Search

and circadian function are leading candidate markers for early relapse identification in MDD. Consumer-grade wearable devices may offer opportunity for remote and real-time examination of dynamic changes in sleep. Objective: We used FitBit data from individuals with recurrent MDD to describe longitudinal associations of sleep duration, quality, and regularity with subsequent depressive relapse and depression severity.Design: Data were collected as part of a longitudinal remote measurement technologies (RMT) cohort study in people with recurrent MDD. Participants: A total of 623 people with MDD wore a FitBit and completed regular outcome assessments via email for a median follow-up of 541 days. Multivariable regression models tested for associations between sleep features and depression outcomes. We considered two samples of people with at least one assessment of relapse (n=213) or at least one assessment of depression severity (n=390). Results: Increased intra-individual variability in total sleep time, greater sleep fragmentation, and later sleep mid-points were associated with worse depression outcomes. Adjusted Population Attributable Fractions (PAFs) suggested that an intervention to increase sleep consistency in adults with MDD could reduce the population risk for depression by up to 18-37%. Conclusion: We found consistent associations between wearable-derived sleep features and the probability of depressive relapse and increased depressive symptom severity. Disordered sleep is prevalent and disruptive, and challenging to capture longitudinally via conventional laboratory sleep assessments. Our study demonstrates a role for consumer-grade activity trackers to predict relapse risk and depression severity in people with recurrent MDD.

Depression shows a metabolomic signature overlapping with that of cardiometabolic conditions. Whether this signature is linked to specific depression profiles remains undetermined. Previous research suggested that metabolic alterations cluster more consistently with depressive symptoms of the atypical spectrum related to energy alterations, such as hyperphagia, weight gain, hypersomnia, fatigue and leaden paralysis. We characterized the metabolomic signature of an “atypical/energy-related” symptom (AES) profile and evaluated its specificity and consistency. Fifty-one metabolites measured using the Nightingale platform in 2 876 participants from the Netherlands Study of Depression and Anxiety were analyzed. An ‘AES profile’ score was based on five items of the Inventory of Depressive Symptomatology (IDS) questionnaire. The AES profile was significantly associated with 31 metabolites including higher glycoprotein acetyls (b=0.13, p=1.35*10-12), isoleucine (b=0.13, p=1.45*10-10), very-low-density lipoproteins cholesterol (b=0.11, p=6.19*10-9) and saturated fatty acid levels (b=0.09, p=3.68*10-10), and lower high-density lipoproteins cholesterol (b=-0.07, p=1.14*10-4). The metabolites were not significantly associated with a summary score of all other IDS items not included in the AES profile. Twenty-five AES-metabolites associations were confirmed in replication analyses in data of 2 015 subjects collected at 6-year follow-up. We identified a specific metabolomic signature – commonly linked to cardiometabolic disorders – associated with a depression profile characterized by atypical, energy-related symptoms. The specific clustering of a metabolomic signature with a clinical profile may identify a more homogenous subgroup of depressed patients at higher cardiometabolic risk, and represent a valuable target for interventions aiming at reducing depression’s detrimental impact on health.

Background: Cognitive symptoms are common during and following episodes of depression. Little is known about the persistence of self-reported and performance-based cognition with depression and functional outcomes. Methods: This is a secondary analysis of a prospective naturalistic observational clinical cohort study of individuals with recurrent Major Depressive Disorder (MDD; N=623). Participants completed app-based self-reported and performance-based cognitive function assessments alongside validated measures of depression, functional disability, and self-esteem every three months. Participants were followed-up for a maximum of 2-years. Multilevel hierarchically nested modelling was employed to explore between- and within-participant variation over time to identify whether persistent cognitive difficulties are related to levels of depression and functional impairment during follow-up.Results: 508 individuals (81.5%) provided data (mean age: 46.6, SD: 15.6; 76.2% female). Increasing persistence of self-reported cognitive difficulty was associated with higher levels of depression and functional impairment throughout the follow-up. In comparison to low persistence of objective cognitive difficulty (75% of timepoints) reported significantly higher levels of depression (B=5.17, SE=2.21, p=0.019) and functional impairment (B=4.82, SE=1.79, p=0.002) over time. Examination of the individual cognitive modules shows that persistently impaired executive function is associated with worse functioning, and poor processing speed is particularly important for worsened depressive symptoms.Conclusions: We replicated previous findings of greater persistence of cognitive difficulty with increasing severity of depression and further demonstrate that these cognitive difficulties are associated with pervasive functional disability. Difficulties with cognition may be an indicator and target for further treatment input.

