Acta Psychiatr Scand 2017: 1–12
All rights reserved
DOI: 10.1111/acps.12772
© 2017 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd
ACTA PSYCHIATRICA SCANDINAVICA
Solar insolation in springtime influences age
of onset of bipolar I disorder
Bauer M, Glenn T, Alda M, Aleksandrovich MA, Andreassen OA,
Angelopoulos E, Ardau R, Ayhan Y, Baethge C, Bharathram SR,
Bauer R, Baune BT, Becerra-Palars C, Bellivier F, Belmaker RH,
Berk M, Bersudsky Y, Bicakci S
ß , Birabwa-Oketcho H, Bjella TD, Bossini L,
Cabrera J, Cheung EYW, Del Zompo M, Dodd S, Donix M, Etain B,
Fagiolini A, Fountoulakis KN, Frye MA, Gonzalez-Pinto A, Gottlieb JF,
Grof P, Harima H, Henry C, Isomets€
a ET, Janno S, Kapczinski F, Kardell
M, Khaldi S, Kliwicki S, K€
onig B, Kot TL, Krogh R, Kunz M, Lafer B,
Landen M, Larsen ER, Lewitzka U, Licht RW, Lopez-Jaramillo C,
MacQueen G, Manchia M, Marsh W, Martinez-Cengotitabengoa M,
Melle I, Meza-Urz
ua F, Yee Ming M, Monteith S, Morken G, Mosca E,
Munoz R, Mythri SV, Nacef F, Nadella RK, Nery FG, Nielsen RE,
O’Donovan C, Omrani A, Osher Y, Østermark Sørensen H, Ouali U, Pica
Ruiz Y, Pilhatsch M, Pinna M, da Ponte FDR, Quiroz D, Ramesar R,
Rasgon N, Reddy MS, Reif A, Ritter P, Rybakowski JK, Sagduyu K,
^
Scippa AM,
Severus E, Simhandl C, Stein DJ, Strejilevich S, Subramaniam M,
Sulaiman AH, Suominen K, Tagata H, Tatebayashi Y, Tondo L,
Torrent C, Vaaler AE, Veeh J, Vieta E, Viswanath B, Yoldi-Negrete M,
Zetin M, Zgueb Y, Whybrow PC. Solar insolation in springtime influences age
of onset of bipolar I disorder.
Objective: To confirm prior findings that the larger the maximum
monthly increase in solar insolation in springtime, the younger the age
of onset of bipolar disorder.
Method: Data were collected from 5536 patients at 50 sites in 32
countries on six continents. Onset occurred at 456 locations in 57
countries. Variables included solar insolation, birth-cohort, family
history, polarity of first episode and country physician density.
Results: There was a significant, inverse association between the
maximum monthly increase in solar insolation at the onset location,
and the age of onset. This effect was reduced in those without a family
history of mood disorders and with a first episode of mania rather than
depression. The maximum monthly increase occurred in springtime.
The youngest birth-cohort had the youngest age of onset. All prior
relationships were confirmed using both the entire sample, and only the
youngest birth-cohort (all estimated coefficients P < 0.001).
Conclusion: A large increase in springtime solar insolation may impact
the onset of bipolar disorder, especially with a family history of mood
disorders. Recent societal changes that affect light exposure (LED
lighting, mobile devices backlit with LEDs) may influence adaptability
to a springtime circadian challenge.
M. Bauer1,* , T. Glenn2, M. Alda3,
M. A. Aleksandrovich4,
O. A. Andreassen5, E. Angelopoulos6,
R. Ardau7, Y. Ayhan8, C. Baethge9,
S. R. Bharathram10, R. Bauer1,
B. T. Baune11, C. Becerra-Palars12,
F. Bellivier13 , R. H. Belmaker14,
M. Berk15,16, Y. Bersudsky14,
S
ß . Bicakci8, H. Birabwa-Oketcho17,
T. D. Bjella5, L. Bossini18,
J. Cabrera19, E. Y. W. Cheung20,
M. Del Zompo7, S. Dodd15,21,
M. Donix1, B. Etain13 ,
A. Fagiolini18, K. N. Fountoulakis22,
M. A. Frye23, A. Gonzalez-Pinto24,
J. F. Gottlieb25, P. Grof26, H. Harima27,
C. Henry28, E. T. Isomets€
a29,30,
31
S. Janno , F. Kapczinski32,
M. Kardell33, S. Khaldi34,†,
S. Kliwicki35, B. K€
onig36, T. L. Kot37,
38
R. Krogh , M. Kunz32, B. Lafer39,
M. Landen40,41, E. R. Larsen38,
U. Lewitzka1, R. W. Licht42,43,
C. Lopez-Jaramillo44, G. MacQueen45,
M. Manchia46, W. Marsh47,
M. Martinez-Cengotitabengoa24,
ua12, M. Yee
I. Melle5, F. Meza-Urz
48
49
Ming , S. Monteith , G. Morken50,51,
E. Mosca7, R. Munoz52, S. V. Mythri53,
F. Nacef54, R. K. Nadella10,
F. G. Nery39, R. E. Nielsen42,43,
C. O’Donovan3, A. Omrani55,
Y. Osher14, H. Østermark Sørensen42,
U. Ouali54, Y. Pica Ruiz56,
M. Pilhatsch1, M. Pinna57, F. D. R. da
Ponte32, D. Quiroz58, R. Ramesar59,
N. Rasgon60, M. S. Reddy53, A. Reif61,
P. Ritter1, J. K. Rybakowski35,
^. M. Scippa63,
K. Sagduyu62, A
1
E. Severus , C. Simhandl36,
D. J. Stein64, S. Strejilevich65,
M. Subramaniam66, A. H. Sulaiman67,
K. Suominen68, H. Tagata27,
Y. Tatebayashi69, L. Tondo70,71,
C. Torrent72, A. E. Vaaler50,51,
J. Veeh61, E. Vieta72 ,
B. Viswanath10, M. Yoldi-Negrete73,
M. Zetin74, Y. Zgueb54,
P. C. Whybrow75
1
Bauer et al.
