SHORT REPORT

Journal of
Sleep
Research

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# Polygenic risk scores for mood disorders and actigraphy estimates of sleep and circadian rhythms: A preliminary study in bipolar disorders

Vincent Hennion<fnref n="1" />,<fnref n="2" />,<fnref n="3" />

Jan Scott<fnref n="4" />

Victoire Martinot<fnref n="2" />,<fnref n="3" />

Ophélia Godin<fnref n="5" />

Cynthia Marie-Claire<fnref n="1" />

Frank Bellivier<fnref n="1" />,<fnref n="2" />,<fnref n="3" />

Stéphane Jamain<fnref n="5" />

Bruno Etain<fnref n="1" />,<fnref n="2" />,<fnref n="3" />

<figure><img src="image_2.png" /></figure>

Correspondence

Bruno Etain, Optimisation Thérapeutique en Neuropsychopharmacologie, INSERM U1144, Université Paris Cité, Paris, France.
Email: bruno.etain@inserm.fr

Funding information

Institut National de la Santé et de la Recherche Médicale; Agence Nationale pour la Recherche, Grant/Award Number: ANR-11-IDEX-0004-02; Assistance Publique des Hôpitaux de Paris, Grant/Award Number: GAN12

Summary

In bipolar disorders, abnormalities of sleep patterns and of circadian rhythms of activity are observed during mood episodes, but also persist during euthymia. Shared vulnerabilities between mood disorders and abnormalities of sleep patterns and circadian rhythms of activity have been suggested. This exploratory study investigated the association between polygenic risk scores for bipolar disorder and major depressive disorder, actigraphy estimates of sleep patterns, and circadian rhythms of activity in a sample of 62 euthymic individuals with bipolar disorder. The polygenic risk score – bipolar disorder and polygenic risk score – major depressive disorder were calculated for three stringent thresholds of significance. Data reduction was applied to aggregate actigraphy measures into dimensions using principal component analysis. A higher polygenic risk score – major depressive disorder was associated with more fragmented sleep, while a higher polygenic risk score – bipolar disorder was associated with a later peak of circadian rhythms of activity. These results remained significant after adjustment for age, sex, bipolar disorder subtype, body mass index, current depressive symptoms, current tobacco use, and medications prescribed at inclusion, but not after correction for multiple testing. In conclusion, the genetic vulnerabilities to major depression and to bipolar disorder might be associated with different abnormalities of sleep patterns and circadian rhythms of activity. The results should be replicated in larger and independent samples.

KEYWORDS

actigraphy, bipolar disorder, circadian rhythms, major depression, polygenic risk scores, sleep

1 | INTRODUCTION

Circadian rhythms are the physiological, physical, and behavioural
changes an organism experiences over a 24 h cycle (from *circa diem*,
i.e., “about one day” in Latin). In humans, circadian rhythms of activity

characterise the variations of levels of physical activity over a 24 h
period. Circadian rhythms of activity can be measured in an ecological
way using actigraphy. An actiwatch is a device resembling a wrist-
watch that contains an accelerometer that detects and scores infor-
mation about the intensity and timing of wrist movements

<footnote n="1">¹Optimisation Thérapeutique en Neuropsychopharmacologie, INSERM U1144, Université Paris Cité, Paris, France</footnote>

<footnote n="2">²Département de Psychiatrie et de Médecine Addictologique, AP-HP Nord, GH Saint-Louis-Lariboisière-Fernand-Widal, DMU Neurosciences, Paris, France</footnote>

<footnote n="3">³Université Paris Cité, Paris, France</footnote>

<footnote n="4">⁴Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, UK</footnote>

<footnote n="5">⁵Université Paris Est Créteil, INSERM, IMRB, Translational Neuropsychiatry, Créteil, France</footnote>

<footnote>This is an open access article under the terms of the [Creative Commons Attribution-NonCommercial-NoDerivs License](https://creativecommons.org/licenses/by-nc-nd/4.0/), which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