We present here a unifying framework for affective phenomena: the Human Affectome. By synthesizing a large body of literature, we have converged on definitions that disambiguate the commonly used terms—affect, feeling, emotion, and mood. Based on this definitional foundation, and under the premise that affective states reflect allostatic concerns, we take a goal-directed, enactive perspective. The human affectome is comprised of allostatic features (valence, motivation, and arousal) and allostatic concerns, which differ in the amount of action required to alleviate allostatic load. Allostatic concerns often fall into three ranges: physiological (the most immediate), operational (intermediate to distal), and global. Global concerns involve summations of overall trajectory, general wellbeing, and self-identity. Within this organizational scheme, the human affectome allows vastly different scientific interests to reside within the same theoretical framework and relate to each other. We hope this framework serves as a common focal point for affective research.

Background: Variation in liability to cannabis use disorder has a strong genetic component (estimated twin and family heritability about 50–70%) and is associated with negative outcomes, including increased risk of psychopathology. The aim of the study was to conduct a large genome-wide association study (GWAS) to identify novel genetic variants associated with cannabis use disorder. Methods: To conduct this GWAS meta-analysis of cannabis use disorder and identify associations with genetic loci, we used samples from the Psychiatric Genomics Consortium Substance Use Disorders working group, iPSYCH, and deCODE (20 916 case samples, 363 116 control samples in total), contrasting cannabis use disorder cases with controls. To examine the genetic overlap between cannabis use disorder and 22 traits of interest (chosen because of previously published phenotypic correlations [eg, psychiatric disorders] or hypothesised associations [eg, chronotype] with cannabis use disorder), we used linkage disequilibrium score regression to calculate genetic correlations. Findings: We identified two genome-wide significant loci: a novel chromosome 7 locus (FOXP2, lead single-nucleotide polymorphism [SNP] rs7783012; odds ratio [OR] 1·11, 95% CI 1·07–1·15, p=1·84 × 10 −9) and the previously identified chromosome 8 locus (near CHRNA2 and EPHX2, lead SNP rs4732724; OR 0·89, 95% CI 0·86–0·93, p=6·46 × 10 −9). Cannabis use disorder and cannabis use were genetically correlated (r g 0·50, p=1·50 × 10 −21), but they showed significantly different genetic correlations with 12 of the 22 traits we tested, suggesting at least partially different genetic underpinnings of cannabis use and cannabis use disorder. Cannabis use disorder was positively genetically correlated with other psychopathology, including ADHD, major depression, and schizophrenia. Interpretation: These findings support the theory that cannabis use disorder has shared genetic liability with other psychopathology, and there is a distinction between genetic liability to cannabis use and cannabis use disorder. Funding: National Institute of Mental Health; National Institute on Alcohol Abuse and Alcoholism; National Institute on Drug Abuse; Center for Genomics and Personalized Medicine and the Centre for Integrative Sequencing; The European Commission, Horizon 2020; National Institute of Child Health and Human Development; Health Research Council of New Zealand; National Institute on Aging; Wellcome Trust Case Control Consortium; UK Research and Innovation Medical Research Council (UKRI MRC); The Brain & Behavior Research Foundation; National Institute on Deafness and Other Communication Disorders; Substance Abuse and Mental Health Services Administration (SAMHSA); National Institute of Biomedical Imaging and Bioengineering; National Health and Medical Research Council (NHMRC) Australia; Tobacco-Related Disease Research Program of the University of California; Families for Borderline Personality Disorder Research (Beth and Rob Elliott) 2018 NARSAD Young Investigator Grant; The National Child Health Research Foundation (Cure Kids); The Canterbury Medical Research Foundation; The New Zealand Lottery Grants Board; The University of Otago; The Carney Centre for Pharmacogenomics; The James Hume Bequest Fund; National Institutes of Health: Genes, Environment and Health Initiative; National Institutes of Health; National Cancer Institute; The William T Grant Foundation; Australian Research Council; The Virginia Tobacco Settlement Foundation; The VISN 1 and VISN 4 Mental Illness Research, Education, and Clinical Centers of the US Department of Veterans Affairs; The 5th Framework Programme (FP-5) GenomEUtwin Project; The Lundbeck Foundation; NIH-funded Shared Instrumentation Grant S10RR025141; Clinical Translational Sciences Award grants; National Institute of Neurological Disorders and Stroke; National Heart, Lung, and Blood Institute; National Institute of General Medical Sciences.

