Educational attainment polygenic risk score and symptom severity change after Internet-delivered cognitive behaviour therapy for depression and anxiety

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Therapist-guided, Internet-delivered cognitive behaviour therapy (ICBT) is an established treatment for depression and anxiety, but a considerable proportion of treated patients do not achieve sufficient improvement. Predicting symptom change from clinical variables alone is challenging. Genetic data could potentially add predictive power and help us understand who will benefit most from ICBT. We conducted a study including 2668 adults (62% women, mean age 35.6 years) from the Swedish MULTI-PSYCH cohort to investigate the association between polygenic risk scores (PRS) from eight psychiatric and cognitive phenotypes and symptom change after ICBT. All participants had been diagnosed with depression, panic disorder or social anxiety disorder and treated with ICBT. The primary clinical outcome was a harmonised score across three different diagnosis-specific symptom rating instruments and measured weekly throughout ICBT. PRS were computed for attention-deficit/hyperactivity disorder, autism spectrum disorder, bipolar disorder, major depressive disorder and schizophrenia, cross-disorder psychopathology, educational attainment, and intelligence, using large discovery data sets. Linear mixed-effects models identified a significant association between the PRS for educational attainment (PRS-EDU) and symptom change (B = -0.73, p = .03), suggesting that a higher PRS-EDU was associated with lower symptom severity. This remained significant after additional covariate adjustment. No other PRS were significant. In the adjusted model, there was a significant PRS-EDU*time interaction, indicating that PRS-EDU also influenced the symptom change rate during treatment. When excluding outliers, the interaction effect was significant in both the crude and adjusted model. While these results await replication, they could have important implications for how the ICBT could be adapted to suit a wider portion of the population. Health sciences/Diseases/Psychiatric disorders/Depression Health sciences/Biomarkers/Predictive markers depression anxiety symptom severity polygenic risk score ICBT Introduction Internet-delivered cognitive behaviour therapy (ICBT) is an effective treatment for anxiety and depressive disorders, with effect sizes comparable to those of in-person CBT( 1 ). However, a substantial proportion of patients do not respond sufficiently to treatment( 2 , 3 ) Considering that depressive and anxiety disorders are among the top ten diseases with the highest global burden( 4 ), the impact of psychotherapeutic non-response is substantial, affecting healthcare utilisation, societal costs and overall public health. There is consequently a need to identify predictors of treatment outcome to inform clinical decision-making and allow for better-tailored intervention for these patients. Earlier studies have found a host of predictors for symptom change; higher or lower baseline symptom severity( 3 , 5 – 8 ), sex( 3 ), education level( 5 ) and psychiatric comorbidity( 3 , 9 ). A recent umbrella review of transdiagnostic predictors in different psychiatric treatments found a number of variables that predict remission and relapse, including depressive symptoms, anxiety symptoms, quality of life, negative life events, functioning, marital status, early access/intervention, age and comorbid physical symptoms/disorders, but could not find predictors for recovery( 9 ). However, the predictive power of these indicators remains insufficient, and there is a need to search for other possible predictors to reach a level of predictive acuity that is acceptable for use to support clinical decision-making, and tailored intervention. Using genetic information for prediction has recently emerged as a complement to clinical and demographic variables. Decades of twin, adoption and other family-based studies have established that mental disorders aggregate in families and are substantially heritable( 10 ). The largest (n cases = 371,184) genome-wide association study (GWAS) of depression identified 243 risk loci associated with the disorder( 11 ). For anxiety, a handful of loci have been found, likely because the largest GWAS was relatively small (n cases = 25453, ( 12 , 13 ). All psychiatric disorders are polygenic, and the possibility of finding single genetic variants, or single nucleotide polymorphisms (SNPs), with substantial individual effects is consequently limited. Instead, the construction of an aggregated polygenic risk score (PRS), derived from the weighted sum of many risk alleles of SNPs, constitutes a useful tool for studying the genetics of common psychiatric disorders. PRS for psychiatric disorders represent a portion of unique variance for the target trait and could prove useful for decision-making involving the allocation of patients to genetically informed tailored treatment( 14 ). Therapygenetics is a relatively new field that aims to identify genetic predictors for psychotherapy outcomes( 15 ). Some early findings include that higher PRS for autism predicted worse outcomes in patients treated with ICBT for depression( 16 ), that PRS for educational attainment was associated with ICBT outcome for depression( 17 ), and outcome, drop-out and remission status in CBT for dental fear( 18 ). A PRS for treatment outcome has not yet been developed as the largest GWAS ( n = 2724) was underpowered to find SNPs associated with CBT outcome( 19 ). To increase the sample size, most studies combine data from different sources. This often renders the aggregated sample highly heterogeneous in terms of treatment type, content, dose, and form of delivery( 15 ). Another way to improve statistical power is to use more homogeneous datasets, i.e., data from highly standardised treatments, such as ICBT, and leveraging repeated weekly symptom measurements instead of only pre-post change in symptoms. The present study investigated associations of PRS from eight psychiatric and cognitive phenotypes (ADHD, autism, bipolar disorder, depression and schizophrenia, cross-disorder PRS, educational attainment and intelligence) with weekly symptom severity change in a sample of 2668 adults with anxiety or depression treated with highly structured, gold-standard ICBT. Methods & Materials Subjects This study was approved by the Regional Ethics Board in Stockholm, Sweden (REPN: 2009/1089-31/2; EPM: 2022-00602-02). The sample was derived from the MULTI-PSYCH cohort, described in detail in(20). In summary, participants were recruited between 2009 and 2018 at the Internet Psychiatry Clinic in Stockholm, which specialises in providing ICBT nationwide as part of the public health care system. A total of 2668 individuals were included in this analysis (depression, n=1300, panic disorder (n=728), or social anxiety disorder, n=640). See Table 1 for participant characteristics. The sample had 88% of individuals with higher education, which is notably high compared to 27% of the Swedish general population in 2016(21). Table 1. Sociodemographic and clinical characteristics at baseline and post-treatment Depression (n=1300) Panic disorder (n=728) Social anxiety (n=640) Total (n=2668) Missing Gender, female 859 (66) 435 (60) 360 (56) 1654 (62) 1 (0) Age 37.6 (11.9) 34.6 (10.8) 32.7 (10.3) 35.6 (11.4) In a relationship 731 (56) 478 (66) 363 (57) 1572 (59) 5 (0) Children 603 (46) 301 (41) 198 (31) 1102 (41) 5 (0) Highest education attained 5 (0) Primary 25 (2) 18 (2) 14 (2) 57 (2) Secondary 116 (9) 82 (11) 56 (9) 254 (10) Higher 1158 (89) 626 (86) 569 (89) 2353 (88) Prior inpatient care 91 (7) 74 (10) 27 (4) 192 (7) 115 (4) Prior suicide attempt 76 (6) 26 (4) 34 (5) 136 (6) 228 (9) Pre-treatment psychiatric comorbidity 395 (30) 274 (38) 212 (33) 881 (33) 108 (4) Pre-treatment psychotropic medication 788 (61) 445 (61) 322 (50) 1555 (58) 454 (17) Number of ICBT modules started 6.90 (3.2) 6.83 (2.9) 7.14 (3.0) 6.94 (3.1) LSAS pre score 70.6 (23.5) 62 (10) LSAS post score 50.1 (24.1) 118 (18) MADRS-S pre score 22.7 (6.3) 14 (1) MADRS-S post score 13.0 (8.0) 248 (19) PDSS-SR pre score 11.0 (4.7) 16 (2.1) PDSS-SR post score 4.98 (4.5) 158 (21.4) Data are integer count (%) or decimal mean (SD). ICBT=Internet-delivered cognitive behaviour therapy, MADRS-S=Montgomery-Åsberg Depression Rating Scale Self-rated, PDSS-SR=Panic Disorder Severity Scale - Self Report, LSAS=Liebowitz Social Anxiety Scale Study Design Intervention The ICBT treatment consisted of 10 treatment modules over 12 weeks for depression(22), panic disorder(23) and social anxiety disorder(24). All participants were treated at the Internet Psychiatry Clinic in Huddinge, Sweden, which specialises in ICBT. The participants did the treatment on a secure platform, including reading 5-20 pages of text per module, and