Significant outcomes
• There was a strong, inverse association between the maximum monthly increase in solar insolation in
•
springtime and the age of onset of bipolar I disorder using a global sample. The effect was reduced in
those without a family history of mood disorders.
There was a large birth-cohort effect, with the youngest group having the youngest onset. Major societal changes that may affect vulnerability to a circadian challenge need investigation: exposure to
LED lighting, mobile devices backlit with LEDs, and the 24-h society.
Limitations
• The data collection process was not standardized. Data on family history or age of onset may be
•
•
1
unreliable.
There was no individual data on behaviours or exposures that affect circadian rhythms.
The sample was not demographically representative of the country populations.
Department of Psychiatry and Psychotherapy, University Hospital Carl Gustav Carus, Technische Universit€at Dresden, Dresden, Germany, 2ChronoRecord Association, Fullerton,
CA, USA, 3Department of Psychiatry, Dalhousie University, Halifax, NS, Canada, 4Soviet Psychoneurological Hospital, Urai, Russia, 5NORMENT – K.G. Jebsen Centre for Psychosis Research, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway, 6Department of Psychiatry,
Medical School, Eginition Hospital, National and Capodistrian University of Athens, Athens, Greece, 7Section of Neurosciences and Clinical Pharmacology, Department of
Biomedical Sciences, University of Cagliari, Sardinia, Italy, 8Department of Psychiatry, Hacettepe University Faculty of Medicine, Ankara, Turkey, 9Department of Psychiatry and
Psychotherapy, University of Cologne Medical School, Cologne, Germany, 10Department of Psychiatry, NIMHANS, Bangalore, India, 11Department of Psychiatry, School of
Medicine, University of Adelaide, Adelaide, SA, Australia, 12National Institute of Psychiatry ‘“Ramon de la Fuente Mu~niz”, Mexico City, Mexico, 13Psychiatry and Addiction
Medicine, Assistance Publique – H^opitaux de Paris, FondaMental Foundation, INSERM UMR-S1144, Denis Diderot University, Rene Descartes University,Paris, France,
14
Department of Psychiatry, Faculty of Health Sciences, Beer Sheva Mental Health Center, Ben Gurion University of the Negev, Beer Sheva, Israel, 15IMPACT Strategic Research
Centre, School of Medicine, Barwon Health, Deakin University, Geelong, Vic., Australia, 16Department of Psychiatry, Orygen, the National Centre for Excellence in Youth Mental
Health, the Centre for Youth Mental Health and the Florey Institute for Neuroscience and Mental Health, University of Melbourne, Parkville, Vic., Australia, 17Butabika Hospital,
Kampala, Uganda, 18Department of Molecular Medicine and Department of Mental Health (DAI), University of Siena and University of Siena Medical Center (AOUS), Siena, Italy,
19
Mood Disorders Clinic, Dr. Jose Horwitz Psychiatric Institute, Santiago de Chile, Chile , 20Department of General Adult Psychiatry, Castle Peak Hospital, Tuen Mun, Hong Kong,
21
Department of Psychiatry, University of Melbourneo, Parkville, Vic, Australia, 22Division of Neurosciences, 3rd Department of Psychiatry, School of Medicine, Aristotle
University of Thessaloniki, Thessaloniki, Greece, 23Department of Psychiatry & Psychology, Mayo Clinic Depression Center, Mayo Clinic, Rochester, MN, USA, 24Department of
Psychiatry, University Hospital of Alava, University of the Basque Country, CIBERSAM, Vitoria, Spain, 25Department of Psychiatry, Feinberg School of Medicine, Northwestern
University, Chicago, IL, USA, 26Mood Disorders Center of Ottawa, University of Toronto, Toronto, ON, Canada, 27Department of Psychiatry, Tokyo Metropolitan Matsuzawa
Hospital, Setagaya, Tokyo, Japan, 28AP-HP, Hopitaux Universitaires Henri Mondor and INSERM U955 (IMRB) and Universite Paris Est and Institut Pasteur, Unite Perception et
Memoire, Paris, France, 29Department of Psychiatry, University of Helsinki and Helsinki University Hospital, Helsinki, Finland, 30National Institute for Health and Welfare,
Helsinki, Finland, 31Department of Psychiatry, University of Tartu, Tartu, Estonia, 32Department of Psychiatry, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil,
33
Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden, 34Private
practice, Tunis, Tunisia, 35Department of Adult Psychiatry, Poznan University of Medical Sciences, Poznan, Poland, 36BIPOLAR Zentrum Wiener Neustadt, Wiener Neustadt,
Austria, 37Khanty-Mansiysk Clinical Psychoneurological Hospital, Khanty-Mansiysk, Russia, 38Department of Affective Disorders, Q, Mood Disorders Research Unit, Aarhus
University Hospital, Aarhus, Denmark, 39Bipolar Disorder Research Program, Department of Psychiatry, University of S~ao Paulo Medical School, S~ao Paulo, Brazil, 40Department
of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, the Sahlgrenska Academy, University of Gothenburg, Gothenburg and M€olndal, Sweden,
