The researcher's guide to selecting biomarkers in mental health studies.

Clinical mental health researchers may understandably struggle with how to incorporate biological assessments in clinical research. The options are numerous and are described in a vast and complex body of literature. Here we provide guidelines to assist mental health researchers seeking to include biological measures in their studies. Apart from a focus on behavioral outcomes as measured via interviews or questionnaires, we advocate for a focus on biological pathways in clinical trials and epidemiological studies that may help clarify pathophysiology and mechanisms of action, delineate biological subgroups of participants, mediate treatment effects, and inform personalized treatment strategies. With this paper we aim to bridge the gap between clinical and biological mental health research by (1) discussing the clinical relevance, measurement reliability, and feasibility of relevant peripheral biomarkers; (2) addressing five types of biological tissues, namely blood, saliva, urine, stool and hair; and (3) providing information on how to control sources of measurement variability.

Psychosocial factors, health behaviors and risk of cancer incidence: Testing interaction and effect modification in an individual participant data meta-analysis.

Depression, anxiety and other psychosocial factors are hypothesized to be involved in cancer development. We examined whether psychosocial factors interact with or modify the effects of health behaviors, such as smoking and alcohol use, in relation to cancer incidence. Two-stage individual participant data meta-analyses were performed based on 22 cohorts of the PSYchosocial factors and CAncer (PSY-CA) study. We examined nine psychosocial factors (depression diagnosis, depression symptoms, anxiety diagnosis, anxiety symptoms, perceived social support, loss events, general distress, neuroticism, relationship status), seven health behaviors/behavior-related factors (smoking, alcohol use, physical activity, body mass index, sedentary behavior, sleep quality, sleep duration) and seven cancer outcomes (overall cancer, smoking-related, alcohol-related, breast, lung, prostate, colorectal). Effects of the psychosocial factor, health behavior and their product term on cancer incidence were estimated using Cox regression. We pooled cohort-specific estimates using multivariate random-effects meta-analyses. Additive and multiplicative interaction/effect modification was examined. This study involved 437,827 participants, 36,961 incident cancer diagnoses, and 4,749,481 person years of follow-up. Out of 744 combinations of psychosocial factors, health behaviors, and cancer outcomes, we found no evidence of interaction. Effect modification was found for some combinations, but there were no clear patterns for any particular factors or outcomes involved. In this first large study to systematically examine potential interaction and effect modification, we found no evidence for psychosocial factors to interact with or modify health behaviors in relation to cancer incidence. The behavioral risk profile for cancer incidence is similar in people with and without psychosocial stress.

Estimating Additive Interaction in Two-Stage Individual Participant Data Meta-Analysis.

Individual participant data (IPD) meta-analysis provides important opportunities to study interaction and effect modification for which individual studies often lack power. While previous meta-analyses have commonly focused on multiplicative interaction, additive interaction holds greater relevance for public health and may in certain contexts better reflect biological interaction. Methodological literature on interaction in IPD meta-analysis does not cover additive interaction for models including binary or time-to-event outcomes. We aimed to describe how the Relative Excess Risk due to Interaction (RERI) and other measures of additive interaction or effect modification can be validly estimated within two-stage IPD meta-analysis. First, we explain why direct pooling of study-level RERI estimates may lead to invalid results. Next, we propose a three-step procedure to estimate additive interaction: 1) estimate effects of both exposures and their product term on the outcome within each individual study; 2) pool study-specific estimates using multivariate meta-analysis; 3) estimate an overall RERI and 95% confidence interval based on the pooled effect estimates. We illustrate this procedure by investigating interaction between depression and smoking and risk of smoking-related cancers using data from the PSYchosocial factors and Cancer (PSY-CA) consortium. We discuss implications of this procedure, including the application in meta-analysis based on published data.

Features of immunometabolic depression as predictors of antidepressant treatment outcomes: pooled analysis of four clinical trials.

