Introduction
Depression, a leading global burden of disease contributor [Reference Vollset, Ababneh, Abate, Abbafati, Abbasgholizadeh and Abbasian1] with 20% lifetime prevalence, affects twice as many women as men [Reference Salk, Hyde and Abramson2]. An initial epidemiological meta-analysis [Reference Sullivan, Neale and Kendler3] suggested an absence of gender differences in the heritability for depression, though a recently updated review showed that the specific behavioural genetics methods used determine whether or not such differences are found [Reference Zhao, Han, Zhao, Jin, Ge and Yang4]. Given the disorder’s high heterogeneity, little evidence exists of single genetic or environmental causes explaining all depressive symptoms [Reference Fried, Flake and Robinaugh5]. Genes certainly contribute to these causes, as monozygotic twins show higher concordance rates for depression diagnosis [Reference Sullivan, Neale and Kendler3], while environmental factors are critical for triggering its polygenic liability [Reference Gariépy, Honkaniemi and Quesnel-Vallée6, Reference Mullins, Power, Fisher, Hanscombe, Euesden and Iniesta7]; namely the inherited predisposition to develop depression that is conferred by the joined action of several “vulnerability” genes. Besides gene–environment interactions, multiple intertwined aetiologies can explain the gender differences, including hormones, genetically determined physiological stress responsiveness, and gender-associated environmental stress exposure [Reference Kuehner8]. Assessing concurrently these factors to determine their individual contributions is complex; however, it is generally accepted that they are directly expressed in people’s cognitive abilities and daily functioning, which in turn are significant predictors of depression [Reference Simons, Jacobs, Derom, Thiery, Jolles and Van Os9, Reference Vicent-Gil, Keymer-Gausset, Serra-Blasco, Carceller-Sindreu, De Diego-Adeliño and Trujols10].
Cognitive dysfunction is a recognised feature of depression [Reference Rock, Roiser, Riedel and Blackwell11], both during an acute episode and following remission [Reference Semkovska, Quinlivan, O’Grady, Johnson, Collins and O’Connor12]. Individuals presenting with depressive symptoms engage less in physical, social, and intellectual leisure activities [Reference Sharifian, Gu, Manly, Schupf, Mayeux and Brickman13], while participation in leisure activities is consistently associated with better cognitive function and a lower risk of cognitive decline [Reference Ihle, Fagot, Vallet, Ballhausen, Mella and Baeriswyl14–Reference Lifshitz-Vahav, Shrira and Bodner18]. Moreover, gender differences exist not only in depression prevalence but also in leisure activity participation and in some cognitive functions. Socially, women tend to establish quicker stronger cooperation with others, while mens cooperation levels increase progressively as the activity develops [Reference Peshkovskaya, Myagkov, Babkina and Lukinova19]. Women engage less than men in physical activities [Reference Guthold, Stevens, Riley and Bull20] and face more barriers to exercise [Reference Vasudevan and Ford21]. Traditional female-gender role responsibilities, such as childcare and domestic chores, impact negatively participation in physical exercise, and make prioritising one’s health more challenging [Reference Verhoef, Love and Rose22–Reference Brown, Brown, Miller and Hansen24]. Gender differences in cognitive function are generally non-significant, with the notable exceptions of women outperforming men in verbal fluency, and men outperforming women in 3D mental rotation [Reference Jäncke25].
Previous studies have rarely explored concomitantly the complex interactions among symptoms, cognitive functions, and leisure activities, or evaluated the unique contribution of each depression risk factor independently from its shared associations with other predictors. One study [Reference Sharifian, Gu, Manly, Schupf, Mayeux and Brickman13] employed structural equation modelling to assess these interrelationships concurrently but aggregated multiple variables into single measures for each of the studied categories. Similarly, another study [Reference Sharifian, Sol, Zaheed, Morris, Palms and Martino26] totalled the performance of five different cognitive domains into a single factor, despite their distinct associations with depressive symptoms. For example, executive function correlates strongly with fatigue, but not with other depressive symptoms (e.g., indecisiveness, appetite changes) [Reference Kraft, Bø, Jonassen, Heeren, Ulset and Stiles27], while loneliness is associated with memory but not with orientation [Reference Sun, Chen, Bai, Zhang, Sha and Su28]. Reducing distinct phenomena to merged single factors prevents the identification of independent interrelations between specific symptoms and specific risk factors. Moreover, neither study examined the effect of gender on those complex associations, despite known gender differences in most of the studied variables.
