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Sunlight-JAD 2014

December 2014

December 2014

Authors:

Christian Simhandl

Sigmund Freud University Vienna

Joaquim Radua

IDIBAPS August Pi i Sunyer Biomedical Research Institute

Benedikt Amann

Mental Health Institute Hospital del Mar

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Research report

Relationship between sunlight and the age of onset of bipolar disorder:

An international multisite study

Michael Bauer

, Tasha Glenn

, Martin Alda

, Ole A. Andreassen

, Elias Angelopoulos

Raffaella Ardau

, Christopher Baethge

, Rita Bauer

, Frank Bellivier

h,i

Robert H. Belmaker

, Michael Berk

k,l,m

, Thomas D. Bjella

, Letizia Bossini

Yuly Bersudsky

, Eric Yat Wo Cheung

,Jörn Conell

, Maria Del Zompo

, Seetal Dodd

k,q

Bruno Etain

r,i

, Andrea Fagiolini

, Mark A. Frye

, Kostas N. Fountoulakis

Jade Garneau-Fournier

, Ana González-Pinto

, Hirohiko Harima

, Stefanie Hassel

Chantal Henry

r,i

, Apostolos Iacovides

, Erkki T. Isometsä

y,z

, Flávio Kapczinski

Sebastian Kliwicki

, Barbara König

, Rikke Krogh

, Mauricio Kunz

, Beny Lafer

Erik R. Larsen

, Ute Lewitzka

, Carlos Lopez-Jaramillo

, Glenda MacQueen

Mirko Manchia

, Wendy Marsh

, Mónica Martinez-Cengotitabengoa

, Ingrid Melle

Scott Monteith

, Gunnar Morken

, Rodrigo Munoz

, Fabiano G. Nery

Claire O’Donovan

, Yamima Osher

, Andrea Pfennig

, Danilo Quiroz

, Raj Ramesar

Natalie Rasgon

, Andreas Reif

, Philipp Ritter

, Janusz K. Rybakowski

Kemal Sagduyu

, Ângela M. Scippa

, Emanuel Severus

, Christian Simhandl

Dan J. Stein

, Sergio Strejilevich

, Ahmad Hatim Sulaiman

, Kirsi Suominen

Hiromi Tagata

, Yoshitaka Tatebayashi

, Carla Torrent

, Eduard Vieta

Biju Viswanath

, Mihir J. Wanchoo

, Mark Zetin

, Peter C. Whybrow

Department of Psychiatry and Psychotherapy, University Hospital Carl Gustav Carus, Technische Universität, Dresden, Germany

ChronoRecord Association, Fullerton, CA, USA

Department of Psychiatry, Dalhousie University, Halifax, NS, Canada

NORMENT –K.G. Jebsen Centre for Psychosis Research, Division of Mental Health and Addiction, Oslo University Hospital &Institute of Clinical Medicine,

Oslo, Norway

Department of Psychiatry, University of Athens Medical School, Eginition Hospital, Athens, Greece

Unit of Clinical Pharmacology, University-Hospital of Cagliari, Sardinia, Italy

Department of Psychiatry and Psychotherapy, University of Cologne Medical School, Cologne, Germany

Psychiatrie, GH Saint-Louis –Lariboisière –F. Widal, APHP, INSERM UMR-S1144, Faculté de Médecine, Université D. Diderot, Paris, France

FondaMental Fondation, Créteil, France

Department of Psychiatry, Faculty of Health Sciences, Ben Gurion University of the Negev, Beer Sheva Mental Health Center, Beer Sheva, Israel

IMPACT Strategic Research Centre, School of Medicine, Deakin University, Geelong, Victoria 3220, Australia

Department of Psychiatry, ORYGEN Youth Health Research Centre, Centre for Youth Mental Health, Australia

The Florey Institute for Neuroscience and Mental Health, University of Melbourne, Parkville, Victoria 3052, Australia

Department of Molecular Medicine and Department of Mental Health (DAI), University of Siena and University of Siena Medical Center (AOUS), Siena, Italy

Department of General Adult Psychiatry, Castle Peak Hospital, Hong Kong

Section of Neurosciences and Clinical Pharmacology, Department of Biomedical Sciences, University of Cagliari, Sardinia, Italy

Department of Psychiatry, University of Melbourne, Parkville, Victoria 3052, Australia

AP-HP, Hopitaux Universitaires Henri Mondor and INSERM U955 (IMRB), Université Paris Est, Creteil, France

Department of Psychiatry &Psychology, Mayo Clinic Depression Center, Mayo Clinic, Rochester, MN, USA

3rd Department of Psychiatry, Division of Neurosciences, School of Medicine, Aristotle University of Thessaloniki, Thessaloniki, Greece

Department of Psychiatry and Behavioral Sciences, Stanford School of Medicine, Palo Alto, CA, USA

Department of Psychiatry, University Hospital of Alava, University of the Basque Country, CIBERSAM, Vitoria, Spain

