4.5 %,CI: 3.7 %–5.5 %) (OR = 1.3; CI 1.28–1.33, p < 0.0001), spring (mean 4.9 %, CI::4 %–6 %), (OR = 1.2; CI 1.17–1.22, p < 0.001) and autumn (5.2 %, CI: 4.3 %–6.3 %) (OR = 1.13; CI 1.11–1.15, p < 0.001). Also, the risk of having a depressive episode was significantly higher in autumn as compared with summer (OR = 1.16; CI: 1.13–1.17, p < 0.0001), and to a lesser degree, when compared to spring (OR = 1.06; CI: 1.04–1.17, p = 0.005), and in spring as compared with summer (OR = 1.1; CI: 1.06–1.10, p = 0.0001). *Winter (December–February), Spring (March–May), Summer (June–August), and Autumn (September–November). Abbreviations: CI: Confidence Interval, OR: Odds Ratio.

The participants from Brown/UPMC had a similar pattern of the monthly prevalence of major depressive episodes, with the highest prevalence in the months of December and January, and the lowest prevalence in the months of July and August (Fig. 2a) (Supplemental material 1). When considering seasons at Brown/UPMC sites, the highest prevalence of major depressive episodes was during winter, and the lowest was during summer (Fig. 2b). In contrast, participants from UCLA showed significantly lower monthly and seasonal prevalence of major depressive episodes (~0.05 %) when compared with Brown/ UPMC. Indeed, in comparison with UCLA, the risk of having a major depressive episode at Brown/UPMC was higher across all months (ORs ranging from 9.1 to 20.1, all p-values < 0.0001). There were no statistically significant monthly or seasonal differences in the prevalence of major depressive episodes at UCLA, showing an absence of seasonal pattern as compared to the Brown/UPMC sites. For both, the monthly and seasonal analyses noted above, adjusting for between-sites statistically significant sociodemographic, clinical differences, and pharmacological treatment, yielded similar results (Fig. 2 and Supplemental material 1). Analyses exploring the interaction effect seasonal patterns of major depressive episodes in participants younger and older than 18 years old (Supplemental material 2), and by BD subtype (Supplemental material 3), also yielded similar results.

Fig. 2.

Fig. 2.

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Monthly (a) and seasonal (b) prevalence of depressive episodes per site.

(a) Participants at the Brown/UPMC site had statistically significantly more prevalence of depressive episodes during the months of December, January, and February, as compared to the months of April (OR = 1.302, p < 0.0001; OR = 1.237, p < 0.0001; OR = 1.192, p < 0.0001), May (OR = 1.319, p < 0.0001; OR = 1.253, p < 0.0001; OR = 1.207, p < 0.0001), June (OR = 1.346, p < 0.0001; OR = 1.278, p < 0.0001; OR = 1.231, p < 0.0001), July (OR = 1.406, p < 0.0001; OR =1.336, p < 0.0001; OR = 1.287, p < 0.0001), August (OR = 1.388, p < 0.0001; OR = 1.319, p < 0.0001; OR = 1.271, p < 0.0001), and September (OR = 1.263, p < 0.0001; OR = 1.199, p < 0.001; OR = 1.155, p = 0.0044) respectively; March as compared to June (OR = 1.133, p = 0.0375), July (OR = 1.185, p = 0.0001), and August (OR = 1.17, p = 0.0007); October as compared to July (OR = 1.157, p = 0.0032), and August (OR = 1.142, p = 0.0149); and November as compared to May (OR = 1.22, p < 0.0001), June (OR = 1.239, p < 0.0001), July (OR = 1.295, p < 0.0001), August (OR = 1.278, p < 0.0001), and September (OR = 1.161, p = 0.0015). Adjusted by age, sex, SES, bipolar subtype, and presence or absence of any type of pharmacological treatment, comorbid anxiety, ADHD and physical/sexual abuse. (b) Regarding seasonality, at the Brown/UPMC sites, the highest prevalence of depressive episodes was during the winter season (4.8 %, range 3.9 %–5.8 %) whereas the lowest prevalence was during the summer season (3.5 %, range 2.8 %–4.3 %). The risk of having a depressive episode was higher in the winter season as compared with the summer (OR = 1.374; p < 0.0001), spring (OR = 1.232,p < 0.0001), and autumn (OR = 1.163, p < 0.0001) seasons; in autumn, as compared to summer (OR = 1.181, p < 0.0001); and in spring, as compared to summer (OR = 1.115, p < 0.0001). The prevalence of depressive episodes at the UCLA site was 0.5 %, without differences across the seasons. Adjusted by age, sex, SES, bipolar subtype, and presence or absence of any type of pharmacological treatment, comorbid anxiety, ADHD and physical/sexual abuse. Adjusted and unadjusted prevalences are depicted in Supplemental Material 1. **Winter (December–February), Spring (March–May), Summer (June–August), and Autumn (September–November). Abbreviations: OR: Odds Ratio.