completing weekly homework assignments. They also communicated with their therapist asynchronously through the platform. The format ensured the treatment content followed protocol and facilitated quality control through automatically distributed questionnaires. For more detail, see (20) . Primary outcome measure Symptom severity was measured weekly from the beginning to end of treatment for the primary outcome using disorder-specific instruments: The Montgomery-Åsberg Depression Rating Scale Self-rated (MADRS-S, (25)) for depression, the Panic Disorder Severity Scale - Self Report (PDSS-SR, (26,27)) for panic disorder, and the Liebowitz Social Anxiety Scale (LSAS, (28))for social anxiety disorder. All instruments were self-rated and completed via the online platform. To harmonise symptom values across diagnoses, original disorder-specific values were scaled to a common metric (score range 0-100, see details in the Supplement). Genotyping Genotyping was done in three batches, on either Illumina HumanOmniExpress BeadChips (Illumina, USA) or Infinium Global Screening Array 1.0 BeadArray (Illumina, Inc., San Diego, CA, USA), at the Department of Genomics, Life and Brain Centre, University of Bonn, Germany. More details on genotyping and quality control steps are described in the Supplement. Target dataset Genotype array data from the target dataset were processed through the Ricopili pipeline v2019_10_15_001 (29). We first used Ricopili to do pre-imputation quality control on array genotypes across each of the three batches using default thresholds (see Supplement). As part of this, we controlled for cryptic relatedness between samples by removing samples where 1) they had a mean PI_HAT relatedness metric above 0.95, 2) they had evidence of being a duplicated sample based on PI_HAT > 0.95, or 3) they had evidence of cryptic relationship based on PI_HAT > 0.2. Genotype imputation was performed using Ricopili using impute2(30) for pre-phasing and minimac3(31) for imputation. To estimate ancestry, principal component analysis (PCA) was done in EIGENSOFT(32) and a value of >6 SD from the mean on any of the first three principal components was considered outlying and therefore removed. PRS were thereafter calculated with PRS-CS(33) from GWAS summary statistics and samples with European ancestry from the 1000 Genomes Project(34). PRS-CS is a Bayesian polygenic prediction method that has demonstrated better predictive accuracy than earlier methods (34) . All genetic analyses were conducted using PLINK 1.9(35). Discovery datasets Discovery datasets from large GWASs were used to create individual-level aggregated PRS for each phenotype. The following discovery datasets were used: ADHD(36), autism spectrum disorder (ASD (37), bipolar disorder, (BPD, (38), major depressive disorder (MDD, (39)) and schizophrenia (SCZ, (40)), cross disorder PRS (41), educational attainment (EDU, (42)) and intelligence (IQ, (43)). The cross disorder PRS includes genetic effects of ADHD, ASD, BPD, MDD and SCZ. The target dataset was not part of these GWAS meta-analyses. Statistical analysis Statistical analysis was done using R(44). To estimate the association between PRS and symptom severity change during ICBT treatment, we used linear mixed effects models for repeated measures (lme4 package, (45)). To obtain p-values for the coefficients of interest we used the package lmerTest(46), which provides p-values via Satterthwaite’s degrees of freedom method. We fitted models that used both the linear and quadratic effects of time, which together provided a better fit for data compared to using either one individually, according to Akaike Information Criterion(47). We estimated the association of time and symptom ratings to investigate the rate of symptom severity change from pre- to post-treatment. For the main analysis, we first tested all PRS as fixed effects in separate models, and a second step, adjusted for age, sex, batch, and the first five principal components for ancestry (PCs). The interpretation of a significant main effect of a PRS was that the PRS had a constant association with symptom severity through the entire treatment period. As a secondary analysis, to investigate the influence of PRS on the rate of change during treatment, we tested for interaction effects (PRS*time) for those PRS that had a significant main effect. A significant PRS*time effect was interpreted as the PRS being associated with the rate of symptom change during the treatment period. To account for dropout in our modelling, we incorporated a pattern mixture term by dummy coding participants into two subgroups, non-completers (1) or completers (0); where participants that started <5 modules were classified as non-completers. ICBT patients could drop out of treatment for reasons related or unrelated to the treatment or symptom change, and reasons are in many cases unknown. By accounting for dropout in our model, we decrease the potential bias of our estimates caused by dropout, which a plain mixed model would otherwise ignore. To control for unwanted genotyping batch-effects, analysis also included three dummies (one per batch). Results Symptom severity There was a significant negative main effect of time (fixed) (B = − 2.47, SE = 0.07, p < 2 × 10 − 16) on symptom severity, showing the expected reduction in symptom severity during the treatment period, see Fig. 1 . This was also true for each diagnosis separately; see Fig. 2 . Symptom severity rating and polygenic risk Results are displayed in Table 2 . PRS-EDU had a significant negative association (B = -0.76, p = .02) in the crude model, which also remained significant when adjusting for PCs and sex (B = -0.70, p = .04). No other PRS showed a significant main effect. The PRS-EDU*time interaction effect was statistically significant (B = 0.07, p = .04) in the adjusted, but not in the crude model (B = 0.07, p = .05). Table 2 PRS estimates for symptom change Crude Adjusted Est. SE p CI Est. SE p CI Attention-deficit hyperactive disorder 0.16 0.34 0.64 -0.50, 0.82 0.02 0.34 0.94 -0.63, 0.68 Autism spectrum disorder -0.15 0.34 0.65 -0.81, 0.50 -0.22 0.34 0.50 -0.88, 0.43 Bipolar disorder -0.22 0.34 0.50 -0.88, 0.43 -0.26 0.34 0.44 -0.94, 0.39 Cross-disorder -0.45 0.34 0.19 -1.10, 0.21 -0.69 0.36 0.05 -1.38, 0.01 Educational attainment -0.76 0.34 0.02* -1.41, -0.10 -0.70 0.34 0.04* -1.36, -0.04 IQ -0.07 0.34 0.84 -0.73, 0.59 -0.03 0.34 0.93 -0.70, 0.64 Major depressive disorder 0.12 0.34 0.72 -0.54, 0.78 0.03 0.34 0.94 -0.65, 0.70 Schizophrenia -0.30 0.34 0.37 -0.96, 0.36 -0.60 0.36 0.10 -1.30, 0.10 PRS-EDU*time interaction 0.07 0.04 0.05 -0.00, 0.14 0.07 0.04 0.04* 0.00 0.14 PRS = Polygenic risk score, Est = estimated effect, SE = standard error, CI = confidence interval. All models included genotyping batch, and dropout status. Adjusted models included control for sex, ancestry PCs. *significant < 0.05 Sensitivity analyses We did outlier analysis post hoc to detect influential cases that may have biased our regression estimates on the PRS-EDU model. We used the influence.ME package ( 48 ) to calculate Cook’s distance for all observations. Possible influential observations were identified with a threshold of a Cook’s Distance greater than 3x the mean, and the analyses were rerun with these outlying observations removed (n = 1632, total n observations = 32,016). This did not result in altered interpretations of the significant results of the main effect for PRS-EDU. However, the PRS-EDU*time effect was significant in the crude model (B = -0.07, p = .04) and the adjusted model (B = -0.07, p = .04) when outliers were removed. An additional post hoc analysis estimated the association between self-reported educational level and symptom severity change during ICBT treatment. The effect was significant, and larger than the main effect both in the crude (B = -2.08, p = .00) and adjusted (B = -2.09, p = .00) model. Discussion ICBT for depression and anxiety disorders is effective, but many patients keep experiencing disabling symptoms after treatment. In this study, we investigated the association between PRS for eight psychiatric and cognitive phenotypes and symptom severity in 2668 adults with depression, panic disorder, or social anxiety disorder who underwent ICBT. We found that a higher PRS for educational attainment was associated with a higher symptom severity reduction during ICBT. Across different sensitivity analyses, the PRS-EDU association remained significant. In addition, there was a significant PRS-EDU*time interaction, suggesting that PRS-EDU also influenced the rate of change of symptoms during treatment. The findings suggest a better and more rapid clinical outcome in individuals with higher PRS-EDU, which is also supported by the observed association between phenotypic education level and symptom severity change. This could be explained by the resemblance between ICBT and a formal education. Both