Prospective cohort study of early biosignatures of response to lithium in bipolar-I-disorders: overview of the H2020-funded R-LiNK initiative

Wearable and Mobile Technologies for the Evaluation and Treatment of Obsessive-Compulsive Disorder: Scoping Review

BACKGROUND

Smartphones and wearable biosensors can continuously and passively measure aspects of behavior and physiology while also collecting data that require user input. These devices can potentially be used to monitor symptom burden; estimate diagnosis and risk for relapse; predict treatment response; and deliver digital interventions in patients with obsessive-compulsive disorder (OCD), a prevalent and disabling psychiatric condition that often follows a chronic and fluctuating course and may uniquely benefit from these technologies.

OBJECTIVE

Given the speed at which mobile and wearable technologies are being developed and implemented in clinical settings, a continual reappraisal of this field is needed. In this scoping review, we map the literature on the use of wearable devices and smartphone-based devices or apps in the assessment, monitoring, or treatment of OCD.

METHODS

In July 2022 and April 2023, we conducted an initial search and an updated search, respectively, of multiple databases, including PubMed, Embase, APA PsycINFO, and Web of Science, with no restriction on publication period, using the following search strategy: ("OCD" OR "obsessive" OR "obsessive-compulsive") AND ("smartphone" OR "phone" OR "wearable" OR "sensing" OR "biofeedback" OR "neurofeedback" OR "neuro feedback" OR "digital" OR "phenotyping" OR "mobile" OR "heart rate variability" OR "actigraphy" OR "actimetry" OR "biosignals" OR "biomarker" OR "signals" OR "mobile health").

RESULTS

We analyzed 2748 articles, reviewed the full text of 77 articles, and extracted data from the 25 articles included in this review. We divided our review into the following three parts: studies without digital or mobile intervention and with passive data collection, studies without digital or mobile intervention and with active or mixed data collection, and studies with a digital or mobile intervention.

CONCLUSIONS

Use of mobile and wearable technologies for OCD has developed primarily in the past 15 years, with an increasing pace of related publications. Passive measures from actigraphy generally match subjective reports. Ecological momentary assessment is well tolerated for the naturalistic assessment of symptoms, may capture novel OCD symptoms, and may also document lower symptom burden than retrospective recall. Digital or mobile treatments are diverse; however, they generally provide some improvement in OCD symptom burden. Finally, ongoing work is needed for a safe and trusted uptake of technology by patients and providers.

Divergent Directionality of Immune Cell-Specific Protein Expression between Bipolar Lithium Responders and Non-Responders Revealed by Enhanced Flow Cytometry

Background and Objectives: There is no biomarker to predict lithium response. This study used CellPrint™ enhanced flow cytometry to study 28 proteins representing a spectrum of cellular pathways in monocytes and CD4⁺ lymphocytes before and after lithium treatment in patients with bipolar disorder (BD). Materials and Methods: Symptomatic patients with BD type I or II received lithium (serum level ≥ 0.6 mEq/L) for 16 weeks. Patients were assessed with standard rating scales and divided into two groups, responders (≥50% improvement from baseline) and non-responders. Twenty-eight intracellular proteins in CD4⁺ lymphocytes and monocytes were analyzed with CellPrint™, an enhanced flow cytometry procedure. Data were analyzed for differences in protein expression levels. Results: The intent-to-treat sample included 13 lithium-responders (12 blood samples before treatment and 9 after treatment) and 11 lithium-non-responders (11 blood samples before treatment and 4 after treatment). No significant differences in expression between the groups was observed prior to lithium treatment. After treatment, the majority of analytes increased expression in responders and decreased expression in non-responders. Significant increases were seen for PDEB4 and NR3C1 in responders. A significant decrease was seen for NR3C1 in non-responders. Conclusions: Lithium induced divergent directionality of protein expression depending on the whether the patient was a responder or non-responder, elucidating molecular characteristics of lithium responsiveness. A subsequent study with a larger sample size is warranted.