Analyses evaluating whether subsyndromal depressive episodes displayed the same monthly and seasonal patterns as the syndromal major depressive episodes yielded similar results.

3.2.2. Mania/hypomania

By month: As shown in Fig. 3a, for the group as a whole, episodes of mania were more prevalent in the month of August, and less prevalent in the month of February; however, differences were not statistically sig-nificant. By season: As shown in Fig. 3b, for the group as a whole, episodes of mania were statistically significantly more prevalent in the summer than in the winter.

Fig. 3.

Fig. 3.

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Monthly (a) and seasonal (b) prevalence of manic episodes.

(a): Episodes of mania were more prevalent in August (1.3 %, CI: 1 %–1.7 %), and less prevalent in February (1.2 %, CI: 0.9 %–1.5 %), but differences were not statistically significant (OR = 1.14; CI 0.95–1.37; p = 0.4). (b) When considering seasonality, episodes of mania were more prevalent in summer (1.3 %, CI: 1 %–1.6 %) than in winter (1.2 %, CI: 0.9 %–1.5 %) (OR = 1.09; CI 1.01–1.18; p = 0.02). No statistically significant differences in the prevalence of mania were found among any other seasons. *Winter (December–February), Spring (March–May), Summer (June–August), and Autumn (September–November). Abbreviations: CI: Confidence Interval, OR: Odds Ratio.

The prevalence of manic episodes was higher at the Brown/UPMC sites than at the UCLA site (1.4 %, 95 % CI: 1 %–1.8 % vs. 0.5 %, 95 % CI: 0.4 %–0.6 %). (Supplemental material 4). However, after adjusting for between-site statistically significant demographic and clinical differences (Fig. 4) there were no differences in the monthly or seasonal prevalence of mania at the Brown/UPMC site. For the UCLA site, there was a trend for more manic episodes in the month of May, as compared to the month of December (Fig. 4a), but there were no seasonal differences (Fig. 4b). Analyses exploring the interaction effect of mania seasonal patterns in participants younger and older than 18 years old, and by BD subtype yielded similar results.

Fig. 4.

Fig. 4.

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Monthly (a) and seasonal (b) prevalence of mania episodes per site.

(a) There were no monthly differences in the prevalence of mania in Brown/UPMC sites. However, for UCLA there was a trend for more manic episodes in May, as compared to December (0.8 %, 95 % CI: 0.5 %−1.1 % vs. 0.3 %, CI: 0.2 %–0.5 %, OR = 2.26, 95 % CI: 0.5–3.5, p = 0.046). (b) There were no seasonal differences in the prevalence of mania in either the Brown/UPMC or UCLA sites. Adjusted by age, sex, SES, bipolar subtype, and presence or absence of any type of pharmacological treatment, comorbid anxiety, ADHD and physical/sexual abuse. **Winter (December–February), Spring (March–May), Summer (June–August), and Autumn (September–November). Abbreviations: CI: Confidence Interval, OR: Odds Ratio.

4. Discussion

In this study we prospectively examined the influence of latitude and seasons on the prevalence of mood polarities in a large sample of BD youth and young adults. In summary, for the entire sample there were statistically significant differences in the monthly and seasonal prevalence of depression for all BD subtypes. Specifically, syndromal and subsyndromal depressive episodes were more prevalent from the months of November to February, and especially in December, compared to August, in the total sample. When analyses were performed taking into account the differences in latitude of the sites, and after adjusting for confounders, a similar pattern of monthly/seasonal mood changes was found at the Brown/UPMC sites. In contrast, the UCLA site showed an overall lower prevalence of major depressive episodes, and subsyndromal symptoms, and an absence of seasonal pattern.