include shared educational components such as reading, writing, memorising, generalising, application of acquired knowledge and homework assignments. Similar to formal education, ICBT requires that the person takes initiative in planning and carrying out homework exercises. A genetic propensity for educational attainment has previously been associated with ICBT outcome for depression( 17 ), and also cognitive ability, as well as a broader suite of beneficial psychological traits, including self-control and interpersonal skills( 49 ). Our results suggest that a genetic predisposition for higher educational attainment, could improve the chances of benefitting from ICBT. While, in Sweden, ICBT is available to all citizens regardless of their educational background, we know that, in practice, patients at the Internet Psychiatry Clinic tend to have higher than average educational level (88% with higher education in the sample). A more far-reaching implication of the findings is that ICBT should probably be further adapted to benefit a wider portion of society by, for example, improving the readability of the written materials or replacing a part of the large amount of text with multi-media content. The sample size was relatively small for a genetic study, which might explain why we were unable to find associations with PRS-IQ even though it has shown a large overlap with PRS-EDU in other studies. A somewhat unexpected finding was the lack of an association between PRS-MDD and other psychiatric PRS and symptom change. Despite using a larger sample, we were not able to replicate our previous finding that PRS-ASD was associated with a faster decrease in depression symptom severity( 16 ). Possible reasons for that include that the sample was still too small to reliably detect associations and the additional cross-disorder heterogeneity in the present larger sample which also included participants with anxiety disorders. Finally, it is possible that the genetic risk associated with developing a mental disorder (as captured by PRS) does not necessarily overlap with the genetics of treatment response. PRS alone will likely not be used to guide clinical decisions in the future, but could be of use when combined in a multi-PRS approach ( 50 , 51 ) and/or in combination with other types of data, like clinical risk scores, in order to find individuals at elevated risk for suboptimal outcomes( 52 ). The utility of PRS in clinical psychiatry should be evaluated in the context of realistic expectations of what PRS can and cannot deliver. Strengths and limitations A strength of this study is the quality of data provided by the highly structured treatment and state-of-the-art clinical routines. All participants were diagnosed by licensed or supervised clinicians in a structured way, providing diagnostic precision. The ICBT treatment format was consistent across individuals and based on gold-standard treatment protocols. Data included weekly symptom severity ratings, which we leveraged using mixed modelling resulting in increased power compared with the more commonly used pre-post-treatment symptom ratings. Another strength was the control for bias from genotype batching and patient dropout. However, there are limitations to consider. To increase sample size and statistical power, we combined data from various clinical diagnoses, which has introduced heterogeneity in the data, although we hold that the larger resulting dataset offsets this limitation and it also increases generalizability to a larger group of ICBT patients. Another possible limitation is that severe MDD cases were excluded a priori, which means that the results may not generalise to more severe individuals. This further highlights the need for external validation of the present findings as well as caution when extrapolating the results. Conclusion In a large sample of participants with depression and anxiety undergoing ICBT, we found that a higher PRS for educational attainment was associated with a more substantial symptom reduction. While these results await replication, they could have important implications for how ICBT should be adapted to suit a wider portion of the population given their genetic predisposition for educational behaviour. Declarations Acknowledgments We gratefully acknowledge the work of research nurse Monica Hellberg, clinicians at Internet Psychiatry Clinic, and the participants. Contributions JB, JW, and CR designed the study. JB drafted the manuscript. JB, JW and MH processed and analysed data. JB, JW, MH, JC, DMC, and CR interpreted the findings, critically revised the manuscript, and approved its final form and submission. Conflicts of interest None. Funding JW and CR gratefully acknowledge funding from the Söderström-König Foundation (SLS-941192 JW, and SLS-994792 JW), FORTE (2018-00221 CR), the Swedish Research Council (2021-06377 JW; 2018-02487 CR) and the Centre for innovative medicine (CIMED 96328 JW; 954440 CR). Data and code availability Patient data are deemed sensitive personal information and therefore the dissemination of individual level data is prohibited by Swedish law. Free third-party access is, therefore, not possible. 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Nat Genet. 2018;50(5):668–81. Schizophrenia Working Group of the Psychiatric Genomics Consortium. Biological insights from 108 schizophrenia-associated genetic loci. Nature. 2014;511(7510):421–7. Cross-Disorder Group of the Psychiatric Genomics Consortium. Identification of risk loci with shared effects on five major psychiatric disorders: a genome-wide analysis. Lancet. 2013;381(9875):1371–9. Okbay A, Beauchamp JP, Fontana MA, Lee JJ, Pers TH, Rietveld CA, et al. Genome-wide association study identifies 74 loci associated with educational attainment. Nature. 2016;533(7604):539–42. Savage JE, Jansen PR, Stringer S, Watanabe K, Bryois J, de Leeuw CA, et al. Genome-wide association meta-analysis in 269,867 individuals identifies new genetic and functional links to intelligence. Nat Genet. 2018;50(7):912–9. Team RC, Others. R: A language and environment for statistical computing. 2013; Available from: http://r.meteo.uni.wroc.pl/web/packages/dplR/vignettes/intro-dplR.pdf Bates D, Mächler M, Bolker B, Walker S. Fitting Linear Mixed-Effects Models Using lme4. J Stat Softw. 2015;67:1–48. Kuznetsova A, Brockhoff PB, Christensen RHB. lmerTest Package: Tests in Linear Mixed Effects Models. J Stat Softw. 2017;82:1–26. Bozdogan H. Model selection and Akaike’s Information Criterion (AIC): The general theory and its analytical extensions. Psychometrika. 1987;52(3):345–70. Nieuwenhuis R, Grotenhuis M, Pelzer B. Influence.ME: Tools for detecting influential data in mixed effects models. R J. 2012;4(2):38. Belsky DW, Moffitt TE, Corcoran DL, Domingue B, Harrington H, Hogan S, et al. The Genetics of Success: How Single-Nucleotide Polymorphisms Associated With Educational Attainment Relate to Life-Course Development. Psychol Sci. 2016;27(7):957–72. Krapohl E, Patel H, Newhouse S, Curtis CJ, von Stumm S, Dale PS, et al. Multi-polygenic score approach to trait prediction. Mol Psychiatry. 2018;23(5):1368–74. Albiñana C, Zhu Z, Schork AJ, Ingason A, Aschard H, Brikell I, et al. Multi-PGS enhances polygenic prediction by combining 937 polygenic scores. Nat Commun. 2023;14(1):4702. Ikeda M, Saito T, Kanazawa T, Iwata N. Polygenic risk score as clinical utility in psychiatry: a clinical viewpoint. J Hum Genet. 2021;66(1):53–60. Additional Declarations The authors have declared there is NO conflict of interest to disclose Supplementary Files eduprsicbtsupplement.doc Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4246791","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":291398928,"identity":"19dc2739-e49e-4463-a37e-76163874c78c","order_by":0,"name":"Julia Bäckman","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4ElEQVRIiWNgGAWjYBACxgbmhgMMDAcYDEC8DwckiNHCiNDCOIMYLSBNDDAtzDwHiNDAPCOx8cAPhjty5uy9j1/bnLFgkG9vIGDHjMSGgz0Mz4wte46bWefckGAwOEPAKpCWAzwMhxM33EhjM875ANQikUCELX9AWu4/YzO2AGqRn/+AsJbDEFvYmB8zAB3GcAO/DgbGnocNh2UMQH5JY2PsOSPBY3CGgMMM25MPf3xTAQqxY8wffhyrk5NvP0BASwOIBEc9AxsoHnkIOIuBQR6JzfyBoPJRMApGwSgYkQAA+BhLYX1hqGgAAAAASUVORK5CYII=","orcid":"","institution":"Karolinska Institutet","correspondingAuthor":true,"prefix":"","firstName":"Julia","middleName":"","lastName":"Bäckman","suffix":""},{"id":291398929,"identity":"eda4402d-aedb-42a0-9d3b-363d6f42f44d","order_by":1,"name":"John Wallert","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"John","middleName":"","lastName":"Wallert","suffix":""},{"id":291398930,"identity":"45dcafd8-1c80-4e1d-9ceb-4f36838c3cc3","order_by":2,"name":"Matthew Halvorsen","email":"","orcid":"","institution":"University of North Carolina at Chapel Hill","correspondingAuthor":false,"prefix":"","firstName":"Matthew","middleName":"","lastName":"Halvorsen","suffix":""},{"id":291398931,"identity":"94262d0e-8b33-497c-954b-e6421db59f8e","order_by":3,"name":"James