Elevated choline in dorsolateral prefrontal cortex of lithium responders with bipolar I disorder

INTRODUCTION

Response to lithium maintenance varies widely across patients with bipolar disorder (BD). The studies on neurochemical correlates of long-term lithium response in BD remain scant.

AIM

To assess the neurochemical profile in DLPFC based on lithium response status among subjects with bipolar I disorder (BD-I) using in vivo MRS.

MATERIALS AND METHOD

This was an observational study of 40 right-handed, euthymic adult participants with DSM-5 BD-I on long-term lithium maintenance with no psychiatric comorbidities (MINI 7.0). Using Alda Lithium Response Scale (LRS), a cut-off ≥ 7 for excellent lithium response, the sample was grouped into study group I for responders and group II for non-responders. All participants were assessed using NIMH Life Chart Method and IGSLI typical/atypical features scale. ¹H-MRS was carried out on a 3 T MR scanner (Achieva, Phillips) using a 32-channel head coil, with a voxel placed at the left DLPFC. LC model was used to measure absolute concentrations of neurochemicals and their ratios in relation to creatine.

RESULTS

Group I (n = 20) was comparable to Group II (n = 20) with respect to demographic and illness profile. The GPC/Cr+PCr ratio was significantly higher (p = 0.028) among excellent lithium responders (0.32 ± 0.20 mmol/l) compared to sub-optimal responders (0.25 ± 0.05 mmol/l). Choline-containing compounds reflect alterations in cell membrane synthesis or myelin turnover, and are a marker of overall cell density. No significant alterations were detected in NAA, glutamate, glutamine, myo-inositol and creatine.

CONCLUSION

The lithium responders exhibited elevated choline (GPC) in the left DLPFC compared to non-responders.

Variability and efficacy in treatment effects on manic symptoms with lithium, anticonvulsants, and antipsychotics in acute bipolar mania: A systematic review and meta-analysis

BACKGROUND

Acute mania is a psychiatric emergency requiring rapid management. However, randomised controlled trials (RCTs) have shown considerable individual differences in treatment effects on manic symptoms with antimanic drugs.

METHODS

We searched the MEDLINE, CENTRAL, EMBASE, PsycINFO, and ClinicalTrials.gov to identify RCTs without language restrictions from inception to April 19, 2022. We included double-blind RCTs of oral antimanic monotherapy versus placebo in adult patients. The primary outcome was variability in improvement of manic symptoms (assessed using the coefficient of variation ratio [CVR]). The secondary outcomes were overall improvement of manic symptoms and acceptability (i.e., discontinuation for any reason). The pooled effects of outcomes were calculated by random-effects meta-analyses using restricted maximum likelihood methods. The quality of the included studies was assessed using the Cochrane Risk of Bias (ROB) Assessment Tool. This study was registered with OSF (DOI:10.17605/OSF.IO/G4JNY).

FINDINGS

We included 39 RCTs (N=12150; mean age=39·9 years, interquartile range [IQR]=38·7-41·1; mean proportion of female=48·6%, IQR=42·3%-52·3%) and investigated 14 antimanic drugs. We found that eight antimanic drugs compared to placebo were associated with lower CVRs (95% confidence interval [CI]; I²), including risperidone (0·51; 0·37-0·70; 0%), haloperidol (0·54; 0·44-0·67; 4%), olanzapine (0·59; 0·44-0·79; 47%), ziprasidone (0·61; 0·53-0·71; 0%), lithium (0·63; 0·52-0·76; 0%), quetiapine (0·65; 0·48-0·87; 2%), aripiprazole (0·68; 0·56-0·84; 25%), and cariprazine (0·70; 0·49-0·99; 28%). There were nine antimanic drugs associated with greater efficacy than placebo, including risperidone (reported as standardised mean difference; 95% CI; I²: 0·64; 0·31-0·97; 15%), haloperidol (0·57; 0·29-0·85; 64%), cariprazine (0·51; 0·24-0·78; 0%), olanzapine (0·44; 0·30-0·58; 0%), lithium (0·42; 0·29-0·55; 0%), ziprasidone (0·42; 0·26-0·58; 0%), quetiapine (0·40; 0·13-0·67; 0%), asenapine (0·40; 0·13-0·67; 0%), and aripiprazole (0·32; 0·14-0·49; 53%). Ziprasidone (reported as risk ratio; 95% CI; I²: 0·83; 0·79-0·89; 0%) and olanzapine (0·63; 0·49-0·80; 35%) were associated with better acceptability relative to placebo. Among the 39 RCTs, none had a high ROB.