Conceptualising the interactions between depressive symptoms and risk factors as a network allows us to consider each element’s individual contribution to the entire system without resorting to the oversimplification of summing up different factors into one. Specifically, when constructing a network where each depressive symptom and risk factor represents a network node, the links (network edges) between each pair of nodes can be studied while accounting for the remaining associations. Traditional models assuming that depressive symptoms are an interchangeable representation of a common cause [Reference Fried, Flake and Robinaugh5], or that cognitive functions stem from a latent single factor are currently challenged [Reference Knyspel and Plomin29]. Conversely, network theory allows to analyse of the complexity of specific interactions among individual symptoms and various risk factors, revealing patterns and connections that cannot be distinguished with traditional methods [Reference Cramer, Van Borkulo, Giltay, Van Der Maas, Kendler and Scheffer30].
Recently, twin data have been modeled using network analyses to explore the heritability of cognitive abilities [Reference Knyspel and Plomin29], anxiety [Reference Olatunji, Christian, Strachan and Levinson31], and depression [Reference Moroń, Mengel-From, Zhang, Hjelmborg and Semkovska32], but again, known differences between women and men were not considered. We aimed to evaluate the gender effects on extended networks of depressive symptoms, cognitive functions, and three types of leisure activities (intellectual, physical, and social) using like-sex monozygotic (MZ) and dizygotic (DZ) twin pairs. This method allows, with astrong control for shared genetic liability for depression, to determine the most influential (central) nodes in a network and how symptoms and risk factors may interact to possibly explain the gender differences in depression expression. Two variables known to significantly influence both depressive symptoms and cognitive function – namely, age [Reference Stordal, Mykletun and Dahl33, Reference Mirowsky and Ross34] and alcohol consumption [Reference Sullivan, Fiellin and O’Connor35] – were used as covariates. Specifically, the study first examined the differences between the two members of a twin pair within sub-groups defined by gender and zygosity (i.e., MZ women, DZ women, MZ men, DZ men). Secondly, the differences between MZ and DZ networks were assessed within each gender group. Finally, the extended networks of depressive symptoms of women and men were directly compared.
Methods
Study population
Participants were collected from two cohort studies of the Danish Twin Registry, the Middle Age Danish Twins (MADT) 2008 survey, and the MIddle Age Danish Twins (MIDT) 2008–2011 survey years [Reference Pedersen, Larsen, Nygaard, Mengel-From, McGue and Dalgård36]. They assessed, respectively, 2,400 and 10,276 Danish twins born between 1931 and 1969. Participants self-reported their gender as part of the demographic data collection. Both studies utilised identical measures for depressive symptoms, cognitive functions, and leisure activities [Reference Pedersen, Larsen, Nygaard, Mengel-From, McGue and Dalgård36]. The following inclusion criteria were applied: (a) both twins of a pair participated, (b) the pair was like-sex (same gender), and (c) the pair’s zygosity was clearly determined.
Measures
The standardised battery of the MADT and MIDT studies [Reference Pedersen, Larsen, Nygaard, Mengel-From, McGue and Dalgård36] included nine depressive symptoms from the Cambridge Mental Disorders of the Elderly Examination [Reference Roth, Tym, Mountjoy, Huppert, Hendrie and Verma37], six neuropsychological tests to estimate cognitive functions, and a scale measuring the frequency of engagement in three types of leisure activities, eight intellectual, six physical and eight social. Age and alcohol consumption were included as covariates due to their established associations with other variables. See Supplementary Table S1 for all measures’ scoring methods and corresponding node names. Descriptive statistics were computed separately for women and men. Categorical variables (e.g., zygosity, education, marital status, and work status) were analysed using Chi-square tests (χ2). Independent samples t-tests were conducted to compare between genders for continuous variables.
Network analysis
Network estimation. Gaussian Graphical Models (GGMs) with graphical Least Absolute Shrinkage and Selection Operator (Glasso) regularisation were used to estimate the undirected networks of the six nodes’ categories – 9 depressive symptoms, 6 cognitive functions, 8 intellectual activities, 6 physical activities, 8 social activities, and 2 covariates. Glasso was applied with Extended Bayesian Information Criterion (EBIC) for model selection [Reference Epskamp and Fried38]. This method estimates 100 models with varying levels of sparsity. The final model is selected based on the lowest EBIC value, determined by hyperparameter gamma (γ), we set at 0.5 to minimise the risk of including spurious edges. To meet the GGM normality requirement, while optimising the original data preservation, nonparanormal transformations were used to normalise variables with absolute skew values >1. Networks were drawn by Cytoscape 3.10.2.