BACKGROUND: Profiling patients on a proposed 'immunometabolic depression' (IMD) dimension, described as a cluster of atypical depressive symptoms related to energy regulation and immunometabolic dysregulations, may optimise personalised treatment. AIMS: To test the hypothesis that baseline IMD features predict poorer treatment outcomes with antidepressants. METHOD: Data on 2551 individuals with depression across the iSPOT-D (n = 967), CO-MED (n = 665), GENDEP (n = 773) and EMBARC (n = 146) clinical trials were used. Predictors included baseline severity of atypical energy-related symptoms (AES), body mass index (BMI) and C-reactive protein levels (CRP, three trials only) separately and aggregated into an IMD index. Mixed models on the primary outcome (change in depressive symptom severity) and logistic regressions on secondary outcomes (response and remission) were conducted for the individual trial data-sets and pooled using random-effects meta-analyses. RESULTS: Although AES severity and BMI did not predict changes in depressive symptom severity, higher baseline CRP predicted smaller reductions in depressive symptoms (n = 376, ßpooled = 0.06, P = 0.049, 95% CI 0.0001-0.12, I2 = 3.61%); this was also found for an IMD index combining these features (n = 372, ßpooled = 0.12, s.e. = 0.12, P = 0.031, 95% CI 0.01-0.22, I2= 23.91%), with a higher - but still small - effect size compared with CRP. Confining analyses to selective serotonin reuptake inhibitor users indicated larger effects of CRP (ßpooled = 0.16) and the IMD index (ßpooled = 0.20). Baseline IMD features, both separately and combined, did not predict response or remission. CONCLUSIONS: Depressive symptoms of people with more IMD features improved less when treated with antidepressants. However, clinical relevance is limited owing to small effect sizes in inconsistent associations. Whether these patients would benefit more from treatments targeting immunometabolic pathways remains to be investigated.

Metabolic syndrome after childhood trauma: a 9-year longitudinal analysis.

BACKGROUND: Childhood trauma (CT) has been cross-sectionally associated with metabolic syndrome (MetS), a group of biological risk factors for cardiometabolic disease. Longitudinal studies, while rare, would clarify the development of cardiometabolic dysregulations over time. Therefore, we longitudinally investigated the association of CT with the 9-year course of MetS components. METHODS: Participants (N = 2958) from the Netherlands Study of Depression and Anxiety were assessed four times across 9 years. The CT interview retrospectively assessed childhood emotional neglect and physical, emotional, and sexual abuse. Metabolic outcomes encompassed continuous MetS components (waist circumference, triglycerides, high-density lipoprotein [HDL] cholesterol, blood pressure [BP], and glucose) and count of clinically elevated MetS components. Mixed-effects models estimated sociodemographic- and lifestyle-adjusted longitudinal associations of CT with metabolic outcomes over time. Time interactions evaluated change in these associations. RESULTS: CT was reported by 49% of participants. CT was consistently associated with increased waist (b = 0.32, s.e. = 0.10, p = 0.001), glucose (b = 0.02, s.e. = 0.01, p < 0.001), and count of MetS components (b = 0.04, s.e. = 0.01, p < 0.001); and decreased HDL cholesterol (b = -0.01, s.e.<0.01, p = .020) and systolic BP (b = -0.33, s.e. = 0.13, p = 0.010). These associations were mainly driven by severe CT and unaffected by lifestyle. Only systolic BP showed a CT-by-time interaction, where CT was associated with lower systolic BP initially and with higher systolic BP at the last follow-up. CONCLUSIONS: Over time, adults with CT have overall persistent poorer metabolic outcomes than their non-maltreated peers. Individuals with CT have an increased risk for cardiometabolic disease and may benefit from monitoring and early interventions targeting metabolism.

The mediating role of health behaviors in the association between depression, anxiety and cancer incidence: an individual participant data meta-analysis.

BACKGROUND: Although behavioral mechanisms in the association among depression, anxiety, and cancer are plausible, few studies have empirically studied mediation by health behaviors. We aimed to examine the mediating role of several health behaviors in the associations among depression, anxiety, and the incidence of various cancer types (overall, breast, prostate, lung, colorectal, smoking-related, and alcohol-related cancers). METHODS: Two-stage individual participant data meta-analyses were performed based on 18 cohorts within the Psychosocial Factors and Cancer Incidence consortium that had a measure of depression or anxiety (N = 319 613, cancer incidence = 25 803). Health behaviors included smoking, physical inactivity, alcohol use, body mass index (BMI), sedentary behavior, and sleep duration and quality. In stage one, path-specific regression estimates were obtained in each cohort. In stage two, cohort-specific estimates were pooled using random-effects multivariate meta-analysis, and natural indirect effects (i.e. mediating effects) were calculated as hazard ratios (HRs). RESULTS: Smoking (HRs range 1.04-1.10) and physical inactivity (HRs range 1.01-1.02) significantly mediated the associations among depression, anxiety, and lung cancer. Smoking was also a mediator for smoking-related cancers (HRs range 1.03-1.06). There was mediation by health behaviors, especially smoking, physical inactivity, alcohol use, and a higher BMI, in the associations among depression, anxiety, and overall cancer or other types of cancer, but effects were small (HRs generally below 1.01). CONCLUSIONS: Smoking constitutes a mediating pathway linking depression and anxiety to lung cancer and smoking-related cancers. Our findings underline the importance of smoking cessation interventions for persons with depression or anxiety.