© 2024 The Author(s). *Journal of Sleep Research* published by John Wiley & Sons Ltd on behalf of European Sleep Research Society.</footnote>
(i.e., physical activity) over consecutive 24 h intervals (Ancoli-Israel
et al., 2003). Actigraphy estimates several parameters of physical
activity: the amplitude (i.e., the difference in activity levels between
the most and the least active periods over a 24 h period), the intra-
daily variability (i.e., the fragmentation of rest-activity periods within
days), the inter-daily stability (i.e., the regularity in day-to-day rest-
activity patterns), and the timing (i.e., the onsets of the most pro-
longed active and inactive periods during the 24 h cycle).

In bipolar disorders (BD), abnormalities of sleep patterns and cir-
cadian rhythms of activity have been described during acute episodes,
with most abnormalities persisting during euthymia or episode remis-
sion. Several meta-analyses of actigraphy studies confirm that euthy-
mic individuals with bipolar disorders display higher sleep
fragmentation, longer sleep onset latency, lower sleep efficiency,
alongside lower stability, higher variability, later peak, and lower
amplitude of circadian rhythms of activity, compared with healthy
controls (De Crescenzo et al., 2017).

Abnormalities of sleep patterns and circadian rhythms of activity
are – at least partly – determined by genetic factors, as shown consis-
tently by results from family studies, twin studies, or genome-wide
association studies. For instance, a genetic contribution to circadian
rhythms of activity and sleep patterns has been suggested in pedi-
grees segregating for bipolar disorders (Pagani et al., 2016). A meta-
analysis of twin studies has demonstrated that sleep duration and
sleep quality were genetically determined (respectively 46% and 44%
of the phenotypic variability) (Kocevska et al., 2021). Finally, a
genome-wide association study of 27 actigraphy-derived physical
activity measurements suggested that most of these phenotypes were
heritable (Qi et al., 2022).

There may be a shared genetic vulnerability between bipolar disor-
ders and abnormalities of sleep patterns and circadian rhythms of activ-
ity, a hypothesis that would also apply to major depressive disorders
(MDD). For example, Mendelian randomisation studies suggest bidirec-
tional causal links between mood disorders (both bipolar and unipolar
disorders) and sleep-related phenotypes (e.g., sleep duration and chron-
otype) (Crinion et al., 2024). Since genetic vulnerabilities to bipolar dis-
orders and MDD also overlap (Liu et al., 2023), it is relevant to take
both into account to explore how they might explain clinical observa-
tions in terms of sleep patterns and circadian rhythms of activity.

To date, polygenic risk scores for MDD and BD have been exclu-
sively studied in association with the clinical expression of bipolar dis-
orders (in terms of age at onset, psychotic symptoms, or disease
course, for instance) (Liu et al., 2023), but never in association with
actigraphy derived parameters. As such, this study is exploratory and
novel, and aims to enrich the current knowledge on how the genetic
susceptibility to mood disorders might be associated with some phe-
notypic characteristics of bipolar disorders, here in terms of sleep pat-
terns and circadian rhythms of activity.

We therefore hypothesised that the genetic vulnerability to bipo-
lar disorder and to major depressive disorder estimated by polygenic
risk scores would be associated with sleep patterns and circadian
rhythms of activity estimates using actigraphy in a sample of euthymic
adults with bipolar disorders.

2 | MATERIALS AND METHODS

This study is based on a subsample of the research protocol registered
on [ClinicalTrials.org](http://ClinicalTrials.org) (NCT02627404), that was approved by a French
ethics committee (see Ethics approval statement). All participants pro-
vided written informed consent.

