Exploring the genetics of lithium response in bipolar disorders

Abstract

Background
Lithium (Li) remains the treatment of choice for bipolar disorders (BP). Its mood-stabilizing effects help reduce the long-term burden of mania, depression and suicide risk in patients with BP. It also has been shown to have beneficial effects on disease-associated conditions, including sleep and cardiovascular disorders. However, the individual responses to Li treatment vary within and between diagnostic subtypes of BP (e.g. BP-I and BP-II) according to the clinical presentation. Moreover, long-term Li treatment has been linked to adverse side-effects that are a cause of concern and non-adherence, including the risk of developing chronic medical conditions such as thyroid and renal disease. In recent years, studies by the Consortium on Lithium Genetics (ConLiGen) have uncovered a number of genetic factors that contribute to the variability in Li treatment response in patients with BP. Here, we leveraged the ConLiGen cohort (N = 2064) to investigate the genetic basis of Li effects in BP. For this, we studied how Li response and linked genes associate with the psychiatric symptoms and polygenic load for medical comorbidities, placing particular emphasis on identifying differences between BP-I and BP-II.

Results
We found that clinical response to Li treatment, measured with the Alda scale, was associated with a diminished burden of mania, depression, substance and alcohol abuse, psychosis and suicidal ideation in patients with BP-I and, in patients with BP-II, of depression only. Our genetic analyses showed that a stronger clinical response to Li was modestly related to lower polygenic load for diabetes and hypertension in BP-I but not BP-II. Moreover, our results suggested that a number of genes that have been previously linked to Li response variability in BP differentially relate to the psychiatric symptomatology, particularly to the numbers of manic and depressive episodes, and to the polygenic load for comorbid conditions, including diabetes, hypertension and hypothyroidism.

Conclusions
Taken together, our findings suggest that the effects of Li on symptomatology and comorbidity in BP are partially modulated by common genetic factors, with differential effects between BP-I and BP-II.

Full text

Herrera‑Riveroetal.
International Journal of Bipolar Disorders (2024) 12:20
https://doi.org/10.1186/s40345‑024‑00341‑y
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International Journal of
Bipolar Disorders
Exploring thegenetics oflithium response
inbipolar disorders
Marisol Herrera‑Rivero1, Mazda Adli2,3, Kazufumi Akiyama4, Nirmala Akula5, Azmeraw T. Amare6,
Raffaella Ardau7, Bárbara Arias8, Jean‑Michel Aubry9,10, Lena Backlund11, Frank Bellivier12, Antonio Benabarre13,
Susanne Bengesser14, Abesh Kumar Bhattacharjee15, Joanna M. Biernacka16,17, Armin Birner14,
Micah Cearns6, Pablo Cervantes18, Hsi‑Chung Chen19, Caterina Chillotti7, Sven Cichon20,21,22, Scott R. Clark6,
Francesc Colom23,24, Cristiana Cruceanu25, Piotr M. Czerski26, Nina Dalkner14, Franziska Degenhardt27,
Maria Del Zompo28, J. Raymond DePaulo29, Bruno Etain12, Peter Falkai30, Ewa Ferensztajn‑Rochowiak31,
Andreas J. Forstner22,27, Josef Frank32, Louise Frisén33, Mark A. Frye17, Janice M. Fullerton34, Carla Gallo35,
Sébastien Gard36, Julie S. Garnham37, Fernando S. Goes29, Maria Grigoroiu‑Serbanescu38, Paul Grof39,
Ryota Hashimoto40, Roland Hasler9, Joanna Hauser26, Urs Heilbronner41, Stefan Herms20,27, Per Hoffmann20,27,
Liping Hou5, Yi‑Hsiang Hsu42, Stephane Jamain43, Esther Jiménez44, Jean‑Pierre Kahn45, Layla Kassem5,
Tadafumi Kato46, John Kelsoe15, Sarah Kittel‑Schneider47, Po‑Hsiu Kuo48, Ichiro Kusumi49, Barbara König50,
Gonzalo Laje5, Mikael Landén51,52, Catharina Lavebratt11, Marion Leboyer53, Susan G. Leckband54,
Mario Maj55, Mirko Manchia56,57, Cynthia Marie‑Claire58, Lina Martinsson59, Michael J. McCarthy15,60,
Susan L. McElroy61, Vincent Millischer11,62, Marina Mitjans24,63, Francis M. Mondimore29, Palmiero Monteleone64,
Caroline M. Nievergelt15, Tomas Novák65, Markus M. Nöthen27, Claire O’Donovan37, Norio Ozaki66,
Sergi Papiol30,41, Andrea Pfennig67, Claudia Pisanu28, James B. Potash29, Andreas Reif68, Eva Reininghaus14,
Hélène Richard‑Lepouriel9,10, Gloria Roberts69, Guy A. Rouleau70, Janusz K. Rybakowski31, Martin Schalling11,
Peter R. Schofield34, Klaus Oliver Schubert6,71, Eva C. Schulte30,41,72, Barbara W. Schweizer29,
Giovanni Severino28, Tatyana Shekhtman15, Paul D. Shilling15, Katzutaka Shimoda73, Christian Simhandl74,
Claire M. Slaney37, Alessio Squassina28, Thomas Stamm2, Pavla Stopkova65, Fabian Streit32, Fasil Tekola‑Ayele75,
Anbupalam Thalamuthu76, Alfonso Tortorella77, Gustavo Turecki25, Julia Veeh68, Eduard Vieta44, Biju Viswanath78,
Stephanie H. Witt32, Peter P. Zandi79, Martin Alda37, Michael Bauer67, Francis J. McMahon5, Philip B. Mitchell69,
Marcella Rietschel32, Thomas G. Schulze29,41,80 and Bernhard T. Baune1,81*
Abstract
Background Lithium (Li) remains the treatment of choice for bipolar disorders (BP). Its mood‑stabilizing effects help
reduce the long‑term burden of mania, depression and suicide risk in patients with BP. It also has been shown to have
beneficial effects on disease‑associated conditions, including sleep and cardiovascular disorders. However, the indi‑
vidual responses to Li treatment vary within and between diagnostic subtypes of BP (e.g. BP‑I and BP‑II) according
to the clinical presentation. Moreover, long‑term Li treatment has been linked to adverse side‑effects that are a cause
*Correspondence:
Bernhard T. Baune
Bernhard.Baune@ukmuenster.de
Full list of author information is available at the end of the article
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Page 2 of 11
Herrera‑Riveroetal. International Journal of Bipolar Disorders (2024) 12:20
of concern and non‑adherence, including the risk of developing chronic medical conditions such as thyroid and renal
disease. In recent years, studies by the Consortium on Lithium Genetics (ConLiGen) have uncovered a number
of genetic factors that contribute to the variability in Li treatment response in patients with BP. Here, we leveraged
the ConLiGen cohort (N = 2064) to investigate the genetic basis of Li effects in BP. For this, we studied how Li response
and linked genes associate with the psychiatric symptoms and polygenic load for medical comorbidities, placing
particular emphasis on identifying differences between BP‑I and BP‑II.