Mental healthcare utilization among head and neck cancer patients: A longitudinal cohort study.

OBJECTIVE: To investigate utilization of mental healthcare among head and neck cancer (HNC) patients from diagnosis to 2 years after treatment, in relation to psychological symptoms, mental disorders, need for mental healthcare, and sociodemographic, clinical and personal factors. METHODS: Netherlands Quality of life and Biomedical Cohort study data as measured before treatment, at 3 and 6 months, and at 1 and 2 years after treatment was used (n = 610). Data on mental healthcare utilization (iMCQ), psychological symptoms (Hospital Anxiety and Depression Scale, Cancer Worry Scale), mental disorders (CIDI interview), need for mental healthcare (Supportive Care Needs Survey Short-Form 34, either as continuous outcome indicating the level of need or dichotomized into unmet need (yes/no)) and several sociodemographic, clinical and personal factors were collected. Factors associated with mental healthcare utilization were investigated using generalized estimating equations (p < 0.05). RESULTS: Of all HNC patients, 5%-9% used mental healthcare per timepoint. This was 4%-14% in patients with mild-severe psychological symptoms, 4%-17% in patients with severe psychological symptoms, 15%-35% in patients with a mental disorder and 5%-16% in patients with an unmet need for mental healthcare. Among all patients, higher symptoms of anxiety, a higher need for mental healthcare, lower age, higher disease stage, lower self-efficacy and higher social support seeking were significantly associated with mental healthcare utilization. CONCLUSION: Mental health care utilization among HNC patients is limited, and is related to psychological symptoms, need for mental healthcare, and sociodemographic, clinical and personal factors.

Longitudinal Assessment of Seasonal Impacts and Depression Associations on Circadian Rhythm Using Multimodal Wearable Sensing: Retrospective Analysis.

BACKGROUND: Previous mobile health (mHealth) studies have revealed significant links between depression and circadian rhythm features measured via wearables. However, the comprehensive impact of seasonal variations was not fully considered in these studies, potentially biasing interpretations in real-world settings. OBJECTIVE: This study aims to explore the associations between depression severity and wearable-measured circadian rhythms while accounting for seasonal impacts. METHODS: Data were sourced from a large longitudinal mHealth study, wherein participants' depression severity was assessed biweekly using the 8-item Patient Health Questionnaire (PHQ-8), and participants' behaviors, including sleep, step count, and heart rate (HR), were tracked via Fitbit devices for up to 2 years. We extracted 12 circadian rhythm features from the 14-day Fitbit data preceding each PHQ-8 assessment, including cosinor variables, such as HR peak timing (HR acrophase), and nonparametric features, such as the onset of the most active continuous 10-hour period (M10 onset). To investigate the association between depression severity and circadian rhythms while also assessing the seasonal impacts, we used three nested linear mixed-effects models for each circadian rhythm feature: (1) incorporating the PHQ-8 score as an independent variable, (2) adding seasonality, and (3) adding an interaction term between season and the PHQ-8 score. RESULTS: Analyzing 10,018 PHQ-8 records alongside Fitbit data from 543 participants (n=414, 76.2% female; median age 48, IQR 32-58 years), we found that after adjusting for seasonal effects, higher PHQ-8 scores were associated with reduced daily steps (ß=-93.61, P<.001), increased sleep variability (ß=0.96, P<.001), and delayed circadian rhythms (ie, sleep onset: ß=0.55, P=.001; sleep offset: ß=1.12, P<.001; M10 onset: ß=0.73, P=.003; HR acrophase: ß=0.71, P=.001). Notably, the negative association with daily steps was more pronounced in spring (ß of PHQ-8 × spring = -31.51, P=.002) and summer (ß of PHQ-8 × summer = -42.61, P<.001) compared with winter. Additionally, the significant correlation with delayed M10 onset was observed solely in summer (ß of PHQ-8 × summer = 1.06, P=.008). Moreover, compared with winter, participants experienced a shorter sleep duration by 16.6 minutes, an increase in daily steps by 394.5, a delay in M10 onset by 20.5 minutes, and a delay in HR peak time by 67.9 minutes during summer. CONCLUSIONS: Our findings highlight significant seasonal influences on human circadian rhythms and their associations with depression, underscoring the importance of considering seasonal variations in mHealth research for real-world applications. This study also indicates the potential of wearable-measured circadian rhythms as digital biomarkers for depression.