For the entire sample, there were more manic episodes during summer compared to winter. After adjusting for confounders, when analyzing the data by the latitude of the sites, no monthly/seasonal differences were found in the prevalence of mania/hypomania in any site.

In line with the existing literature, we found a higher prevalence of major depressive episodes and subsyndromal symptoms during the months with fewer daylight hours, coinciding with the winter season (Akhter et al., 2013; Geoffroy et al., 2014; Rosenthal et al., 1986). The lower number of major depressive episodes and the different distribu-tion throughout the months at the UCLA site compared to the Brown/ UPMC sites could be explained, at least in part, by its more temperate climate, with fewer climatic differences between the months throughout the year. In addition, a different lifestyle in California, with more year-round outdoor activities, could also explain its lower number of major depressive episodes (Thompson Coon et al., 2011). On the other hand, a recent study of 7488 adults with BD-I across different countries repre-senting a wide range of latitudes in both hemispheres showed that larger changes in solar insolation throughout the year was associated with a greater likelihood for depression (Bauer et al., 2022). Similarly, we found a significantly higher likelihood of major depressive episodes at the Brown/UPMC sites as compared to the UCLA site, and a significant difference in the month-by-site prevalence rate of major depressive episodes between the Brown/UPMC and UCLA sites. There were no differences among BD suptypes, suggesting the inherent relationship between major depressive episodes and less light exposure (Bauer et al., 2022).

It is important to note the possibility that the school calendar was a confounding factor, considering the higher prevalence of major depressive episodes in winter, when the students are attending class and subject to more academic and social stressors. However, the fact that there is no difference in the prevalence of major depressive episodes across months at the UCLA site, and that there is a lower prevalence of major depressive episodes at the Brown/UPMC sites during months in which students are still attending school (April, May and beginning of June), makes this possibility unlikely. In addition, the higher percentage of major depressive episodes in winter than in any other season at the Brown/UPMC site, leads us to think that the difference in the prevalence of major depressive episodes between winter and summer is not only due to the absence of school in summer.

Regarding manic/hypomanic episodes, for the group as a whole, our results are similar to some studies that reported a higher prevalence of manic episodes during the months with more daylight hours, and summer or spring seasons (Aguglia et al., 2018; Fellinger et al., 2019; Geoffroy et al., 2014). However, the results were not as strong as the differences found for the depressive episodes. This may be attributable to the low prevalence of manic/hypomanic episodes in our sample, which usually respond better to treatment than the depressive episodes (Baldessarini et al., 2019), thus reducing the probability to detect statistically significant seasonal effects (Bauer et al., 2009; Murray et al., 2011). Similar prevalences of manic/hypomanic episodes have been reported in other studies (Birmaher et al., 2009; Judd et al., 2002; Peel et al., 2007; Perlis et al., 2006). Moreover, other adult prospective studies have not found significant associations between manic episodes and brighter daylight hour months (Bauer et al., 2009; Murray et al., 2011).

In the United States, around the age of 18, many adolescents leave the family home to go to college or to work and live independently. Their schedules, behavioral habits (e.g., sleep), exposure to different stressors, and risk-taking behaviors (e.g., substance abuse), can be very different from those experienced when living at home, and could potentially influence mood patterns. However, there were no monthly and seasonal mood patterns differences between participants younger and older than 18, strengthening the findings that seasonal changes affect the polarity of mood episodes in people with BD.