Recent growth in remote studies has shown the effectiveness of digital health technologies in recruiting and monitoring the health and behavior of large and diverse populations of interest in real-world settings. However, retaining and engaging participants to monitor their long-term health trajectories has remained a significant challenge. Uneven participant engagement combined with attrition over the course of the study could lead to imbalanced study cohort and data collection, which may severely impact the generalizability of real-world evidence.We report findings from long-term participant retention and engagement patterns in a multinational remote digital depression study with up to two years of real-world behavior monitoring. In total, real-world engagement data from 614 participants with 14,964 surveys and 135,014 days of phone passive and wearable (Fitbit) data were analyzed using survival and unsupervised clustering methods. A considerable proportion of participants (N=415; 67.6%) were retained during the first 43 weeks of the study. Clustering participants’ long-term usage data of study apps and wearables showed three distinct subgroups with different engagement levels (most, middle, and least). Notable findings comparing participants' characteristics across these subgroups were: 1.) Participants (33.2%; N= 204) with the highest baseline depression severity (4 points higher PHQ8 score, p < .01) were in the least engaged group (median bi-weekly surveys completed = 4) compared to the most engaged group (37.6%; N = 231) that on average completed 20 bi-weekly surveys. 2.) A considerable proportion (44.6%; N = 91) of participants in the least engaged group still contributed wearable data for up to 10 months. 3.) The participants in the least engaged group also took significantly longer in responding to surveys in naturalistic settings (3.8 hours more, p < .001) and were younger (age difference = 5 years, p < .01) in comparison to participants in the most engaged group. Our findings show various factors such as socio-demographics, app usage behavior, and depression severity can be linked to the long-term retention and density of real-world data collected in remote digital research studies. Finally, passive data gathered from wearables without additional participant burden showed advantages over active survey data for long-term monitoring, providing greater contiguity and duration of data collected. Together these findings could inform the design of future remote digital health research studies to enable equitable and balanced health data collection from diverse target populations.

Background: Major depressive disorder (MDD) is a highly heterogenous disease, both in terms of clinical profiles and pathobiological alterations. Recently, immunometabolic dysregulations were shown to be correlated with atypical, energy-related symptoms but less so with the Melancholic or Anxious distress symptom dimensions of depression in The Netherlands Study of Depression and Anxiety (NESDA) study. In this study, we aimed to replicate these immunometabolic associations and to characterize the metabolomic correlates of each of the three MDD dimensions. Methods: Using three clinical rating scales, Melancholic, and Anxious distress, and Immunometabolic (IMD) dimensions were characterized in 158 patients who participated in the Predictors of Remission to Individual and Combined Treatments (PReDICT) study and from whom plasma and serum samples were available. The NESDA-defined inflammatory index, a composite measure of interleukin-6 and C-reactive protein, was measured from pre-treatment plasma samples and a metabolomic profile was defined using serum samples analyzed on three metabolomics platforms targeting fatty acids and complex lipids, amino acids, acylcarnitines, and gut microbiome-derived metabolites among other metabolites of central metabolism. Results: The IMD clinical dimension and the inflammatory index were positively correlated (r = 0.19, p = 0.019) after controlling for age, sex, and body mass index, whereas the Melancholic and Anxious distress dimensions were not, replicating the previous NESDA findings. The three symptom dimensions had distinct metabolomic signatures using both univariate and set enrichment statistics. IMD severity correlated mainly with gut-derived metabolites and a few acylcarnitines and long chain saturated free fatty acids. Melancholia severity was significantly correlated with several phosphatidylcholines, primarily the ether-linked variety, lysophosphatidylcholines, as well as several amino acids. Anxious distress severity correlated with several medium and long chain free fatty acids, both saturated and polyunsaturated ones, sphingomyelins, as well as several amino acids and bile acids. Conclusion: The IMD dimension of depression appears reliably associated with markers of inflammation. Metabolomics provides powerful tools to inform about depression heterogeneity and molecular mechanisms related to clinical dimensions in MDD, which include a link to gut microbiome and lipids implicated in membrane structure and function.