41
Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden, 42Unit for Psychiatric Research, Aalborg University Hospital, Psychiatry,
Aalborg, Denmark, 43Department of Clinical Medicine, Aalborg University, Aalborg, Denmark, 44Mood Disorders Program, Hospital Universitario San Vicente Fundacion, Research
Group in Psychiatry, Department of Psychiatry, Faculty of Medicine, Universidad de Antioquia, Medellín, Colombia, 45Department of Psychiatry, Faculty of Medicine, University of
Calgary, Calgary, AB, Canada, 46Section of Psychiatry, Department of Medical Science and Public Health, University of Cagliari, Cagliari, Italy, 47Department of Psychiatry,
University of Massachusetts, Worcester, MA, USA, 48Department of General Psychiatry, Mood Disorders Unit, Institute of Mental Health, Singapore City, Singapore, 49Traverse
City Campus, Michigan State University College of Human Medicine, Traverse City, MI, USA, 50Department of Mental Health, Norwegian University of Science and Technology
– NTNU, Trondheim, Norway, 51Department of Psychiatry, St Olavs’ University Hospital, Trondheim, Norway, 52Department of Psychiatry, University of California San Diego, San
Diego, CA, USA, 53Asha Bipolar Clinic, Asha Hospital, Hyderabad, Telangana, India, 54Razi Hospital, Faculty of Medicine, University of Tunis-El Manar, Tunis, Tunisia, 55Tunisian
del Pedregal”, Mexico City, Mexico, 57Lucio Bini Mood Disorder Center, Cagliari, Italy,
Bipolar Forum, Erable Medical Cabinet 324, Tunis, Tunisia, 56Hospital “Angeles
58
Deparment of Psychiatry, Diego Portales University, Santiago de Chile, Chile, 59UCT/MRC Human Genetics Research Unit, Division of Human Genetics, Institute of Infectious
Disease and Molecular Medicine, University of Cape Town, Cape Town, South Africa, 60Department of Psychiatry and Behavioral Sciences, Stanford School of Medicine, Palo
Alto, CA, USA, 61Department of Psychiatry, Psychosomatic Medicine and Psychotherapy, University Hospital Frankfurt, Johann Wolfgang Goethe-Universit€at Frankfurt am Main,
Frankfurt am Main, Germany, 62Department of Psychiatry, University of Missouri Kansas City School of Medicine, Kansas City, MO, USA, 63Department of Neuroscience and
Mental Health, Federal University of Bahia, Salvador, Brazil, 64Department of Psychiatry, MRC Unit on Risk & Resilience in Mental Disorders, University of Cape Town, Cape
Town, South Africa, 65Bipolar Disorder Program, Neuroscience Institute, Favaloro University, Buenos Aires, Argentina, 66Research Division, Institute of Mental Health, Singapore
City, Singapore, 67Department of Psychological Medicine, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia, 68Department of Social Services and Health Care,
Psychiatry, City of Helsinki, Helsinki, Finland, 69Schizophrenia & Affective Disorders Research Project, Tokyo Metropolitan Institute of Medical Science, Seatagaya, Tokyo, Japan,
70
McLean Hospital-Harvard Medical School, Boston, MA, USA, 71Mood Disorder Lucio Bini Centers, Cagliari e Roma, Italy, 72Clinical Institute of Neuroscience, Hospital Clinic,
University of Barcelona, IDIBAPS, CIBERSAM,Barcelona, Catalonia, Spain, 73Consejo Nacional de Ciencia y Tecnología - Instituto Nacional de Psiquiatría Ramon de la Fuente
Mu~niz, Ciudad de Mexico, Mexico, 74Department of Psychology, Chapman University, Orange, CA, USA and 75Department of Psychiatry and Biobehavioral Sciences, Semel
Institute for Neuroscience and Human Behavior University of California Los Angeles (UCLA), Los Angeles, CA, USA
2
Solar insolation and onset of bipolar disorder
Key words: bipolar disorder; circadian rhythm; solar insolation; epidemiology
Michael Bauer, Department of Psychiatry and Psychotherapy, University Hospital Carl Gustav Carus, Technische Universit€at Dresden, Fetscherstr. 74, 01307 Dresden, Germany.
E-mail: michael.bauer@uniklinikum-dresden.de
†
Deceased in 2016.
Accepted for publication June 16, 2017
Introduction
There is considerable evidence of circadian rhythm
dysfunction in patients with bipolar disorder
including disturbances in the sleep/wake cycle,
activity patterns, melatonin secretion, as well as
suggestive associations with clock gene polymorphisms and epigenetic alterations (1–6). Clinical
symptoms that are frequently reported include
sleep timing disturbances (7), irregular daily schedules (8), and an evening preference (9). These
symptoms of circadian disruption may occur during episodes, while euthymic (7, 10–13), and in
those at high risk for bipolar disorder (7, 12, 14,
15). Even small changes to circadian rhythms such
as the shift to daylight savings time may have
adverse mental health consequences (16). In the
future, circadian symptoms and clock gene polymorphisms may help define endophenotypes of
bipolar disorder, including early onset (17–19).