2.1 | Sample characteristics

Participants were European adult out-patients diagnosed with bipolar
disorders type 1 or 2 (according to DSM-IV criteria). Participants were
euthymic at inclusion, that is, no hospitalisation, no mood recurrence,
nor medication modification (assessed by clinical interviews) for at
least 3 months before inclusion and scores lower than 8 both on the
Montgomery-Asberg Depression Rating Scale (MADRS) and
the Young Mania Rating Scale (YMRS). Exclusion criteria were: current
alcohol or substance use disorder (except tobacco); diagnosed sleep
disorder (sleep apnea, narcolepsy, etc.), and/or undertaking shift work
and/or any recent life event disrupting sleep patterns or circadian
rhythms of activity.

## 2.2 | Actigraphy recording

Participants were asked to wear continuously an actiwatch (AW-7
CamNtech) on their non-dominant wrist for 21 days and to press a
button each day when they went to sleep and got out of bed. An acti-
watch is a device resembling a wristwatch that contains an accelerom-
eter that detects, scores, and stores information about the intensity
and timing of wrist movements over consecutive 24 h intervals. For
this study, an epoch of 1 min (i.e., activity level is recorded every
minute) and an average sensitivity threshold was chosen. On day
21, data were extracted from the Actiwatch and standard actimetry
parameters were calculated using the Actiwatch Activity and Sleep
Analysis software (CamNtech 7.28). Table 1 describes actigraphy
variables.

2.3 | Polygenic risk scores

A polygenic risk score (PRS) is an estimate of an individual's genetic
liability to a disease, calculated according to the individual's genotype
profile and based on summary statistics of genome-wide association
studies data comparing cases and controls. The classical PRS calcula-
tion combines the effect sizes of multiple single nucleotide polymor-
phisms identified by genome-wide association studies into a single
aggregated score. A PRS is expected to reflect individuals' disease risk
(here for major depression or bipolar disorder), meaning that the PRS
for a given disorder is higher when individuals carry a greater number
of at-risk genetic variants (Choi et al., 2020).

Genomic DNA was isolated from venous blood sample using stan-
dard procedures. Genotyping was performed using the Infinium
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<table><caption>TABLE 1 Summary of available actigraphy estimates of sleep and circadian rhythms of activity and medians (IQR) in the studied sample.</caption><thead><tr><th>Actigraphy variable</th><th>Definition</th><th>Median</th><th>IQR</th></tr></thead><tbody><tr><td>Assumed sleep</td><td>Time between reported sleep onset and offset (minus WASO) (hours)</td><td>8.24</td><td>7.47 8.87</td></tr><tr><td>Sleep latency</td><td>Time between reported bedtime and sleep onset (minutes)</td><td>11.34</td><td>8.19 19.21</td></tr><tr><td>Sleep efficiency</td><td>Sleep duration divided by the total time spent in bed (%)</td><td>84.83</td><td>82.26 86.93</td></tr><tr><td>Wake after sleep onset (WASO)</td><td>Total time of nocturnal waking (minutes)</td><td>51.12</td><td>35.46 66.69</td></tr><tr><td>Fragmentation index (FI)</td><td>Amount of time associated with movement during the sleep period (%)</td><td>30.28</td><td>24.34 36.73</td></tr><tr><td>Mean activity score during sleep</td><td>Average count per active epoch during sleep</td><td>11.42</td><td>7.82 16.36</td></tr><tr><td>Inter-daily stability (IS)</td><td>Degree of regularity in day-to-day rest-activity patterns</td><td>0.47</td><td>0.36 0.55</td></tr><tr><td>Intra-daily variability (IV)</td><td>Degree of fragmentation of rest-activity periods within days</td><td>0.83</td><td>0.70 0.93</td></tr><tr><td>L5</td><td>Average activity across the least active 5 hour period during 24 h</td><td>873</td><td>636 1371</td></tr><tr><td>L5 onset</td><td>L5 start time (hours)</td><td>1</td><td>0 2</td></tr><tr><td>M10</td><td>Average activity level for the most active 10 hours period during 24 h</td><td>14,773</td><td>12,432 20,669</td></tr><tr><td>M10 onset</td><td>M10 start time (hours)</td><td>9</td><td>8 11</td></tr><tr><td>Amplitude</td><td>Difference in the amount of activity between the most active and least active periods</td><td>13,647</td><td>11,275 20,013</td></tr><tr><td>Relative amplitude</td><td>Amplitude divided by (M10 + L5)</td><td>0.89</td><td>0.84 0.92</td></tr></tbody></table>

Abbreviation: IQR, interquartile range.

