# Epigenetic markers of lithium response in bipolar disorder: optimization of a Methylation-Sensitive High-Resolution Melting assay (MS-HRM) and relevance in combination with clinical factors

Céline Bourdon, Cindie Courtin, Frank Bellivier, Jan Scott, Bruno Etain,
Cynthia Marie-Claire

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## To cite this version:

Céline Bourdon, Cindie Courtin, Frank Bellivier, Jan Scott, Bruno Etain, et al.. Epigenetic markers of lithium response in bipolar disorder: optimization of a Methylation-Sensitive High-Resolution Melting assay (MS-HRM) and relevance in combination with clinical factors. Journal of Affective Disorders, 2026, 392, pp.120177. 10.1016/j.jad.2025.120177. hal-05285314

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Research paper

# Epigenetic markers of lithium response in bipolar disorder: optimization of a Methylation-Sensitive High-Resolution Melting assay (MS-HRM) and relevance in combination with clinical factors

Céline Bourdon<sup>a, *,</sup>, Cindie Courtin<sup>a</sup>, Frank Bellivier<sup>a,b,c</sup>, Jan Scott<sup>d</sup>, Bruno Etain<sup>a,b,c</sup>,
Cynthia Marie-Claire<sup>a</sup>

<figure><img src="image_4.png" /></figure>

## ARTICLE INFO

### Keywords:

- Bipolar disorder
- Lithium
- Biomarker
- Response
- MS-HRM
- DNA methylation

## ABSTRACT

Lithium (Li) is the most frequently recommended first-line treatment in bipolar disorder (BD).

Nevertheless, response to Li is highly variable between individuals and no clinical or biological biomarkers have been consistently associated to long-term response. We previously identified 3 differentially methylated regions (DMRs) that discriminated good from non-responders to Li. In this study, we validated 4 additional DMRs using Methylation Specific High-Resolution Melting (MS-HRM), a PCR-based low-cost method with easy trans- ferability from bench to bedside. In 61 individuals with BD-I, we investigated associations between these 7 DMRs and Li response, alone or combined with demographic and clinical variables. Using 7 DMRs and 6 demographic and clinical variables, 90 % of the BD-I individuals were correctly classified according to their response status (AUC = 0.95). In particular, combining DNA methylation markers with clinical predictors improved the iden- tification of non-responders (83 % correctly classified). This epigenetic signature might be useful in clinical practice especially as it is imperative to avoid exposing individuals to Li if they have a very low likelihood of response.

## 1. Introduction

Bipolar disorder (BD) is a prevalent and severe psychiatric disorder characterized by the recurrence of (hypo)-manic and depressive episodes (Zhou et al., 2021). Bipolar disorder affects more than 1 % of the general population and is one of the most burdensome disorders worldwide in terms of disability-adjusted life-year and is the 6th leading cause of disability in working age adults (GBD 2019 Mental Disorders Collaborators, 2022).

In BD, Lithium (Li) is the most frequently recommended first-line treatment in clinical practice guidelines, for mood relapse prevention (Kishi et al., 2021; Grande et al., 2016), acute manic episodes and sui- cide prevention (Tondo et al., 2021). However, about 30–55 % of in- dividuals prescribed Li do not have the good outcome in terms of decreased frequency and/or severity of mood recurrences (Burgess et al., 2001). As no predictive stratification criteria are currently

available, most individuals who may benefit from a mood stabilizer are offered an extended trial of Li (up to 2 years) to see if the frequency or severity of BD episodes is reduced. Longitudinal research indicates that an individuals' response to Li can be broadly classified as: Good (GR), Partial (PaR) and the Non-Responders (NR), with each category repre- senting about a third of individuals exposed to Li (Manchia et al., 2013). Although both genetic and clinical factors have been found to be asso- ciated with response to Li, no robust combination of predictors has been identified that identify both GR, but also NR (Grillault Laroche et al., 2020; Hui et al., 2019; Senner et al., 2021). The early identification of NR is a clinical imperative in the field of BD as it is unhelpful and potentially distressing for individuals to undergo an extended trial of Li if the risks are likely to significantly outweigh any potential benefits (Scott et al., 2018).