Some of the current treatments for bipolar disorder act directly or indirectly on circadian mechanisms, which may contribute to therapeutic effects
(2). Lithium modulates the expression of central
and peripheral clock genes (20–23), and the amplitude and timing of effects may differ between
responders and non-responders (24). Lithium also
has phase-delaying properties and may resynchronize overly fast circadian rhythms (2, 21). Other
treatments for bipolar disorder that may modify
circadian systems include valproate (2, 25) light
and dark therapy and sleep deprivation (1, 26),
and blue light blocking glasses (27).
The importance of the circadian system to
human health is becoming clearer from recent
research, as summarized briefly. Over the course of
evolution, life on Earth has adapted to the Sun
(28). Humans have endogenous circadian timing
cycles for nearly every physiological, metabolic,
and behavioural system allowing for anticipation
of light and dark, and adaptation to seasonal
changes and environmental challenge (29–31). The
cycle length of endogenous circadian rhythms is
not exactly 24 h and must be regularly synchronized to the natural 24-h light–dark cycle that
arises from one rotation of the Earth on its axis
(32). Sunlight is the primary and most potent
signal that entrains human circadian systems to
the natural environment. Specialized non-visual
receptors, photosensitive retinal ganglion cells
(pRGC), express the melanopsin photopigment
that is sensitive to short-wavelength blue light (33).
The pRGC detect environmental fluctuations in
light and project primarily to non-visual centers of
the brain including the suprachiasmatic nucleus
(SCN) in the hypothalamus (34). The SCN is the
master pacemaker over a system that includes circadian clock genes expressed in the SCN, and
throughout the rest of the brain, peripheral tissues
and cells (35, 36). The signals from the pRGC
entrain the SCN (37), which integrates the external
signals with signals from internal activities, and in
turn entrains the peripheral circadian clocks (35,
36). Timekeeping for optimal diurnal physiological
processes and mental health requires both synchronization of the circadian systems with the natural
environment, and internal system-wide circadian
coordination.
Aims of the study
As the onset of bipolar disorder is highly variable,
it is important to understand the factors that may
influence it, including environmental (38, 39). We
previously found a large, significant, inverse relation between the maximum monthly increase in
solar insolation (incoming solar radiation striking
the Earth’s surface) and the age of onset of bipolar
I disorder (40, 41). This effect was reduced in those
without a family history of mood disorders and
was smaller for those with a first episode of mania
rather than depression. The aim of this analysis
was to confirm that the relations found previously
were not sample specific, by repeating the analyses
using significantly more data from geographically
dispersed countries.
Methods
The data were collected by researchers at 50 collection sites in 32 countries. In the Northern Hemisphere, the collection sites were Aalborg,
Denmark; Aarhus, Denmark; Ankara, Turkey;
Athens, Greece; Bangalore, India; Barcelona,
3
Bauer et al.
Spain; Beer Sheva, Israel; Cagliari, Italy (2 sites);
Calgary, Canada; Dresden, Germany; Halifax,
Canada; Helsinki, Finland; Hong Kong; Hyderabad, India; Kampala, Uganda; Kansas City, KS,
USA; Khanti-Mansiysk, Russia; Kuala Lumpur,
Malaysia; Los Angeles, CA, USA; Medellı́n,
Colombia; Mexico City, Mexico; Oslo, Norway;
Ottawa, Canada; Palo Alto, CA, USA; Paris,
France; Poznan, Poland; Rochester, MN, USA;
San Diego, CA, USA; Siena, Italy; Singapore;
Stockholm/Gothenburg, Sweden; Tartu, Estonia;
Thessaloniki, Greece; Tokyo, Japan; Trondheim,
Norway; Tunis, Tunisia; Vitoria, Spain; Wiener
Neustadt, Austria; Worcester, MA, USA, and
W€
urzburg, Germany. In the Southern Hemisphere,
the collection sites were: Adelaide, Australia; Buenos Aires, Argentina; Cape Town, South Africa;
Melbourne/Geelong, Australia; Porto Alegre,
Brazil; Salvador, Brazil; Santiago, Chile (2 sites);
and S~
ao Paulo, Brazil. This analysis combined
newly collected data with data collected and analyzed previously.
Approval for this study was obtained from local
institutional review boards according to local
requirements. All patients had a diagnosis of bipolar disorder according to DSM-IV criteria from a
psychiatrist, with age of onset defined as the first
occurrence of an episode of mania, hypomania, or
depression. Other patient data in this analysis were
sex, family history of a mood disorder in any first
degree relative, and polarity of the first episode.
Data were obtained retrospectively by direct
questioning, reviewing records, or both.
Solar insolation
Solar insolation is defined as the amount of electromagnetic energy from the Sun received on Earth
for a given surface area at a given time, expressed
in kilowatt h/square meter/day (kWh/m2/day)
(42). Several factors determine the intensity of
solar insolation including the angle at which the
Sun’s rays strike the Earth’s surface, time of day,
latitude, atmospheric conditions, surface reflection,
and the Earth’s tilt. The tilt of Earth’s axis relative
to the plane of its orbit around the Sun results in
the seasonal changes in solar insolation and day
length (43). The pattern of monthly changes in
solar insolation varies by latitude, with very little
monthly change near the equator and larger
monthly changes as one nears the poles. However,
locations at the same latitude may have different
patterns due to local conditions such as altitude,
cloud cover, and proximity to bodies of water.