Results We found that clinical response to Li treatment, measured with the Alda scale, was associated with a dimin‑
ished burden of mania, depression, substance and alcohol abuse, psychosis and suicidal ideation in patients with BP‑I
and, in patients with BP‑II, of depression only. Our genetic analyses showed that a stronger clinical response to Li
was modestly related to lower polygenic load for diabetes and hypertension in BP‑I but not BP‑II. Moreover, our results
suggested that a number of genes that have been previously linked to Li response variability in BP differentially relate
to the psychiatric symptomatology, particularly to the numbers of manic and depressive episodes, and to the poly‑
genic load for comorbid conditions, including diabetes, hypertension and hypothyroidism.
Conclusions Taken together, our findings suggest that the effects of Li on symptomatology and comorbidity in BP
are partially modulated by common genetic factors, with differential effects between BP‑I and BP‑II.
Keywords Bipolar disorder, Lithium treatment, Psychiatric symptoms, Comorbidity, Genetics
Background
Lithium (Li) is the first-line maintenance treatment for
bipolar disorders (BP). Multiple beneficial properties have
been attributed to Li, including mood stabilization, car-
dio- and neuroprotection, circadian regulation, immu-
nomodulation, and suicide prevention in patients with BP
(Geoffroy etal. 2016; Volkmann etal. 2020; Xu etal. 2021;
Queissner etal. 2021; Miller & McCall 2022; Rybakowski
2022; Chen etal. 2023; Szałach etal. 2023). Li is not exempt
from acute side-effects, the most frequent being gastroin-
testinal complaints, that may cause non-adherence. How-
ever, it is the long-term adverse effects, including thyroid
and kidney problems (Volkmann etal. 2020; Ferensztajn-
Rochowiak etal. 2021), that cause most concern.
Individual responses to Li vary according to the clini-
cal presentation of the disease. Reportedly, only about
30% of patients with BP have a full response to Li treat-
ment. Various clinical, psychosocial and demographic fac-
tors that affect Li response have been described (Nunes
etal. 2020; Ferensztajn-Rochowiak etal. 2021). Moreover,
genetic studies have established Li response as a polygenic
trait (Papiol etal. 2022). Previous work performed by the
Consortium on Lithium Genetics (ConLiGen) has offered
significant insights into the molecular mechanisms con-
tributing to Li response (Amare etal. 2023), as well as the
links with the polygenic scores of other psychiatric dis-
orders (Amare etal. 2018; Schubert etal. 2021; Coombes
etal. 2021) and with suicidal behavior (Yoshida etal. 2019)
in BP. However, the relationships between Li response and
disease features, particularly comorbidity, remain largely
unexplored. Moreover, most studies have made no distinc-
tion between different diagnostic groups. Here, we used
data from ConLiGen participants (N = 2064) to explore
how the genetic factors that contribute to Li response
variability in patients with BP are associated with specific
psychiatric symptoms and the polygenic load (i.e. genetic
risk) for medical comorbid conditions, and whether these
relationships differ between BP types I and II.
Methods
Study population
e ConLiGen cohort has been described elsewhere
(Hou etal. 2016). Briefly, between 2003 and 2013, Con-
LiGen recruited over 2500 Li-treated individuals with
bipolar spectrum disorders at various sites in Europe,
the United States, Australia and East-Asia. e inclusion
criteria consisted of a diagnosis of bipolar disorder type I
(BP-I) or type II (BP-II), schizoaffective bipolar disorder
or bipolar disorder not otherwise specified in accordance
with the criteria established in the Diagnostic and Statis-
tical Manual of Mental Disorders (DSM) versions III or
IV, as well as Li treatment that lasted a minimum of six
months with no additional mood stabilizers. Long-term
responses to Li treatment were assessed using the Alda
scale, where an A subscale rates the degree of response
in the range 0–10 and a B subscale reflects the relation-
ship between improvement and treatment. A total score,
ranging from 0–10, is obtained by subtracting the B score
from the A score (Manchia etal. 2013). Negative scores
are set to 0. Here, we used a sample of 2064 ConLiGen
participants with complete covariate phenotypes: sex,
age-at-onset (AAO), age at recruitment (i.e. sample col-
lection), diagnosis and recruitment site (used to establish
population).
e Ethics Committee at the University of Heidel-
berg provided central approval for ConLiGen. Writ-
ten informed consent from all participants was
obtained according to the study protocols of each of the
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Herrera‑Riveroetal. International Journal of Bipolar Disorders (2024) 12:20
participating sites and their institutions. All procedures
were performed in accordance with the guidelines of the
Declaration of Helsinki.
Genotype data
Genotyping, quality control (QC) and imputation of the
ConLiGen cohort has been described elsewhere (Hou
etal. 2016). Briefly, DNA genotyping by array was per-
formed from peripheral blood samples in two batches of
similar composition, originally referred to as “GWAS1”
(N = 1162) and “GWAS2” (N = 1401). Standard proce-
dures for QC and imputation using the 1000 Genomes
Project reference panel were employed. Here, we used
an updated ConLiGen dataset we previously described
in detail (Herrera-Rivero et al. 2024), in which we re-
imputed the combined ConLiGen batches using the
Haplotype Reference Consortium (HRC) panel. is pro-
cedure increased the number of markers and the qual-
ity of the dataset, increasing its suitability for polygenic
score (PGS) analyses. Single nucleotide polymorphisms
(SNPs) in 37 genes that were previously reported to con-
tribute to Li response in ConLiGen following a gene-level
genome-wide analysis (Amare etal. 2023) were extracted
from the dataset using a window of ± 1 kb from the start
and end positions of the gene (according to the Ensembl
hg19 genome build). Our final dataset contained 9374
SNPs corresponding to 34 Li response-linked genes and
2064 individuals with BP, from which 1669 had a diagno-
sis of BP-I and 370 of BP-II.
Phenotypes
Li response
We used the total Alda score as a measure of Li
response. is was available for all 2064 individuals
included in our study.
Psychiatric symptoms
Here, the psychiatric symptoms corresponded to the
numbers of episodes of depression and mania, the pres-
ence of psychosis, alcohol and substance abuse, and of
suicidal ideation. ese variables were available for a
maximum of 853 individuals from the GWAS1 batch.
Genetic risk formedical comorbidities
Based on the literature, we identified various conditions
that are comorbid in BP and searched the PGS Catalog
(Lambert etal. 2021) for publicly available PGSs for these.