Global Screening Arrays-24 v2.0 (Illumina Inc.), according to the manufacturer's protocol (~660,000 single-nucleotide polymorphisms). Quality control of genotypic data, family relationships, and ancestry were performed using the procedure described previously (Courtois et al., 2020). PRS were calculated for each individual using the PRSice software (v.2.0.15) (Euesden et al., 2015) and based on the Psychiatric Genomics Consortium summary statistics for bipolar disorders (Mullins et al., 2021) and major depressive disorders (Wray et al., 2018) in European individuals only. We restricted the analyses to the three most stringent PRS-BD and PRS-MDD (threshold *p* values of 0.00005, 0.0001, and 0.001) and did not use the more recently suggested Bayesian approaches for PRS calculations.

## 2.4 | Statistical analysis

Analyses were performed using SPSS. Continuous variables are presented with medians and interquartile range (IQR).

Given the redundancy and correlations in-between actigraphy parameters, we first undertook two principal component analyses (PCA) with an Oblimin rotation, including first only sleep estimates and then only circadian rhythms of activity estimates. We employed this method in prior studies to enable data reduction and the generation of sleep and circadian rhythms of activity dimensions (Ferrand et al., 2022). The Kaiser-Meyer-Olkin index (KMO) and Bartlett's test of sphericity measured PCA appropriateness. The number of extracted components relied on Eigenvalues >1. Factor loadings of >0.50 were considered significant.

Next, we undertook correlations between each PCA component, polygenic risk scores for depression and bipolar disorder, followed by linear regression analyses using each PCA component as a dependent

variable, polygenic risk scores for depression or for bipolar disorder as independent variables, with adjustments for sex, age, bipolar disorder subtype, BMI (body mass index), residual depressive symptoms (MADRS score), current tobacco use, and medications prescribed at inclusion. We applied a correction for multiple testing (*p* < 0.0016).

# 3 | RESULTS

## 3.1 | Sample characteristics

The initial sample included 109 participants, among which 62 had a blood sample. All were of European descent. Among these, 38 were females and 44 had bipolar disorder type 1. The median age at inclusion was 42 (IQR 33-54). The median age at bipolar disorder onset was 23 (IQR 19-30), with a median number of mood episodes of 6 (IQR 4-8). The median body mass index was 25 (IQR 23-28). The median MADRS score at inclusion was 2 (IQR 0-4). About half (47%) of the individuals were current smokers. Current psychotropic medications included lithium (63%), anticonvulsants (47%), atypical antipsychotics (31%), antidepressants (27%), and benzodiazepines, and/or hypnotics (8%). Table 1 shows actigraphy variables, their respective definitions, medians, and IQR.

## 3.2 | Principal component analyses

A first PCA included six actigraphy sleep estimates (KMO = 0.62; Bartlett's test of sphericity *p* < 0.001) and extracted two components. The first one was labelled "sleep fragmentation" (high wake after sleep onset, high fragmentation index, high mean active score during
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sleep, and low sleep efficiency) and explained 55% of the variance. The second one was labelled “good sleep quality” (long sleep duration and low sleep latency), and explained 20% of the variance.

A second PCA included eight actigraphy estimates of circadian rhythms of activity (KMO = 0.65; Bartlett's test of sphericity *p* < 0.001) and extracted three components. The first one was labelled “robustness of circadian rhythms of activity” (high M10, high amplitude, high inter-daily stability, and low intra-daily variability) and explained 41% of the variance. The second one was labelled “late chronotype” (late M10 onset and late L5 onset, meaning delayed peaks of active and inactive periods over the 24 h period) and explained 30% of the variance. The third one was labelled “high amplitude of circadian rhythms of activity” (low L5 and high relative amplitude) and explained 14% of the variance (see Tables S1 and S2 for details).