All solar insolation data were obtained from the
National Aeronautics and Space Administration
4
(NASA) Surface Meteorological and Solar Energy
(SSE) database version 6.0, which is based on global data collected by satellite for 22 years between
1983 and 2005 (42). The average monthly solar
insolation data are available with a spatial resolution of 1° 9 1° latitude/longitude. The actual
onset locations were grouped into reference onset
locations, which represent all locations in the
1° 9 1° grid of latitude and longitude. The number
of reference onset locations from a collection site
varied greatly, influenced by country size, migration patterns, and cultural factors. The reference
onset locations were used in all analyses. All solar
insolation data were shifted by 6 months for onset
locations in the Southern Hemisphere to compare
with data from the Northern Hemisphere. Solar
insolation is an appropriate variable for a sample
with multiple birth-cohorts as the incoming global
average, annual mean solar insolation has
remained essentially unchanged over the last
2000 years (44).
The monthly change in solar insolation was calculated as the difference between the current
month minus the previous month. The maximum
monthly increase in solar insolation was defined as
the largest monthly increase over the year. The
interaction between the maximum monthly
increase in solar insolation 9 family history and
the maximum monthly increase in solar insolation
9 polarity of first episode were also analyzed.
Birth-cohort
The birth-cohort was included in all analyses as an
older age of onset of bipolar disorder in older
cohorts was reported in many studies (40, 41, 45–
47). Three birth-cohort groups were created, for
those born before 1940, born between 1940 and
1959, and born 1960 or later, consistent with prior
research (40, 41, 45–47). In this sample, 34.6% of
the patients with bipolar I disorder were born
before 1960.
Country specific variables
As the onset of bipolar disorder spans several decades, an older mean age of onset would be
expected in a country with an older median age
(48, 49), and country median age was included in
the prior analyses. The country median age also
provides information about country socioeconomic characteristics (50). However, in the current
sample, there was a 31-year difference in median
age between the oldest country (Japan 46.9 years)
and the youngest (Uganda 15.7 years) and more
socioeconomic variation among countries.
Solar insolation and onset of bipolar disorder
Additional socioeconomic measures were obtained
to explain country specific differences in age of
onset: physician density of any specialty per 1000
population, GDP per capita, total health expenditures as a per cent of GDP, and the Gini index of
income inequality (50).
Bipolar I disorder
Only data from patients with a diagnosis of bipolar
I disorder were included to be consistent with our
prior studies, and because there was a large imbalance in the per cent of patients with a diagnosis of
bipolar I disorder at the collection sites, varying
from 99% to 23%. Also, there was a potential for
bias related to age of onset for those who received
a diagnosis before the criteria were expanded to
include bipolar II disorder.
Statistics
Estimates of the effects of solar insolation on the
age of onset were calculated using generalized estimating equations (GEE) to account for the correlated data and unbalanced number of data points
within each onset location (cluster). The GEE
model uses a population-based or marginal
approach to estimate the effect across the entire
population rather than within a cluster (51). An
exchangeable correlation matrix was selected for
the GEE models, which is appropriate for a large
number of clusters including many with a single
observation (52). Models were estimated for all
patients, and after excluding the patients born
before 1960 as in our prior studies. In all GEE models, the dependent variable was the age of onset.
Sidak’s adjustment for multiple comparisons was
used to make pair-wise comparisons between the
birth-cohorts. A significance level of 0.01 was used
for all evaluations. The corrected quasi-likelihood
independence model criterion was used to assist
with model evaluation (53). SPSS version 24 (IBM,
Armonk, NY, USA) was used for all analyses.
available for 5055 of the 5536 patients (91.3%),
and of the 5055 patients, the polarity was mania
in 2543 (50.3%) and depression in 2512 (49.7%).
The unadjusted mean age of onset was 25.4
10.6 years.
The onset of bipolar disorder for the 5536
patients occurred in 456 unique onset locations in
57 countries. The average number of patients in
each onset location was 12.1, with 240 of the 5536
patients (4.3%) in an onset location of one. Of
the 5536 patients, 4283 (77.4%) had onset in the
Northern Hemisphere and 1253 (22.6%) in the
Southern Hemisphere (Table 1). The number of
patients, onset locations, and onset countries in
this study was considerably larger than in our prior
studies (Table 2).
Solar insolation
The largest maximum monthly increase in solar
insolation occurred in the northern latitudes such
as the Nordic countries, Russia, Estonia, and
Canada, and in warm dry areas in Chile, USA,
Mexico, Greece, and South Africa. The smallest
changes occurred near the equator in Uganda,
Colombia, Malaysia, and Brazil (Table 3). The
maximum monthly increase in solar insolation
occurred in springtime in both hemispheres.
Table 1. Latitude of patient onset locations
Degrees latitude
(north and south)*
Number of
patients
Number of
onset locations
473
413
315
1957
1654
464
259
1
5536
30
54
34
120
157
46
14
1
456
0–9
10–19
20–29
30–39
40–49
50–59
60–69
70–79
Total
*1253 in the Southern Hemisphere.