Weight files for the calculation of PGSs for various traits,
such as disorders of sleep and metabolism, were down-
loaded from the PGS Catalog and used for allelic scoring
in the total ConLiGen sample with plink 1.9 (Chang etal.
2015). Standardized sum scores were used for analysis.
Because of incomplete compatibility between PGS SNPs
and variants in the ConLiGen dataset, only PGSs with
compatibility > 78% were used. ese corresponded to
the following traits: chronotype (PGS ID: PGS002209),
sleep duration (PGS ID: PGS002196), insomnia (PGS ID:
PGS002149), hypertension (PGS ID: PGS002047), hypo-
thyroidism (PGS ID: PGS001816) and type 2 diabetes
(PGS ID: PGS003118) (Privé etal. 2022; Ma etal. 2022)
(Suppl.Table1). Traits excluded due to lower compatibil-
ity included cardiovascular disorders, obesity, migraine
and asthma.
Statistical analyses
Associations between total Alda scores and psychiat-
ric symptoms were tested using robust linear/logis-
tic regression models with the “robustbase” R package
(nmax = 853). Models were adjusted for sex, AAO
and age. Associations between total Alda scores and
PGSs for comorbid conditions were tested using par-
tial Spearman correlation with the “ppcor” R pack-
age (nmax = 2064). Models were adjusted for sex, AAO,
age and population. SNP-phenotype associations were
tested using linear/logistic regression models with
plink 1.9. Models were adjusted for sex, AAO, age,
population, total Alda score and the first eight dimen-
sions coming from a principal components analysis
of the genotypes. When testing associations using all
individuals, all models were also adjusted for the dif-
ferential BP diagnosis. All associations were also tested
separately for BP-I and BP-II. For exploratory purposes,
significance was set to nominal (i.e. unadjusted) p < 0.05
and p < 0.01 for total Alda score and SNP-phenotype
associations, respectively.
Results
To explore how Li response genes are associated with
specific psychiatric symptoms and the poygenic load
for medical comorbid conditions, and whether these
relationships differ between BP types I and II, we used
a sample of 2064 individuals with BP from the ConLi-
Gen cohort. From these, 1197 (58%) were females, 1669
(80.1%) had a diagnosis of BP-I and 370 (17.9%) were
diagnosed with BP-II. e mean AAO in the sample was
25 ± 11 years, while the mean age at recruitment was
47 ± 14 years. e mean total Alda score was 4.22 ± 3.16
points, with 29.8% of the patients being categorized as
good responders (total Alda score ≥ 7). Compared to
BP-I, BP-II patients were slightly older at disease onset
(28 ± 12 vs 24 ± 10 years) and recruitment (50 ± 14 vs
47 ± 14 years), and had higher rates of females (61.9%
vs 57.2%) and good Li responders (34.1% vs 28.2%).
However, the mean total Alda scores were very similar
(4.6 ± 3.2 vs 4.2 ± 3.1 points).
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Herrera‑Riveroetal. International Journal of Bipolar Disorders (2024) 12:20
First, we explored the association between Li response
and psychiatric symptoms/PGSs for comorbid condi-
tions. Using a nominal significance threshold (p < 0.05),
we found that the total Alda scores showed a nega-
tive relationship with all psychiatric symptom variables
in all BP (nmax = 835) and BP-I (nmax = 665) individu-
als. However, in BP-II individuals (nmax = 153), the total
Alda scores showed a negative relationship only with
the number of depressive episodes (Fig. 1A). Notice-
ably, these results survived false discovery rate correc-
tion (FDR < 0.05). Furthermore, the total Alda scores
also correlated negatively with the PGSs for diabetes and
hypertension in all BP (N = 2064) and BP-I (N = 1669)
individuals, and with the PGS for insomnia in all BP, BP-I
and BP-II (N = 370) individuals (Fig.1B). However, none
of the nominal associations with PGSs survived FDR cor-
rection in our sample.
Second, we explored the association between genes
previously linked to Li response and psychiatric symp-
toms/PGSs for comorbid conditions. Using a nominal
significance threshold (p < 0.01) as indicative of sugges-
tive association, we found that 32 of the 34 genes tested
were suggested to associate with specific psychiatric
symptoms and/or PGSs for comorbid conditions (Fig.2,
Suppl.Tables.2–7). e most significant hits were for the
number of manic episodes, with SLC13A3 as top gene in
BP-I and TNRC6C in BP-II, followed by the number of
depressive episodes, with MTSS1 as top gene in BP-I and
DNAH14 in BP-II (Table1).
Taken together, 22 of the 34 genes tested were nomi-
nally associated with at least one psychiatric symptom
and one PGS in at least one of the tests performed (i.e. all
BP, BP-I and BP-II). Noticeably, some of the Li response
genes were suggested to associate with all the pheno-
types that we studied in at least one of the tests. We
also observed that genes with the most overlaps, includ-
ing RNLS, GRIN2A, CSMD2, DNAH14 and T TC39B
(Table2), represented the most significant hits obtained
in BP-I or BP-II for various PGSs for comorbid condi-
tions (Table1).
Finally, we looked into the overlapping and non-
overlapping genes between the BP-I and BP-II analyses
(Table3). Here, we observed that, for example, GRIN2A
was suggested to relate to the number of depressive epi-
sodes, the presence of alcohol abuse, and the polygenic
contribution to chronotype, diabetes and hypertension in
both major types of BP. However, it was suggested to be
linked to the presence of psychosis and suicidal ideation,
and the polygenic contribution to sleep duration and
hypothyroidism in BP-I only, while relating to the num-
ber of manic episodes and the genetic load for insomnia
only in BP-II.
Discussion
We showed that positive responses to Li treatment in
patients with BP are generally more beneficial to those
patients diagnosed with BP-I than to those with a BP-II
diagnosis, and that genes linked to Li response also con-
tribute to the clinical presentation of the disorder in
terms of psychiatric symptomatology and, potentially,
the risk of medical comorbid conditions. is may partly
explain why Li responses usually vary according to clini-
cal features, and why clinical and psychosocial factors
can only partially predict Li responses (Tondo etal. 2001;
Ferensztajn-Rochowiak etal. 2021).
Fig. 1 Links between phenotypes and Li responses in ConLiGen. A Association test results between total Alda scores and psychiatric symptoms.