Network centrality. For each node, centrality measures for strength and expected influence (EI) were calculated with standardised z-scores for each independent network. Strength represents the sum of absolute values of the node’s connections to neighboring nodes, while EI refers to the net sum, accounting for positive and negative values [Reference Epskamp, Borsboom and Fried39].
The network stability was quantified with the correlation stability coefficient (CS-coefficient). It represents the maximum number of cases that can be dropped to retain a centrality correlation of at least 0.7. CS-coefficient values above 0.25 indicate stable networks, although traditionally, values above 0.5 are preferable. Edge accuracy was estimated with bootstrapped 95% confidence intervals (CIs), with narrower CIs suggesting more reliable networks. Bootstrapped difference tests were also performed to evaluate if centrality and edge-weights were stable [Reference Epskamp and Fried38].
The network comparison test (NCT) with 1000 permutations [Reference Van Borkulo, Van Bork, Boschloo, Kossakowski, Tio and Schoevers40] was used for all between-networks comparisons, using indices of global strength invariance (S) and maximum difference (M) of network invariance. Firstly, differences between co-twin pairs were examined within each of the following sub-groups obtained by crossing gender with zygosity: MZ women, DZ women, MZ men, and DZ men. To mitigate randomness due to arbitrary assignment, we reassigned each of these datasets 1,000 times and obtained two averaged networks representing the independent networks of each co-twin [Reference Moroń, Mengel-From, Zhang, Hjelmborg and Semkovska32]. For every reassignment iteration, the NCT evaluated differences in global strength and structure with S and M indices. The 1,000 p-values distribution of each index was used to identify significant differences among the reassignments. Secondly, MZ and DZ networks were compared within each gender group. Finally, overall differences between the two genders were assessed.
Results
Participants
Applying the inclusion criteria to the merged MADT and MIDT databases led to the identification of 1,876 twin pairs (N = 3,752), consisting of 2,040 women (918 MZ and 1,122 DZ) and 1,712 men (730 MZ and 982 DZ). Table 1 presents the socio-demographic characteristics of the sample. Figure 1 illustrates the three-level sample’s subdivisions as required by the network analysis design.
Descriptive statistics of the sample and between-gender differences
Note: *p < 0.05, **p < 0.01, ***p < 0.001.
a Controlled for age and alcohol consumption.
b Age range: women, 40.28–79.53; men, 40.29–79.93.
c Reverse coded.
Diagram of the network comparisons conducted at three levels of analysis: (1) between co-twins of the same zygosity, (2) between zygosity types, and (3) between genders.
Network comparisons of co-twins within gender by zygosity sub-groups
Networks were constructed and compared between co-twins within each zygosity-gender pair. All networks were stable. After the 1,000 reassignments, the p-values for the between-co-twins comparisons did not show any significant differences across all four groups in terms of global strength and network invariance, with all p-values >0.94, as shown in Supplementary Table S2. For MZ/DZ women, and MZ/DZ men, see Supplementary Figures S1, S7, S13, S19 for networks, Figures S2, S8, S14, S20 for centrality, Figures S3–S6, S9–S12, S15–S18, S20–S24, and Table S3 for stability.
Network comparisons of MZ and DZ within each gender
After confirming the absence of significant differences between co-twins, sub-groups were merged to compare MZ to DZ networks within each gender, i.e., MZ women vs DZ women and MZ men vs DZ men. All networks were stable. In women, MZ and DZ networks were comparable in both global strength (S = 1.82, p = 0.210) and structure (M = 0.11, p = 0.483). Similarly, in men, MZ and DZ networks did not differ significantly neither in global strength (S = 1.11, p = 0.441), nor in structure (M = 0.16, p = 0.227), as shown in Supplementary Figure S27, S34 and Table S4. For MZ and DZ in each gender, see Supplementary Figures S25, S32 for networks, S26 and S33 for centrality, S28-S31, S35-S38 and Table S5 for stability.
Network comparisons between genders
Networks’ stability. Given the lack of significant zygosity effects, the MZ and DZ samples were further merged to construct two networks for women and men (Figure 2). The networks were highly stable, with CS-coefficient values of 0.75 (see Supplementary Figures S39–S42 and Table S6).
Women’s and men’s GGM networks of depressive symptoms, cognitive functions, frequency of leisure activities, and covariates. Green lines represent positive partial correlations, and red lines represent negative partial correlations.