The results of this study need to be considered in light of the following limitations. First, the COBY study is a naturalistic study with no healthy comparison cohort, mainly included self-reported non-His-panic White individuals recruited at University centers, limiting the generalizability of the findings. Second, while light is the primary syn-chronizer of circadian rhythmicity, other potentially confounding variables not examined in this study (e.g., stress, behavioral habits) may also affect circadian mechanisms, trigger mood episodes in people with BD, and attenuate or magnify the seasonal effects as a consequence (Mistlberger and Skene, 2004). Third, heterogeneous results might be obtained when latitude is used as an approximation for sunlight exposure. In fact, although some studies have reported differences in the prevalence of mood polarities according to latitude (Friedman et al., 2006), others do not (Bauer et al., 2009). We evaluated the temporal association of changes in the prevalence and risk for mood episodes within months, seasons, and latitude, as a proxy for light exposure. In our sample, latitude varied from 41.82◦ (Providence, Rhode Island, USA) and 40.44◦ (Pittsburgh, Pennsylvania, USA) to 34.05◦ (Los Angeles, California, USA), from the equator. Apart from their different latitudes, the Brown and UPMC sites share the same temperate climate type, whereas UCLA has a more tropical climate type, following the Köppen climate classification (Peel et al., 2007). These different types of climate between these sites could explain, at least in part, the different probabilities of mood polarities between them (Bauer et al., 2009, 2022). However, considering that locations at the same latitude may have different solar insolation (Bauer et al., 2012; Rosenthal et al., 2020), and the effects of modernization (e.g., staying indoors in air-conditioned environments, sedentary lifestyle, electronic media, trav-eling long distances for work), some have questioned the validity of latitude as an adequate indicator for sunlight exposure (Godar, 2005). In fact, some studies suggest that lifestyle (including outdoor work and recreation) may be a better determinant of sunlight exposure than latitude (Godar, 2005). Fourth, participants lived within a reasonable dis-tance of the study sites and, over time, some of them might have moved away, which could potentially change their location latitude and sea-sonality. Lastly, although we did not find medication effects on the seasonality of mood episodes, these results need to be taken with caution given the naturalistic aspect of this study.

4.1. Clinical implications

Despite the awareness of the existing evidence that the amount of exposure to light during the year affects the course of mood disorders, and that there are adjunctive treatments (e.g., light therapy for depression) that can prevent and ameliorate mood symptoms (Wirz-Justice and Terman, 2012), the implementation of these treatments is very limited (Gottlieb et al., 2019). Interventions based on the control of environmental stimuli that act on biological rhythms (i.e., chronotherapy light therapy, medications) and psychosocial interventions, such as interpersonal and social rhythm therapy, are warranted (Gottlieb et al., 2019).

Light therapy combined with selective serotonin reuptake inhibitors leads to a rapid improvement (within a week) of depressive symptoms (Wirz-Justice and Terman, 2012). This intervention might be indicated in those patients with depressive symptoms and suicidal ideation in whom a rapid reduction of depressive symptoms is imperative. Finally, the evidence that some subjects show a greater risk of suffering depressive symptoms in those months of less light justifies to carry out a closer follow-up in those young people with risk factors for a major depressive disorder.

5. Conclusion

Our results give further evidence for the influence of latitude and seasonal differences in sun exposure on the rates of major depressive episodes and subsyndromal depressive episodes, with these episodes being significantly more prevalent during the winter, even after controlling for confounders (e.g., school vacations). These results were less evident for manic/hypomanic episodes which were more prevalent during the summer.

Supplementary Material

Supplemental Data

Acknowledgments

The authors wish to thank the Course and Outcome of Bipolar Youth (COBY) study participants and their families for their participation, and the COBY research team. The authors would also like to acknowledge the Alicia Koplowitz Foundation for its immense support, as well as Rita Scholle of UPMC Western Psychiatric Hospital, for assistance with the preparation of the manuscript, Stacia Friedman-Hill, PhD, and Shelli Avenevoli, PhD, of NIMH, for their continued encouragement and support.

This article is dedicated to the memory of Dr. Michael Strober, who died during the preparation of this manuscript.

Funding

This research was supported by the National Institute of Mental Health (NIMH): Course and Outcome of Bipolar Youth (COBY) study grants RO1 MH059929 (PI: Birmaher), RO1 MH59691 (PIs: Keller/Yen), and RO1 MH59977 (PI: Strober), and Predicting Adult Outcomes in Bipolar Youth (PROBY) study grants RO1 MH112544 (PI: Birmaher), and RO1 MH5270580 (PI: Yen); and the Alicia Koplowitz Foundation (Estrada-Prat, Romero), Madrid, Spain. The funding sources have not been involved in the study design, collection, analysis, interpretation of data, writing of the report, or the decision to submit to this Journal.

Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi.org/10.1016/j.jad.2024.10.115.