## 3.3 | Associations between actigraphy components, and polygenic risk scores for MDD and BD

Spearman correlations tests identified associations between the two most stringent polygenic risk scores for depression and the sleep fragmentation component, and between the two most stringent polygenic risk scores for bipolar disorder and the late chronotype component (Table 2). A higher polygenic risk score for depression was associated with a higher sleep fragmentation. A higher polygenic risk score for bipolar disorder was associated with a higher value on the late chronotype component (i.e., corresponding to a later peak of both active and inactive periods over the 24 h cycle).

Associations were no longer significant when the threshold for polygenic risk scores calculation was higher than *p* = 0.001. No

association was observed between polygenic risk scores and other components. No association resisted a correction for multiple testing. Of note, no correlation was observed between the polygenic risk scores for depression and bipolar disorder at the two lowest thresholds (*p* values between 0.21 and 0.77).

After adjustments for potential confounding factors in linear regressions, the associations between the most stringent polygenic risk scores for depression and sleep fragmentation and between the most stringent polygenic risk scores for bipolar disorder and late chronotype remained significant at *p* < 0.05 (*p* = 0.038 and *p* = 0.014, respectively) (see Tables 3 and 4). These associations did not resist a correction for multiple testing.

## 4 | DISCUSSION

This study is the first to explore associations between polygenic risk scores for mood disorders and actigraphy-based sleep patterns and circadian rhythms of activity in a clinical sample of euthymic individuals with bipolar disorders. We suggest that sleep fragmentation might be associated with a higher polygenic risk score for depression, while later peaks of circadian rhythms of activity (i.e., later M10 onset and L5 onset) might be associated with a higher polygenic risk score for bipolar disorder. These associations resisted adjustment for several potential confounding factors that also may influence sleep patterns and circadian rhythms of activity (including medications), although were not significant after correction for multiple testing in the relatively small sample used.

There is a widespread hypothesis in the literature suggesting that the abnormalities of sleep patterns and circadian rhythms of activity are a core vulnerability to bipolar disorder. This is supported by the fact that these abnormalities pre-exist the onset of bipolar disorder

<table><caption>TABLE 2 Spearman correlation tests between PRS-BD, PRS-MDD, and sleep and circadian rhythms components.</caption><thead><tr><th rowspan="2">Variable</th><th rowspan="2"></th><th colspan="2">Sleep components</th><th colspan="3">Circadian rhythms components</th></tr><tr><th>"Sleep fragmentation"</th><th>"Good sleep quality"</th><th>"Robustness"</th><th>"Late chronotype"</th><th>"High amplitude"</th></tr></thead><tbody><tr><td rowspan="2">PRS MDD (0.00005)</td><td>Rho</td><td>0.29</td><td>-0.04</td><td>-0.19</td><td>-0.09</td><td>-0.15</td></tr><tr><td>p</td><td>0.02</td><td>NS</td><td>NS</td><td>NS</td><td>NS</td></tr><tr><td rowspan="2">PRS MDD (0.0001)</td><td>Rho</td><td>0.31</td><td>0.07</td><td>-0.15</td><td>-0.14</td><td>-0.14</td></tr><tr><td>p</td><td>0.01</td><td>NS</td><td>NS</td><td>NS</td><td>NS</td></tr><tr><td rowspan="2">PRS MDD (0.001)</td><td>Rho</td><td>0.16</td><td>-0.12</td><td>-0.19</td><td>-0.15</td><td>-0.15</td></tr><tr><td>p</td><td>NS</td><td>NS</td><td>NS</td><td>NS</td><td>NS</td></tr><tr><td rowspan="2">PRS BD (0.00005)</td><td>Rho</td><td>-0.02</td><td>-0.16</td><td>-0.17</td><td>0.36</td><td>-0.04</td></tr><tr><td>p</td><td>NS</td><td>NS</td><td>NS</td><td>0.004</td><td>NS</td></tr><tr><td rowspan="2">PRS BD (0.0001)</td><td>Rho</td><td>-0.05</td><td>-0.21</td><td>-0.23</td><td>0.32</td><td>-0.05</td></tr><tr><td>p</td><td>NS</td><td>NS</td><td>NS</td><td>0.01</td><td>NS</td></tr><tr><td rowspan="2">PRS BD (0.001)</td><td>Rho</td><td>-0.001</td><td>0.006</td><td>-0.07</td><td>0.18</td><td>-0.04</td></tr><tr><td>p</td><td>NS</td><td>NS</td><td>NS</td><td>NS</td><td>NS</td></tr></tbody></table>