Nodes’ centrality and edges. See full results in Supplementary Figure S43 and Tables S7–S10. The gendered networks showed similar patterns in nodes’ centrality which reflects the importance of nodes (Supplementary Figure S43). For both, the same depressive symptoms, Sad (strengthwomen = 1.08, EIwomen = 1.03; strengthmen = 1.08, EImen = 1.07) and HappyNow (strengthwomen = 1.03, EIwomen = 0.86; strengthmen = 0.92, EImen = 0.79), and the same physical activity Exercised (strengthwomen = 1.05, EIwomen = 0.86; strengthmen = 1.02, EImen = 0.97), were among the top five most influential nodes. The latter also included intellectual leisure activities, that was going to the Museum (strength = 1.01; EI = 1.01) for women, or to the Library (strength = 0.99; EI = 0.81), and to Courses (strength = 0.96; EI = 0.94) for men. The most central cognitive function was DelayedRecall (strength = 1.04; EI = 0.64) for women and WorkingMemory (strength = 0.86; EI = 0.83) for men.
The two genders showed similar edges with the top five absolute partial correlation values. These concerned cognitive functions – Learning-DelayedRecall (pr women = 0.55; pr men = 0.45), WorkMemo-AuditAtt (pr women = 0.33; pr men = 0.30), social activities – MeetTwin-PhoneTwin (pr women = 0.53; pr men = 0.54), Dinner-FriendsDinner (pr women = 0.33; pr men = 0.35), and physical leisure activities, where the edge with the highest value was Yoga-Exercised for women (pr = 0.33), and Sport-Exercised for men (pr = 0.42). All these correlations were positive, indicating strong connectivity within the corresponding risk factor’s individual nodes.
NCT. Figure 3 showed that the gendered networks were significantly different in both global strength (S = 1.87, p = 0.022) and structure (M = 0.14, p = 0.009). Table 2 lists all nodes with significantly different between-gender centrality values. All indicated stronger connectivity of the corresponding nodes in women.
The network comparison test’s distribution of the 1,000 permutations of global strength and maximum difference indices in women and men. The red marker indicates the observed test statistic within the permutation test, highlighting its position to assess statistical significance.
Nodes with significant differences in strength centrality between women and men
Note. *p < 0.05, **p < 0.01, ***p < 0.001.
When examining the edges connecting depressive symptoms with the remaining network nodes, 10 were found to be significant in women and only one in men. Table 3 details all edges involving depressive symptoms with significant between-gender differences. Non-significant edges within each gendered network are listed in Supplementary Tables S11–S12. Figure 1c and d visualize the gendered sub-networks centred on depressive symptoms and related risk factors to better illustrate where the core differences between women and men were concentrated. Two of these edges with significant differences concerned positive correlations between depressive symptoms. Specifically, women showed significantly stronger associations for Lonely-WorthNothing (pr women = 0.151, pr men = 0.065; p = 0.030) and for Tense-Outlook (pr women = 0.118, pr men = 0.019; p = 0.009). The remaining results concerned significantly stronger negative edges linking depressive symptoms with either cognitive functions or leisure activities. The only exception was the positive association WriteStory-HappyNow. Except Outlook-Courses, all these edges linking depressive symptoms with risk factors were null in men.
Between-gender comparisons on edges with significant differences involving at least one depressive symptom
Note. pr , partial correlation.
*p < 0.05, **p < 0.01, ***p < 0.001.
Discussion
The study aimed to evaluate gender differences within and between like-sex co-twins. The observed networks were stable and without significant differences when co-twins were compared to each other, regardless of the pair’s zygosity or gender. Given these similarities, samples were pooled so to compare MZ to DZ women, and MZ to DZ men, leading to non-significantly different networks. However, when comparing the gendered networks (all women vs all men), significant differences in global strength, global structure, and local structure emerged. Specifically, women’s networks were denser (more interconnected) and showed significantly stronger associations both within depressive symptoms and between depressive symptoms and risk factors (i.e., cognition and leisure activities). Traditional models compare MZ and DZ twins to determine the relative contribution of genetic and environmental factors to the observed differences in variances/covariance within twin pairs [Reference Rijsdijk41]. Similarly, the network comparison test evaluates the overall differences in interconnectivity between co-twins first, and between MZ and DZ twins second, in order to examine these contributions at the network level [Reference Moroń, Mengel-From, Zhang, Hjelmborg and Semkovska32]. Previous research using network analyses in MZ and DZ twins has not considered gender effects [Reference Knyspel and Plomin29, Reference Olatunji, Christian, Strachan and Levinson31, Reference Moroń, Mengel-From, Zhang, Hjelmborg and Semkovska32] and focused on within-symptoms [Reference Olatunji, Christian, Strachan and Levinson31] or within-cognitive functions [Reference Knyspel and Plomin29] analyses. Overall, our results suggest that between-gender differences in the extended networks of depressive symptoms and the studied risk factors may be predominantly environmentally determined.