Department of Psychiatry, Tokyo Metropolitan Matsuzawa Hospital, Setagaya, Tokyo, Japan

Department of Psychiatry, Faculty of Medicine, University of Calgary, Calgary, AB, Canada

Department of Psychiatry, Institute of Clinical Medicine, University of Helsinki, Finland

National Institute for Health and Welfare, Helsinki, Finland

Laboratory of Molecular Psychiatry, Hospital de Clínicas de Porto Alegre, Federal University of Rio Grande do Sul, Porto Alegre, Brazil

Department of Adult Psychiatry, Poznan University of Medical Sciences, Poznan, Poland

BIPOLAR Zentrum Wiener Neustadt, Wiener Neustadt, Austria

Department of Affective Disorders, Q, Mood Disorders Research Unit, Aarhus University Hospital, Denmark

Bipolar Disorder Research Program, Department of Psychiatry, University of São Paulo Medical School, São Paulo, Brazil

Contents lists available at ScienceDirect

journal homepage: www.elsevier.com/locate/jad

Journal of Affective Disorders

http://dx.doi.org/10.1016/j.jad.2014.05.032

Journal of Affective Disorders 167 (2014) 104–111

Mood Disorders Program, Fundacion San Vicente de Paul, Department of Psychiatry, Universidad de Antioquia, Medellín, Colombia

Department of Psychiatry, University of Massachusetts, Worcester, MA, USA

Michigan State University College of Human Medicine, Traverse City Campus, Traverse City, MI, USA

Department of Neuroscience, NTNU, and St Olavs’University Hospital, Trondheim, Norway

Department of Psychiatry, University of California San Diego, San Diego, CA, USA

Deparment of Psychiatry, Diego Portales University, Santiago, Chile

UCT/MRC Human Genetics Research Unit, Institute of Infectious Disease and Molecular Medicine, University of Cape Town, Cape Town, South Africa

Department of Psychiatry, Psychosomatics and Psychotherapy, University of Würzburg, Würzburg, Germany

Department of Psychiatry, University of Missouri Kansas City, School of Medicine, Kansas City, MO, USA

Department of Neuroscience and Mental Health, Federal University of Bahia, Salvador, Brazil

Department of Psychiatry, University of Cape Town, Cape Town, South Africa

Bipolar Disorder Program, Neuroscience Institute, Favaloro University, Buenos Aires, Argentina

Department of Psychological Medicine, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia

City of Helsinki, Department of Social Services and Health Care, Psychiatry, Helsinki, Finland

Schizophrenia &Affective Disorders Research Project, Tokyo Metropolitan Institute of Medical Science, Seatagaya, Tokyo, Japan

Clinical Institute of Neuroscience, Hospital Clinic, University of Barcelona, IDIBAPS, CIBERSAM, Barcelona, Catalonia, Spain

Department of Psychiatry, NIMHANS, Bangalore 560029, India

Department of Psychology, Chapman University, Orange, CA, USA

Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, University of California Los Angeles (UCLA),

Los Angeles, CA, USA

article info

Article history:

Received 28 February 2014

Received in revised form

20 May 2014

Accepted 21 May 2014

Available online 29 May 2014

Keywords:

Bipolar disorder

Age of onset

Sunlight

Insolation

abstract

Background: The onset of bipolar disorder is inﬂuenced by the interaction of genetic and environmental

factors. We previously found that a large increase in sunlight in springtime was associated with a lower

age of onset. This study extends this analysis with more collection sites at diverse locations, and includes

family history and polarity of ﬁrst episode.

Methods: Data from 4037 patients with bipolar I disorder were collected at 36 collection sites in 23

countries at latitudes spanning 3.2 north (N) to 63.4 N and 38.2 south (S) of the equator. The age of onset

of the ﬁrst episode, onset location, family history of mood disorders, and polarity of ﬁrst episode were

obtained retrospectively, from patient records and/or direct interview. Solar insolation data were

obtained for the onset locations.

Results: There was a large, signiﬁcant inverse relationship between maximum monthly increase in solar

insolation and age of onset, controlling for the country median age and the birth cohort. The effect was

reduced by half if there was no family history. The maximum monthly increase in solar insolation

occurred in springtime. The effect was one-third smaller for initial episodes of mania than depression.

The largest maximum monthly increase in solar insolation occurred in northern latitudes such as Oslo,

Norway, and warm and dry areas such as Los Angeles, California.

Limitations: Recall bias for onset and family history data.

Conclusions: A large springtime increase in sunlight may have an important inﬂuence on the onset of

bipolar disorder, especially in those with a family history of mood disorders.

1. Introduction

Sunlight provides warmth, stimulates vision, initiates vitamin

D synthesis, and plays a fundamental role in how the circadian

clock adapts human physiology and behavior to the alternation of

day and night (Berson, 2003; Brainard and Haniﬁn, 2005; Hatori

and Panda, 2010). Circadian rhythms are involved in regulation

of mood (Albrecht, 2010; McClung, 2013) and abnormalities

in circadian rhythms are thought to underlie bipolar disorder

(Goodwin and Jamison, 1990; Mansour et al., 2005; McClung,

2007). We previously found that the larger the springtime increase

in solar electromagnetic energy striking the surface of the earth

(insolation) at the onset location, the younger the age of onset of

bipolar disorder (Bauer et al., 2012).