Crowley","email":"","orcid":"https://orcid.org/0000-0001-9051-1557","institution":"UNC Chapel Hill","correspondingAuthor":false,"prefix":"","firstName":"James","middleName":"","lastName":"Crowley","suffix":""},{"id":291398932,"identity":"dce4aae3-6727-45bd-bfae-5e34a20861cf","order_by":4,"name":"David Mataix-Cols","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"David","middleName":"","lastName":"Mataix-Cols","suffix":""},{"id":291398933,"identity":"893e5c00-5d8a-403c-82e4-de86070a29da","order_by":5,"name":"Christian Rück","email":"","orcid":"https://orcid.org/0000-0002-8742-0168","institution":"Karolinska Institutet","correspondingAuthor":false,"prefix":"","firstName":"Christian","middleName":"","lastName":"Rück","suffix":""}],"badges":[],"createdAt":"2024-04-10 10:35:53","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4246791/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4246791/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":63562164,"identity":"2a07d335-11a8-40db-8ffe-ccf63f122f0d","added_by":"auto","created_at":"2024-08-29 14:55:19","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":481422,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4246791/v1/679d4929-faba-4641-8e6d-d77441834cbb.pdf"},{"id":57954288,"identity":"1fff2654-4ec5-42a0-9f95-5bed5fe80de1","added_by":"auto","created_at":"2024-06-07 23:15:15","extension":"doc","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":580096,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"eduprsicbtsupplement.doc","url":"https://assets-eu.researchsquare.com/files/rs-4246791/v1/c4bceb2576dd308288d34d8c.doc"}],"financialInterests":"The authors have declared there is \u003cb\u003eNO\u003c/b\u003e conflict of interest to disclose","formattedTitle":"Educational attainment polygenic risk score and symptom severity change after Internet-delivered cognitive behaviour therapy for depression and anxiety","fulltext":[{"header":"Introduction","content":"\u003cp\u003eInternet-delivered cognitive behaviour therapy (ICBT) is an effective treatment for anxiety and depressive disorders, with effect sizes comparable to those of in-person CBT(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). However, a substantial proportion of patients do not respond sufficiently to treatment(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) Considering that depressive and anxiety disorders are among the top ten diseases with the highest global burden(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e), the impact of psychotherapeutic non-response is substantial, affecting healthcare utilisation, societal costs and overall public health. There is consequently a need to identify predictors of treatment outcome to inform clinical decision-making and allow for better-tailored intervention for these patients.\u003c/p\u003e \u003cp\u003eEarlier studies have found a host of predictors for symptom change; higher or lower baseline symptom severity(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan additionalcitationids=\"CR6 CR7\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e), sex(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e), education level(\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e) and psychiatric comorbidity(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). A recent umbrella review of transdiagnostic predictors in different psychiatric treatments found a number of variables that predict remission and relapse, including depressive symptoms, anxiety symptoms, quality of life, negative life events, functioning, marital status, early access/intervention, age and comorbid physical symptoms/disorders, but could not find predictors for recovery(\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). However, the predictive power of these indicators remains insufficient, and there is a need to search for other possible predictors to reach a level of predictive acuity that is acceptable for use to support clinical decision-making, and tailored intervention.\u003c/p\u003e \u003cp\u003eUsing genetic information for prediction has recently emerged as a complement to clinical and demographic variables. Decades of twin, adoption and other family-based studies have established that mental disorders aggregate in families and are substantially heritable(\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). The largest (n cases\u0026thinsp;=\u0026thinsp;371,184) genome-wide association study (GWAS) of depression identified 243 risk loci associated with the disorder(\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). For anxiety, a handful of loci have been found, likely because the largest GWAS was relatively small (n cases\u0026thinsp;=\u0026thinsp;25453, (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). All psychiatric disorders are polygenic, and the possibility of finding single genetic variants, or single nucleotide polymorphisms (SNPs), with substantial individual effects is consequently limited. Instead, the construction of an aggregated polygenic risk score (PRS), derived from the weighted sum of many risk alleles of SNPs, constitutes a useful tool for studying the genetics of common psychiatric disorders. PRS for psychiatric disorders represent a portion of unique variance for the target trait and could prove useful for decision-making involving the allocation of patients to genetically informed tailored treatment(\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTherapygenetics is a relatively new field that aims to identify genetic predictors for psychotherapy outcomes(\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). Some early findings include that higher PRS for autism predicted worse outcomes in patients treated with ICBT for depression(\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e), that PRS for educational attainment was associated with ICBT outcome for depression(\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e), and outcome, drop-out and remission status in CBT for dental fear(\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). A PRS for treatment outcome has not yet been developed as the largest GWAS (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2724) was underpowered to find SNPs associated with CBT outcome(\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). To increase the sample size, most studies combine data from different sources. This often renders the aggregated sample highly heterogeneous in terms of treatment type, content, dose, and form of delivery(\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). Another way to improve statistical power is to use more homogeneous datasets, i.e., data from highly standardised treatments, such as ICBT, and leveraging repeated weekly symptom measurements instead of only pre-post change in symptoms.\u003c/p\u003e \u003cp\u003eThe present study investigated associations of PRS from eight psychiatric and cognitive phenotypes (ADHD, autism, bipolar disorder, depression and schizophrenia, cross-disorder PRS, educational attainment and intelligence) with weekly symptom severity change in a sample of 2668 adults with anxiety or depression treated with highly structured, gold-standard ICBT.\u003c/p\u003e"},{"header":"Methods \u0026 Materials","content":"\u003ch3\u003eSubjects\u003c/h3\u003e\n\u003cp\u003eThis study was approved by the Regional Ethics Board in Stockholm, Sweden (REPN: 2009/1089-31/2; EPM: 2022-00602-02). The sample was derived from the MULTI-PSYCH cohort, described in detail in(20). In summary, participants were recruited between 2009 and 2018 at the Internet Psychiatry Clinic in Stockholm, which specialises in providing ICBT nationwide as part of the public health care system. A total of 2668 individuals were included in this analysis (depression, n=1300, panic disorder (n=728), or social anxiety disorder, n=640). See \u003cstrong\u003eTable 1\u003c/strong\u003e for participant characteristics. The sample had 88% of individuals with higher education, which is notably high compared to 27% of the Swedish general population in 2016(21).