Shown are the nominal p‑values (−log10) and z‑values (effect) obtained from robust linear/logistic regression models. B Correlation test results
between total Alda scores and PGSs for comorbid conditions. Shown are the nominal p‑values (−log10) and correlation coefficients (effect)
obtained from partial correlation models using the Spearman method
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Herrera‑Riveroetal. International Journal of Bipolar Disorders (2024) 12:20
Often, the efficacy of Li treatment in BP is assessed
without making distinction between BP types and/or is
focused on manic-depressive episodes, with disregard
of other disease-associated afflictions. However, some
studies have shown that Li impacts differently the fre-
quency and duration of mood episodes in BP-I and BP-II
(Tondo etal. 2001), which might relate to stronger effects
on acute manic than depressive episodes (Fountoulakis
etal. 2022). Moreover, it is plausible that the beneficial
effects of Li treatment on psychiatric symptomatology
are related to its effects on other health issues associ-
ated with BP, such as improving inflammation and sleep
(Geoffroy etal. 2016; Szałach etal. 2023). e results of
our study are in agreement. When we explored the asso-
ciation between Li response and psychiatric symptoms/
PGSs for comorbid conditions, our observations sug-
gested that better responses to Li treatment diminish the
burden of most psychiatric symptoms in patients with
BP-I, but only that of depression in patients with BP-II,
and that better Li response differentially correlates with
lower genetic burden predisposing to comorbid condi-
tions, such as insomnia, diabetes and hypertension. In
addition, when we explored the association between
genes previously linked to Li response and psychiat-
ric symptoms/PGSs for comorbid conditions, we found
that Li response genes were more strongly associated
with manic than depressive episodes in both BP-I and
BP-II, and that Li response genes were modestly but dif-
ferentially associated with other features relevant to the
clinical presentation, including, for example, suicidal
ideation, psychosis and polygenic load for insomnia and
hypothyroidism, in both BP-I and BP-II. Noticeably, the
fact that the results of our genetic analyses did not exactly
match those obtained for the total Alda score, where the
positive effects of Li showed a clear bias towards BP-I,
also suggest important gene-environment interactions.
Despite the exploratory character of our genetic study,
we believe that it suggests plausible candidate genes
and offers some valuable insights into the molecular
mechanisms underlying inter-individual variability in Li
response. For example, renalase (RNLS) was one of the
most highlighted genes in our study. In addition to its link
to Li response in BP (Amare etal. 2023), serum renalase
levels have been reported to be lower in patients with
schizophrenia (SCZ) than in control individuals (Catak
et al. 2019), and Li response was previously shown to
inversely associate with the genetic risk for SCZ (Amare
etal. 2018). RNLS is thought to modulate blood pressure
and cardiac function, and has been associated with meta-
bolic and cardiovascular alterations as well as kidney
disease (Vijayakumar & Mahapatra 2022), all of which
are affected by Li. Similar are the cases of CSMD2 and
GRIN2A, which are involved in the control of the com-
plement cascade and N-methyl-D-aspartate (NMDA)
receptor activity, respectively. Polymorphisms in both
genes have also been associated with SCZ (Tang etal.
2006; Håvik etal. 2011) and their respective functions are
Fig. 2 Visual integration of nominal findings for Li response genes. Shapes depict the diagnostic group analyzed while colors refer
to the phenotypes nominally associated with the gene in our analyses, except for the blue color, which localized even the genes not analyzed
in this study that were reported by Amare et al. 2023 as contributors to Li response in ConLiGen
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Herrera‑Riveroetal. International Journal of Bipolar Disorders (2024) 12:20
reported targets of Li effects (Ghasemi & Dehpour 2011;
Yu etal. 2015).
e investigation of how Li response measured by
the Alda scale and Li response genes associate with the
genetic predisposition to comorbid (medical) conditions
is an important strength of our study. To our knowl-
edge, this has not been investigated before. A high rate
of medical comorbidity in BP, including cardiometabolic
conditions, thyroid and kidney disease, is associated
with worse clinical presentation and course, as well as
higher mortality and increased socioeconomic burden
(Sylvia etal. 2015). Although the risk of comorbidity can
be exacerbated by pharmacological treatment, as dis-
cussed above, Li has shown beneficial effects on various
Table 1 Phenotype‑based summary of findings for the association analyses between Li response genes and psychiatric symptoms/
PGSs for comorbid conditions in ConLiGen
Phenotype N # Cases # Controls # SNPs p < 0.01 # Genes Top gene Top # SNPs
p < 0.01 Lowest p
All BP
# Manic episodes 724 – – 38 9 SLC13A3 11 2.48E−08
# Depressive episodes 789 – – 225 12 FGD4 75 5.15E−06
Alcohol abuse 835 140 695 114 9 ELOVL6 5 1.11E−04
Substance abuse 832 135 697 143 9 ADGRD1 45 4.17E−04
Psychosis 692 342 350 55 11 GRIN2A 12 7.83E−04
Suicidal ideation 660 321 339 10 6 DNAH14 1 2.31E−03
Insomnia PGS 2064 – – 57 8 CSMD2 6 1.73E−04
Sleep duration PGS 2064 – – 211 12 DNAH14 133 1.12E−04
Chronotype PGS 2064 – – 81 7 GRIN2A 47 4.06E−04
Diabetes PGS 2064 – – 111 12 CSMD2 33 6.28E−04
Hypertension PGS 2064 – – 34 7 TTC39B 5 9.57E−05
Hypothyroidism PGS 2064 – – 82 7 MTSS1 42 4.73E−04
BP-I diagnosis
# Manic episodes 641 – – 48 10 SLC13A3 11 2.15E−08
# Depressive episodes 632 – – 193 13 MTSS1 12 1.52E−06
Alcohol abuse 665 129 536 131 9 CSMD2 52 1.34E−04
Substance abuse 662 121 541 121 5 ADGRD1 52 4.13E−04
Psychosis 564 318 246 87 10 CSMD2 21 7.17E−04
Suicidal ideation 530 264 266 41 6 MTSS1 1 2.15E−04
Insomnia PGS 1669 – – 48 6 ALPK1 4 3.92E−04
Sleep duration PGS 1669 – – 174 11 RNLS 3 4.37E−05
Chronotype PGS 1669 – – 35 5 RNLS 2 1.76E−04
Diabetes PGS 1669 – – 74 13 TTC39B 1 6.78E−04
Hypertension PGS 1669 – – 29 7 TTC39B 1 6.81E−04
Hypothyroidism PGS 1669 – – 38 8 CSMD2 4 6.95E−04