Table 2. Number of patients by study
Results
Patients and onset locations
Study date
Data were collected for 7392 patients with bipolar
disorder. Of these, 5536 had a diagnosis of bipolar
I disorder and were included in the analysis. Of the
5536 patients, 3221 (58.2%) were female, and 2314
(41.8%) were male. Family history was available
for 4698 of the 5536 patients (84.9%), and of the
4698 patients, 2567 (54.6%) had a positive family
history. The polarity of the first episode was
2012 (40)
2014 (41)
2017
Per cent increase
between 2012
and 2017 studies (%)
Per cent increase
between 2014
and 2017 studies (%)
Number of patients
with bipolar I disorder
Number of
onset locations
Number of
onset
countries
2414
4037
5536
129
180
318
456
153
24
43
57
138
37
43
33
5
Bauer et al.
Excluding the locations near the equator that have
little change to solar insolation throughout the
year, the maximum increase occurred between
February and March at 41.1% of onset locations,
between March and April at 35.4% of onset locations, and between April and May at 13.4% of
onset locations.
Model estimates
The best model to assess the relation between solar
insolation and the age of onset included the interaction of the maximum monthly increase in solar
insolation 9 family history, the birth-cohort and
the physician density, as shown in Table 4. There
was a significant inverse relation between the
Table 3. Some examples of the maximum monthly increase in solar insolation at
onset locations
Onset location
Kampala, Uganda
Medellín, Colombia
Hong Kong
Kuala Lumpur, Malaysia
Salvador, Brazil
Bangalore, India
S~ao Paulo, Brazil
Tokyo, Japan
Singapore
Hyderabad, India
Mexico City, Mexico
Boston, MA, USA
Rochester, MN, USA
Porto Alegre, Brazil
Nova Scotia, Canada
Adelaide, Australia
Thessaloniki, Greece
Tunis, Tunisia
Melbourne, Australia
Barcelona, Spain
Paris, France
Ankara, Turkey
San Diego, CA, USA
Buenos Aries, Argentina
Cagliari, Sardinia, Italy
Dresden, Germany
Bordeaux, France
Calgary, Canada
Beer Sheva, Israel
Valparaiso, Chile
Tartu, Estonia
Athens, Greece
Los Angeles, CA, USA
Santiago, Chile
Helsinki, Finland
Cape Town, South Africa
Talca, Chile
Oslo, Norway
Khanti-Mansiysk, Russia
Stockholm, Sweden
Trondheim, Norway
6
Maximum monthly
increase in solar
insolation
(kWh/m2/day)
0.3
0.3
0.6
0.6
0.6
0.7
0.7
0.7
0.7
0.8
0.9
1.0
1.0
1.0
1.1
1.1
1.1
1.1
1.1
1.2
1.2
1.2
1.2
1.2
1.3
1.3
1.3
1.4
1.4
1.4
1.4
1.5
1.5
1.5
1.5
1.5
1.6
1.6
1.6
1.6
1.7
Latitude
0.3
6.3
22.5
3.2
12.9
12.9
23.5
35.7
1.3
17.4
19.4
42.2
44.0
30.0
45.1
34.9
40.6
36.8
37.5
41.4
48.9
39.9
32.4
34.6
39.2
51.1
44.8
51.1
31.2
33.0
58.4
38.0
34.0
33.3
60.2
33.9
35.4
59.9
61.0
59.3
63.4
N
N
N
N
S
N
S
N
N
N
N
N
N
S
N
S
N
N
S
N
N
N
N
S
N
N
N
N
N
S
N
N
N
S
N
S
S
N
N
N
N
maximum monthly increase in solar insolation and
the age of onset, labeled Model 1. For every
0.1 kWh/m2/day increase in the maximum
monthly increase in solar insolation, there was
approximately a 0.57-year (6.8 months) decrease
in the age of onset. Alternatively, comparing the
largest (1.7) to the smallest (0.3) maximum
monthly increase in solar insolation, Model 1 suggests an 8-year decrease in the age of onset (1.4
range in maximum monthly increase in solar insolation * 5.702 estimated coefficient). This effect
was reduced by about 30% if there was no family
history. The inverse relation was also found when
including the interaction of the maximum monthly
increase in solar insolation 9 polarity of first episode, the birth-cohort and the physician density,
labeled Model 2, with the effect about 20% smaller
for a first episode of mania (5.3 months). The
results were similar when models 1 and 2 were estimated excluding the patients born before 1960 and
the birth-cohort (Models 3 and 4 in Table 5).
Of the 5536 patients, 287 (5.1%) were born
before 1940, 1631 (29.5%) were born between 1940
and 1959, and 3618 (65.4%) were born in 1960 or
later. The birth-cohort was significantly associated
with age of onset (P < 0.001). In Model 1, compared to the youngest birth-cohort born in 1960 or
later, those born before 1940 had an onset
15.7 years older, and those born between 1940 and
1959 had an onset 7.7 years older.
The model estimates were improved when physician density was used to explain socioeconomic
differences rather than the country median age (40,
41). Because the model has changed, these results
cannot be directly compared with our prior studies. The other economic variables were not significant or the models were not as good. The
collection site was considered to be an adequate
proxy for the onset location at some sites: Barcelona, Cape Town, Helsinki, Melbourne/Geelong,
Salvador, Stockholm/Gothenburg, and W€
urzburg.
The best models were estimated excluding all data
from these collection sites and results remained significant (P < 0.001). Compared with other solar
insolation variables, models including the maximum monthly increase in solar insolation
remained the best.