The identification of biological markers associated with Li response is an important first step toward a personalized medicine. Epigenetic

<footnote><sup>a</sup> Université Paris Cité, Inserm, Optimisation Thérapeutique en Neuropsychopharmacologie, UMR-S 1144, F-75006 Paris, France</footnote>

<footnote><sup>b</sup> Département de Psychiatrie et de Médecine Addictologique, Hôpitaux Lariboisière-Fernand Widal, GHU APHP.Nord – Université Paris Cité, Paris F-75010, France</footnote>

<footnote><sup>c</sup> Fondation Fondamental, F-94010 Créteil, France</footnote>

<footnote><sup>d</sup> Institute of Neuroscience, Newcastle University, Newcastle upon Tyne NE4 5PL, UK</footnote>

<footnote>* Corresponding author at: Faculté de Pharmacie – Université de Paris Cité, INSERM UMR-S 1144, 4 avenue de l'Observatoire, 75006 Paris, France.
E-mail address: celine.bourdon@inserm.fr (C. Bourdon).</footnote>
biomarkers appear particularly promising in this context. We previously
performed the first genome-wide analysis of DNA methylation profiles
among individuals with BD type I (BD-I) assessed for their response to
long-term treatment with Li. We identified differentially methylated
regions (DMRs) discriminating GR from NR, after selecting only those
regions that were not impacted by co-prescribed treatments that are - by
definition - more frequent in NR (Marie-Claire et al., 2020). We then
used Methylation Specific High-Resolution Melting (MS-HRM), a PCR
based method that can be implemented in any medical laboratory at low
cost and minimal equipment, to validate 3 DMRs that discriminated GR
from NR in an extended cohort of 70 individuals with BD-I (Marie-Claire
et al., 2022). Also, in an independent cohort, we showed that combining
selected clinical variables with the 3 DMRs improved the performance of
the MS-HRM-based biomarkers in discriminating between groups
(Marie-Claire et al., 2023). The present study aims at further improving
the performance of the combination of MS-HRM-based biomarkers and
clinical predictors to discriminate individuals that benefited from Li
treatment (GR and PaR) from those who did not (NR) in an extended
cohort.

## 2. Experimental procedures

2.1. Cohort

Participants were recruited from a university-affiliated psychiatric
department in France (between 2009 and 2020). The cohort comprised
61 euthymic individuals whose clinical presentation met diagnostic
criteria for BD-I, according to a SCID assessment (Structured Clinical
Interview for DSMIV Disorders). Twenty one of participants eligible for
this study were reported upon in our previous publication (Marie-Claire
et al., 2023) (Supplementary data 1).

For this study, the additional inclusion criteria were as follow:
Caucasian origin, age 18–75, a score of less than 10 on the Montgomery-
Asberg depression rating scale (Montgomery and Asberg, 1979) and less
than 12 at the Young Mania Rating Scale (Young et al., 1978) at baseline
assessment. Exclusion criteria were: inability to provide informed con-
sent, current acute mood episode of any polarity, or dementia.

All 61 individuals provided written informed consent to participate
in the study of clinical and genetic factors in BD. This research protocol
(Clinical Trials Number NCT02627404) was approved by the French
medical ethics committee (Comité de Protection des Personnes (CPP)—
IDRCB2008 AO1465 50 VI – Pitié-Salpetrière 118–08) and carried out
according to the approved guidelines. Clinical characteristics to be
included in the analysis were selected in the database on the basis of a
previous review by Grillault Laroche et al. (Grillault Laroche et al.,
2020).