BP-II diagnosis
# Manic episodes 68 – – 113 10 TNRC6C 3 3.76E−79
# Depressive episodes 141 – – 128 11 DNAH14 6 3.12E−08
Alcohol abuse 153 7 146 7 5 TNRC6C 2 1.80E−03
Substance abuse 153 8 145 0 0 – – –
Psychosis 115 12 103 353 7 TMEM131 46 1.08E−03
Suicidal ideation 118 48 70 79 7 TTC39B 24 2.49E−03
Insomnia PGS 370 – – 209 7 GRIN2A 38 2.65E−04
Sleep duration PGS 370 – – 64 9 DNAH14 16 2.95E−04
Chronotype PGS 370 – – 32 7 GRIN2A 19 1.81E−03
Diabetes PGS 370 – – 97 9 MTSS1 6 2.01E−04
Hypertension PGS 370 – – 130 10 TMEM196 27 3.21E−04
Hypothyroidism PGS 370 – – 70 7 BMF 12 1.92E−04
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Herrera‑Riveroetal. International Journal of Bipolar Disorders (2024) 12:20
systems. erefore, it becomes crucial to gain a better
understanding of the relationship between the effects of
Li and medical comorbidity in BP. In this context, even
when our PGS analyses resulted in only nominally signifi-
cant findings, these suggested that common genetic fac-
tors link Li response and other conditions, particularly
insomnia, in BP, and pinpointed potential contributing
genes. In BP, sleep disturbances, from which the most
frequent is insomnia, are not only highly prevalent, but
an important predictor of quality of life, mood swings,
suicide attempts, cognitive function and relapse rates
(Steardo etal. 2019). erefore, our observations might
have implications for the prediction of Li response in BP
Table 2 Gene‑based summary of findings for the association analyses between Li response genes and psychiatric symptoms/PGSs for
comorbid conditions in ConLiGen
Gene Chr Gene start (−1kb) Gene end (+ 1kb) # tested SNPs Psychiatric
phenotype count PGS phenotype
count Max. #
phenotypes
All BP-I BP-II All BP-I BP-II
CSMD2 1 33,978,609 34,632,443 1064 4 5 3 5 5 5 12
S100A11 1 152,003,982 152,021,383 14 0 1 0 0 0 0 1
SLC9C2 1 173,468,603 173,573,233 179 2 2 1 1 2 0 5
DNAH14 1 225,082,964 225,587,996 1417 5 5 3 3 3 5 11
TMEM131 2 98,371,799 98,613,388 358 0 0 1 0 0 1 2
RBM47 4 40,424,272 40,633,892 164 0 0 3 0 0 1 4
ELOVL6 4 110,966,002 111,121,355 261 2 2 3 2 3 1 7
ALPK1 4 113,205,665 113,364,776 301 1 2 3 4 3 4 10
ZBTB2 6 151,684,252 151,713,683 43 1 1 2 1 0 0 3
TMEM196 7 19,757,933 19,814,221 108 2 1 1 1 0 1 4
ERVW-1 7 92,096,694 92,108,300 19 0 0 0 0 0 0 0
FAM133B 7 92,189,107 92,220,708 50 1 0 0 0 0 0 1
MTSS1 8 125,562,031 125,741,730 499 4 4 1 2 3 4 8
TTC39B 9 15,162,620 15,308,358 408 3 3 4 4 5 2 11
TOR1B 9 132,564,432 132,574,560 20 0 0 0 1 0 0 1
TOR1A 9 132,574,223 132,587,413 32 0 0 0 1 0 0 1
TYSND1 10 71,896,737 71,907,432 40 0 1 1 0 0 0 2
RNLS 10 90,032,621 90,345,287 628 5 5 3 6 6 4 12
FANK1 10 127,584,108 127,699,161 250 1 1 0 2 3 0 4
FGD4 12 32,551,463 32,799,984 882 5 3 3 5 2 3 12
OR2AP1 12 55,967,199 55,970,128 7 1 0 0 1 1 1 3
ADGRD1 12 131,437,452 131,627,014 603 5 5 1 6 3 4 12
RGCC 13 42,030,695 42,046,018 35 1 1 0 1 1 0 2
BMF 15 40,379,091 40,402,093 16 0 0 0 0 0 1 1
GRIN2A 16 9,851,376 10,277,611 1624 5 4 3 3 5 4 12
CHP2 16 23,764,948 23,771,272 10 0 0 0 0 0 0 0
MYLK3 16 46,739,891 46,825,319 0 0 0 0 0 0 0 0
C16orf87 16 46,829,519 46,866,323 0 0 0 0 0 0 0 0
TRAF4 17 27,070,002 27,078,974 8 2 0 0 0 1 1 4
TMEM98 17 31,253,928 31,273,124 33 0 0 0 0 1 1 2
CDK3 17 73,995,987 74,003,080 4 1 1 0 0 0 0 1
TNRC6C 17 75,999,249 76,105,916 153 2 2 2 3 2 2 7
GNG8 19 47,136,333 47,138,942 0 0 0 0 0 0 0 0
ZFP28 19 57,049,317 57,069,169 46 0 1 0 0 0 0 1
OCSTAMP 20 45,168,585 45,180,213 10 0 0 0 0 0 1 1
SLC13A3 20 45,185,463 45,305,714 58 1 1 1 1 0 0 3
CRYBB3 22 25,594,817 25,604,330 31 2 2 1 0 1 3 5
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

Page 8 of 11
Herrera‑Riveroetal. International Journal of Bipolar Disorders (2024) 12:20
patients as well as for disease management. Nevertheless,
more studies will be required.
Conclusions
Taken together, our findings suggest that the effects of Li
on symptomatology and comorbidity in BP are partially
modulated by common genetic factors, with differential
effects between BP-I and BP-II. ese findings might
pave the way towards the development of more personal-
ized treatment strategies for patients with BP.
Abbreviations
AAO Age at disease onset
BP Bipolar disorders
ConLiGen Consortium on Lithium Genetics
Li Lithium
PGS Polygenic score
SNP Single nucleotide polymorphism
Supplementary Information
The online version contains supplementary material available at https:// doi.
org/ 10. 1186/ s40345‑ 024‑ 00341‑y.
Supplementary Material 1.
Author contributions
MHR: study conception and design, data analysis, manuscript preparation.
BTB: study conception, supervision, manuscript editing. All other authors are
ConLiGen members, which contributed to the clinical and genetic data used
in the study, and provided overall feedback on the manuscript.
Funding
Open Access funding enabled and organized by Projekt DEAL. The study was
supported by the joint project “Individualisation in Changing Environments”
(InChangE) of the universities of Münster and Bielefeld, Germany. The project
received funding from the programme "Profilbildung 2020", an initiative of the
Ministry of Culture and Science of the State of Northrhine Westphalia. The sole
responsibility for the content of this publication lies with the authors. The pri‑
mary sources of funding for ConLiGen were grants RI 908/7‑1, FOR2107 and RI
908/11‑1 from the Deutsche Forschungsgemeinschaft (Marcella Rietschel) and
grant NO 246/10‑1 (Markus M. Nöthen) and grant ZIA‑MH00284311 from the
Intramural Research Program of the National Institute of Mental Health (Clini‑
calTrials.gov identifier: NCT00001174). The genotyping was funded in part
by the German Federal Ministry of Education and Research through the Inte‑
grated Network IntegraMent (Integrated Understanding of Causes and Mech‑
anisms in Mental Disorders), under the auspices of the e:Med Programme
(Thomas G. Schulze, Marcella Rietschel and Markus M. Nöthen). The Canadian
part of the study was supported by grant #166098 from the Canadian
Institutes of Health Research and by a grant from Genome Atlantic/Research
Nova Scotia (Martin Alda). Collection and phenotyping of the Australian
University of New South Wales sample was funded by Program Grant 1037196
from the Australian National Health and Medical Research Council (Philip B.