The emergence of bipolar disorder involves the interaction of

complex genetic mechanisms (Burmeister et al., 2008; Craddock

and Sklar, 2013; Petronis, 2003) and environmental factors

(Tsuchiya et al., 2003). Based on 6 studies of 2509 patients with

bipolar I disorder, the weighted mean age of onset falls into

3 groups, having peaks at ages 18.1, 26.9 and 42.7 years, with

55% of patients in the middle or late onset groups (Bellivier et al.,

2001, 2003; González Pinto et al., 2009; Hamshere et al., 2009; Lin

et al., 2006; Manchia et al., 2008). This broad range of onset and

the polygenic basis of bipolar disorder suggest that environmental

factors have an inﬂuential role (Burmeister et al., 2008; Craddock

and Sklar, 2013; Wright et al., 2003). Environmental factors

associated with a younger age of onset are cannabis use

(González-Pinto et al., 2008; Lagerberg et al., 2011), stressful life

events (Horesh et al., 2011) and childhood abuse (Garno et al.,

2005; Leverich et al., 2002), while neurological illness is associated

with an older onset (Depp and Jeste, 2004). The purpose of this

study was to repeat our prior investigation of the association

between solar insolation and the age of onset of bipolar disorder

using a substantially larger sample, and including information on

family history and polarity of the ﬁrst episode.

2. Methods

2.1. Patient data

All patients included in this study had a diagnosis of bipolar

disorder according to DSM-IV criteria made by a psychiatrist.

Approval for this study was obtained from institutional review

boards according to local requirements. Patient data were

Corresponding author. Tel.: þ49 351 458 0; fax: þ49 30 450 51 79 62.

E-mail address: michael.bauer@uniklinikum-dresden.de (M. Bauer).

M. Bauer et al. / Journal of Affective Disorders 167 (2014) 104–111 105

obtained retrospectively at 36 collection sites in 23 countries.

In 20 sites (Athens, Greece; Bangalore, India; Buenos Aires,

Argentina; Cagliari, Sardinia, Italy; Dresden, Germany; Halifax,

Canada; Helsinki, Finland; Hong Kong; Kansas City, KS, USA;

Los Angeles, CA, USA; Medellín, Colombia; Oslo, Norway; Paris,

France; Porto Alegre, Brazil; Rochester, MN, USA; San Diego, CA,

USA; Santiago, Chile; Siena, Italy; Thessaloniki, Greece; Würzburg,

Germany), data were gathered by direct interviews and reviewing

records, in 8 sites (Barcelona, Spain; Melbourne/Geelong,

Australia; Palo Alto, CA, USA; São Paulo, Brazil; Salvador, Brazil;

Trondheim, Norway; Vitoria-Basque Country, Spain; Worcester,

MA, USA) primarily by direct interviews, and at the remaining

8 sites (Aarhus, Denmark; Beer Sheva, Israel; Calgary, Canada;

Cape Town, South Africa; Kuala Lumpur, Malaysia; Poznan, Poland;

Tokyo, Japan; Wiener Neustadt, Austria) primarily by reviewing

records.

Variables included sex, date of birth, age of onset, location of

onset, family history of any mood disorder in a ﬁrst degree

relative, and polarity of the ﬁrst episode (depressed, manic or

hypomanic). The age of onset was deﬁned as the ﬁrst occurrence

of an episode of depression, mania or hypomania according to

DSM-IV criteria.

2.2. Potential confounders

Apart from any solar insolation effects, there were two potential

age-related confounders. The country median age varied by about 20

years between the oldest (Japan 45.8 years) and youngest (South

Africa at 25.5) collection sites. For a disease with a variable age of

onset that spans several decades, an older age of onset would be

expected in a country with an older population (Chen et al., 1993;

Heimbuch et al., 1980). The country median age was included in all

models to reﬂect the differences in country population.

Previous research reported a large birth cohort effect in bipolar

disorder with an older onset in older cohorts (Chengappa et al.,

2003), and that less than 10% of people develop bipolar disorder after

age 50 (Vasudev and Thomas, 2010). Since the current data includes a

largepercentageofpeoplebornbefore1960(36.8%),threebirth

cohort groups were created: born before 1940, born between 1940

and 1959, and born after 1959 (Chengappa et al., 2003). The birth

cohort grouping variable was included in all models.

There was a large imbalance in the percent of patients with a

diagnosis of bipolar I disorder as compared to other bipolar

subtypes. Since the percent of bipolar I varied from 23% to 99%,

with 40% of collection sites having Z80% of patients with bipolar I

disorder, only patients with a diagnosis of bipolar I disorder were

included in the analysis.