Even though physical activity and sedentary behavior are moderately heritable, little is known about the mechanisms that influence these traits. Here, we combine data for up to 674,980 individuals from 51 studies in a trans-ancestry meta-analysis of genome-wide association studies for self-reported moderate-to-vigorous intensity physical activity during leisure time (MVPA); leisure screen time (LST); sedentary commuting; and sedentary behavior at work. We identify 99 loci that associate with at least one trait. Loci associated with LST are enriched for genes whose expression in skeletal muscle is altered by resistance training. Molecular dynamics simulations suggest that the Glu to Ala substitution encoded by rs2229456 (ACTN3) – associated with more MVPA – disrupts salt bridge interactions and makes the alpha actinin 3 filaments more flexible. In isolated type IIA muscle fibers, the Ala-encoding allele is associated with lower maximal force and power during an isometric contraction, suggesting protection from exercise-induced muscle damage. Finally, Mendelian Randomization analyses show that the causal effect of LST on BMI is 2-3 times larger than the effect of body mass index (BMI) on LST, and that beneficial effects of LST and MVPA on several risk factors and diseases are mediated or confounded by BMI. Taken together, our results provide mechanistic insights into the regulation of MVPA and into the role of LST and MVPA in disease prevention. These insights may facilitate the development of tailored physical activity interventions.

Objectives We report on the acceptability, feasibility, dose-response relationship and adherence of two nutritional strategies to improve mood (multinutrient supplements; food-related behavioural activation (F-BA)) studied in a randomised controlled depression prevention trial (the Multi-country cOllaborative project on the rOle of Diet, Food-related behaviour, and Obesity in the prevention of Depression (MooDFOOD) Trial). We also assessed baseline determinants of adherence and assessed whether better adherence resulted in lower depressive symptoms. Design Randomised controlled trial with a 2x2 factorial design conducted between 2015 and 2017. Setting Germany, the Netherlands, UK and Spain. Participants Community sample of 1025 overweight adults with elevated depressive symptoms without a current episode of major depressive disorder. Main eligibility criteria included age (18-75 years), being overweight or obese, and having at least mild depressive symptoms, shown by a Patient Health Questionnaire Score of >= 5. A total of 76% of the sample was retained at the 12-month follow-up. Interventions Daily nutritional supplements versus pill placebo or an F-BA therapy, delivered in individual and group sessions versus no behavioural intervention over a 1-year period. Primary and secondary outcome measures Primary outcome: self-reported acceptability of the interventions. Secondary outcomes: adherence and self-reported depressive symptoms. Results Most participants reported that the F-BA was acceptable (83.61%), feasible to do (65.91%) and would recommend it to a friend (84.57%). Individual F-BA sessions (88.10%) were significantly more often rated as positive than group F-BA sessions (70.17%) and supplements (28.59%). There were statistically significant reductions in depressive symptoms for those who both adhered to the F-BA intervention and had a history of depression (B=-0.08, SE=0.03, p=0.012) versus those who had no history of depression. Supplement intake had no effect on depressive symptoms irrespective of adherence. Conclusions F-BA may have scope for development as a depression prevention intervention and public health strategy but further refinement and testing are needed., This work was supported by the European Union FP7 MooDFOOD Project 'Multi-country collaborative project on the role of Diet, food related behaviour, and Obesity in the prevention of Depression' (grant agreement no. 613598). This work is supported in the UK by the National Institute for Health Research (NIHR), through the Primary Care Research Network and the NIHR Exeter Clinical Research Facility.

BACKGROUND: Recent genome-wide association studies (GWASs) identified the first genetic loci associated with attention-deficit/hyperactivity disorder (ADHD) and autism spectrum disorder (ASD). The next step is to use these results to increase our understanding of the biological mechanisms involved. Most of the identified variants likely influence gene regulation. The aim of the current study is to shed light on the mechanisms underlying the genetic signals and prioritize genes by integrating GWAS results with gene expression and DNA methylation (DNAm) levels.METHODS: We applied summary-data-based Mendelian randomization to integrate ADHD and ASD GWAS data with fetal brain expression and methylation quantitative trait loci, given the early onset of these disorders. We also analyzed expression and methylation quantitative trait loci datasets of adult brain and blood, as these provide increased statistical power. We subsequently used summary-data-based Mendelian randomization to investigate if the same variant influences both DNAm and gene expression levels.RESULTS: We identified multiple gene expression and DNAm levels in fetal brain at chromosomes 1 and 17 that were associated with ADHD and ASD, respectively, through pleiotropy at shared genetic variants. The analyses in brain and blood showed additional associated gene expression and DNAm levels at the same and additional loci, likely because of increased statistical power. Several of the associated genes have not been identified in ADHD and ASD GWASs before.CONCLUSIONS: Our findings identified the genetic variants associated with ADHD and ASD that likely act through gene regulation. This facilitates prioritization of candidate genes for functional follow-up studies.