Footnotes

CRediT authorship contribution statement

Xavier Estrada-Prat: Writing – review & editing, Writing – original draft, Visualization, Methodology, Investigation, Formal analysis, Conceptualization. Soledad Romero: Writing – review & editing, Writing – original draft, Visualization, Validation, Supervision, Resources, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. Roger Borras: Validation, Supervision, Software, Resources, Methodology, Investigation, Formal analysis, Data curation. John Merranko: Writing – review & editing, Visualization, Methodology, Funding acquisition, Formal analysis, Data curation, Conceptualization. Tina Goldstein: Writing – review & editing, Funding acquisition, Formal analysis, Data curation. Danella Hafeman: Writing – review & editing, Funding acquisition, Formal analysis, Data curation. Heather Hower: Writing – review & editing, Funding acquisition, Formal analysis, Data curation. Shirley Yen: Writing – review & editing, Funding acquisition, Formal analysis, Data curation. Jeffrey Hunt: Writing – review & editing, Funding acquisition, Formal analysis, Data curation. Benjamin Goldstein: Writing – review & editing, Funding acquisition, Formal analysis, Data curation. Neal Ryan: Writing – review & editing, Funding acquisition, Formal analysis, Data curation. Rasim Diler: Writing – review & editing, Funding acquisition, Formal analysis, Data curation. Michael Strober: Writing – review & editing, Funding acquisition, Formal analysis, Data curation. MaryKay Gill: Writing – review & editing, Writing – original draft. Boris Birmaher: Writing – review & editing, Writing – original draft, Visualization, Validation, Supervision, Resources, Methodology, Investigation, Funding acquisition, Formal analysis, Data curation, Conceptualization.

Declaration of competing interest

Dr. X. Estrada-Prat and Dr. S. Romero received a grant from the Alicia Koplowitz Foundation. Dr. T. Goldstein reports grants from the National Institute of Mental Health (NIMH), The American Foundation for Suicide Prevention (AFSP), University of Pittsburgh Clinical and Translational Science Institute (CTSI) and The Pittsburgh Foundation and royalties from Guilford Press, outside the submitted work. Dr. Birmaher reports grants from NIMH, during the conduct of the study; royalties from Random House, Woltas Kluwer (UpToDate) and Lippin-cott, Williams & Wilkins, outside of the submitted work. Ms. Hower has received research support from NIMH, and honoraria from the Department of Defense (DOD). Dr. Strober has received research support from NIMH, and support from the Resnick Endowed Chair in Eating Disor-ders. Dr. Hafeman reports grants from NIMH. Dr. B. Goldstein reports grants from Brain & Behavior Research Foundation, Brain Canada, Ca-nadian Institutes of Health Research, Heart & Stroke Foundation, and the University of Toronto Department of Psychiatry. Dr. Yen has received research support from NIMH, the National Center for Compli-mentary and Integrative Health (NCCIH), and the AFSP. Dr. Ryan reports grants from NIH and The American Foundation for Suicide Prevention (AFSP). Dr. Diler has received NIMH grants. Dr. Jeffrey Hunt receives support from NIMH and an editorial stipend from John Wiley Publishers. Mr. Roger Borras, Mr. Merranko, Ms. Gill report no financial relationships with commercial interests.

Data availability

The investigative team contributes data and associated documenta-tion in compliance with NIMH Data Sharing Policy through the National Database for Clinical Trials Related to Mental Illness (NDCT) as per NOT-MH-14–015. Per the NIMH Data Repositories Data Access Agree-ment and Use Certification, in order to gain access to data from this project, outside Investigators must submit a detailed proposal of the study aims, hypotheses to be tested, variables/constructs and analytic approach to be used.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplemental Data
NIHMS2153486-supplement-Supplemental_Data.docx (40.9KB, docx)

Data Availability Statement

The investigative team contributes data and associated documenta-tion in compliance with NIMH Data Sharing Policy through the National Database for Clinical Trials Related to Mental Illness (NDCT) as per NOT-MH-14–015. Per the NIMH Data Repositories Data Access Agree-ment and Use Certification, in order to gain access to data from this project, outside Investigators must submit a detailed proposal of the study aims, hypotheses to be tested, variables/constructs and analytic approach to be used.

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