\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"647\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"6\" valign=\"top\"\u003e\n \u003cp\u003eTable 1. Sociodemographic and clinical characteristics at baseline and post-treatment\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.31782945736434%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.178294573643411%\" valign=\"top\"\u003e\n \u003cp\u003eDepression\u003c/p\u003e\n \u003cp\u003e(n=1300)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.573643410852712%\" valign=\"top\"\u003e\n \u003cp\u003ePanic disorder\u003c/p\u003e\n \u003cp\u003e(n=728)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.573643410852712%\" valign=\"top\"\u003e\n \u003cp\u003eSocial anxiety\u003c/p\u003e\n \u003cp\u003e(n=640)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.178294573643411%\" valign=\"top\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003cp\u003e(n=2668)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.178294573643411%\" valign=\"top\"\u003e\n \u003cp\u003eMissing\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.31782945736434%\" valign=\"top\"\u003e\n \u003cp\u003eGender, female\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.178294573643411%\" valign=\"top\"\u003e\n \u003cp\u003e859 (66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.573643410852712%\" valign=\"top\"\u003e\n \u003cp\u003e435 (60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.573643410852712%\" valign=\"top\"\u003e\n \u003cp\u003e360 (56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.178294573643411%\" valign=\"top\"\u003e\n \u003cp\u003e1654 (62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.178294573643411%\" valign=\"top\"\u003e\n \u003cp\u003e1 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.31782945736434%\" valign=\"top\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.178294573643411%\" valign=\"top\"\u003e\n \u003cp\u003e37.6 (11.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.573643410852712%\" valign=\"top\"\u003e\n \u003cp\u003e34.6 (10.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.573643410852712%\" valign=\"top\"\u003e\n \u003cp\u003e32.7 (10.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.178294573643411%\" valign=\"top\"\u003e\n \u003cp\u003e35.6 (11.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.178294573643411%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.31782945736434%\" valign=\"top\"\u003e\n \u003cp\u003eIn a relationship\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.178294573643411%\" valign=\"top\"\u003e\n \u003cp\u003e731 (56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.573643410852712%\" valign=\"top\"\u003e\n \u003cp\u003e478 (66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.573643410852712%\" valign=\"top\"\u003e\n \u003cp\u003e363 (57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.178294573643411%\" valign=\"top\"\u003e\n \u003cp\u003e1572 (59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.178294573643411%\" valign=\"top\"\u003e\n \u003cp\u003e5 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.31782945736434%\" valign=\"top\"\u003e\n \u003cp\u003eChildren\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.178294573643411%\" valign=\"top\"\u003e\n \u003cp\u003e603 (46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.573643410852712%\" valign=\"top\"\u003e\n \u003cp\u003e301 (41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.573643410852712%\" valign=\"top\"\u003e\n \u003cp\u003e198 (31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.178294573643411%\" valign=\"top\"\u003e\n \u003cp\u003e1102 (41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.178294573643411%\" valign=\"top\"\u003e\n \u003cp\u003e5 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.31782945736434%\" valign=\"top\"\u003e\n \u003cp\u003eHighest education attained\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.178294573643411%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.573643410852712%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.573643410852712%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.178294573643411%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.178294573643411%\" valign=\"top\"\u003e\n \u003cp\u003e5 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.31782945736434%\" valign=\"top\"\u003e\n \u003cp\u003ePrimary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.178294573643411%\" valign=\"top\"\u003e\n \u003cp\u003e25 (2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.573643410852712%\" valign=\"top\"\u003e\n \u003cp\u003e18 (2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.573643410852712%\" valign=\"top\"\u003e\n \u003cp\u003e14 (2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.178294573643411%\" valign=\"top\"\u003e\n \u003cp\u003e57 (2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.178294573643411%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.31782945736434%\" valign=\"top\"\u003e\n \u003cp\u003eSecondary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.178294573643411%\" valign=\"top\"\u003e\n \u003cp\u003e116 (9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.573643410852712%\" valign=\"top\"\u003e\n \u003cp\u003e82 (11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.573643410852712%\" valign=\"top\"\u003e\n \u003cp\u003e56 (9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.178294573643411%\" valign=\"top\"\u003e\n \u003cp\u003e254 (10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.178294573643411%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.31782945736434%\" valign=\"top\"\u003e\n \u003cp\u003eHigher\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.178294573643411%\" valign=\"top\"\u003e\n \u003cp\u003e1158 (89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.573643410852712%\" valign=\"top\"\u003e\n \u003cp\u003e626 (86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.573643410852712%\" valign=\"top\"\u003e\n \u003cp\u003e569 (89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.178294573643411%\" valign=\"top\"\u003e\n \u003cp\u003e2353 (88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.178294573643411%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.31782945736434%\" valign=\"top\"\u003e\n \u003cp\u003ePrior inpatient care\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.178294573643411%\" valign=\"top\"\u003e\n \u003cp\u003e91 (7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.573643410852712%\" valign=\"top\"\u003e\n \u003cp\u003e74 (10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.573643410852712%\" valign=\"top\"\u003e\n \u003cp\u003e27 (4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.178294573643411%\" valign=\"top\"\u003e\n \u003cp\u003e192 (7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.178294573643411%\" valign=\"top\"\u003e\n \u003cp\u003e115 (4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.31782945736434%\" valign=\"top\"\u003e\n \u003cp\u003ePrior suicide attempt\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.178294573643411%\" valign=\"top\"\u003e\n \u003cp\u003e76 (6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.573643410852712%\" valign=\"top\"\u003e\n \u003cp\u003e26 (4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.573643410852712%\" valign=\"top\"\u003e\n \u003cp\u003e34 (5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.178294573643411%\" valign=\"top\"\u003e\n \u003cp\u003e136 (6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.178294573643411%\" valign=\"top\"\u003e\n \u003cp\u003e228 (9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.31782945736434%\" valign=\"top\"\u003e\n \u003cp\u003ePre-treatment psychiatric comorbidity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.178294573643411%\" valign=\"top\"\u003e\n \u003cp\u003e395 (30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.573643410852712%\" valign=\"top\"\u003e\n \u003cp\u003e274 (38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.573643410852712%\" valign=\"top\"\u003e\n \u003cp\u003e212 (33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.178294573643411%\" valign=\"top\"\u003e\n \u003cp\u003e881 (33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.178294573643411%\" valign=\"top\"\u003e\n \u003cp\u003e108 (4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.31782945736434%\" valign=\"top\"\u003e\n \u003cp\u003ePre-treatment psychotropic medication\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.178294573643411%\" valign=\"top\"\u003e\n \u003cp\u003e788 (61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.573643410852712%\" valign=\"top\"\u003e\n \u003cp\u003e445 (61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.573643410852712%\" valign=\"top\"\u003e\n \u003cp\u003e322 (50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.178294573643411%\" valign=\"top\"\u003e\n \u003cp\u003e1555 (58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.178294573643411%\" valign=\"top\"\u003e\n \u003cp\u003e454 (17)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.31782945736434%\" valign=\"top\"\u003e\n \u003cp\u003eNumber of ICBT modules started\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.178294573643411%\" valign=\"top\"\u003e\n \u003cp\u003e6.90 (3.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.573643410852712%\" valign=\"top\"\u003e\n \u003cp\u003e6.83 (2.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.573643410852712%\" valign=\"top\"\u003e\n \u003cp\u003e7.14 (3.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.178294573643411%\" valign=\"top\"\u003e\n \u003cp\u003e6.94 (3.