Note: In bold *p* values < 0.05 (no correction being applied).

Abbreviations: BD, bipolar disorder; MMD, major depressive disorder; NS, not statistically significant at *p* < 0.05; PRS, polygenic risk score.
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<table><caption>**TABLE 3** Multivariate analysis between PRS-MDD and sleep fragmentation.</caption><thead><tr><th>Variable</th><th>Beta</th><th>SE</th><th>t</th><th>p</th><th>Tolerance</th><th>VIF</th></tr></thead><tbody><tr><td>Intercept</td><td>0.33</td><td>1.301</td><td>0.254</td><td>0.801</td><td></td><td></td></tr><tr><td>PRS-MDD (0.00005)</td><td>191.457</td><td>90.012</td><td>2.127</td><td>0.038*</td><td>0.858</td><td>1.165</td></tr><tr><td>Sex</td><td>-0.392</td><td>0.270</td><td>-1.45</td><td>0.153</td><td>0.869</td><td>1.150</td></tr><tr><td>Age</td><td>-0.015</td><td>0.010</td><td>-1.514</td><td>0.136</td><td>0.819</td><td>1.222</td></tr><tr><td>BD type</td><td>0.297</td><td>0.347</td><td>0.855</td><td>0.396</td><td>0.607</td><td>1.648</td></tr><tr><td>BMI</td><td>0.029</td><td>0.032</td><td>0.905</td><td>0.370</td><td>0.835</td><td>1.198</td></tr><tr><td>MADRS</td><td>0.101</td><td>0.069</td><td>1.451</td><td>0.153</td><td>0.788</td><td>1.270</td></tr><tr><td>Current smoking</td><td>-0.036</td><td>0.272</td><td>-0.134</td><td>0.894</td><td>0.818</td><td>1.222</td></tr><tr><td>Lithium</td><td>0.315</td><td>0.326</td><td>0.967</td><td>0.338</td><td>0.608</td><td>1.645</td></tr><tr><td>Anticonvulsants</td><td>0.558</td><td>0.315</td><td>1.768</td><td>0.083</td><td>0.607</td><td>1.647</td></tr><tr><td>Antipsychotics</td><td>-0.212</td><td>0.286</td><td>-0.740</td><td>0.463</td><td>0.865</td><td>1.156</td></tr><tr><td>Antidepressants</td><td>0.027</td><td>0.337</td><td>0.081</td><td>0.936</td><td>0.665</td><td>1.504</td></tr><tr><td>Benzodiazepines</td><td>-0.929</td><td>0.494</td><td>-1.879</td><td>0.066</td><td>0.830</td><td>1.205</td></tr></tbody></table>

Note: Beta are not standardised. *Uncorrected *p* value.

Abbreviations: BD, bipolar disorder; BMI, body mass index; MADRS, Montgomery-Asberg depression rating scale; MMD, major depressive disorder; PRS, polygenic risk score; SE, standard error; VIF, variance inflation factor.