## 2.2. Classification of lithium response

Response to Li was rated using the Alda scale (Grof et al., 2002). This
scale is widely used to assess response to mood stabilizers and can be
used as a continuous or categorical measure of Li response. The Alda
comprises two sub-scales, the first scale (scale A) is used to make a
retrospective assessment of the individuals' response to the introduction
of Li (rated from 0 to 10, with low scores indicating less benefit). The
second subscale (scale B) comprises five items (each rated 0–2) that may
confound the assessment of Li response (such as the prescription of
polypharmacy, levels of medication adherence, etc.). Overall response is
then estimated by subtracting the B score from the A score. Typically,
individuals with a total score $\ge$ 7 are categorized as GR, those with a
total score $\le$ 3 are categorized as NR, and those with a total score be-
tween 3 and 7 are categorized as PaR (Manchia et al., 2013). As we
wished to identify predictors of NR, we combined the PaR and GR into
one category (herewith, GR + PaR).

2.3. DNA isolation

A blood sample was collected at inclusion and whole blood DNA was
extracted using the automated Maxwell 16 DNA Purification Instrument
and the dedicated Maxwell® RSC Blood DNA Kit (Promega). All DNA
samples were stored at -80 °C.

2.4. Targeted bisulfite sequencing and selection of DMRs of interest

Targeted bisulfite sequencing was performed by the Diagenode DNA
methylation service (Diagenode). Briefly, DNA concentration of the
samples was measured using Qubit® dsDNA BR Assay Kit (Thermo
Fisher Scientific). DNA quality of the samples was assessed with the
Fragment Analyzer™ and the DNF-488 High Sensitivity genomic DNA
Analysis Kit (Agilent). Based on the previously identified 66 DMRs
associated with the response to Li in BD1 individuals, independently of
the co-medication status (atypical antipsychotics, antidepressants, an-
ticonvulsants and number of psychotropic drugs) but also current Li
treatment (Marie-Claire et al., 2020), a custom panel was created
(Qiagen). The 161 custom primers covered 82.2 % of the requested
CpGs. The EpiTect Fast Bisulfite Conversion kit (Qiagen) was used for
DNA Bisulfite Conversion. Bisulfite converted DNA was used for subse-
quent end repair, adapter ligation, target PCR enrichment for 8 cycles
and amplification of 19 cycles using the QIA-seq Targeted Methyl Panel.
Libraries were purified with Qiaseq beads after ligation, target enrich-
ment and amplification. The quality and quantity of the libraries was
assessed with the Fragment AnalyzerTM and the DNF-488 High Sensi-
tivity genomic DNA Analysis Kit (Agilent). Libraries were pooled for
sequencing in an equimolar way. The pooled libraries were sequenced
on a Miseq platform (Illumina) using 150 bp paired-end sequencing with
the MiSeq Reagent kit v2 (300-cycles).

In order to select DMRs associated with Li response status for further
MSH-RM validation, we used 3 groups based on the Alda score (GR, PaR
and NR). Then, a Kruskal-Wallis statistical test was performed in order to
determine the associations (p < 0.05) between groups and the methyl-
ation percentage for each DMR (Supplementary data 2).