Depression, anxiety, and the risk of cancer: An individual participant data meta-analysis.

BACKGROUND: Depression and anxiety have long been hypothesized to be related to an increased cancer risk. Despite the great amount of research that has been conducted, findings are inconclusive. To provide a stronger basis for addressing the associations between depression, anxiety, and the incidence of various cancer types (overall, breast, lung, prostate, colorectal, alcohol-related, and smoking-related cancers), individual participant data (IPD) meta-analyses were performed within the Psychosocial Factors and Cancer Incidence (PSY-CA) consortium. METHODS: The PSY-CA consortium includes data from 18 cohorts with measures of depression or anxiety (up to N = 319,613; cancer incidences, 25,803; person-years of follow-up, 3,254,714). Both symptoms and a diagnosis of depression and anxiety were examined as predictors of future cancer risk. Two-stage IPD meta-analyses were run, first by using Cox regression models in each cohort (stage 1), and then by aggregating the results in random-effects meta-analyses (stage 2). RESULTS: No associations were found between depression or anxiety and overall, breast, prostate, colorectal, and alcohol-related cancers. Depression and anxiety (symptoms and diagnoses) were associated with the incidence of lung cancer and smoking-related cancers (hazard ratios [HRs], 1.06-1.60). However, these associations were substantially attenuated when additionally adjusting for known risk factors including smoking, alcohol use, and body mass index (HRs, 1.04-1.23). CONCLUSIONS: Depression and anxiety are not related to increased risk for most cancer outcomes, except for lung and smoking-related cancers. This study shows that key covariates are likely to explain the relationship between depression, anxiety, and lung and smoking-related cancers. PREREGISTRATION NUMBER: https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=157677.

Depression, cardiometabolic disease, and their co-occurrence after childhood maltreatment: an individual participant data meta-analysis including over 200,000 participants.

BACKGROUND: Childhood maltreatment is associated with depression and cardiometabolic disease in adulthood. However, the relationships with these two diseases have so far only been evaluated in different samples and with different methodology. Thus, it remains unknown how the effect sizes magnitudes for depression and cardiometabolic disease compare with each other and whether childhood maltreatment is especially associated with the co-occurrence ("comorbidity") of depression and cardiometabolic disease. This pooled analysis examined the association of childhood maltreatment with depression, cardiometabolic disease, and their comorbidity in adulthood. METHODS: We carried out an individual participant data meta-analysis on 13 international observational studies (N = 217,929). Childhood maltreatment comprised self-reports of physical, emotional, and/or sexual abuse before 18 years. Presence of depression was established with clinical interviews or validated symptom scales and presence of cardiometabolic disease with self-reported diagnoses. In included studies, binomial and multinomial logistic regressions estimated sociodemographic-adjusted associations of childhood maltreatment with depression, cardiometabolic disease, and their comorbidity. We then additionally adjusted these associations for lifestyle factors (smoking status, alcohol consumption, and physical activity). Finally, random-effects models were used to pool these estimates across studies and examined differences in associations across sex and maltreatment types. RESULTS: Childhood maltreatment was associated with progressively higher odds of cardiometabolic disease without depression (OR [95% CI] = 1.27 [1.18; 1.37]), depression without cardiometabolic disease (OR [95% CI] = 2.68 [2.39; 3.00]), and comorbidity between both conditions (OR [95% CI] = 3.04 [2.51; 3.68]) in adulthood. Post hoc analyses showed that the association with comorbidity was stronger than with either disease alone, and the association with depression was stronger than with cardiometabolic disease. Associations remained significant after additionally adjusting for lifestyle factors, and were present in both males and females, and for all maltreatment types. CONCLUSIONS: This meta-analysis revealed that adults with a history of childhood maltreatment suffer more often from depression and cardiometabolic disease than their non-exposed peers. These adults are also three times more likely to have comorbid depression and cardiometabolic disease. Childhood maltreatment may therefore be a clinically relevant indicator connecting poor mental and somatic health. Future research should investigate the potential benefits of early intervention in individuals with a history of maltreatment on their distal mental and somatic health (PROSPERO CRD42021239288).

