Mental health in UK Biobank: development, implementation and results from an online questionnaire completed by 157 366 participants - RETRACTED

Transdiagnostic clustering and network analysis for questionnaire-based symptom profiling and drug recommendation in the UK Biobank and a Korean cohort

Clinical decision support systems (CDSSs) play a critical role in enhancing the efficiency of mental health care delivery and promoting patient engagement. Transdiagnostic approaches that utilize raw psychological and biological data enable personalized patient profiling and treatment. This study introduces a CDSS incorporating symptom profiling and drug recommendation for mental health care. Among the UK Biobank cohort, we analyzed 157,348 participants for symptom profiling and 14,358 participants with a drug prescription history for drug recommendation. Among the 1307 patients in the Samsung Medical Center cohort, 842 were eligible for analysis. Symptom profiling utilized demographic and questionnaire data, employing conventional clustering and community detection methods. Identified clusters were explored using diagnostic mapping, feature importance, and scoring. For drug recommendation, we employed cluster- and network-based approaches. The analysis identified nine clusters using k-means clustering and ten clusters with the Louvain method. Clusters were annotated for distinct features related to depression, anxiety, psychosis, drug addiction, and self-harm. For drug recommendation, drug prescription probabilities were retrieved for each cluster. A recommended list of drugs, including antidepressants, antipsychotics, mood stabilizers, and sedative-hypnotics, was provided to individual patients. This CDSS holds promise for efficient personalized mental health care and requires further validation and refinement with larger datasets, serving as a valuable tool for mental healthcare providers.

Association between visual impairment and psychosis: A longitudinal study and nested case-control study of adults

BACKGROUND

Theories propose that visual impairment might increase the risk of psychosis, and vice versa. We aimed to investigate the relationship between visual impairment and psychosis in the UK Biobank cohort.

STUDY DESIGN

In a nested case control study of ~116,000 adults, we tested whether a Schizophrenia Spectrum Disorder (SSD) diagnosis as exposure was associated with visual impairment. We also tested longitudinally whether poorer visual acuity, and thinner retinal structures on Optical Coherence Tomography (OCT) scans in 2009 were associated with psychotic experiences in 2016. We adjusted for age, sex, depression and anxiety symptoms; and socioeconomic variables and vascular risk factors where appropriate. We compared complete case with multiple imputation models, designed to reduce bias potentially introduced by missing data.

RESULTS

People with visual impairment had greater odds of SSD than controls in multiply imputed data (Adjusted Odds Ratio [AOR] 1.42, 95 % Confidence Interval [CI] 1.05-1.93, p = 0.021). We also found evidence that poorer visual acuity was associated with psychotic experiences during follow-up (AOR per 0.1 point worse visual acuity score 1.06, 95 % CI 1.01-1.11, p = 0.020; and 1.04, 95 % CI 1.00-1.08, p = 0.037 in right and left eye respectively). In complete case data (15 % of this cohort) we found no clear association, although confidence intervals included the multiple imputation effect estimates. OCT measures were not associated with psychotic experiences.

CONCLUSIONS

Our findings highlight the importance of eye care for people with psychotic illnesses. We could not conclude whether visual impairment is a likely causal risk factor for psychosis.

Disentangling sex differences in the shared genetic architecture of posttraumatic stress disorder, traumatic experiences, and social support with body size and composition