2.3. Solar insolation data

All solar insolation data were obtained from the USA National

Aeronautics and Space Administration (NASA) Surface Meteorol-

ogy and Solar Energy (SSE) Version 6.0 database, which is based on

data collected over a 22 year time period from 1983 to 2005

(NASA, 2012). Average monthly solar insolation data are available

for the entire globe with a spatial resolution of 1 11latitude/

longitude. Solar insolation is a measure of the electromagnetic

energy from the sun received for a given surface area on earth at

a given time, expressed in kWh/m

/day (kilowatt hours/square

meter/day). Solar insolation is not evenly distributed across the

earth's surface. The solar insolation varies with the annual changes

to the earth–sun relationship (angle of incidence which the sun's

rays strike the earth, the day length, and the latitude), reﬂection,

scattering and absorption of the sun's rays by clouds and aerosols

in the atmosphere, and reﬂection back into space by snow, ice, and

desert sand on the earth's surface (NASA, 2010).

The overall pattern of monthly changes to solar insolation

varies greatly by latitude. At the equator, there is very little change

to solar insolation throughout the year. Generally, the closer to the

poles, the greater the range in solar insolation across the year. See

Fig. 1 for examples of monthly changes in insolation at collection

sites in this study. Additionally, locations at the same latitude may

have very different solar insolation due to local conditions such as

cloud cover, altitude, and proximity to large bodies of water. For

example, Rome, Italy and Chicago IL, USA have the same latitude of

41.91north (N) and the same hours of daylight. However the

maximum monthly increase in solar insolation is 1.4 kWh/m

/day

in Rome but only 1.01 kWh/m

/day in Chicago, occurring between

February and March for both cities.

2.4. Solar insolation variables

Onset location data from every patient were grouped into

reference cities. Each reference city represents all locations within

the 1 11grid of latitude and longitude. For example, Dresden

with latitude of 51.1 N and 13.81east (E) is the reference city for

all onset locations between 51 and 521N, and 13 and 141E. The

monthly solar insolation data for reference cities in the southern

hemisphere were shifted by 6 months to be comparable with

monthly solar insolation in the northern hemisphere. The number

of reference cities from each collection site varied considerably

reﬂecting country size, migration patterns and cultural differences.

Monthly, yearly and seasonal variables were created using the

solar insolation data for each reference city including the monthly

increase and decrease in solar insolation, the yearly cumulative solar

insolation, and the yearly minimum and maximum monthly solar

insolation. The interaction between maximum monthly increase in

solar insolation family history, and maximum monthly increase in

solar insolation polarity of ﬁrst episode were also analyzed.

2.5. Country speciﬁc variables

The variables available for each country for each reference city

included the lifetime prevalence of bipolar I disorder, the country

median age, and the country sex ratio for ages 15–64 (CIA World

Factbook).

Fig. 1. Comparison of monthly solar insolation pattern at northern, equatorial and

temperate latitudes. The pattern of monthly solar insolation at Helsinki, Finland

(60.2 N), San Francisco, CA, USA (37.8 N) and Medellín, Columbia (6.3 N).

M. Bauer et al. / Journal of Affective Disorders 167 (2014) 104–111106

2.6. Statistics

Generalized Estimating Equations (GEE) models were used to

estimate the effect of solar insolation on the age of onset. The GEE

method corrects for the correlated data within each reference city

(cluster) (Zeger and Liang, 1986), estimates the difference in

magnitude of the association between two variables through the

use of interaction terms, and estimates the effect across the entire

population rather than within a cluster. All GEE models used age of

onset as the dependent variable. An exchangeable correlation

matrix was chosen, as appropriate for a large number of clusters

with variable cluster sizes including many with a single observa-

tion (Stedman et al., 2008; Zeger and Liang, 1986). To further

evaluate the birth cohort effect, models were also estimated that

excluded both patients born before 1960 and the birth cohort

variable. The model ﬁt was assessed using the quasi-likelihood

independent model criterion that is suitable for GEE (Pan, 2001).

A signiﬁcance level of 0.01 was used to evaluate estimated

coefﬁcients. Sidak's adjustment was used to evaluate multiple

comparisons at the 0.01 level. SPSS Version 22 was used for all

analyses.

3. Results

Data were collected for a total of 5465 patients. Of the 5465

patients, 4037 had a diagnosis of bipolar I disorder, 1236 had

bipolar II and 192 had bipolar NOS. Only the 4037 patients with

a diagnosis of bipolar I disorder were included in the analysis. The

4037 patients included 2414 from the prior analysis (Bauer et al.,

2012). Of the 4037 patients, 2374 (58.8%) were female and 1663

(41.2%) were male. Family history was available for 3334 (82.6%) of

the 4037 patients and polarity of ﬁrst episode was available for

3600 (89.2%).