To describe the direct and indirect costs for people with anxiety and depressive disorders where guidelines are adhered to and patients' perceived needs are fully met.Data were derived from the Netherlands Study of Depression and Anxiety. At baseline, adult patients were interviewed and they completed questionnaires to measure DSM-IV diagnoses, socio-demographic characteristics and perceived need for care. Actual care data were also derived from electronic medical records. Criteria for guideline adherence were based on general practice guidelines, issued by the Dutch College of General Practitioners. Direct and indirect costs were inferred from the Perceived Need for Care Questionnaire administered at baseline, and the Trimbos and iMTA questionnaire on Costs associated with Psychiatric illness administered at 1-year follow-up.For 568 patients with a current anxiety or depressive disorder a complete dataset on health care use and absenteeism was available. Guideline adherence was significantly associated with increased care use and corresponding costs, while fully met perceived need was unrelated to costs. Socio-demographic characteristics, severity of symptoms and guideline adherence all affected the societal costs of patients with fully met perceived needs compared with patients with perceived unmet needs.It appears that guideline-concordant care for anxiety and depression costs more than non-concordant care, while care that has fulfilled all of a patient's needs seems not to be more expensive than care that has not met all perceived needs. However, randomized controlled trials should first confirm this conclusion.

To investigate the association between the extent of pain and the severity of psychological distress in neck and upper limb pain, and to establish whether extent of pain is associated with consultation frequency in primary care.The study population was selected from responders to a general health survey conducted in a general practice in North Staffordshire, UK. Responders indicating pain in the neck or upper limb area were included. The survey included the Hospital Anxiety and Depression Scale (HADS). Consultation data were retrieved for a period of 12 months following the survey.A total of 867 responders had experienced neck-upper limb pain in the month preceding the survey (33% of all responders). Responders with more generalized pain within the neck-upper limb area had significantly higher HADS scores compared to responders with pain in one area only, particularly for depression (median scores 5 vs 3 points). Annual consultation frequency was also higher among responders with generalized pain [adjusted OR for high consultation frequency (or = 7 visits vs 0-2 visits) 1.6, 95% CI 1.1 to 2.4]. When the analysis was restricted to consultations specifically related to neck-upper limb pain, the association between extent of pain and consultation frequency was weak and not statistically significant.Our survey revealed a significant association between extent of pain in the neck-upper limb area and psychological distress, although scores for anxiety and depression were generally low, with only a small proportion of responders reporting moderate or severe symptoms. Responders with both generalized pain and depressive symptoms were more likely to consult their family doctor, but not specifically for musculoskeletal pain. These results confirm the hypothesis that general psychological well being rather than specific somatic symptoms predict consultation frequency.

Gait is an essential manifestation of depression. Laboratory gait characteristics have been found to be closely associated with depression. However, the gait characteristics of daily walking in real-world scenarios and their relationships with depression are yet to be fully explored. This study aimed to explore associations between depression symptom severity and daily-life gait characteristics derived from acceleration signals in real-world settings. In this study, we used two ambulatory datasets: a public dataset with 71 elder adults' 3-day acceleration signals collected by a wearable device, and a subset of an EU longitudinal depression study with 215 participants and their phone-collected acceleration signals (average 463 hours per participant). We detected participants' gait cycles and force from acceleration signals and extracted 20 statistics-based daily-life gait features to describe the distribution and variance of gait cadence and force over a long-term period corresponding to the self-reported depression score. The gait cadence of faster steps (75th percentile) over a long-term period has a significant negative association with the depression symptom severity of this period in both datasets. Daily-life gait features could significantly improve the goodness of fit of evaluating depression severity relative to laboratory gait patterns and demographics, which was assessed by likelihood-ratio tests in both datasets. This study indicated that the significant links between daily-life walking characteristics and depression symptom severity could be captured by both wearable devices and mobile phones. The gait cadence of faster steps in daily-life walking has the potential to be a biomarker for evaluating depression severity, which may contribute to clinical tools to remotely monitor mental health in real-world settings.