Jerome Clifford Foo

Despite extensive research on DNA methylation signatures associated with alcohol use disorder (AUD), findings are often inconsistent. Here we conducted a large-scale meta-analysis of epigenome-wide association studies (EWASs) to identify reliable, reproducible epigenetic markers of AUD. Seven cohorts, comprising 3,775 individuals (1,325 with AUD), contributed to this meta-analysis. Downstream analyses included differentially methylated regions, overrepresentation analyses and the construction of a methylation risk score. We identified 118 significant CpG sites associated with AUD, with the strongest association found at cg24889777 ( P = 5.12 × 10 ⁻¹⁷ ). CpG sites were enriched for pathways related to GTPase signaling, transmembrane transporter activity and EWAS signals of alcohol consumption. The methylation risk score explained 2.06% of the variance in alcohol consumption and predicted heavy drinking in an independent cohort ( N = 2,534, area under the curve = 0.657). This analysis offers key insights into the epigenetic mechanisms of AUD and lays the groundwork for future research on methylation risk scores for the diagnosis, prognosis and treatment of AUD.

Problematic Sexual Behaviour (PSB) is defined as difficult to control recurrent sexual behaviours that continue despite adverse consequences, leading to social and functional impairment. There is debate whether PSB is a disorder of compulsion or addiction; PSB often co-occurs with neuropsychiatric disorders, but further elucidation regarding underlying biology is required. A deficiency in reward neurotransmitter systems (reward deficiency syndrome: RDS) may underlie a shared vulnerability to addiction. We conducted the first case-control genome wide association study (GWAS) of PSB in patients (n=448), and comparison participants with (n=196) and without PSB (n=1488). We used polygenic risk scores (PRS) to test genetic overlap with related psychiatric, behavioural and personality phenotypes. Three models were used: 1) All-PSB (patient + comparison) vs. controls, 2) Patient-PSB vs controls, and 3) RDS (yes/no). Results suggested genetic overlap of PSB with psychiatric conditions, with PRS for major depression, substance use, and others associated with PSB status. PRS for related behavioural phenotypes (e.g., externalizing, age at first sex, number of lifetime sexual partners) and personality traits were also associated with PSB. The patient model showed stronger associations than the All-PSB model, and RDS had both shared and distinct genetics with PSB. As expected with the sample size, only suggestive hits were observed with single variant and gene-based tests. PSB may share genetic mechanisms with various conditions. Further research in larger cohorts is needed to better understand the underlying genetics and environmental factors involved, and to improve diagnostic classification, intervention and treatment prospects.

Objectives: Non-suicidal self-injury (NSSI) and self-harming sexual behaviours share functional and behavioural overlaps. However, the relationship between NSSI and problematic sexual behaviour (PSB) remains underexplored. This study aimed to investigate the association between NSSI and PSB in two cohorts - a non-clinical university cohort and a clinical PSB patient cohort. Methods: Data were collected from 2,189 university participants and 477 clinical PSB patients. NSSI was assessed via self-report, and PSB was measured with the Sexual Addiction Screening Test-Revised (SAST-R) Core. The four core addictive dimensions of PSB: relationship disturbance, loss of control, preoccupation, and affect disturbance, were also evaluated. Logistic regression analyses were conducted to examine the association between PSB (presence/absence and severity) and NSSI, looking at effects of gender and contributions of addictive dimensions of PSB. Results: Rates of NSSI were similar in the university (7.1%) and patient (5.7%) cohorts; stratified by gender, a higher proportion of women PSB patients had NSSI compared to in the university cohort (29.3% vs 9.3%). In the university group, who had milder PSB than patients, PSB was associated with NSSI (OR=2.11, p<0.001); a significant gender by PSB interaction was found showing that women with PSB were over four times more likely to have NSSI than men without PSB (OR=4.44, p=0.037). In contrast, PSB severity was not associated with NSSI in PSB patients (OR=1.10, p=0.25). Associations of the addictive dimensions of PSB with NSSI were observed only in the subgroup of university women, in the 'preoccupation' dimension (p<0.001). Conclusions: Our findings highlight gender-specific patterns in the association between PSB and NSSI, suggesting the need for further research and possibly targeted prevention and intervention strategies in women.