Discussion
Despite increasing the number of patients, onset
locations, and countries, the findings from our
prior studies were confirmed. The maximum
monthly increase in solar insolation was inversely
associated with the age of onset of bipolar disorder. The effect was reduced in those without a
Solar insolation and onset of bipolar disorder
Table 4. Estimated coefficients of parameters explaining age of onset of bipolar I disorder
99% Confidence
interval
Parameters
Model 1* N = 4698
Maximum monthly increase in solar insolation
No family history 9 maximum monthly
increase in solar insolation
Model 2† N = 5055
Maximum monthly increase in solar insolation
First episode manic 9 maximum monthly
increase in solar insolation
Coefficient estimate
Standard error
Lower
Upper
Coefficient significance
Wald chi-square
P
5.702
1.733
0.932
0.247
8.102
1.097
3.301
2.369
37.423
49.288
<0.001
<0.001
5.491
1.037
1.008
0.269
8.088
0.345
2.895
1.729
29.685
14.901
<0.001
<0.001
*Dependent variable: Age of Onset. Model: intercept, physicians per 1000 onset country population, maximum monthly increase in solar insolation, no family history 9
maximum monthly increase in solar insolation and birth-cohort group.
†Dependent variable: Age of Onset. Model: intercept, physicians per 1000 onset country population, maximum monthly increase in solar insolation, first episode manic 9
maximum monthly increase in solar insolation and birth-cohort group.
Table 5. Estimated coefficients of parameters explaining age of onset for patients with bipolar I disorder born in 1960 or later
99% Confidence
interval
Parameters
Coefficient estimate
Model 3* N = 3101
Maximum monthly increase in solar insolation
No family history 9 maximum monthly increase in solar insolation
Model 4† N = 3308
Maximum monthly increase in solar insolation
First episode manic 9 maximum monthly increase in solar insolation
Standard error
Lower
Upper
Coefficient significance
Wald chi-square
P
4.676
1.240
0.838
0.255
6.835
0.584
2.517
1.897
31.118
23.694
<0.001
<0.001
4.626
1.524
0.831
0.279
6.766
0.805
2.487
2.242
31.023
29.854
<0.001
<0.001
*Dependent variable: Age of Onset. Model: intercept, physicians per 1000 onset country population, maximum monthly increase in solar insolation, and no family
history 9 maximum monthly increase in solar insolation.
†Dependent variable: Age of Onset. Model: intercept physicians per 1000 onset country population, maximum monthly increase in solar insolation, and first episode
manic 9 maximum monthly increase in solar insolation.
family history of a mood disorder and with an
onset of mania rather than depression. This confirmation using a larger and more diverse sample
with collection and onset locations on six continents, and when only including the youngest birthcohort, suggests that these findings are not due to
chance. As with the prior studies, the maximum
monthly increase in solar insolation occurred in
springtime. The effect was related to the size of the
maximum monthly increase, regardless if starting
from a low level of solar insolation at very northern latitude, or from a medium level at a mid-latitude desert.
From a clinical perspective, physicians should
recognize the potential for a younger age of onset
in locations with a large increase in sunlight in
springtime, and the potential for an older onset in
areas with little seasonal change. The interaction
with family history suggests that a genetic predisposition to bipolar disorder involves circadian dysregulation (18). The findings also emphasize the
importance of obtaining a family history from all
patients, especially as the age of onset was younger
in the youngest birth-cohort. Early onset bipolar
disorder is associated both with family history and
with poorer outcomes (54–57).
These models were different from our prior analyses due to the inclusion of physician density.
Using data from such diverse countries, it is not
surprising that having an important socioeconomic
variable in addition to the maximum monthly
increase in solar insolation improved the model for
age of onset. The physician workforce directly
impacts health outcomes (58, 59) and varies greatly
between high- and low-income countries and
within high-income regions, and especially for
mental health (60, 61).
Recent and remarkable societal changes relating
to light exposure may be contributing to the
increased vulnerability to a springtime circadian
challenge in the youngest birth-cohort and may be
of particular concern for future generations. These
include the conversion from incandescent to LED
(light-emitting diode) lighting, the use of mobile
7
Bauer et al.
devices backlit with LEDs, and the rise of a 24-h
society. The Nobel Prize in physics in 2014 was
awarded to Isamu Akasaki, Hiroshi Amano, and
Shuji Nakamura for the invention of the blue LED
in the 1990s, which enabled a new method for
semiconductors to create white light (62). LEDs
are energy-efficient, requiring 85% less energy than
incandescent light bulbs (63), and are long-lasting.
Conversion to LEDs has been extremely rapid,
with 69% of all light bulbs sold worldwide
expected to be LEDs by 2020 (63). However, differences in the properties of LEDs may have physiological impact. Incandescent lights have a
dominant wavelength of 574 nm, close to the peak
sensitivity of photopic photoreception of 555 nm
(64, 65). In contrast, white LEDs have a dominant
wavelength of 482 nm, in the blue light range,
close to the peak sensitivity for circadian photoreception (~480 nm for melanopsin and ~460 nm for
melatonin suppression) (33, 64, 66).
Compared to the past, many people today experience darker days and brighter nights (28). In the
daytime, people are spending more time indoors
and all indoor lighting (incandescent and LED) is
vastly dimmer and has a different spectral pattern
than the Sun. Unlike with vision, people are not
aware if they do not receive enough daytime light
for circadian needs (65). In the night-time, indoor
lighting is a necessity, but long-term effects of
exposure to LEDs after sunset on the circadian
system are unknown. Sufficient darkness during
the dark phase of the light–dark cycle is needed for
melatonin secretion and optimal entrainment (67).