2.5. Methylation Sensitive High-Resolution Melting (MS-HRM)

Primers were designed for each of the DMRs selected after targeted
bisulfite sequencing and after statistical comparisons between groups.
Due to technical issues, notably during the primers design step, most
DMRs had to be excluded for further validation. A list of 7 DMRs were
then obtained: 4 DMRs added to the 3 ones previously described and
tested in our laboratory (Marie-Claire et al., 2023, 2022). Whole blood
DNA samples were treated with sodium bisulfite, using the EZ DNA
methylation kit (Zymo Research, CA, USA). Human methylated and
unmethylated DNA standards (Milipore) were diluted before bisulfite
conversion to obtain 0, 10, 20, 30, 40, 50, 60, 70, 80, 90 and 100 %
methylated and unmethylated ratios. Bisulfite modified DNA was
quantified using a NanoDrop spectrophotometer. The primer design was
performed using the Bisearch online tool (http://bisearch.enzim.hu/),
as previously described (Marie-Claire et al., 2022). Methylation Sensi-
tive High-Resolution Melting (MS-HRM) (Sestáková et al., 2019) PCR
reactions were performed on a CFX384 Touch Real-Time PCR Detection
System (Biorad Laboratories) in a final volume of 10 µL: 200 nM of each
primer, 5 µL of Precision Melt Supermix (Biorad Laboratories) and 10 ng
of bisulfite treated DNA. The first step was the initial denaturation (95
°C during 3 min), followed by 45 cycles of 10 s at 95 °C, 30 s at 50 °C, 30
s at 72 °C. The HRM step consisted of a denaturation of all the products
at 95 °C for 30 s, with an annealing at 60 °C for 1 min. Samples were then
warmed, slowly, to 95 °C at 0.2 °C per second, holding for 10 s after each
stepwise increment and fluorescence data were collected. Each sample
was analyzed in triplicate. Peak-heights were calculated with the CFX
Maestro Software (Version 2.2, Bio-Rad Laboratories, Inc., Hercules, CA,
USA). Linear curves of the peak-heights of the temperature melting (T_m)
first derivative of HRM curves against the methylation percentage of the standard were plotted (Tse et al., 2011). The individuals performing the experiments were blinded to response status of the participants.

## 2.6. Statistical analysis

All statistical analyses were performed using R version 4.2.1 (www.r-project.org), and the JASP software (JASP Team, 2024). In this exploratory study a p-value < 0.05 was considered statistically significant, without correction for multiple testing.

To select the DMRs to be tested using MS-HRM, we used non-parametric Kruskal-Wallis test (to analyze the percentage of methylation between NR, PaR and GR) As several demographic and clinical variables can confound research into mood stabilizers and BD the following were included as co-variates in the analyses: age, sex, cigarette smoking status, lifetime number of hospitalizations, age at onset of BD, polarity at onset, psychotic symptoms at onset, family history of BD, lifetime alcohol misuse, lifetime cannabis misuse, lifetime panic disorders, Li prescribed as the first mood stabilizer (vs 2nd or 3rd choice, etc.). We did not include some other variables such as number and frequency of episodes as these are assessed in the Alda scale.

We used logistic regression analyses to explore the utility of DMRs (the set of 3 DMRs and the set of 7 DMRs), and clinical variables, and the combination of all these variables to classify study participants into the two Li response categories (GR + PaR versus NR). To reduce the number of clinical variables and to prevent from potential collinearity between clinical variables, we used a Backward stepwise regression for these variables, while all DMRs were considered into the models without backward selection.

We used the same approach for performing Receiver Operating Characteristic (ROC) curve analyses, with findings reported as the estimated Area Under the Curve (AUC). The pROC and ROCR package were used in order to create and compare ROC curves (Robin et al., 2011; Sing et al., 2005). The analysis is refined using k-fold statistics and the caret R package (Kuhn, 2008). As age and sex are known founders of findings from research into mood stabilizers and BD, the regression and ROC analyses are all adjusted for these two variables (we provide some findings regarding this in Supplement data 3).

# 3. Results

## 3.1. Characteristics of Li response groups

Sixty-one participants with BD-I were included in this study, with a slight majority of females (52.5 %) and a mean age at interview around 43 years old. Description of the cohort and by Li response groups are shown in Table 1. Eighteen out of 61 individuals were classified as NR (29.5 %). Only the lifetime number of hospitalizations differed significantly between the GR + PaR and the NR groups (p = 0.038).