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.178294573643411%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.31782945736434%\" valign=\"top\"\u003e\n \u003cp\u003eLSAS pre score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.178294573643411%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.573643410852712%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.573643410852712%\" valign=\"top\"\u003e\n \u003cp\u003e70.6 (23.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.178294573643411%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.178294573643411%\" valign=\"top\"\u003e\n \u003cp\u003e62 (10)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.31782945736434%\" valign=\"top\"\u003e\n \u003cp\u003eLSAS post score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.178294573643411%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.573643410852712%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.573643410852712%\" valign=\"top\"\u003e\n \u003cp\u003e50.1 (24.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.178294573643411%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.178294573643411%\" valign=\"top\"\u003e\n \u003cp\u003e118 (18)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.31782945736434%\" valign=\"top\"\u003e\n \u003cp\u003eMADRS-S pre score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.178294573643411%\" valign=\"top\"\u003e\n \u003cp\u003e22.7 (6.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.573643410852712%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.573643410852712%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.178294573643411%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.178294573643411%\" valign=\"top\"\u003e\n \u003cp\u003e14 (1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.31782945736434%\" valign=\"top\"\u003e\n \u003cp\u003eMADRS-S post score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.178294573643411%\" valign=\"top\"\u003e\n \u003cp\u003e13.0 (8.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.573643410852712%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.573643410852712%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.178294573643411%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.178294573643411%\" valign=\"top\"\u003e\n \u003cp\u003e248 (19)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.31782945736434%\" valign=\"top\"\u003e\n \u003cp\u003ePDSS-SR pre score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.178294573643411%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.573643410852712%\" valign=\"top\"\u003e\n \u003cp\u003e11.0 (4.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.573643410852712%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.178294573643411%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.178294573643411%\" valign=\"top\"\u003e\n \u003cp\u003e16 (2.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.31782945736434%\" valign=\"top\"\u003e\n \u003cp\u003ePDSS-SR post score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.178294573643411%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.573643410852712%\" valign=\"top\"\u003e\n \u003cp\u003e4.98 (4.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.573643410852712%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.178294573643411%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.178294573643411%\" valign=\"top\"\u003e\n \u003cp\u003e158 (21.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"6\" valign=\"top\"\u003e\n \u003cp\u003eData are integer count (%) or decimal mean (SD). ICBT=Internet-delivered cognitive behaviour therapy, MADRS-S=Montgomery-\u0026Aring;sberg Depression Rating Scale Self-rated, PDSS-SR=Panic Disorder Severity Scale - Self Report, LSAS=Liebowitz Social Anxiety Scale\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch3\u003e\u0026nbsp;\u003c/h3\u003e\n\u003ch2\u003eStudy Design\u003c/h2\u003e\n\u003ch3\u003eIntervention\u003c/h3\u003e\n\u003cp\u003eThe ICBT treatment consisted of 10 treatment modules over 12 weeks for depression(22), panic disorder(23) and social anxiety disorder(24). \u0026nbsp;All participants were treated at the Internet Psychiatry Clinic in Huddinge, Sweden, which specialises in ICBT. The participants did the treatment on a secure platform, including reading 5-20 pages of text per module, and completing weekly homework assignments. They also communicated with their therapist asynchronously through the platform. The format ensured the treatment content followed protocol and facilitated quality control through automatically distributed questionnaires. For more detail, see (20)\u003cstrong\u003e.\u003c/strong\u003e\u003c/p\u003e\n\u003ch3\u003ePrimary outcome measure\u003c/h3\u003e\n\u003cp\u003eSymptom severity was measured weekly from the beginning to end of treatment for the primary outcome using disorder-specific instruments: The Montgomery-\u0026Aring;sberg Depression Rating Scale Self-rated (MADRS-S, (25)) for depression, the Panic Disorder Severity Scale - Self Report (PDSS-SR, (26,27)) for panic disorder, and the Liebowitz Social Anxiety Scale (LSAS, (28))for social anxiety disorder. All instruments were self-rated and completed via the online platform. To harmonise symptom values across diagnoses, original disorder-specific values were scaled to a common metric (score range 0-100, see details in the Supplement).\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003eGenotyping\u003c/h3\u003e\n\u003cp\u003eGenotyping was done in three batches, on either Illumina HumanOmniExpress BeadChips (Illumina, USA) or Infinium Global Screening Array 1.0 BeadArray (Illumina, Inc., San Diego, CA, USA), at the Department of Genomics, Life and Brain Centre, University of Bonn, Germany. More details on genotyping and quality control steps are described in the Supplement.\u003c/p\u003e\n\u003ch3\u003eTarget dataset\u003c/h3\u003e\n\u003cp\u003eGenotype array data from the target dataset were processed through the Ricopili pipeline v2019_10_15_001\u0026nbsp;(29). We first used Ricopili to do pre-imputation quality control on array genotypes across each of the three batches using default thresholds (see Supplement). As part of this, we controlled for cryptic relatedness between samples by removing samples where 1) they had a mean PI_HAT relatedness metric above 0.95, 2) they had evidence of being a duplicated sample based on PI_HAT \u0026gt; 0.95, or 3) they had evidence of cryptic relationship based on PI_HAT \u0026gt; 0.2. Genotype imputation was performed using Ricopili using impute2(30)\u0026nbsp;for pre-phasing and minimac3(31)\u0026nbsp;for imputation. To estimate ancestry, principal component analysis (PCA) was done in EIGENSOFT(32)\u0026nbsp;and a value of \u0026gt;6 SD from the mean on any of the first three principal components was considered outlying and therefore removed. PRS were thereafter calculated with PRS-CS(33)\u0026nbsp;from GWAS summary statistics and samples with European ancestry from the 1000 Genomes Project(34). PRS-CS is a Bayesian polygenic prediction method that has demonstrated better predictive accuracy than earlier methods\u003ca href=\"https://paperpile.com/c/g1aUtp/Kf5o\"\u003e(34)\u003c/a\u003e. All genetic analyses were conducted using PLINK 1.9(35).\u003c/p\u003e\n\u003ch3\u003eDiscovery datasets\u003c/h3\u003e\n\u003cp\u003eDiscovery datasets from large GWASs were used to create individual-level aggregated PRS for each phenotype. The following discovery datasets were used: ADHD(36), autism spectrum disorder (ASD\u0026nbsp;(37), bipolar disorder, (BPD,\u0026nbsp;(38), major depressive disorder (MDD,\u0026nbsp;(39)) and schizophrenia (SCZ,\u0026nbsp;(40)), cross disorder PRS\u0026nbsp;(41), educational attainment (EDU,\u0026nbsp;(42)) and intelligence (IQ,\u0026nbsp;(43)). The cross disorder PRS\u003csup\u003e\u0026nbsp;\u003c/sup\u003eincludes genetic effects of ADHD, ASD, BPD, MDD and SCZ. The target dataset was not part of these GWAS meta-analyses.\u003c/p\u003e\n\u003ch2\u003eStatistical analysis\u003c/h2\u003e\n\u003cp\u003eStatistical analysis was done using R(44). To estimate the association between PRS and symptom severity change during ICBT treatment, we used linear mixed effects models for repeated measures (lme4 package,\u0026nbsp;(45)). To obtain p-values for the coefficients of interest we used the package lmerTest(46), which provides p-values via Satterthwaite\u0026rsquo;s degrees of freedom method. We fitted models that used both the linear and quadratic effects of time, which together provided a better fit for data compared to using either one individually, according to Akaike Information Criterion(47). We estimated the association of time and symptom ratings to investigate the rate of symptom severity change from pre- to post-treatment.