Dissection of depression heterogeneity using proteomic clusters.

BACKGROUND: The search for relevant biomarkers of major depressive disorder (MDD) is challenged by heterogeneity; biological alterations may vary in patients expressing different symptom profiles. Moreover, most research considers a limited number of biomarkers, which may not be adequate for tagging complex network-level mechanisms. Here we studied clusters of proteins and examined their relation with MDD and individual depressive symptoms. METHODS: The sample consisted of 1621 subjects from the Netherlands Study of Depression and Anxiety (NESDA). MDD diagnoses were based on DSM-IV criteria and the Inventory of Depressive Symptomatology questionnaire measured endorsement of 30 symptoms. Serum protein levels were detected using a multi-analyte platform (171 analytes, immunoassay, Myriad RBM DiscoveryMAP 250+). Proteomic clusters were computed using weighted correlation network analysis (WGCNA). RESULTS: Six proteomic clusters were identified, of which one was nominally significantly associated with current MDD (p = 9.62E-03, Bonferroni adj. p = 0.057). This cluster contained 21 analytes and was enriched with pathways involved in inflammation and metabolism [including C-reactive protein (CRP), leptin and insulin]. At the individual symptom level, this proteomic cluster was associated with ten symptoms, among which were five atypical, energy-related symptoms. After correcting for several health and lifestyle covariates, hypersomnia, increased appetite, panic and weight gain remained significantly associated with the cluster. CONCLUSIONS: Our findings support the idea that alterations in a network of proteins involved in inflammatory and metabolic processes are present in MDD, but these alterations map predominantly to clinical symptoms reflecting an imbalance between energy intake and expenditure.

A network analysis of depressive symptoms and metabolomics.

BACKGROUND: Depression is associated with metabolic alterations including lipid dysregulation, whereby associations may vary across individual symptoms. Evaluating these associations using a network perspective yields a more complete insight than single outcome-single predictor models. METHODS: We used data from the Netherlands Study of Depression and Anxiety (N = 2498) and leveraged networks capturing associations between 30 depressive symptoms (Inventory of Depressive Symptomatology) and 46 metabolites. Analyses involved 4 steps: creating a network with Mixed Graphical Models; calculating centrality measures; bootstrapping for stability testing; validating central, stable associations by extra covariate-adjustment; and validation using another data wave collected 6 years later. RESULTS: The network yielded 28 symptom-metabolite associations. There were 15 highly-central variables (8 symptoms, 7 metabolites), and 3 stable links involving the symptoms Low energy (fatigue), and Hypersomnia. Specifically, fatigue showed consistent associations with higher mean diameter for VLDL particles and lower estimated degree of (fatty acid) unsaturation. These remained present after adjustment for lifestyle and health-related factors and using another data wave. CONCLUSIONS: The somatic symptoms Fatigue and Hypersomnia and cholesterol and fatty acid measures showed central, stable, and consistent relationships in our network. The present analyses showed how metabolic alterations are more consistently linked to specific symptom profiles.

Inflammation and depression in young people: a systematic review and proposed inflammatory pathways.

Depression onset peaks during adolescence and young adulthood. Current treatments are only moderately effective, driving the search for novel pathophysiological mechanisms underlying youth depression. Inflammatory dysregulation has been shown in adults with depression, however, less is known about inflammation in youth depression. This systematic review identified 109 studies examining the association between inflammation and youth depression and showed subtle evidence for inflammatory dysregulation in youth depression. Longitudinal studies support the bidirectional association between inflammation and depression in youth. We hypothesise multiple inflammatory pathways contributing to depression. More research is needed on anti-inflammatory treatments, potentially tailored to individual symptom profiles.

The course of health-related quality of life in the first 2 years after a diagnosis of head and neck cancer: the role of personal, clinical, psychological, physical, social, lifestyle, disease-related, and biological factors.