## 3.2. MS-HRM technique

The selection process of the DMRs is described in Fig. 1. Among the 63 DMRs not previously validated using MS-HRM, 57 could be sequenced using bisulfite targeted sequencing, among which 13 DMRs displayed differences by Kruskal-Wallis tests (Supplementary data 2). Among these DMRs, primers were not designable due to multiple reasons (GC rich sequences, multiple CpG in the primers, melting temperature different between primers, short length of DMRs) for 9 DMRs. This selection process resulted in 4 DMRs with primers being designable and satisfactory results (DMR 7291, DMR 17987, DMR 63769 and DMR 79888). The raw first derivative of the HRM curves obtained for the 0 % (unmethylated) and 100 % (fully methylated) DNA standards are presented in Fig. 2. We added the 3 previously validated DMRs (DMR 17107, DMR 24332 and DMR 106540) for a total of 7 DMRs to be included in the analyses. Peak-heights were used to generate the

<table><caption>**Table 1**
Socio-demographic and clinical characteristics of the sample (N = 61).</caption><thead><tr><th rowspan="2">Variables</th><th>Whole sample</th><th>GR + PaR</th><th>NR</th><th rowspan="2">P-value (t-test or Mann-Whitney)</th></tr><tr><th colspan="3">N (%) or Mean (SD)</th></tr></thead><tbody><tr><td>N</td><td>61</td><td>43</td><td>18</td><td></td></tr><tr><td>Age</td><td>43.1<br>(12.0)</td><td>42.8<br>(12.8)</td><td>43.7<br>(10.2)</td><td>0.800</td></tr><tr><td>Sex (females)</td><td>32 (52.5)<br>(%)</td><td>23<br>(53.5)<br>(%)</td><td>9 (50 %)</td><td>0.812</td></tr><tr><td>Number of hospitalizations</td><td>4.18<br>(3.62)</td><td>3.39<br>(2.39)</td><td>6.06<br>(5.17)</td><td><strong>0.038</strong></td></tr><tr><td>Age at onset</td><td>25.4<br>(10.1)</td><td>24.7<br>(10.5)</td><td>27.2<br>(9.2)</td><td>0.124</td></tr><tr><td>Duration of disease</td><td>17.7<br>(10.2)</td><td>18.1<br>(10.4)</td><td>16.5<br>(10.0)</td><td>0.558</td></tr><tr><td>Manic polarity at onset</td><td>19 (31.1)<br>(%)</td><td>11<br>(25.6)<br>(%)</td><td>8 (44.4)<br>(%)</td><td>0.153</td></tr><tr><td>Psychotic symptoms at onset</td><td>15 (24.6)<br>(%)</td><td>10<br>(23.3)<br>(%)</td><td>5 (27.8)<br>(%)</td><td>0.708</td></tr><tr><td>Family history of BD</td><td>20 (32.8)<br>(%)</td><td>14<br>(32.6)<br>(%)</td><td>6 (33.3)<br>(%)</td><td>0.961</td></tr><tr><td>Current tobacco use</td><td>30 (49.2)<br>(%)</td><td>19<br>(44.2)<br>(%)</td><td>11<br>(61.1)<br>(%)</td><td>0.235</td></tr><tr><td>Lifetime alcohol misuse</td><td>16 (26.2)<br>(%)</td><td>9 (20.9)<br>(%)</td><td>7 (38.9)<br>(%)</td><td>0.152</td></tr><tr><td>Lifetime cannabis misuse</td><td>8 (13.1)<br>(%)</td><td>4 (22.2)<br>(%)</td><td>4 (9.3)<br>(%)</td><td>0.181</td></tr><tr><td>Lifetime panic disorders</td><td>12 (19.7)<br>(%)</td><td>6 (13.9)<br>(%)</td><td>6 (33.3)<br>(%)</td><td>0.087</td></tr><tr><td>Lithium as first prescribed stabilizer</td><td>16 (26.2)<br>(%)</td><td>9 (20.9)<br>(%)</td><td>7 (38.9)<br>(%)</td><td>0.113</td></tr></tbody></table>

BD: bipolar disorder, GR: good responders, PaR; partial responders, NR: Non responders.

The number in bold in the table is statistically significant (p < 0.05).

standard curves for determination of percentage of methylation for the seven DMRs analyzed. A total of 13 genes were found associated with these regions as presented in Supplementary data 4 (FAM24B, RTKN, DCTN1, STK32C, DPYSL4, WDR60, ZAP70, CXXC11, TMEM131, TGFBR3, CDC7, VIPR2, THBS1). They are involved in pathways including signal transduction, immune system, neuronal system or gene expression. Pathway enrichment analyses were not significant probably due to the small number of genes.