\u003c/p\u003e\n\u003cp\u003eFor the main analysis, we first tested all PRS as fixed effects in separate models, and a second step, adjusted for age, sex, batch, and the first five principal components for ancestry (PCs). The interpretation of a significant main effect of a PRS was that the PRS had a constant association with symptom severity through the entire treatment period. As a secondary analysis, to investigate the influence of PRS on the rate of change during treatment, we tested for interaction effects (PRS*time) for those PRS that had a significant main effect. A significant PRS*time effect was interpreted as the PRS being associated with the rate of symptom change during the treatment period.\u003c/p\u003e\n\u003cp\u003eTo account for dropout in our modelling, we incorporated a pattern mixture term by dummy coding participants into two subgroups, non-completers (1) or completers (0); where participants that started \u0026lt;5 modules were classified as non-completers. ICBT patients could drop out of treatment for reasons related or unrelated to the treatment or symptom change, and reasons are in many cases unknown. By accounting for dropout in our model, we decrease the potential bias of our estimates caused by dropout, which a plain mixed model would otherwise ignore. To control for unwanted genotyping batch-effects, analysis also included three dummies (one per batch).\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eSymptom severity\u003c/p\u003e \u003cp\u003eThere was a significant negative main effect of time (fixed) (B\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;2.47, SE\u0026thinsp;=\u0026thinsp;0.07, p\u0026thinsp;\u0026lt;\u0026thinsp;2 \u0026times; 10\u0026thinsp;\u0026minus;\u0026thinsp;16) on symptom severity, showing the expected reduction in symptom severity during the treatment period, see \u003cb\u003eFig.\u0026nbsp;1\u003c/b\u003e. This was also true for each diagnosis separately; see \u003cb\u003eFig.\u0026nbsp;2\u003c/b\u003e.\u003c/p\u003e \u003cp\u003eSymptom severity rating and polygenic risk\u003c/p\u003e \u003cp\u003eResults are displayed in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. PRS-EDU had a significant negative association (B = -0.76, p\u0026thinsp;=\u0026thinsp;.02) in the crude model, which also remained significant when adjusting for PCs and sex (B = -0.70, p\u0026thinsp;=\u0026thinsp;.04). No other PRS showed a significant main effect. The PRS-EDU*time interaction effect was statistically significant (B\u0026thinsp;=\u0026thinsp;0.07, p\u0026thinsp;=\u0026thinsp;.04) in the adjusted, but not in the crude model (B\u0026thinsp;=\u0026thinsp;0.07, p\u0026thinsp;=\u0026thinsp;.05).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePRS estimates for symptom change\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003eCrude\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c9\" namest=\"c6\"\u003e \u003cp\u003eAdjusted\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEst.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eEst.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eCI\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAttention-deficit hyperactive disorder\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.50, 0.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.63, 0.68\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAutism spectrum disorder\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.81, 0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.88, 0.43\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBipolar disorder\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.88, 0.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.94, 0.39\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCross-disorder\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-1.10, 0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-1.38, 0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducational attainment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.02*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-1.41, -0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.04*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-1.36, -0.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIQ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.73, 0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.70, 0.64\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMajor depressive disorder\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.54, 0.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.65, 0.70\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSchizophrenia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.96, 0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-1.30, 0.10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePRS-EDU*time interaction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.00, 0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.04*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.00 0.14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"9\" nameend=\"c9\" namest=\"c1\"\u003e \u003cp\u003ePRS\u0026thinsp;=\u0026thinsp;Polygenic risk score, Est\u0026thinsp;=\u0026thinsp;estimated effect, SE\u0026thinsp;=\u0026thinsp;standard error, CI\u0026thinsp;=\u0026thinsp;confidence interval. All models included genotyping batch, and dropout status. Adjusted models included control for sex, ancestry PCs. *significant\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eSensitivity analyses\u003c/p\u003e \u003cp\u003eWe did outlier analysis post hoc to detect influential cases that may have biased our regression estimates on the PRS-EDU model. We used the influence.ME package (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e) to calculate Cook\u0026rsquo;s distance for all observations. Possible influential observations were identified with a threshold of a Cook\u0026rsquo;s Distance greater than 3x the mean, and the analyses were rerun with these outlying observations removed (n\u0026thinsp;=\u0026thinsp;1632, total n observations\u0026thinsp;=\u0026thinsp;32,016). This did not result in altered interpretations of the significant results of the main effect for PRS-EDU. However, the PRS-EDU*time effect was significant in the crude model (B = -0.07, p\u0026thinsp;=\u0026thinsp;.04) and the adjusted model (B = -0.07, p\u0026thinsp;=\u0026thinsp;.04) when outliers were removed.\u003c/p\u003e \u003cp\u003eAn additional post hoc analysis estimated the association between self-reported educational level and symptom severity change during ICBT treatment. The effect was significant, and larger than the main effect both in the crude (B = -2.08, p\u0026thinsp;=\u0026thinsp;.00) and adjusted (B = -2.09, p\u0026thinsp;=\u0026thinsp;.00) model.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eICBT for depression and anxiety disorders is effective, but many patients keep experiencing disabling symptoms after treatment. In this study, we investigated the association between PRS for eight psychiatric and cognitive phenotypes and symptom severity in 2668 adults with depression, panic disorder, or social anxiety disorder who underwent ICBT. We found that a higher PRS for educational attainment was associated with a higher symptom severity reduction during ICBT. Across different sensitivity analyses, the PRS-EDU association remained significant. In addition, there was a significant PRS-EDU*time interaction, suggesting that PRS-EDU also influenced the rate of change of symptoms during treatment.\u003c/p\u003e \u003cp\u003eThe findings suggest a better and more rapid clinical outcome in individuals with higher PRS-EDU, which is also supported by the observed association between phenotypic education level and symptom severity change. This could be explained by the resemblance between ICBT and a formal education. Both include shared educational components such as reading, writing, memorising, generalising, application of acquired knowledge and homework assignments. Similar to formal education, ICBT requires that the person takes initiative in planning and carrying out homework exercises. A genetic propensity for educational attainment has previously been associated with ICBT outcome for depression(\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e), and also cognitive ability, as well as a broader suite of beneficial psychological traits, including self-control and interpersonal skills(\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e). Our results suggest that a genetic predisposition for higher educational attainment, could improve the chances of benefitting from ICBT. While, in Sweden, ICBT is available to all citizens regardless of their educational background, we know that, in practice, patients at the Internet Psychiatry Clinic tend to have higher than average educational level (88% with higher education in the sample). A more far-reaching implication of the findings is that ICBT should probably be further adapted to benefit a wider portion of society by, for example, improving the readability of the written materials or replacing a part of the large amount of text with multi-media content.