The mean age of the patients was 48.1714.5 years. For

comparison with prior international studies, the unadjusted age

of onset for the 4037 patients was 25.4710.7 years, similar to

previous reports of 25.7 years, n¼1665 (Baldessarini et al., 2012)

and 25.6 years, n¼1041 ( Morselli et al., 2003). Of the 4037

patients, 220 (5.4%) were born before 1940, 1267 (31.4%) were

born between 1940 and 1959, and 2550 (63.2%) were born after

1959. As expected, country median age and birth cohort were

signiﬁcantly associated with the age of onset (both po0.001).

Although data for the 4037 patients were collected at 36

collection sites in 23 countries, the onset locations were in 318

unique reference cities or clusters in 43 countries. The onset loca-

tion was in northern hemisphere for 2994 patients and in the

southern hemisphere for 1043 patients. The distribution of the

onset locations across latitudes is shown in Table 1. The mean

number of patients in each reference city was 12.7 with 4.3% of the

4037 patients in a reference city of one.

The best ﬁtting model included the interaction of the max-

imum monthly increase in solar insolation family history, the

country median age and the birth cohort. This is labeled Model 1.

The primary result was an inverse relationship between maximum

monthly increase in solar insolation and age of onset, which was

reduced by about 50% if there was no family history. To put these

results in perspective, when comparing 2 regions with a difference

of 0.1 kWh/m

/day in the maximum monthly increase in solar

insolation, the region with the larger increase is associated with a

0.49 year or nearly 6 months younger age of onset when there is a

family history of bipolar disorder, as compared to the region with

the smaller increase. If there is no family history, this difference is

reduced to 0.26 year or about 3 months. There were similar results

when Model 1 was run excluding patients born before 1960 and

the birth cohort. Model 2 included the interaction of the max-

imum monthly increase in solar insolation polarity of ﬁrst

episode, the country median age and the birth cohort, and was

also signiﬁcant. The results also showed the inverse relationship

between maximum monthly increase in solar insolation and age

of onset, but the effect was about 1/3 smaller for initial episodes

of mania. Similar results for Model 2 were also obtained

when excluding patients born before 1960 and the birth cohort.

See Tables 2 and 3.

The maximum monthly increase in solar insolation occurred in

springtime: between February and March in 40% of onset loca-

tions, between March and April in 38% of onset locations, and

between April and May in 11% of onset locations, excluding the

locations near the equator that have little monthly change in solar

insolation throughout the year. The maximum increase in solar

insolation occurred in diverse latitudes. Table 4 shows the mean

age of onset adjusted for country median age and birth cohort by

the maximum monthly increase in solar insolation.

The collection site was thought to adequately serve as a proxy

for the speciﬁc onset location for patients from Barcelona, Cape

Town, Helsinki, Melbourne/Geelong, Porto Alegro, São Paulo,

Salvador, Vitoria, and Würzburg. The best ﬁtting model was run

excluding all these sites and the magnitude of the estimated

coefﬁcients did not change substantially and remained signiﬁcant

at the 0.01 level.

The age of onset was also associated with the range of monthly

solar insolation but the model was not as good a ﬁt as with the

maximum monthly increase. As in our prior study, the yearly

cumulative solar insolation, the maximum decrease in monthly

solar insolation, the yearly minimum and maximum monthly solar

insolation, latitude, sex, country prevalence of bipolar I disorder,

and country sex ratio were not signiﬁcantly associated with the

age of onset.

4. Discussion

The maximum monthly increase in solar insolation in spring-

time was inversely associated with the age of onset of bipolar

disorder, but this effect was reduced by half in those without

a family history of mood disorders. This ﬁnding replicated the

results of our initial study (Bauer et al., 2012) with a sample that is

67% larger in size and contains 77% more reference cities. The

interaction with family history suggests there may be a genetic

predisposition to some physiological responses to sunlight, and

highlights the importance of obtaining a family history from all

patients. This ﬁnding is also consistent with many prior reports

that family history is associated with a younger age of onset

(Baldessarini et al., 2012; Lin et al., 2006; Post et al., 2013;

Schürhoff et al., 2000).

Both the collection sites and the onset locations were distrib-

uted across all latitudes in both hemispheres, and represent a wide

Table 1

Patient onset location by latitude.

Degrees latitude (north and south)

Number of patients

0–9 309

10–19 200

20–29 286

30–39 1222

40–49 1475

50–59 297

60–69 247

70–79 1

Total 4037

1043 in southern hemisphere.

M. Bauer et al. / Journal of Affective Disorders 167 (2014) 104–111 107

range of solar insolation proﬁles including arid, sub-arctic, equa-

torial as well as temperate. In locations that experience a large

increase in sunlight in springtime, detailed questioning to detect

symptoms of bipolar disorder, and closer monitoring of patients

and their adolescent children may be indicated. Conversely, in

locations with little change in sunlight throughout the year, the

onset of bipolar disorder may be at an older age than expected

from studies conducted in temperate climates. The effect of the

maximum monthly increase in solar insolation on the age of onset

is one-third smaller for those with a ﬁrst episode of mania rather

than depression, in line with prior observation that a ﬁrst episode

of depression occurs at a younger age than mania (Forty et al.,

2009; Ortiz et al., 2011; Perlis et al., 2005).