## 3.3. Association between DMRs and Li response

Logistic regression analysis of the 3 previously identified DMRs correctly classified 98 % of the GR + PaR and 22 % of the NR (AUC = 0.686; 95 % CI: 0.542–0.830). Logistic regression analysis of the 7 DMRs correctly classified 91 % of GR + PaR and 50 % of NR. As shown in Fig. 3, the AUC for the 7 DMRs (AUC = 0.846; 95 % CI: 0.747–0.945) is significantly different from the AUC for 3 DMRs (DeLong test p-value: 0.029).

## 3.4. Association between demographic and clinical variables and Li response

Clinical characteristics to be included in the next analysis were selected on the basis of a previous review by Grillault Laroche et al. (2020) when available in the cohort database. As shown in Table 2, after adjusting the analysis for age and sex, 4 clinical variables (lifetime number of hospitalizations, polarity at onset, lifetime panic disorders and Li prescribed as the 1st mood stabilizer) were retained in the final model on the basis of their significance using backward stepwise
<figure><img src="image_5.png" /><figcaption>Fig. 1. Flowchart of the selection process of new MS-HRM assays of DMRs.</figcaption></figure>

<figure><img src="image_6.png" /><figcaption>Fig. 2. Raw melt curves (in triplicates) for 100 % methylated (M) and 0 % methylated (UM) bisulfite treated DNA standards. (a) DMR7291, (b) DMR17978, (c) DMR79888, (d) DMR63769.</figcaption></figure>

regression. This model correctly classified 91 % of the GR + PaR and 39 % of the NR (AUC = 0.810 95 % CI: 0.697–0.923).

### 3.5. Combination of DMRs and clinical variables in association with Li response

The final analyses explored the classification of Li responders when the 7 DMRs were combined with the 4 clinical variables (with analyses adjusted for age and sex). The combination of 7 DMRs and 4 clinical variables correctly classified 93 % of GR + PaR (93 %) and 83 % of NR. As shown in Fig. 4, this model led to a significant improvement of the

classification with an AUC = 0.950 95 % CI: 0.895–1.000 (p-value = 0.0128 vs clinical variables alone, and p-value vs the 7 DMRs alone: 0.015).

Finally, we performed k-fold cross-validation in order to reduce the likelihood of overfitting in the whole cohort. The resulting AUC was 0.8625, 95 % CI: 0.784–0.936. An analysis including the combination of clinical variables and DMR only for the newly included patients (n = 40) has been performed and the results are shown in Supplementary data 5.
<figure><img src="image_7.png" /><figcaption>**Fig. 3.** Receiver operating characteristics (ROC) curves for Li response classification (GR + PaR vs. NR) using 3 DMRs (previously validated MS-HRM assay) and with 7 DMRs (optimized MS-HRM assay).</figcaption></figure>

<table><caption>**Table 2**
Backward stepwise regression analysis on clinical variables.</caption><thead><tr><th rowspan="2">Estimate</th><th colspan="3">Wald test</th></tr><tr><th>Wald statistic</th><th>df</th><th>p-Value</th></tr></thead><tbody><tr><td>(Intercept)</td><td>0.171</td><td>1</td><td>0.679</td></tr><tr><td>Age</td><td>0.051</td><td>1</td><td>0.822</td></tr><tr><td>Sex</td><td>0.967</td><td>1</td><td>0.325</td></tr><tr><td>Number of hospitalizations</td><td>6.012</td><td>1</td><td>0.014</td></tr><tr><td>Polarity at onset</td><td>4.979</td><td>1</td><td>0.026</td></tr><tr><td>Panic disorders</td><td>4.213</td><td>1</td><td>0.040</td></tr><tr><td>Li prescribed at the 1st mood stabilizer</td><td>4.383</td><td>1</td><td>0.036</td></tr></tbody></table>