There is a well-known association of traumatic experiences and posttraumatic stress disorder (PTSD) with body size and composition, including consistent differences between sexes. However, the biology underlying these associations is unclear. To understand the genetic underpinnings of this complex relationship, we investigated genome-wide datasets informative of African and European ancestries from the Psychiatric Genomic Consortium, the UK Biobank, the GIANT Consortium, and the Million Veteran Program. We used genome-wide association statistics to estimate sex-specific genetic correlations (r g ) of traumatic experiences, social support, and PTSD with multiple anthropometric traits. After multiple testing corrections (false discovery rate, FDR q < 0.05), we observed 58 significant r g relationships in females (e.g., childhood physical abuse and body mass index, BMI r g  = 0.245, p = 3.88 × 10-10) and 21 significant r g relationships in males (e.g., been involved in combat or exposed to warzone and leg fat percentage; r g  = 0.405, p = 4.42 × 10-10). We performed causal inference analyses of these genetic overlaps using Mendelian randomization and latent causal variable approaches. Multiple female-specific putative causal relationships were observed linking body composition/size with PTSD (e.g., leg fat percentage→PTSD; beta = 0.319, p = 3.13 × 10-9), traumatic experiences (e.g., childhood physical abuse→waist circumference; beta = 0.055, p = 5.07 × 10-4), and childhood neglect (e.g., "someone to take you to doctor when needed as a child"→BMI; beta = -0.594, p = 1.09 × 10-5). In males, we observed putative causal effects linking anthropometric-trait genetic liabilities to traumatic experiences (e.g., BMI→childhood physical abuse; beta = 0.028, p = 8.19 × 10-3). Some of these findings were replicated in individuals of African descent although the limited sample size available did not permit us to conduct a sex-stratified analysis in this ancestry group. In conclusion, our findings provide insights regarding sex-specific causal networks linking anthropometric traits to PTSD, traumatic experiences, and social support.

Diet, Physical Activity, and Disinhibition in Middle-Aged and Older Adults: A UK Biobank Study

Disinhibition is a prominent feature of multiple psychiatric disorders, and has been associated with poor long-term somatic outcomes. Modifiable lifestyle factors including diet and moderate-to-vigorous physical activity (MVPA) may be associated with disinhibition, but their contributions have not previously been quantified among middle-aged/older adults. Here, among N = 157,354 UK Biobank participants aged 40-69, we extracted a single disinhibition principal component and four dietary components (prudent diet, elimination of wheat/dairy/eggs, meat consumption, full-cream dairy consumption). In addition, latent profile analysis assigned participants to one of five empirical dietary groups: prudent-moderate, unhealthy, restricted, meat-avoiding, low-fat dairy. Disinhibition was regressed on the four dietary components, the dietary grouping variable, and self-reported MVPA. In men and women, disinhibition was negatively associated with prudent diet, and positively associated with wheat/dairy/eggs elimination. In men, disinhibition was also associated with consumption of meat and full-cream dairy products. Comparing groups, disinhibition was lower in the prudent-moderate diet (reference) group compared to all other groups. Absolute βs ranged from 0.02-0.13, indicating very weak effects. Disinhibition was not associated with MVPA. In conclusion, disinhibition is associated with multiple features of diet among middle-aged/older adults. Our findings foster specific hypotheses (e.g., early malnutrition, elevated immune-response) to be tested in alternative study designs.

Automated classification of depression from structural brain measures across two independent community-based cohorts

Major depressive disorder (MDD) has been the subject of many neuroimaging case-control classification studies. Although some studies report accuracies ≥80%, most have investigated relatively small samples of clinically-ascertained, currently symptomatic cases, and did not attempt replication in larger samples. We here first aimed to replicate previously reported classification accuracies in a small, well-phenotyped community-based group of current MDD cases with clinical interview-based diagnoses (from STratifying Resilience and Depression Longitudinally cohort, 'STRADL'). We performed a set of exploratory predictive classification analyses with measures related to brain morphometry and white matter integrity. We applied three classifier types-SVM, penalised logistic regression or decision tree-either with or without optimisation, and with or without feature selection. We then determined whether similar accuracies could be replicated in a larger independent population-based sample with self-reported current depression (UK Biobank cohort). Additional analyses extended to lifetime MDD diagnoses-remitted MDD in STRADL, and lifetime-experienced MDD in UK Biobank. The highest cross-validation accuracy (75%) was achieved in the initial current MDD sample with a decision tree classifier and cortical surface area features. The most frequently selected decision tree split variables included surface areas of bilateral caudal anterior cingulate, left lingual gyrus, left superior frontal, right precentral and paracentral regions. High accuracy was not achieved in the larger samples with self-reported current depression (53.73%), with remitted MDD (57.48%), or with lifetime-experienced MDD (52.68-60.29%). Our results indicate that high predictive classification accuracies may not immediately translate to larger samples with broader criteria for depression, and may not be robust across different classification approaches.