Mitchell, Peter R. Schofield, Janice M. Fullerton), and acknowledges support
from Lansdowne Foundation, Betty Lynch OAM (dec) and the Janette Mary
O’Neill Fellowship. AT Amare is currently supported by National Health and
Medical Research Council (NHMRC) Emerging Leadership (EL1) Investigator
Grant (APP2008000). The collection of the Barcelona sample was supported
by grants PI080247, PI1200906, PI12/00018, 2014SGR1636, 2014SGR398, and
MSII14/00030 from the Centro de Investigación en Red de Salud Mental,
Institut d’Investigacions Biomèdiques August Pi i Sunyer, the Centres de
Recerca de Catalunya Programme/Generalitat de Catalunya, and the Miguel
Servet II and Instituto de Salud Carlos III. The Swedish Research Council, the
Stockholm County Council, Karolinska Institutet and the Söderström‑Königska
Foundation supported this research through grants awarded to Lena Back‑
lund, Louise Frisen, Catharina Lavebratt and Martin Schalling. The collection of
the Geneva sample was supported by grants Synapsy–The Synaptic Basis of
Mental Diseases 51NF40‑158776 and 32003B‑125469 from the Swiss National
Foundation. The work by the French group was supported by INSERM (Institut
National de la Santé et de la Recherche Médicale), AP‑HP (Assistance Publique
des Hôpitaux de Paris), the Fondation FondaMental (RTRS Santé Mentale),
and the labex Bio‑PSY (Investissements d’Avenir program managed by the
ANR under reference ANR‑11‑IDEX‑0004–02). The collection of the Romanian
sample was supported by a grant from UEFISCDI, Bucharest, Romania (grants
PCCA‑89/2012; PCE‑203/2021) to Maria Grigoroiu‑Serbanescu. The collection
Table 3 Li response genes nominally associated with psychiatric symptoms/PGSs for comorbid conditions in ConLiGen. Shown are
the overlapping and non‑overlapping genes between BP‑I and BP‑II diagnostic groups
Phenotype BP-I only BP-II only Overlap
# Manic episodes ADGRD1, FANK1, FGD4, SLC13A3, SLC9C2 ALPK1, CSMD2, ELOVL6, GRIN2A, TTC39B CRYBB3, DNAH14, RNLS, TNRC6C, ZBTB2
# Depressive episodes ADGRD1, CDK3, MTSS1, RGCC, S100A11,
TTC39B, TYSND1
ELOVL6, RBM47, SLC13A3, TMEM196,
ZBTB2
ALPK1, CSMD2, DNAH14, FGD4, GRIN2A,
RNLS
Alcohol abuse ADGRD1, CRYBB3, CSMD2, DNAH14,
ELOVL6, RNLS, SLC9C2
ALPK1, FGD4, TNRC6C GRIN2A, TTC39B
Substance abuse ADGRD1, CSMD2, MTSS1, RNLS, TTC39B – –
Psychosis ALPK1, FGD4, GRIN2A, MTSS1, TMEM196,
TNRC6C, ZFP28
ADGRD1, RBM47, TMEM131, TTC39B CSMD2, DNAH14, ELOVL6
Suicidal ideation ADGRD1, CSMD2, DNAH14, GRIN2A,
MTSS1
FGD4, MTSS1, RBM47, SLC9C2, TTC39B,
TYSND1
RNLS
Insomnia PGS ALPK1, CSMD2, T TC39B ADGRD1, GRIN2A, OR2AP1, TMEM131 DNAH14, MTSS1, RNLS
Sleep duration PGS ELOVL6, FANK1, GRIN2A, RNLS, SLC9C2 FGD4, RBM47, TMEM98 ADGRD1, ALPK1, CRYBB3, CSMD2, DNAH14,
TTC39B
Chronotype PGS ELOVL6, RNLS CRYBB3, DNAH14, MTSS1, TNRC6C ALPK1, CSMD2, GRIN2A
Diabetes PGS ADGRD1, FANK1, OR2AP1, SLC9C2, TTC39B ALPK1, TNRC6C CSMD2, DNAH14, ELOVL6, FGD4, GRIN2A,
MTSS1, RNLS
Hypertension PGS FANK1, TNRC6C, TRAF4 ADGRD1, CRYBB3, CSMD2, DNAH14,
OCSTAMP, TMEM196
FGD4, GRIN2A, RNLS, TTC39B
Hypothyroidism PGS GRIN2A, TMEM98, TNRC6C, TTC39B ALPK1, BMF, TRAF4 ADGRD1, CSMD2, MTSS1, RNLS
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

Page 9 of 11
Herrera‑Riveroetal. International Journal of Bipolar Disorders (2024) 12:20
of the Czech sample was supported by the project Nr. LO1611 with a financial
support from the MEYS under the NPU I program and by the Czech Science
Foundation, grant Nr. 17‑07070S. Biju Viswanath is funded by the Intermedi‑
ate (Clinical and PublicHealth) Fellowship (IA/CPHI/20/1/505266) of the DBT/
Wellcome Trust India Alliance.
Availability of data and materials
The data that support the findings of this study are available from ConLiGen,
but restrictions apply to their availability.
Declarations
Ethics approval and consent to participate
The Ethics Committee at the University of Heidelberg provided central
approval for ConLiGen. Written informed consent from all participants was
obtained according to the study protocols of each of the participating sites
and their institutions. All procedures were performed in accordance with the
guidelines of the Declaration of Helsinki.
Consent for publication
Not applicable.