## 4. Discussion

Despite considerable research efforts, there is no usable clinical predictor or biomarker of Li response in BD. This study aimed at improving a recently described MS-HRM assay that used 3 DMRs to discriminate between individuals who benefited from Li from those who did not. In this larger study of 61 euthymic individuals with BD-I, we optimized and validated a MS-HRM assay for 7 DMRs and found that this set of 7 DMRs better discriminated the GR + PaR group from NR cases. Logistic regression analyses (adjusted for age and sex) demonstrated that this set of 7 DMRs was as accurate as a subset of 4 clinical variables in classifying individuals into the correct Li response category. Further, the combination of the 7 DMR and 4 clinical variables provided the optimal classification, improved the AUC and critically, significantly improved the identification on NR cases. As such, we suggest that this study provides new evidence that combining clinical variables and epigenetic biomarkers significantly increases our ability to stratify individuals with BD-I into Li responders and non-responders. In the following paragraphs, we examine the evidence to support this proposal.

In the first instance, we report the selection of 4 additional DMRs from the 66 DMRs previously identified (Marie-Claire et al., 2020) and their validation using MS-HRM. As noted, the AUCs for the 3 DMRs

<figure><img src="image_8.png" /><figcaption>**Fig. 4.** Receiver operating characteristics (ROC) curves of the identification of GR + PaR vs NR individuals using 4 clinical variables alone or combined with 7 DMRs (optimized MS-HRM assay) adjusted for age and sex.</figcaption></figure>

(previously studied) versus the 7 DMRs were significantly different (respectively 0.686 and 0.846 after adjustment for age and sex). Moreover, whilst the correct classification of GR + PaR was similar with 3 and 7 DMRs (respectively 97.7 % and 86.0 %), the model employing 7 DMRs correctly classified significantly more NR cases (from 50.0 % versus 16.7 %). Therefore, the optimized signature might be more accurate than the original one in discriminating groups of individuals according to their response to Li.

Second, the classification with 7 DMRs performed as efficiently as the one with 4 clinical variables (respectively AUC = 0.846 and AUC = 0.810). Therefore, the MS-HRM assay might be proposed as a proxy for selected clinical variables that can predict Li response. If our findings are replicated and confirmed in larger independent cohorts, then it might be that these 7 DMR could be seen as a more objective measure than clinical information (e.g. the recording of clinical factors such as age at onset of BD or family history is known to be unreliable unless resource intensive assessments are used). Thirteen genes were found associated with the 7 DMRs. Among those 3 have been suggested to be associated with BD: a splice variant in *DCTN1* has been associated with the etiology of BD and MDD (Hallen and Cooper, 2020), *WDR60* has been associated with psychoses in BD, SCZ and MDD (Xu et al., 2014) and *TGFBR3* has been associated with the etiology of BD and MDD (Zhang and Chen, 2020).

Third, the best classification model was obtained by combining 7 DMRs with 4 clinical variables. In this model, the AUC was 0.95 with a correct classification of 93 % of GR + PaR and 83 % of NR. As previously described (Marie-Claire et al., 2023), the combination performed better than the assay alone or the clinical variables alone primarily because of the improvement in the correct classification of NR. From a clinical point of view, it is as, important to identify individuals who are unlikely to respond to Li as it is to identify those who are likely to respond. Indeed, many clinicians and patients would argue that it is more important to identify NR cases as early as possible to avoid prolonged exposure to a trial of Li that is not only unlikely to offer any significant benefits but also, given the narrow therapeutic window for Li, may actually have negative or adverse effects in terms of side effects but also in under-mining patient confidence in medications in general (Malhi et al., 2020).