PURPOSE: The aim of this prospective cohort study was to estimate the relationship between the course of HRQOL in the first 2 years after diagnosis and treatment of head and neck cancer (HNC) and personal, clinical, psychological, physical, social, lifestyle, HNC-related, and biological factors. METHODS: Data were used from 638 HNC patients of the NETherlands QUality of life and BIomedical Cohort study (NET-QUBIC). Linear mixed models were used to investigate factors associated with the course of HRQOL (EORTC QLQ-C30 global quality of life (QL) and summary score (SumSc)) from baseline to 3, 6, 12, and 24 months after treatment. RESULTS: Baseline depressive symptoms, social contacts, and oral pain were significantly associated with the course of QL from baseline to 24 months. Tumor subsite and baseline social eating, stress (hyperarousal), coughing, feeling ill, and IL-10 were associated with the course of SumSc. Post-treatment social contacts and stress (avoidance) were significantly associated with the course of QL from 6 to 24 months, and social contacts and weight loss with the course of SumSc. The course of SumSc from 6 to 24 months was also significantly associated with a change in financial problems, speech problems, weight loss, and shoulder problems between baseline and 6 months. CONCLUSION: Baseline clinical, psychological, social, lifestyle, HNC-related, and biological factors are associated with the course of HRQOL from baseline to 24 months after treatment. Post-treatment social, lifestyle, and HNC-related factors are associated with the course of HRQOL from 6 to 24 months after treatment.

Challenges in Using mHealth Data From Smartphones and Wearable Devices to Predict Depression Symptom Severity: Retrospective Analysis.

BACKGROUND: Major depressive disorder (MDD) affects millions of people worldwide, but timely treatment is not often received owing in part to inaccurate subjective recall and variability in the symptom course. Objective and frequent MDD monitoring can improve subjective recall and help to guide treatment selection. Attempts have been made, with varying degrees of success, to explore the relationship between the measures of depression and passive digital phenotypes (features) extracted from smartphones and wearables devices to remotely and continuously monitor changes in symptomatology. However, a number of challenges exist for the analysis of these data. These include maintaining participant engagement over extended time periods and therefore understanding what constitutes an acceptable threshold of missing data; distinguishing between the cross-sectional and longitudinal relationships for different features to determine their utility in tracking within-individual longitudinal variation or screening individuals at high risk; and understanding the heterogeneity with which depression manifests itself in behavioral patterns quantified by the passive features. OBJECTIVE: We aimed to address these 3 challenges to inform future work in stratified analyses. METHODS: Using smartphone and wearable data collected from 479 participants with MDD, we extracted 21 features capturing mobility, sleep, and smartphone use. We investigated the impact of the number of days of available data on feature quality using the intraclass correlation coefficient and Bland-Altman analysis. We then examined the nature of the correlation between the 8-item Patient Health Questionnaire (PHQ-8) depression scale (measured every 14 days) and the features using the individual-mean correlation, repeated measures correlation, and linear mixed effects model. Furthermore, we stratified the participants based on their behavioral difference, quantified by the features, between periods of high (depression) and low (no depression) PHQ-8 scores using the Gaussian mixture model. RESULTS: We demonstrated that at least 8 (range 2-12) days were needed for reliable calculation of most of the features in the 14-day time window. We observed that features such as sleep onset time correlated better with PHQ-8 scores cross-sectionally than longitudinally, whereas features such as wakefulness after sleep onset correlated well with PHQ-8 longitudinally but worse cross-sectionally. Finally, we found that participants could be separated into 3 distinct clusters according to their behavioral difference between periods of depression and periods of no depression. CONCLUSIONS: This work contributes to our understanding of how these mobile health-derived features are associated with depression symptom severity to inform future work in stratified analyses.

The role of depressive symptoms and symptom dimensions in actigraphy-assessed sleep, circadian rhythm, and physical activity.