Additionally, about 80% of the world’s population
lives where the night sky brightness is above the
threshold for light pollution (68), and high-intensity LED streetlights pose a health threat (69, 70).
International lighting industry standards were
optimized for vision. The complexity of non-visual
physiological responses and the need to balance
benefits and harmful effects pose many challenges
for developing new standards (29, 70, 71). The
intensity, spectrum, duration, and timing of all
lighting, and prior light and dark exposure may all
impact circadian entrainment (29,71–72).
Digital devices, such as smartphones, eReaders,
tablets, video games, computer screens, and TV
sets, are backlit with LED light to enhance the
daytime brightness and contrast (73, 74). While
well suited for the screen size of mobile devices,
LEDs emit bright blue light. In the USA, 72% of
adolescents used a cell phone in the hour before
bedtime (75), and many adolescents in Belgium
used a cell phone after lights out (76). Recent
investigations report an association between evening use of technology (smartphones, computers,
8
and eReaders) and reduced melatonin secretion in
healthy adults and adolescents (77–79), and
decreased sleep in children and adults (75, 77, 80,
81). In a systematic review of 67 international studies of children and adolescents, increasing screen
time was associated with an adverse sleep outcome
(82). The heavy use of mobile technology by children and adolescents is of particular concern.
Early pubertal children have increased sensitivity
to evening light as measured by melatonin suppression (83), and transmission of blue light in the
young is much greater than in the old due to aging
of the crystalline lens and loss of pupil area
(65, 84).
Daily patterns of light exposure reflect individual preferences and societal requirements (29, 35).
Recently, competitiveness, consumer demand, and
globalization are creating a 24-h society (85). The
continuous provision of goods and services offers
considerable customer convenience, but requires
that many people work non-traditional and irregular work schedules, including late night and early
morning. Yet people with bipolar disorder may be
especially vulnerable to the circadian disruption
experienced by shift workers (86, 87). The importance of regularity in daily patterns of exposure to
circadian stimuli may be greatest for individuals
who have suboptimal circadian function (88). The
increase in irregular work schedules is occurring
worldwide (89, 90), with 35% of employed
persons in the USA having flexible work hours in
2012 (91).
Limitations
The process of data gathering was not standardized across all collection sites, although based on
the DSM-IV criteria. Family history data were not
validated and may be unreliable. There may be
recall bias with self-reported age of onset in relation to episodes early in life or of less severity. The
sample was not demographically representative of
the country populations. However, the unadjusted
mean age of onset in the sample of 25.4
10.6 years was similar to that in other international studies: 25.7 years for bipolar I disorder (54)
and 25.6 years for any bipolar disorder (92).
Although 79% of the samples were from the
Northern Hemisphere, about 87.5% of the world’s
population lives there (93). There was no individual data on sun exposure, sun-related behaviours,
shift work, technology use, skin type, serum vitamin D levels (94), or retinal abnormalities (95, 96).
There was also no individual data on perinatal
light exposure, which may impact future circadian
resilience (97, 98). Other functions stimulated by
Solar insolation and onset of bipolar disorder
blue light including direct enhancement of cognition (99) were not considered. Societal changes
unrelated to light were not considered, including
the tumultuous events of the twentieth century that
impacted the older birth-cohorts (46). There was a
downward bias in the age of onset of the youngest
birth-cohort due to the absence of individuals with
late onset bipolar disorder, ascertainment bias,
and the potential for earlier mortality in those with
early onset (48, 100, 101). Data from the Southern
Hemisphere was shifted by 6 months, which discounts the cultural dimensions of seasonality. The
model results show an association but cannot show
causality. However, the impacts of circadian disruption on the course bipolar disorder needs to be
studied, whether or not the association is causative
(102).
In conclusion, a large increase in the maximum
monthly solar insolation in springtime may influence the onset of bipolar disorder, especially for
patients with a family history of mood disorders.
The larger the maximum monthly increase in solar
insolation, the younger the onset of bipolar disorder. With the youngest age of onset in the youngest
birth-cohort, recent societal changes that may
impact adaptability to a circadian challenge need
investigation. These include LED lighting, mobile
technology backlit with LEDs, and 24-h lifestyles.
While country conditions such as physician density
are beyond the individual’s ability to control, individual behaviour directly impacts light exposure
and darkness at night. Perhaps, treatment recommendations for bipolar disorder may include optimum daytime and night-time light exposure,
including from technology.
Acknowledgements
Michael Berk is supported by an NHMRC Senior Principal
Research Fellowship (1059660). Ole A Andreassen, Thomas
DBjella and Ingrid Melle are supported by Research Council
of Norway (223273) and KG Jebsen Stiftelsen. Ravi Nadella
has received funding from the Accelerator program for Discovery in Brain disorders using Stem cells (ADBS), jointly funded
by the Department of Biotechnology, Government of India,
and the Pratiksha trust. Biju Viswanath has received funding
by Department of Science and Technology INSPIRE scheme,
Government of India. Mikael Landen was supported by grants
from the Swedish Research Council (K2014-62X-14647-12-51
and K2010-61P-21568-01-4), the Swedish foundation for
Strategic Research (KF10-0039), and the Swedish Federal
Government under the LUA/ALF agreement (ALF 20130032,
ALFGBG-142041).
We thank Haydeh Olofsson for valuable data management
support.
Declarations of interest
The authors declare that they have no conflict of interests.
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