We acknowledge that, although our study has many strengths, there are several weaknesses. One major limitation is the cohort size and the inclusion of some individuals from our previous study of DMRs. The latter means the current cohort is not truly independent, even if the analysis of new individuals only is significant, we cannot exclude a bias
due to the fact that all the subjects were included in the same cohort. Whilst the overall cohort size (n = 61) limited the demographic and clinical covariates that we could assess and reduced statistical power of the analyses. Also, since this is exploratory, we did not apply any correction for multiple testing. Also, whilst the Alda scale is widely used in genetics research exploring Li response, there are some climimetric and psychometric weakness in the scale and, most importantly, Li response is assessed retrospectively rather than prospectively. The retrospective design of this study does not allow to determine whether the observed methylation differences pre-existed to Li initiation and could therefore be used as a predictive biomarker of Li response, or whether the differences were induced by Li or other factors (BD course during Li trial for instance). So, whilst our study represents an important step forward, we recognize the need for similar studies in larger independent cohorts. It is important to note that, in this context, a recent study has identified a DNA methylation signature, enriched in genes related to the GSK3β pathway, allowing to discriminate 26 GR from 37 NR (Zafrilla-López et al., 2024). Confirming that the use of epigenetic markers as discriminants for Li response is of clinical interest in BD.

## 5. Conclusion

Our findings show that a minimal set of 7 DMRs and 4 clinical variables can differentiate between individuals with BD-I who benefit from Li treatment (GR + PaR) and those who did not (NR). Critically, this model accurately identifies 4 out of 5 NR cases. Further, MS-HRM is a cost-effective method that can be easily implemented in clinical laboratories due to its minimal equipment requirements. However, its potential transferability from bench to bedside necessitate prospective validation in an independent and larger cohort. If the findings are replicated, the combination of these epigenetic biomarkers of response to Li with clinical variables could represent an important step toward precision psychiatry in BD. Importantly, the H2020-funded R-LiNK study offers the opportunity to test the utility of the DMRs (with or without clinical variables) in a prospectively ascertained clinical cohort of well-characterized BD-I cases who will be assessed before and after initiation of Li treatment (Scott et al., 2019).

Supplementary data to this article can be found online at https://doi.org/10.1016/j.jad.2025.120177.

## CRediT authorship contribution statement

**Céline Bourdon:** Writing – original draft, Methodology, Investigation, Formal analysis. **Cindie Courtin:** Writing – review & editing, Methodology, Investigation, Formal analysis. **Frank Bellivier:** Writing – review & editing, Supervision. **Jan Scott:** Writing – review & editing. **Bruno Etain:** Writing – review & editing, Validation, Conceptualization. **Cynthia Marie-Claire:** Writing – review & editing, Validation, Conceptualization.

## Funding source

This research was supported by INSERM (Institut National de la Santé et de la Recherche Médicale), AP-HP (Assistance Publique des Hôpitaux de Paris), Université de Paris (IDEX Prematuration 2021 to C. M-C), Inserm Tranfert (CoPoc funding 2021 to C. M-C) and the Milken Institute and Baszucki Brain Research Fund (Bipolar Disorder Therapeutic Research Grant 2021 to C. M-C). These funding sources had no role in the study design, data collection, analysis, preparation of the manuscript, or decision to submit the manuscript for publication.

## Declaration of competing interest

None to be declared in links with this study.

## Acknowledgements

We thank the clinicians and nursing staff of the French Network of Centers for Expertise in BD for the recruitment and clinical assessment of participants. The biobank is hosted at the Plateforme de Ressources Biologique, Hôpital Henri Mondor, Créteil (Dr. C. Barau). Dr. C. Marie-Claire is supported by the Centre National de la Recherche Scientifique. This research was supported by INSERM (Institut National de la Santé et de la Recherche Médicale), AP-HP (Assistance Publique des Hôpitaux de Paris), Université de Paris (IDEX Prematuration 2021 to C. M-C), Inserm Tranfert (CoPoc funding 2021 to C. M-C) and the Milken Institute and Baszucki Brain Research Fund (Bipolar Disorder Therapeutic Research Grant 2021 to C. M-C).

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