BACKGROUND: Considering the heterogeneity of depression, distinct depressive symptom dimensions may be differentially associated with more objective actigraphy-based estimates of physical activity (PA), sleep and circadian rhythm (CR). We examined the association between PA, sleep, and CR assessed with actigraphy and symptom dimensions (i.e. mood/cognition, somatic/vegetative, sleep). METHODS: Fourteen-day actigraphy data of 359 participants were obtained from the Netherlands Study of Depression and Anxiety. PA, sleep, and CR estimates included gross motor activity (GMA), sleep duration (SD), sleep efficiency (SE), relative amplitude between daytime and night-time activity (RA) and sleep midpoint. The 30-item Inventory of Depressive Symptomatology was used to assess depressive symptoms, which were categorised in three depression dimensions: mood/cognition, somatic/vegetative, and sleep. RESULTS: GMA and RA were negatively associated with higher score on all three symptom dimensions: mood/cognition (GMA: ß = -0.155, p < 0.001; RA: ß = -0.116, p = 0.002), somatic/vegetative (GMA: ß = -0.165, p < 0.001; RA: ß = -0.133, p < 0.001), sleep (GMA: ß = -0.169, p < 0.001; RA: ß = -0.190, p < 0.001). The association with sleep was more pronounced for two depression dimensions: longer SD was linked to somatic/vegetative (ß = 0.115, p = 0.015) dimension and lower SE was linked to sleep (ß = -0.101, p = 0.011) dimension. CONCLUSION: As three symptom dimensions were associated with actigraphy-based low PA and dampened CR, these seem to be general indicators of depression. Sleep disturbances appeared more linked to the somatic/vegetative and sleep dimensions; the effectiveness of sleep interventions in patients reporting somatic/vegetative symptoms may be explored, as well as the potential of actigraphy to monitor treatment response to such interventions.

Investigating data-driven biological subtypes of psychiatric disorders using specification-curve analysis.

BACKGROUND: Cluster analyses have become popular tools for data-driven classification in biological psychiatric research. However, these analyses are known to be sensitive to the chosen methods and/or modelling options, which may hamper generalizability and replicability of findings. To gain more insight into this problem, we used Specification-Curve Analysis (SCA) to investigate the influence of methodological variation on biomarker-based cluster-analysis results. METHODS: Proteomics data (31 biomarkers) were used from patients (n = 688) and healthy controls (n = 426) in the Netherlands Study of Depression and Anxiety. In SCAs, consistency of results was evaluated across 1200 k-means and hierarchical clustering analyses, each with a unique combination of the clustering algorithm, fit-index, and distance metric. Next, SCAs were run in simulated datasets with varying cluster numbers and noise/outlier levels to evaluate the effect of data properties on SCA outcomes. RESULTS: The real data SCA showed no robust patterns of biological clustering in either the MDD or a combined MDD/healthy dataset. The simulation results showed that the correct number of clusters could be identified quite consistently across the 1200 model specifications, but that correct cluster identification became harder when the number of clusters and noise levels increased. CONCLUSION: SCA can provide useful insights into the presence of clusters in biomarker data. However, SCA is likely to show inconsistent results in real-world biomarker datasets that are complex and contain considerable levels of noise. Here, the number and nature of the observed clusters may depend strongly on the chosen model-specification, precluding conclusions about the existence of biological clusters among psychiatric patients.

Association of inflammation with depression and anxiety: evidence for symptom-specificity and potential causality from UK Biobank and NESDA cohorts.

We examined whether inflammation is uniformly associated with all depressive and anxiety symptoms, and whether these associations are potentially causal. Data was from 147,478 individuals from the UK Biobank (UKB) and 2,905 from the Netherlands Study of Depression and Anxiety (NESDA). Circulating C-reactive protein (CRP) was measured in both cohorts and interleukin-6 (IL-6) in NESDA. Genetic instruments for these proteins were obtained from published GWAS and UKB. Depressive and anxiety symptoms were assessed with self-report questionnaires. In NESDA, neurovegetative (appetite, sleep, psychomotor) symptoms were disaggregated as increased vs. decreased. In joint analyses, higher CRP was associated with depressive symptoms of depressed mood (OR = 1.06, 95% CI = 1.05-1.08), altered appetite (OR = 1.25, 95%CI = 1.23-1.28), sleep problems (OR = 1.05, 95%CI = 1.04-1.06), and fatigue (OR = 1.12, 95% CI = 1.11-1.14), and with anxiety symptoms of irritability (OR = 1.06, 95% CI = 1.05-1.08) and worrying control (OR = 1.03, 95% CI = 1.02-1.04). In NESDA, higher IL-6 was additionally associated with anhedonia (OR = 1.30, 95% CI = 1.12-1.52). Higher levels of both CRP (OR = 1.27, 95% CI = 1.13-1.43) and IL-6 (OR = 1.26, 95% CI = 1.07-1.49) were associated with increased sleep. Higher CRP was associated with increased appetite (OR = 1.21, 95% CI = 1.08-1.35) while higher IL-6 with decreased appetite (OR = 1.45, 95% CI = 1.18-1.79). In Mendelian Randomisation analyses, genetically predicted higher IL-6 activity was associated with increased risk of fatigue (estimate = 0.25, SE = 0.08) and sleep problems (estimate = 0.19, SE = 0.07). Inflammation was associated with core depressive symptoms of low mood and anhedonia and somatic/neurovegetative symptoms of fatigue, altered sleep and appetite changes. Less consistent associations were found for anxiety. The IL-6/IL-6R pathway could be causally linked to depression. Experimental studies are required to further evaluate causality, mechanisms, and usefulness of immunotherapies for depressive symptoms.