Both longstanding clinical observation of circadian dysfunction

in bipolar disorder, and active ongoing research into circadian

genes and phenotypes support the concept that a large monthly

increase in solar insolation may be associated with disease onset

(Goodwin and Jamison, 1990; Mansour et al., 2005; McCarthy

et al., 2012; McClung, 2007; Whybrow, 1997). The recent progress

in understanding how light changes are unconsciously captured

and transmitted to the circadian system (Benarroch, 2011; Foster

and Hankins, 2007; Hatori and Panda, 2010) may someday help to

explain the solar insolation ﬁndings in this study. Photosensitive

retinal ganglion cells (pRGC) containing the pigment melanopsin

mediate a broad range of non-image forming functions including

circadian synchronization, melatonin suppression and alertness

(Berson, 2003, Gooley et al., 2003; Zaidi et al., 2007). The intrinsic

photosensitivity of pRGC is specialized to detect ﬂuctuations in

intensity of environmental light (Berson, 2003, Hatori and Panda,

2010) and the signals are sent to the circadian pacemaker in the

suprachiasmatic nucleus (SCN). The peak spectral absorption for

non-image forming functions is at short wavelength of visible

light, or blue light: 480 nm for of melanopsin (Berson et al.,

2002), 460 nm for melatonin suppression (Brainard et al., 2001),

and 470 nm for alertness (Stephenson et al., 2012). Similarly, the

dominant wavelength of morning sunlight was measured at

477 nm in the USA (Gallagher III et al., 1996; Turner and

Mainster, 2008).

Although people who live at latitudes with a short daylength have

less exposure to solar insolation during winter, indoor lighting is

optimized for vision (Andersen et al., 2012; van Bommel, 2006; van

Bommel and van den Beld, 2004). In contrast to circadian photo-

reception, peak absorptions for vision are at longer wavelength:

rod-mediated dim light vision at 506 nm or green light, and cone-

mediated bright/moderate light vision at 555 nm or green-yellow

light (Turner and Mainster, 2008). Standard lighting, such as incan-

descent, ﬂuorescent warm, and low pressure sodium lamps, has

a dominant wavelength of about 575 nm (Bellia et al., 2011)andless

than 5% of the intensity of sunlight. However, unlike with vision,

insufﬁcient circadian light exposure is not perceived (Turner et al.,

2010). There are ongoing efforts to develop new standards for indoor

lighting that address circadian as well as visual effects, and will

consider the spectrum, intensity, timing of exposure, duration of

signal, and ocular physiology (Andersen et al., 2012; Bellia et al.,

2011; van Bommel and van den Beld, 2004).

While blue light may be the most important component

of solar insolation from a circadian perspective, its role in the

emergence of bipolar disorder is not known. The spectral compo-

sition of sunlight varies with the time of day, season and latitude

(Thorne et al., 2009) and younger people may be particularly

Table 2

Estimated parameter coefﬁcients explaining age of onset with all patients

Parameter Coefﬁcient

estimate

Standard

error

99% Conﬁdence interval Coefﬁcient signiﬁcance

Lower Upper Wald chi-square P

Model 1 N¼3334

Maximum monthly increase in solar insolation 4.862 1.496 8.678 0.973 10.412 0.001

No family history maximum monthly increase in solar insolation 2.310 0.311 1.510 3.110 55.271 o0.001

Model 2 N¼3600

Maximum monthly increase in solar insolation 4.948 1.732 9.409 0.487 8.161 0.004

First episode manic maximum monthly increase in solar insolation 1.663 0.400 0.633 2.694 17.301 o0.001

GEE model estimated age of onset using a constant, the country of onset median age, birth cohort groups and the listed parameters with an exchangeable correlation

structure in each cluster. The Wald hypothesis test degrees of freedom were 1 for all models.

256 Onset locations. All Sidak pairwise mean age of onset comparisons for birth cohort and family history were signiﬁcantly different at the o0.001 level.

265 Onset locations. All Sidak pairwise mean age of onset comparisons for birth cohort and ﬁrst episode were signiﬁcantly different at the o0.001 level.

Table 3

Estimated parameter coefﬁcients explaining age of onset with patients born after 1959

Parameter Coefﬁcient

estimate

Standard

error

99% Conﬁdence interval Coefﬁcient signiﬁcance

Lower Upper Wald Chi-square P

Model 1 N¼2091

Maximum monthly increase in solar insolation 4.623 1.407 8.247 0.998 10.792 0.001

No family history maximum monthly increase in solar insolation 1.809 0.265 1.127 2.492 46.622 o0.001

Model 2 N¼2271

Maximum monthly increase in solar insolation 4.880 1.429 8.561 1.199 11.661 0.001

First episode manic maximum monthly increase in solar insolation 2.213 0.337 1.345 3.081 43.129 o0.001

GEE model estimated age of onset using a constant, the country of onset median age and the listed parameters with an exchangeable correlation structure in each

cluster. The Wald hypothesis test degrees of freedom were 1 for all models.