Electroconvulsive therapy (ECT) is an effective antidepressant treatment. The mechanisms behind the therapeutic effect are not fully understood, and reliable biomarkers for response are needed. Epigenetic modifications, such as DNA methylation (DNAm), can reflect both genetic and environmental impacts; they may shed light on the mechanisms behind treatment effects and they have the potential to inform response prediction. We performed an epigenome-wide association study (EWAS) in peripheral blood from patients before and after ECT in a Norwegian cohort (n = 65). The methylation levels of 12 differentially methylated CpG positions (DMPs) and 18 differentially methylated regions (DMRs) were significantly associated with percent clinical response. In addition, 29 DMPs and 23 DMRs were significantly associated with remission (Montgomery and Åsberg Depression Rating Scale MADRS < 10 post treatment). Two DMRs were also significantly associated with percent response at baseline and four DMRs were significantly associated with remission at baseline (FDR < 0.05). We did not identify any longitudinal (pre-post) changes in DNAm. We further performed the first meta-analysis (n = 99) between ECT cohorts, combining this Norwegian cohort and a German ECT cohort (n = 34). Seven of the DMRs found to be associated with response in the meta-analyses were previously identified in the Norwegian or the German cohort (FDR < 0.05). Methylation risk scores (MS) calculated using DMPs associated with ECT in the Norwegian cohort showed promising association with response to ECT in the German cohort (p = 0.06). Finally, we found increased neutrophil to lymphocyte ratios, calculated from estimated cell proportions, to be associated with remission (p < 0.003) in the Norwegian cohort.

Despite extensive research on DNA methylation (DNAm) signatures associated with alcohol use disorder (AUD), findings are often inconsistent and not replicated. We conducted a large-scale meta-analysis of epigenome-wide association studies (EWAS) to identify reliable, reproducible epigenetic markers of AUD. Seven cohorts, comprising 3,775 individuals (1,325 with AUD), contributed to this meta-analysis within the framework of the Psychiatric Genomics Consortium Substance Use Disorders Epigenetics Working Group. Downstream analyses included the identification of differentially methylated regions, overrepresentation analyses, and the construction of a methylation risk score (MRS). We identified 118 significant CpG sites associated with AUD, with the strongest association found at cg24889777 ( p =5.12×10 ^-17 ) in the long non-coding RNA LOC100505942. CpG sites were enriched for pathways related to GTPase signaling and transmembrane transporter activity, as well as EWAS signals of alcohol consumption. The MRS explained 10.44% of variance in heavy drinking in an independent cohort (N=2,534, AUC=0.657). This large-scale meta-analysis offers key insights into the epigenetic mechanisms of AUD and lays the groundwork for future research on methylation risk scores for the diagnosis, prognosis, and treatment in AUD.

Background: Impulsivity is a key feature of bipolar disorder (BD) associated with various negative outcomes. Recent use of ecological momentary assessment (EMA) has allowed for nuanced examination of the mechanisms of mood and impulsivity dysregulation. However, few existing studies have used an ecological momentary assessment of impulsivity in multiplex families with BD and examined its associations with mood. Objective: Using EMA, this study investigated the concurrent and predictive relationships between impulsivity and mood. Methods: Multiplex family members with BD (BDF, n = 8), unaffected family members (FC, n = 6), individuals with BD not from families (BDC, n = 8) and healthy controls (HC, n = 8), completed daily EMA surveys about mood and impulsivity for 6–12 weeks. Mixed-effects regression concurrent and lagged models were employed to analyze the relationship between impulsivity and mood. Results: The BDF (Diff = −31.70, p = 0.001) and BDC (Diff = −25.74, p = 0.007) groups had a significantly lower mean in mood scores compared to the HC group but not compared to the FC group. There were no significant differences in the mean impulsivity scores between the groups. Time-lagged analyses revealed a significant negative association between prior impulsivity and mood at the next assessment independent of diagnosis (OR=0.939, p = 0.002). However, the opposite relationship between prior mood and impulsivity was not significant (OR=0.996, p = 0.135). Conclusions: These results contribute to the understanding of the complex interactions between BD, the genetic load of the disorder, impulsivity and mood. Furthermore, these findings indicate the potential benefits of addressing impulsivity as a means to improve mood outcomes at an early stage.

Background: While the health benefits of physical activity are well-known, adherence to regular physical activity remains a major challenge. Just-in-time adaptive intervention (JITAI) has been proposed as one method to increase physical activity by delivering an intervention at a time when individuals are more likely to make behavioral changes. However, most studies that have implemented JITAI have used uniform intervention criteria (UIC) across participants rather than personalized intervention criteria (PIC) for the individual. Objective: The objective of this paper was to examine the effectiveness of using JITAI implemented with PIC to increase physical activity. Methods: Healthy university students wore a wrist activity monitor for 2 weeks. Participants were divided into 2 groups, which received JITAI to promote physical activity according to either PIC or UIC. In the first week, the mean distance moved and sedentary time per hour for each participant were calculated to derive PIC. UIC was obtained from a 2-week study with a different sample (n=47) conducted under the same conditions. In the second week, JITAI prompts were sent every hour if both of the following criteria were met: the distance moved was shorter, and sedentary time was longer than PIC or UIC. Differences in changes in physical activity as a result of implementing interventions according to PIC and UIC were analyzed using multilevel models. Results: We analyzed data from 28 healthy university students (18‐23 y old, female n=12). Both PIC (P<.001) and UIC (P<.001) significantly increased physical activity in the first hour after JITAI was received. In that first hour, PIC increased physical activity more than UIC; more calories were burned (P=.02), more steps were taken (P=.007), and distance moved was increased (P=.003). However, over the course of the week, the use of JITAI did not significantly increase physical activity levels. Conclusions: Our results appear to suggest that PIC-based JITAI is more effective than UIC-based JITAI, consistent with the idea of a need for precision health approaches. Further research is needed to develop effective long-term intervention designs with sustainable effects.