Competing interests
Eduard Vieta has received grants and served as consultant, advisor or CME
speaker for the following entities: AB‑Biotics, Abbvie, Almirall, Allergan,
Angelini, AstraZeneca, Bristol‑Myers Squibb, Dainippon Sumitomo Pharma,
Farmindustria, Ferrer, Forest Research Institute, Gedeon Richter, GH Research,
Glaxo‑Smith‑Kline, Janssen, Lundbeck, Orion, Otsuka, Pfizer, Roche, Rovi,
Sanofi‑Aventis, Servier, Shire, Sunovion, Takeda, the Brain and Behaviour Foun‑
dation, the Spanish Ministry of Science and Innovation (CIBERSAM), the Stan‑
ley Medical Research Institute and Viatris. Michael Bauer has received grants
from the Deutsche Forschungsgemeinschaft (DFG), and Bundesministeriums
für Bildung und Forschung (BMBF), and served as consultant, advisor or CME
speaker for the following entities: Allergan, Aristo, Janssen, Lilly, Lundbeck,
neuraxpharm, Otsuka, Sandoz, Servier and Sunovion outside the submitted
work. Sarah Kittel‑Schneider has received grants and served as consultant,
advisor or speaker for the following entities: Medice Arzneimittel Pütter GmbH
and Takeda. Bernhard Baune has received grants and served as consultant,
advisor or CME speaker for the following entities: AstraZeneca, Bristol‑Myers
Squibb, Janssen, Lundbeck, Otsuka, Servier, the National Health and Medical
Research Council, the Fay Fuller Foundation, the James and Diana Ramsay
Foundation. Tadafumi Kato received honoraria for lectures, manuscripts, and/
or consultancy, from Kyowa Hakko Kirin Co, Ltd, Eli Lilly Japan K.K., Otsuka
Pharmaceutical Co, Ltd, GlaxoSmithKline K.K., Taisho Toyama Pharmaceuti‑
cal Co, Ltd, Dainippon Sumitomo Pharma Co, Ltd, Meiji Seika Pharma Co,
Ltd, Pfizer Japan Inc., Mochida Pharmaceutical Co, Ltd, Shionogi & Co, Ltd,
Janssen Pharmaceutical K.K., Janssen Asia Pacific, Yoshitomiyakuhin, Astellas
Pharma Inc, Wako Pure Chemical Industries, Ltd, Wiley Publishing Japan,
Nippon Boehringer Ingelheim Co Ltd, Kanae Foundation for the Promotion of
Medical Science, MSD K.K., Kyowa Pharmaceutical Industry Co, Ltd and Takeda
Pharmaceutical Co, Ltd. Tadafumi Kato also received a research grant from
Takeda Pharmaceutical Co, Ltd. Peter Falkai has received grants and served as
consultant, advisor or CME speaker for the following entities Abbott, Glaxo‑
SmithKline, Janssen, Essex, Lundbeck, Otsuka, Gedeon Richter, Servier and
Takeda as well as the German Ministry of Science and the German Ministry of
Health. Eva Reininghaus has received grants and served as consultant, advisor
or CME speaker for the following entities: Janssen and Institut Allergosan.
Mikael Landén has received lecture honoraria from Lundbeck. Kazufumi
Akiyama has received consulting honoraria from Taisho Toyama Pharmaceuti‑
cal Co, Ltd. Scott Clark has received grants, or data and served as consultant,
advisor or CME speaker for the following entities: Otsuka Austalia, Lundbeck
Australia, Janssen‑Cilag Australia, Servier Australia,Viatris. Bruno Etain received
honoraria from Sanofi Aventis. The rest of authors have no conflicts of interest
to disclose.
Author details
1 Department of Psychiatry, University of Münster and Joint Institute
for Individualisation in a Changing Environment (JICE), University of Münster
and Bielefeld University, Albert‑Schweitzer‑Campus 1, Building A9, 48149 Mün‑
ster, Germany. 2 Department of Psychiatry and Psychotherapy, Charité,
Universitätsmedizin Berlin, Campus Charité Mitte, Berlin, Germany. 3 Fliedner
Klinik Berlin, Berlin, Germany. 4 Department of Biological Psychiatry and Neuro‑
science, Dokkyo Medical University School of Medicine, Mibu, Japan.
5 Intramural Research Program, National Institute of Mental Health, National
Institutes of Health, US Department of Health & Human Services, Baltimore,
USA. 6 Discipline of Psychiatry, School of Medicine, University of Adelaide,
Adelaide, SA, Australia. 7 Unit of Clinical Pharmacology, Hospital University
Agency of Cagliari, Cagliari, Italy. 8 Unitat de Zoologia i Antropologia Biològica
(Dpt. Biologia Evolutiva, Ecologia i Ciències Ambientals), Facultat de Biologia
and Institut de Biomedicina (IBUB), University of Barcelona, CIBERSAM,
Barcelona, Spain. 9 Department of Psychiatry, Division of Psychiatric Specialities,
Geneva University Hospitals, Geneva, Switzerland. 10 Faculty of Medicine,
University of Geneva, Geneva, Switzerland. 11 Department of Molecular
Medicine and Surgery and Center for Molecular Medicine at Karolinska
University Hospital, Karolinska Institute, Stockholm, Sweden. 12 Département
de Psychiatrie et de Médecine Addictologique, INSERM UMR‑S 1144,
Université Paris Cité, AP‑HP, Groupe Hospitalier Saint‑Louis‑Lariboisière, F.
Widal, Paris, France. 13 Bipolar Disorder Program, Institute of Neuroscience,
Hospital Clinic, University of Barcelona, IDIBAPS, CIBERSAM, Barcelona, Spain.
14 Department of Psychiatry and Psychotherapeutic Medicine, Research Unit
for Bipolar Affective Disorder, Medical University of Graz, Graz, Austria.
15 Department of Psychiatry, University of California San Diego, San Diego, USA.
16 Department of Health Sciences Research, Mayo Clinic, Rochester, USA.
17 Department of Psychiatry and Psychology, Mayo Clinic, Rochester, USA.
18 The Neuromodulation Unit, McGill University Health Centre, Montreal,
Canada. 19 Department of Psychiatry & Center of Sleep Disorders, National
Taiwan University Hospital, Taipei, Taiwan. 20 Human Genomics Research
Group, Department of Biomedicine, University Hospital Basel, Basel,
Switzerland. 21 Institute of Medical Genetics and Pathology, University Hospital
Basel, Basel, Switzerland. 22 Institute of Neuroscience and Medicine (INM‑1),
Research Center Jülich, Jülich, Germany. 23 Mental Health Research Group,
IMIM‑Hospital del Mar, Barcelona, Spain. 24 Centro de Investigación Biomédica
en Red de Salud Mental (CIBERSAM), Instituto de Salud Carlos III, Madrid, Spain.
25 Douglas Mental Health University Institute, McGill University, Montreal,
Canada. 26 Psychiatric Genetic Unit, Poznan University of Medical Sciences,
Poznań, Poland. 27 Institute of Human Genetics, University of Bonn, School
of Medicine & University Hospital Bonn, Bonn, Germany. 28 Department
of Biomedical Sciences, University of Cagliari, Cagliari, Italy. 29 Department
of Psychiatry and Behavioral Sciences, Johns Hopkins University, Baltimore,
USA. 30 Department of Psychiatry and Psychotherapy, Ludwig‑Maximilian‑
University Munich, Munich, Germany. 31 Department of Adult Psychiatry,
Poznan University of Medical Sciences, Poznań, Poland. 32 Department
of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health,
Medical Faculty Mannheim, University of Heidelberg, Heidelberg, Germany.