216 Onset locations.

223 Onset locations.

M. Bauer et al. / Journal of Affective Disorders 167 (2014) 104–111108

sensitive to a springtime increase in blue light. Young eyes only

need about half the circadian illuminance as middle aged eyes due

to age-related decreases in pupil area and crystalline lens trans-

mission (Turner et al., 2010). In animal studies, abnormal light

cycles increased depression-like behavior and impaired learning in

normal mice, but not in mice without pRGC (LeGates et al., 2012).

Blue light exposure also stimulates cognitive brain activity in

normal and blind individuals as detected by fMRI (Stephenson

et al., 2012; Vandewalle et al., 2007; Vandewalle et al., 2013).

Preliminary studies of patients with seasonal affective disorder

(SAD) found blue light treatment to be more effective than red

light (Anderson et al., 2009; Glickman et al., 2006 ), and that

sequence variation in the melanopsin gene may increase vulner-

ability to SAD (Roecklein et al., 2009).

Other recent evidence supports the importance of a large monthly

increase in solar insolation. In patients with depression, there were

more early responders to paroxetine when sunlight was increasing

during springtime (Tomita et al., 2012). Seasonal variation with an

increase in the spring–summer months was reported in the serum

concentration of brain-derived neurotropic factor (BDNF) (Molendijk

et al., 2012) and in serotonin turnover in cerebrospinal ﬂuid (Luykx

et al., 2013), and these may be involved in the pathophysiology of

depression. Dose dependent suppression of melatonin by light (West

et al., 2011) may be exaggerated in bipolar I disorder (Hallam et al.,

2009). Vitamin D synthesis in skin requires sunlight exposure. Low

vitamin D concentrations were associated with diverse psychiatric

disorders (Berk et al., 2008), and by meta-analysis, with depression

(Anglin et al., 2013; Annweiler et al., 2013) and poor cognition (Balion

et al., 2012). Finally, some patients with depression show anomalies in

the retinal response to light (Fountoulakis, 2010; Fountoulakis et al.,

2005).

4.1. Limitations

There are several limitations to this study. The diagnosis

of bipolar disorder was based on the DSM-IV criteria, but the

processes of diagnostic assessment and data gathering were not

standardized across the collection sites. Self-reported age of onset

data are subject to recall bias, however this approach was used in

related research (Baldessarini et al., 2012; Forty et al., 2009; Lin

et al., 2006; Perlis et al., 2005). Family history data is often

inaccurate and more reliable for severe disorders (Hardt and

Franke, 2007), and may be inﬂuenced by cultural attitudes towards

mental illness (Karasz, 2005). Individual exposure to sunlight such

as for outdoor workers was not assessed, although most people in

industrialized countries work indoors and have indoor hobbies

(Godar, 2005; Pergams and Zaradic, 2008). This study did not

include other environmental factors that may affect the age of

onset such as drug abuse, or factors known to disrupt circadian

rhythms such as night shift work or irregular lifestyles (Kapczinski

Table 4

Mean adjusted age of onset by maximum monthly increase in solar insolation (kWh/m

/day) groups.

Maximum monthly

increase in solar

insolation (kWh/m

/day)

Mean adjusted

age of onset

Number of onset

reference sites

Number of

patients

Percent of

patients

Example locations Latitude (deg)

o0.75 27.53 22 779 19 Kuala Lumpur, Malaysia 3.17 N

Medellín, Columbia 6.29 N

Bangalore, India 12.98 N

Salvador, Brazil 12.98 S

Hong Kong, China 22.25 N

Tokyo, Japan 35.69 N

Z0.75 and o1.0 22.47 47 133 3 Kedah, Malaysia 6.13 N

Kolar, India 13.13 N

Miami, FL, USA 25.78 N

Princeton, NJ, USA 40.35 N

Boston, MA, USA 42.35 N

Z1.0 and o1.25 20.97 136 1756 44 Porto Alegre, Brazil 30.03 S

San Diego, CA, USA 32.71 N

Buenos Aires, Argentina 34.60 S

Melbourne, Australia 37.81 S

Thessaloniki, Greece 40.64 N

Barcelona, Spain 41.38 N

Siena, Italy 43.32 N

Rochester, MN, USA 44.02 N

Nova Scotia, Canada 45.10 N

Vienna, Austria 48.20 N

Paris, France 48.87 N

Würzburg, Germany 49.79 N

Z1.25 and o1.5 20.82 85 707 18 Beer Sheva, Israel 31.25 N

Valparaiso, Chile 33.05 S

San Francisco, CA, USA 37.78 N

Sardinia, Italy 39.22 N

Bordeaux, France 44.83 N

Calgary, Canada 51.08 N

Poznan, Poland 51.42 N

Z1.5 19.32 28 662 16 Santiago, Chile 33.45 S

Cape Town, South Africa 33.92 S

Los Angeles, CA, USA 34.05 N

Aarhus, Denmark 56.16 N

Helsinki, Finland 60.18 N

Trondheim, Norway 63.42 N

Total 318 4037 100

M. Bauer et al. / Journal of Affective Disorders 167 (2014) 104–111 109

et al., 2011; Rosa et al., 2013). Shifting data from the southern

hemisphere by six months ignores local cultural dimensions of

seasonality.