\u003c/p\u003e \u003cp\u003eThe sample size was relatively small for a genetic study, which might explain why we were unable to find associations with PRS-IQ even though it has shown a large overlap with PRS-EDU in other studies. A somewhat unexpected finding was the lack of an association between PRS-MDD and other psychiatric PRS and symptom change. Despite using a larger sample, we were not able to replicate our previous finding that PRS-ASD was associated with a faster decrease in depression symptom severity(\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). Possible reasons for that include that the sample was still too small to reliably detect associations and the additional cross-disorder heterogeneity in the present larger sample which also included participants with anxiety disorders. Finally, it is possible that the genetic risk associated with developing a mental disorder (as captured by PRS) does not necessarily overlap with the genetics of treatment response.\u003c/p\u003e \u003cp\u003ePRS alone will likely not be used to guide clinical decisions in the future, but could be of use when combined in a multi-PRS approach (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e) and/or in combination with other types of data, like clinical risk scores, in order to find individuals at elevated risk for suboptimal outcomes(\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e). The utility of PRS in clinical psychiatry should be evaluated in the context of realistic expectations of what PRS can and cannot deliver.\u003c/p\u003e \u003cp\u003eStrengths and limitations\u003c/p\u003e \u003cp\u003eA strength of this study is the quality of data provided by the highly structured treatment and state-of-the-art clinical routines. All participants were diagnosed by licensed or supervised clinicians in a structured way, providing diagnostic precision. The ICBT treatment format was consistent across individuals and based on gold-standard treatment protocols. Data included weekly symptom severity ratings, which we leveraged using mixed modelling resulting in increased power compared with the more commonly used pre-post-treatment symptom ratings. Another strength was the control for bias from genotype batching and patient dropout. However, there are limitations to consider. To increase sample size and statistical power, we combined data from various clinical diagnoses, which has introduced heterogeneity in the data, although we hold that the larger resulting dataset offsets this limitation and it also increases generalizability to a larger group of ICBT patients. Another possible limitation is that severe MDD cases were excluded a priori, which means that the results may not generalise to more severe individuals. This further highlights the need for external validation of the present findings as well as caution when extrapolating the results.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn a large sample of participants with depression and anxiety undergoing ICBT, we found that a higher PRS for educational attainment was associated with a more substantial symptom reduction. While these results await replication, they could have important implications for how ICBT should be adapted to suit a wider portion of the population given their genetic predisposition for educational behaviour.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAcknowledgments\u003c/h2\u003e\n\u003cp\u003eWe gratefully acknowledge the work of research nurse Monica Hellberg, clinicians at Internet Psychiatry Clinic, and the participants.\u003c/p\u003e\n\u003ch2\u003eContributions\u003c/h2\u003e\n\u003cp\u003eJB, JW, and CR designed the study. JB drafted the manuscript. JB, JW and MH processed and analysed data. JB, JW, MH, JC, DMC, and CR interpreted the findings, critically revised the manuscript, and approved its final form and submission.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eConflicts of interest\u003c/h2\u003e\n\u003cp\u003eNone.\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eJW and CR gratefully acknowledge funding from the S\u0026ouml;derstr\u0026ouml;m-K\u0026ouml;nig Foundation (SLS-941192 JW, and SLS-994792 JW), FORTE (2018-00221 CR), the Swedish Research Council (2021-06377 JW; 2018-02487 CR) and the Centre for innovative medicine (CIMED 96328 JW; 954440 CR).\u003c/p\u003e\u003ch2\u003eData and code availability\u003c/h2\u003e \u003cp\u003ePatient data are deemed sensitive personal information and therefore the dissemination of individual level data is prohibited by Swedish law. Free third-party access is, therefore, not possible. Given obtained ethical approval and approval by the MULTI-PSYCH investigators, data may be used in further analyses. R code for generating the present results is available upon request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eHedman-Lagerl\u0026ouml;f E, Carlbring P, Sv\u0026auml;rdman F, Riper H, Cuijpers P, Andersson G. Therapist-supported Internet-based cognitive behaviour therapy yields similar effects as face-to-face therapy for psychiatric and somatic disorders: an updated systematic review and meta-analysis. World Psychiatry. 2023;22(2):305\u0026ndash;14.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAndersson G, Carlbring P, Rozental A. Response and Remission Rates in Internet-Based Cognitive Behavior Therapy: An Individual Patient Data Meta-Analysis. Front Psychiatry. 2019;10:749.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRozental A, Andersson G, Carlbring P. 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J Hum Genet. 2021;66(1):53\u0026ndash;60.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"depression, anxiety, symptom severity, polygenic risk score, ICBT","lastPublishedDoi":"10.21203/rs.3.rs-4246791/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4246791/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eDepressive and anxiety disorders are among the leading causes of disability worldwide. Therapist-guided, Internet-delivered cognitive behaviour therapy (ICBT) is an established treatment for depression and anxiety, but a considerable proportion of treated patients do not achieve sufficient improvement. Predicting symptom change from clinical variables alone is challenging. Genetic data could potentially add predictive power and help us understand who will benefit most from ICBT. We conducted a study including 2668 adults (62% women, mean age 35.6 years) from the Swedish MULTI-PSYCH cohort to investigate the association between polygenic risk scores (PRS) from eight psychiatric and cognitive phenotypes and symptom change after ICBT. All participants had been diagnosed with depression, panic disorder or social anxiety disorder and treated with ICBT. The primary clinical outcome was a harmonised score across three different diagnosis-specific symptom rating instruments and measured weekly throughout ICBT. PRS were computed for attention-deficit/hyperactivity disorder, autism spectrum disorder, bipolar disorder, major depressive disorder and schizophrenia, cross-disorder psychopathology, educational attainment, and intelligence, using large discovery data sets. Linear mixed-effects models identified a significant association between the PRS for educational attainment (PRS-EDU) and symptom change (B = -0.73, p\u0026thinsp;=\u0026thinsp;.03), suggesting that a higher PRS-EDU was associated with lower symptom severity. This remained significant after additional covariate adjustment. No other PRS were significant. In the adjusted model, there was a significant PRS-EDU*time interaction, indicating that PRS-EDU also influenced the symptom change rate during treatment. When excluding outliers, the interaction effect was significant in both the crude and adjusted model. While these results await replication, they could have important implications for how the ICBT could be adapted to suit a wider portion of the population.\u003c/p\u003e","manuscriptTitle":"Educational attainment polygenic risk score and symptom severity change after Internet-delivered cognitive behaviour therapy for depression and anxiety","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-06-07 23:15:10","doi":"10.21203/rs.3.rs-4246791/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"8f4312b7-c600-4bb8-86c3-d30e3bad81b8","owner":[],"postedDate":"June 7th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":30695141,"name":"Health sciences/Diseases/Psychiatric disorders/Depression"},{"id":30695142,"name":"Health sciences/Biomarkers/Predictive markers"}],"tags":[],"updatedAt":"2024-08-29T14:47:12+00:00","versionOfRecord":[],"versionCreatedAt":"2024-06-07 23:15:10","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4246791","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4246791","identity":"rs-4246791","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}