The gendered networks’ comparison revealed several significant differences. Among depressive symptoms, worthlessness and subjective tension were more central in women. Edges worthlessness-loneliness and pessimistic outlook-subjective tension were stronger in women. These results are consistent with both the established higher prevalence of depressive symptoms in women [Reference Salk, Hyde and Abramson2, Reference Kuehner8] and the network theory of psychopathology, postulating that more densely connected symptom networks are associated with a stronger predisposition to depression [Reference Cramer, Van Borkulo, Giltay, Van Der Maas, Kendler and Scheffer30].
Moreover, the women’s networks showed significantly stronger negative associations between depressive symptoms and physical or social leisure activities, including worthlessness - frequency of calling family/friends, worthlessness – frequency of visiting family/friends for dinner, and subjective tension – strenuous sports engagement. Subjective tension often indicates anxiety, which is closely related to a pessimistic view of the future [Reference Altemus, Sarvaiya and Neill Epperson42]. This relationship is further evidenced by women being more likely to engage in rumination, a cognitive process that can perpetuate negative thinking and hinder the ability to maintain a positive outlook [Reference Altemus, Sarvaiya and Neill Epperson42]. Socialisation and cultural expectations play a significant role in shaping how genders perceive their futures. Women are often socialised to prioritise the quality of relationships and emotional connectedness, which, can lead to feelings of worthlessness and pessimism regarding their future outlook [Reference Weisberg, DeYoung and Hirsh43]. This is further supported by the higher links’ strength of worthlessness-loneliness, frequency of feeling happy - frequency of calling family/friends, and sadness-associations in women relative to men. Research indicates that women exhibit greater emotional awareness and regulation skills, which can lead to a more nuanced view of their social world and own future [Reference Sweeny and Andrews44]. However, this emotional intelligence may also result in heightened sensitivity to relationships’ quality and life’s uncertainties, potentially contributing to a more pessimistic outlook [Reference Sweeny and Andrews44]. Social and cultural factors, including stereotyped expectations, could also underlie the stronger association between subjective tension-strenuous sports engagement in women. Body image concerns and traditional female domestic responsibilities can increase tension and impede physical activity prioritization [Reference Vasudevan and Ford21], while the latter can prevent tension release.
Other connections linking depressive symptoms and leisure activities that showed significantly stronger, negative correlations in women relative to the absence of such correlations (zero values) in men, involved pessimistic outlook - frequency of biking, and current unhappiness, with intellectual activities. Regardless of gender, depression is linked to lower participation in physical and other leisure activities [Reference Sharifian, Gu, Manly, Schupf, Mayeux and Brickman13]. Moreover, women and men differ in how much they engage in physical activities, with women reporting more obstacles to exercising and less control over their exercise choices [Reference Vasudevan and Ford21]. Interestingly, the only association that was significantly stronger in men concerned higher pessimistic outlook with lower engagement with courses. These results suggest that engaging in physical, intellectual, or social activities may be associated with fewer depressive symptoms, especially in women. However, our cross-sectional design precludes determining if this engagement directly intervenes in reducing depression. Nevertheless, the value of participation in leisure activities relative to depressive symptoms appears particularly relevant for women. Studies suggest that women may derive greater emotional and psychological benefits from social interactions and recreational activities, which could be due to the relational nature of women’s socialisation [Reference Ryba and Hopko45]. Gender differences in coping strategies also shape how individuals respond to stressors. Research indicates that women are more likely to engage in emotion-focused coping strategies, including seeking social support and participating in activities that promote emotional well-being [Reference Ryba and Hopko45]. This difference in coping styles might explain the reasons behind the stronger associations between leisure activities’ engagement and depressive symptoms in women relative to men.