<table><caption>**TABLE 4** Multivariate analysis between PRS-BD and late chronotype.</caption><thead><tr><th>Variable</th><th>Beta</th><th>SE</th><th>t</th><th>p</th><th>Tolerance</th><th>VIF</th></tr></thead><tbody><tr><td>Intercept</td><td>3.917</td><td>1.404</td><td>2.789</td><td>0.008</td><td></td><td></td></tr><tr><td>PRS-BD (0.00005)</td><td>458.022</td><td>179.301</td><td>2.554</td><td>0.014*</td><td>0.902</td><td>1.108</td></tr><tr><td>Sex</td><td>-0.224</td><td>0.262</td><td>-0.852</td><td>0.398</td><td>0.870</td><td>1.150</td></tr><tr><td>Age</td><td>-0.020</td><td>0.010</td><td>-2.130</td><td>0.038*</td><td>0.826</td><td>1.211</td></tr><tr><td>BD type</td><td>0.039</td><td>0.325</td><td>0.121</td><td>0.904</td><td>0.652</td><td>1.535</td></tr><tr><td>BMI</td><td>-0.014</td><td>0.031</td><td>-0.436</td><td>0.665</td><td>0.824</td><td>1.213</td></tr><tr><td>MADRS</td><td>0.106</td><td>0.067</td><td>1.593</td><td>0.118</td><td>0.804</td><td>1.244</td></tr><tr><td>Current smoking</td><td>0.285</td><td>0.261</td><td>1.092</td><td>0.280</td><td>0.836</td><td>1.196</td></tr><tr><td>Lithium</td><td>-0.483</td><td>0.318</td><td>-1.522</td><td>0.135</td><td>0.603</td><td>1.659</td></tr><tr><td>Anticonvulsants</td><td>-0.535</td><td>0.306</td><td>-1.747</td><td>0.087</td><td>0.608</td><td>1.644</td></tr><tr><td>Antipsychotics</td><td>-0.165</td><td>0.281</td><td>-0.588</td><td>0.559</td><td>0.843</td><td>1.186</td></tr><tr><td>Antidepressants</td><td>-0.211</td><td>0.328</td><td>-0.642</td><td>0.524</td><td>0.663</td><td>1.508</td></tr><tr><td>Benzodiazepines</td><td>-0.394</td><td>0.481</td><td>-0.818</td><td>0.417</td><td>0.826</td><td>1.210</td></tr></tbody></table>

Note: Beta are not standardised. *Uncorrected *p* value.

Abbreviations: BD, bipolar disorder; BMI, body mass index; MADRS, Montgomery-Asberg depression rating scale; MMD, major depressive disorder; PRS, polygenic risk score; SE, standard error; VIF, variance inflation factor.

(Scott, Etain, et al., 2022), persist during euthymia (De Crescenzo et al., 2017), can predict outcomes of the disorder (Scott et al., 2020) and might be “reversed” in individuals who respond to lithium (Scott, Hennion, et al., 2022). Since bipolar disorder, major depression and abnormalities of sleep patterns and circadian rhythms of activity are both heritable, a between-phenotypes shared vulnerability has been hypothesised. As mentioned in the introduction, this has been explored by Mendelian randomisation studies (Crinion et al., 2024) demonstrating links between bipolar disorder or major depression to chronotype, sleep duration, or sleep disorders. We provided here an additional argument in favour of this hypothesis with the association between a higher polygenic risk score for depression and a higher sleep fragmentation, and with the association between a higher

polygenic risk score for bipolar disorder and a later peak of circadian rhythms of activity (i.e., later starts of the most active and the most inactive periods during the 24 h cycle). Polygenic risk scores for depression and bipolar disorder might also influence the clinical expression of bipolar disorders (e.g., BD subtype, age at onset, course, etc) (van Loo et al., 2023), that in turn modify the observed sleep patterns and circadian rhythms of activity, this hypothesis has not been explored in this study.