4.2. Conclusions

In conclusion, the monthly increase in solar insolation may

have a signiﬁcant impact on the age of onset of bipolar disorder,

especially in those with a family history of mood disorders.

The larger the maximum monthly increase in solar insolation

in springtime, the younger the onset of bipolar disorder. A ﬁrst

episode of depression occurred at a younger age than mania,

despite the effects of a large springtime increase in solar insola-

tion. Research into the effects of the duration, intensity, timing and

wavelength of light is needed in bipolar disorder.

Role of funding source

This work was funded in part by the Canadian Institutes of Health Research

(MA, Grant number 64410); the Research Council of Norway (OAA Grant numbers

213837; 223273; 217776); South-East Norway Health Authority (OAA, Grant

number 2013-123); a NHMRC Senior Principal Research Fellowship (M Berk, Grant

number 1059660; INSERM (BE, Grant number C0829) and APHP (BE, Grant number

AOR11096); the Spanish Government (AGP, Grant numbers PS09/02002 CIBER

Network; EC10-333, PI10/01430, PI10/01746, PI11/01977, PI11/02708, 2011/1064,

11-BI-01, 1677-DJ-030, EC10-220); European Regional Development Funds (Grant

numbers UE/2012/FI-STAR, UE/2013/TENDERMH, UE/2013/MASTERMIND), grants

from Spanish Government (Grant numbers PI10/01430, PI10/01746, EC10-220,

EC10-333, PI11/01977, 20111064, PI11/02708, PI12/02077, PI13/02252, PI13/

00451), local grants from the Basque Government (Grant numbers 200911147,

20 1011117 0, 201011 20 09 , 2011111110, 20 11111113) ; th e Basque Foundation for

Health Innovation and Research (Grant number BIO12/AL/002); the Spanish Clinical

Research Network (Grant numbers CAIBER;1392-D-079) and the University of the

Basque Country (Grant number IT679-13); Stanley Research Foundation (Grant

number 03-RC-003); the Research Council of Norway (IM, Grant numbers

ES488722, ES421716); the Regional Health Authority of South Eastern Norway

(IM, Grants number 2011085, 2013088); DFG (AR, Grant numbers SFB TRR 58, B06,

Z02); the DFG and Länder funds (AR, Grant number RTG1252/2); Medical Research

Council of South Africa (DJS); Spanish Ministry of Economy and Competitiveness

(EV, Grants number PI12/00912, PN 2008-2011); the Instituto de Salud Carlos III-

Subdirección General de Evaluación y Fomento de la Investigación (EV); Fondo

Europeo de Desarrollo Regional Unión Europea. Una manera de hacer Europa (EV);

CIBERSAM (EV); the Comissionat per a Universitats i Recerca del DIUE de la

Generalitat de Catalunya to the Bipolar Disorders Group (EV, Grant number 2009

SGR 1022). MB, EA, RA, CB, FB, RB, RHB, TDB, LB, YB, EYWC, MDZ, SD, AF, MAF, KNF,

JGF, TG, HH, SH, CH, AI, ETI, FK, SK, BK, RK, MK, BL, ERL, CLJ, UL, GM, MM, WM, SM,

RM, FGN, CO, YO, AP, DQ, RR, NR, PR, JKR, KS, AMS, ES, CS, SS, AHS, KS, HT, YT, CT, BV,

MJW, MZ and PCW have no speciﬁc funding to acknowledge. The funding sources

had no involvement in the study design, collection, analysis and interpretation of

data, in writing the report, and in the decision to submit for publication.

Conﬂict of interest

The authors have no conﬂict of interest to declare.

Acknowledgments

We would like to thank the International Society of Bipolar Disorders for

supporting this project.

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The Role of Vitamin D in Depression: From a Curious Idea to a Therapeutic Option

Article

Full-text available

Nov 2013
J CLIN PSYCHIAT

Because this piece does not have an abstract, we have provided for your benefit the first 3 sentences of the full text. Besides its classical function of bone metabolism regulation, vitamin D exhibits multiple nonskeletal effects, including in the brain. In particular, relationships between vitamin D status and mood have been repeatedly examined in the last decade. The findings enable causal reasoning and offer new therapeutic perspectives. Hypovitaminosis D, which affects more than 1 billion people worldwide, has recently received a structured critical evaluation as a biological component related to depression.

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Full-text available

Mar 2012

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Jun 2013

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Oct 2013

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Jul 2013
J COGNITIVE NEUROSCI

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Oct 2013
ACTA PSYCHIAT SCAND

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