Within both women’s and men’s networks, cognitive functions ranked among the most central elements and were strongly interconnected. However, few between-gender differences involved cognition. Specifically, delayed verbal memory was more central in women, whereas pessimistic outlook was more strongly connected negatively to both delayed verbal memory and verbal fluency in women. The associations between depressive symptoms and cognitive functions are known to be bidirectional [Reference Semkovska46], but in midlife, cognition does not predict future depressive symptoms, while depressive symptoms do predict lower future cognitive function, especially memory [Reference Semkovska46, Reference Hopper, Grady, Best and Stinchcombe47]. Moreover, longitudinal research indicates that baseline memory is not associated with depression at follow-up in women aged over 65 [Reference Hopper, Grady, Best and Stinchcombe47]. Thus, our results may suggest that, in women, a pessimistic outlook could be the key bridge node through which depressive symptoms exercise their deleterious effect over long-term memory. However, this hypothesis needs to be verified by longitudinal research. Importantly, leisure activities appear overall more central to the extended networks of depressive symptoms than cognitive functions. Withdrawal from leisure activities is well-documented in depressed individuals [Reference Fancourt and Tymoszuk48]. Despite being cross-sectional, our findings allow to speculate that this association may emerge prior to a depression diagnosis, and be more prominent in women, supporting the hypothesis that intellectual, physical, and social leisure activities may serve as protective factors against depression [Reference Fancourt and Tymoszuk48, Reference Bone, Bu, Fluharty, Paul, Sonke and Fancourt49]. Nevertheless, future longitudinal studies are necessary to determine if such causal relationships exist.
The study has the following limitations. As research participants were middle-aged Danish twins, results are not necessarily generalisable to other ethnicities, age-groups, or broader populations. Moreover, contemporaneous networks based on GGMs do not possess causal inference capabilities. Therefore, significant associations should be interpreted with caution. The HardWork item primarily referred to family/work hard physical duties rather than leisure; nonetheless, we have retained it within the physical activities’ category to determine overall physical engagement. This inclusion did not significantly affect the results, as HardWork remained in the periphery of all networks. In future research, methodology such as longitudinal network analysis coupled with Bayesian network reasoning could be used to evaluate the directionality in observed significant edges, and thus further clarify gender differences. Our study did not evaluate the specific genetic, shared, and nonshared environmental portions of the studied variables’ variance. Future research could therefore apply traditional twin-design structural equation modelling (SEM) [Reference Rijsdijk41], alongside the network analyses, to determine the exact genetic and environmental contributions to the observed results. Our study did not include an assessment of lifestyle factors, which encompass a broad range of daily behaviours, such as smoking, substance consumption, and nutritional intake [Reference Anderson, Kurz, Szabo, McGuire and Frankfurt50, Reference Scariot, Garbuio, Pelosi, Pedroso, Silva and Berigo51]. Both leisure and lifestyle factors represent essential aspects of daily life, and can act as risk factors for depression. Network models of the relationship between lifestyle factors and depression have already been studied [Reference Anderson, Kurz, Szabo, McGuire and Frankfurt50–Reference Wang, Xu, Gao, Tan, Zheng and Hou52]. Future research should integrate both lifestyle factors and leisure activities for a more thorough assessment of their combined impact on depression.
Combining network analyses with a twin design enables the evaluation of the independent associations between depressive symptoms and risk factors, and the study of how gender may affect these relationships. This approach contributes towards current intervention science efforts towards the identification of the diverse mechanisms leading to depression, and the associated personalised targets for prevention [Reference Ciarrochi53]. Although the present novel findings require replication in other representative samples, they do indicate that personalised prevention should take gender into consideration and integrate the promotion of relevant leisure activities.
Supplementary material
The supplementary material for this article can be found at http://doi.org/10.1192/j.eurpsy.2025.31.
Data availability statement
In compliance with Danish and EU regulations, the transfer and dissemination of individual-level data from Danish registries require prior authorization from the Danish Data Protection Agency. Current local data protection policies prohibit the sharing of individual-level data in public databases. Data request can be directed through https://www.sdu.dk/en/forskning/dtr. Data access has been purchased from the Danish Twin Registry.
Acknowledgements
The authors are thankful to Jakob Mortensen and Lars Hvidberg from the Danish Twin Research Center for their technical support with server maintenance, software version management, and ensuring the stability of the computational infrastructure necessary for this research, and to Lisbeth Aagaard Larsen for advice on legal and ethical registry data management.
Financial support
This work was supported by a Lundbeck Foundation Ascending Investigator Award 2022 to Maria Semkovska, which had no further role in the study design, data collection, analyses, report writing or the decision to submit this paper for publication.
Competing interests
Both authors report no financial relationships with commercial interests.
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