The findings should be considered as preliminary because of the relatively small, selected convenience sample of individuals and the findings no longer met statistical significance when analyses were corrected for multiple testing. The sample size of 62 participants has a power of 0.80 to identify a Spearman correlation coefficient above
0.36 with an alpha = 0.05, therefore not excluding a lack of power in the analysis. Nevertheless, it is worth highlighting that whilst this study is small for polygenic risk score analyses, the sample size would be deemed large for studies of actigraphy in mood disorders. Furthermore, we assessed participants some years after the onset of the disorder, and sleep patterns and circadian rhythms of activity might have been modified by the course of the disorder, by past and currently prescribed medications (Hennion et al., 2024) (which does not seem to be the case here for medications at inclusion) and other psychosocial factors including the loss of social entrainment (in those who were no longer employed) or alternatively, by the use of psychological interventions that aim at regulating sleep or circadian rhythms of activity. As well as trying to replicate the findings in independent samples of individuals with bipolar disorder, it is increasingly important to compare individuals with bipolar disorder with those with other psychiatric disorders, such as anxiety, major depression or psychosis, to examine whether there is any specificity to the sleep patterns and/or circadian rhythms of activity associated with bipolar disorder. Finally, since no control group with calculated polygenic risk scores and actigraphy recordings was available for this study, we were not able to conclude whether the observed associations were specific to individuals with bipolar disorder, or would also be observed (possibly at a lower magnitude) among controls.

## 5 | CONCLUSION

We suggest that the genetic vulnerability to bipolar disorder and to major depression, as assessed by polygenic risk scores, might differently influence the phenotypic expression in terms of sleep and circadian rhythms of activity estimated by actigraphy in euthymic individuals with bipolar disorder. The genetic vulnerability to major depression might influence sleep fragmentation, while the genetic vulnerability to bipolar disorder might influence the timing of circadian rhythms of activity. If confirmed in independent samples, the findings indicate that studies exploring polygenic risk scores alongside objective, ecological recordings of sleep patterns and circadian rhythms of activity can provide additional insights regarding genetic vulnerabilities and sleep-wake cycles that extend beyond evidence accrued from studies that rely on self-report of sleep profiles.

## ACKNOWLEDGEMENTS

We express our thanks to the individuals who participated to the present study. We thank the clinicians and nursing staff of the Center for Expertise in bipolar disorders for the recruitment and clinical characterisation of individuals with bipolar disorders. We acknowledge V. Benard, H. Brochard, C. Boudebesse, M. Charron, L. Ferrand, P.A. Geoffroy, G. Gross, J. Maruani, M. Meyrel, and S. Yeim.

## FUNDING INFORMATION

This research was funded by Institut National de la Santé et de la Recherche Médicale (INSERM), and Assistance Publique des Hôpitaux de Paris (AP-HP). This work has also been funded by the

Investissements d'Avenir programme managed by the Agence Nationale pour la Recherche (ANR) under reference ANR-11-IDEX-0004-02 (Labex BioPsy). 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.

## CONFLICT OF INTEREST STATEMENT

B.E. has received honoraria for consulting from Sanofi. F.B. is an advisor on mental health to the French government. All other authors have no conflict of interest regarding this work.

## DATA AVAILABILITY STATEMENT

The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions.

## ORCID

Bruno Etain https://orcid.org/0000-0002-5377-1488

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## SUPPORTING INFORMATION

Additional supporting information can be found online in the Supporting Information section at the end of this article.

**How to cite this article:** Hennion, V., Scott, J., Martinot, V., Godin, O., Marie-Claire, C., Bellivier, F., Jamain, S., & Etain, B. (2024). Polygenic risk scores for mood disorders and actigraphy estimates of sleep and circadian rhythms: A preliminary study in bipolar disorders. *Journal of Sleep Research*, e14307. https://doi.org/10.1111/jsr.14307