Within-day bidirectional associations between physical activity and affect: A real-time ambulatory study in persons with and without depressive and anxiety disorders.

BACKGROUND: Ambulatory assessments offer opportunities to study physical activity level (PAL) and affect at the group and person-level. We examined bidirectional associations between PAL and affect in a 3-h timeframe and evaluated whether associations differ between people with and without current or remitted depression/anxiety. METHODS: Two-week ecological momentary assessment (EMA) and actigraphy data of 359 participants with current (n = 93), remitted (n = 176), or no (n = 90) Composite International Diagnostic Interview depression/anxiety diagnoses were obtained from the Netherlands Study of Depression and Anxiety. Positive affect (PA) and negative affect (NA) were assessed by EMA 5 times per day. Average PAL between EMA assessments were calculated from actigraphy data. RESULTS: At the group-level, higher PAL was associated with subsequent higher PA (b = 0.109, p < .001) and lower NA (b = -0.043, p < .001), while higher PA (b = 0.066, p < .001) and lower NA (b = -0.053, p < .001) were associated with subsequent higher PAL. The association between higher PAL and subsequent lower NA was stronger for current depression/anxiety patients than controls (p = .01). At the person-level, analyses revealed heterogeneity in bidirectional associations. CONCLUSIONS: Higher PAL may improve affect, especially among depression/anxiety patients. As the relationships vary at the person-level, ambulatory assessments may help identify who would benefit from behavioral interventions.

Remote Assessment of Disease and Relapse in Major Depressive Disorder (RADAR-MDD): recruitment, retention, and data availability in a longitudinal remote measurement study.

BACKGROUND: Major Depressive Disorder (MDD) is prevalent, often chronic, and requires ongoing monitoring of symptoms to track response to treatment and identify early indicators of relapse. Remote Measurement Technologies (RMT) provide an opportunity to transform the measurement and management of MDD, via data collected from inbuilt smartphone sensors and wearable devices alongside app-based questionnaires and tasks. A key question for the field is the extent to which participants can adhere to research protocols and the completeness of data collected. We aimed to describe drop out and data completeness in a naturalistic multimodal longitudinal RMT study, in people with a history of recurrent MDD. We further aimed to determine whether those experiencing a depressive relapse at baseline contributed less complete data. METHODS: Remote Assessment of Disease and Relapse - Major Depressive Disorder (RADAR-MDD) is a multi-centre, prospective observational cohort study conducted as part of the Remote Assessment of Disease and Relapse - Central Nervous System (RADAR-CNS) program. People with a history of MDD were provided with a wrist-worn wearable device, and smartphone apps designed to: a) collect data from smartphone sensors; and b) deliver questionnaires, speech tasks, and cognitive assessments. Participants were followed-up for a minimum of 11 months and maximum of 24 months. RESULTS: Individuals with a history of MDD (n = 623) were enrolled in the study,. We report 80% completion rates for primary outcome assessments across all follow-up timepoints. 79.8% of people participated for the maximum amount of time available and 20.2% withdrew prematurely. We found no evidence of an association between the severity of depression symptoms at baseline and the availability of data. In total, 110 participants had > 50% data available across all data types. CONCLUSIONS: RADAR-MDD is the largest multimodal RMT study in the field of mental health. Here, we have shown that collecting RMT data from a clinical population is feasible. We found comparable levels of data availability in active and passive forms of data collection, demonstrating that both are feasible in this patient group.