Background: Mobile devices for remote monitoring are inevitable tools to support treatment and patient care, especially in recurrent diseases such as major depressive disorder. The aim of this study was to learn if machine learning (ML) models based on longitudinal speech data are helpful in predicting momentary depression severity. Data analyses were based on a dataset including 30 inpatients during an acute depressive episode receiving sleep deprivation therapy in stationary care, an intervention inducing a rapid change in depressive symptoms in a relatively short period of time. Using an ambulatory assessment approach, we captured speech samples and assessed concomitant depression severity via self-report questionnaire over the course of 3 weeks (before, during, and after therapy). We extracted 89 speech features from the speech samples using the Extended Geneva Minimalistic Acoustic Parameter Set from the Open-Source Speech and Music Interpretation by Large-Space Extraction (audEERING) toolkit and the additional parameter speech rate. Objective: We aimed to understand if a multiparameter ML approach would significantly improve the prediction compared to previous statistical analyses, and, in addition, which mechanism for splitting training and test data was most successful, especially focusing on the idea of personalized prediction. Methods: To do so, we trained and evaluated a set of >500 ML pipelines including random forest, linear regression, support vector regression, and Extreme Gradient Boosting regression models and tested them on 5 different train-test split scenarios: a group 5-fold nested cross-validation at the subject level, a leave-one-subject-out approach, a chronological split, an odd-even split, and a random split. Results: In the 5-fold cross-validation, the leave-one-subject-out, and the chronological split approaches, none of the models were statistically different from random chance. The other two approaches produced significant results for at least one of the models tested, with similar performance. In total, the superior model was an Extreme Gradient Boosting in the odd-even split approach (R²=0.339, mean absolute error=0.38; both P<.001), indicating that 33.9% of the variance in depression severity could be predicted by the speech features. Conclusions: Overall, our analyses highlight that ML fails to predict depression scores of unseen patients, but prediction performance increased strongly compared to our previous analyses with multilevel models. We conclude that future personalized ML models might improve prediction performance even more, leading to better patient management and care.

Background: Impulsivity is a key feature of bipolar disorder (BD) associated with various negative outcomes. Recent use of ecological momentary assessment (EMA) has allowed for nuanced examination of the mechanisms of mood and impulsivity dysregulation. However, few existing studies have used an ecological momentary assessment of impulsivity in multiplex families with BD and examined its associations with mood. Objective: Using EMA, this study investigated the concurrent and predictive relationships between impulsivity and mood. Methods: Multiplex family members with BD (n=8), unaffected family members (n=6), individuals with BD not from families (n=8) and healthy controls (n=8), completed daily EMA surveys about mood and impulsivity for 6-12 weeks. Mixed-effects regression concurrent and lagged models were employed to analyze the relationship between impulsivity and mood. Results: The diagnosis of BD was associated with lower mood and higher impulsivity levels. Belonging to a multiplex family (i.e. high genetic load for BD) was associated with lower mood but not with higher impulsivity. BD participants showed a negative association between impulsivity and concurrent mood measured with EMA. Time-lagged analyses revealed a significant negative association between prior impulsivity and mood at the next assessment independent of diagnosis. Conclusions: These results contribute to the understanding of the complex interactions between BD, the genetic load of the disorder, impulsivity and mood. Furthermore, these findings indicate the potential benefits of addressing impulsivity as a means to improve mood outcomes at an early stage.

We conducted the largest genome-wide meta-analysis of borderline personality disorder (BPD) to date, with a discovery sample of 12,339 cases and 1,041,717 controls, and a replication study of 685 cases and 107,750 controls (all participants of European ancestry). We identified 11 independent associated genomic loci, and nine risk genes in the gene-based analysis. We observed a single-nucleotide polymorphism (SNP) heritability of 17.3% and derived polygenic scores (PGS) predicted 4.6% of the phenotypic variance in BPD on the liability scale. BPD showed the strongest positive genetic correlations with GWAS of posttraumatic stress disorder, depression, attention deficit hyperactivity disorder, antisocial behavior, and measures of suicide and self-harm. Phenome-wide association analyses using BPD-PGS confirmed these associations and additionally revealed associations with general medical conditions including obstructive pulmonary disease and diabetes. The present analyses highlight BPD as a polygenic disorder, with the genetic risk showing substantial overlap with psychiatric and physical health conditions.