33 Centre for Psychiatry Research, Department of Clinical Neuroscience,
Karolinska Institutet, Stockholm, Sweden. 34 Neuroscience Research, Australia
and School of Biomedical Sciences, University of New South Wales, Sydney,
Australia. 35 Laboratorios de Investigación y Desarrollo, Facultad de Ciencias y
Filosofía, Universidad Peruana Cayetano Heredia, San Martín de Porres, Peru.
36 Service de Psychiatrie, Hôpital Charles Perrens, Bordeaux, France. 37 Depart‑
ment of Psychiatry, Dalhousie University, Halifax, Canada. 38 Biometric
Psychiatric Genetics Research Unit, Alexandru Obregia Clinical Psychiatric
Hospital, Bucharest, Romania. 39 Mood Disorders Center of Ottawa, Ottawa,
Canada. 40 Department of Pathology of Mental Diseases, National Institute
of Mental Health, National Center of Neurology and Psychiatry, Tokyo, Japan.
41 Institute of Psychiatric Phenomics and Genomics (IPPG), University Hospital,
LMU Munich, Munich, Germany. 42 Program for Quantitative Genomics,
Harvard School of Public Health and HSL Institute for Aging Research, Harvard
Medical School, Boston, USA. 43 Univ. Paris Est Créteil, INSERM, IMRB,
Translational Neuropsychiatry, Fondation FondaMental, Créteil, France.
44 Bipolar and Depressive Disorders Unit, Institute of Neuroscience, Hospital
Clinic, University of Barcelona, IDIBAPS, CIBERSAM, ISCIII, Barcelona, Spain.
45 Service de Psychiatrie et Psychologie Clinique, Centre Psychothérapique de
Nancy ‑ Université, Nancy, France. 46 Department of Psychiatry & Behavioral
Science, Graduate School of Medicine, Juntendo University, Tokyo, Japan.
47 Department of Psychiatry, Psychosomatic Medicine and Psychotherapy,
University Hospital Würzburg, Würzburg, Germany. 48 Department of Public
Health & Institute of Epidemiology and Preventive Medicine, College of Public
Health, National Taiwan University, Taipei, Taiwan. 49 Department of Psychiatry,
Hokkaido University Graduate School of Medicine, Sapporo, Japan.
50 Department of Psychiatry and Psychotherapeutic Medicine, Landesklinikum
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

Page 10 of 11
Herrera‑Riveroetal. International Journal of Bipolar Disorders (2024) 12:20
Neunkirchen, Neunkirchen, Austria. 51 Institute of Neuroscience and Physiol‑
ogy, The Sahlgrenska Academy at the Gothenburg University, Gothenburg,
Sweden. 52 Department of Medical Epidemiology and Biostatistics, Karolinska
Institutet, Stockholm, Sweden. 53 Univ. Paris Est Créteil, INSERM, IMRB,
Translational Neuropsychiatry, AP‑HP, Mondor University Hospital, DMU
Impact, Fondation FondaMental, Créteil, France. 54 Office of Mental Health, VA
San Diego Healthcare System, California, USA. 55 Department of Psychiatry,
University of Campania ‘Luigi Vanvitelli’, Caserta, Italy. 56 Section of Psychiatry,
Department of Medical Sciences and Public Health, University of Cagliari,
Cagliari, Italy. 57 Department of Pharmacology, Dalhousie University, Halifax,
Canada. 58 Université Paris Cité, Inserm UMR‑S 1144, Optimisation Thérapeu‑
tique en Neuropsychopharmacologie, 75006 Paris, France. 59 Department
of Clinical Neurosciences, Karolinska Institutet, Stockholm, Sweden.
60 Department of Psychiatry, VA San Diego Healthcare System, San Diego, CA,
USA. 61 Department of Psychiatry, Lindner Center of Hope/University
of Cincinnati, Cincinnati, USA. 62 Department of Psychiatry and Psychotherapy,
Comprehensive Center for Clinical Neurosciences and Mental Health, Medical
University of Vienna, Vienna, Austria. 63 Department of Genetics, Microbiology
and Statistics, Faculty of Biology, Institut de Biomedicina de La Universitat de
Barcelona (IBUB), University of Barcelona, Barcelona, Spain. 64 Department
of Medicine, Surgery and Dentistry ‘Scuola Medica Salernitana’, University
of Salerno, Baronissi, Italy. 65 National Institute of Mental Health, Klecany, Czech
Republic. 66 Department of Psychiatry & Department of Child and Adolescent
Psychiatry, Nagoya University Graduate School of Medicine, Nagoya, Japan.
67 Department of Psychiatry and Psychotherapy, University Hospital Carl
Gustav Carus, Medical Faculty, Technische Universität Dresden, Dresden,
Germany. 68 Department of Psychiatry, Psychosomatic Medicine and Psycho‑
therapy, University Hospital Frankfurt, Frankfurt, Germany. 69 School
of Psychiatry, University of New South Wales, Sydney, Australia. 70 Montreal
Neurological Institute and Hospital, McGill University, Montreal, Canada.
71 Northern Adelaide Local Health Network, Mental Health Ser vices, Adelaide,
Australia. 72 Department of Psychiatry and Psychotherapy, University Hospital
Bonn, Medical Faculty University of Bonn, Bonn, Germany. 73 Department
of Psychiatry, Dokkyo Medical University School of Medicine, Mibu, Japan.
74 Medical Faculty, Bipolar Center Wiener Neustadt, Sigmund Freud University,
Vienna, Austria. 75 Epidemiology Branch, Division of Intramural Population
Health Research, Eunice Kennedy Shriver National Institute of Child Health
and Human Development, National Institutes of Health, Bethesda, USA.
76 Centre for Healthy Brain Ageing (CHeBA), School of Psychiatry, University
of New South Wales, Sydney, Australia. 77 Department of Psychiatry, University
of Perugia, Perugia, Italy. 78 Department of Psychiatry, National Institute
of Mental Health and Neurosciences, Bangalore 560029, India. 79 Department
of Mental Health, Johns Hopkins Bloomberg School of Public Health,
Baltimore, USA. 80 Department of Psychiatry and Behavioral Sciences, Norton
College of Medicine, SUNY Upstate Medical University, Syracuse, NY, USA.
81 Department of Psychiatry, Melbourne Medical School, University of Mel‑
bourne and The Florey Institute of Neuroscience and Mental Health, The
University of Melbourne, Melbourne, Australia.
Received: 28 November 2023 Accepted: 2 May 2024
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