INTERPRETATION

We demonstrated that eight antimanic drugs were associated with lower variability and better efficacy than placebo, suggesting that these antimanic drugs were associated with more homogenous and predictable improvements of manic symptoms in patients with acute mania.

FUNDING

The study was supported by from the Ministry of Science and Technology (MOST-110-2314-B-016-035, MOST-111-2314-B-016-054), Medical Affairs Bureau (MND-MAB-D-111102), and Tri-service General Hospital (TSGH-E-111229).

An ecological study of objective rest-activity markers of lithium response in bipolar-I-disorder

BACKGROUND

Despite its pivotal role in prophylaxis for bipolar-I-disorders (BD-I), variability in lithium (Li) response is poorly understood and only a third of patients show a good outcome. Converging research strands indicate that rest-activity rhythms can help characterize BD-I and might differentiate good responders (GR) and non-responders (NR).

METHODS

Seventy outpatients with BD-I receiving Li prophylaxis were categorized as GR or NR according to the ratings on the retrospective assessment of response to lithium scale (Alda scale). Participants undertook 21 consecutive days of actigraphy monitoring of sleep quantity (SQ), sleep variability (SV) and circadian rhythmicity (CR).

RESULTS

Twenty-five individuals were categorized as GR (36%). After correcting statistical analysis to minimize false discoveries, four variables (intra-daily variability; median activity level; amplitude; and relative amplitude of activity) significantly differentiated GR from NR. The odds of being classified as a GR case were greatest for individuals showing more regular/stable CR (1.41; 95% confidence interval (CI) 1.08, 2.05; p < 0.04). Also, there was a trend for lower SV to be associated with GR (odds ratio: 0.56; 95% CI 0.31, 1.01; p < 0.06).

CONCLUSIONS

To our knowledge, this is the largest actigraphy study of rest-activity rhythms and Li response. Circadian markers associated with fragmentation, variability, amount and/or amplitude of day and night-time activity best-identified GR. However, associations were modest and future research must determine whether these objectively measured parameters, singly or together, represent robust treatment response biomarkers. Actigraphy may offer an adjunct to multi-platform approaches aimed at developing personalized treatments or stratification of individuals with BD-I into treatment-relevant subgroups.

Is Poor Lithium Response in Individuals with Bipolar Disorder Associated with Increased Degradation of Tryptophan along the Kynurenine Pathway? Results of an Exploratory Study

Bipolar disorder is associated with an inflammation-triggered elevated catabolism of tryptophan to the kynurenine pathway, which impacts psychiatric symptoms and outcomes. The data indicate that lithium exerts anti-inflammatory effects by inhibiting indoleamine-2,3-dioxygenase (IDO)-1 activity. This exploratory study aimed to investigate the tryptophan catabolism in individuals with bipolar disorder (n = 48) compared to healthy controls (n = 48), and the associations with the response to mood stabilizers such as lithium, valproate, or lamotrigine rated with the Retrospective Assessment of the Lithium Response Phenotype Scale (or the Alda scale). The results demonstrate an association of a poorer response to lithium with higher levels of kynurenine, kynurenine/tryptophan ratio as a proxy for IDO-1 activity, as well as quinolinic acid, which, overall, indicates a pro-inflammatory state with a higher degradation of tryptophan towards the neurotoxic branch. The treatment response to valproate and lamotrigine was not associated with the levels of the tryptophan metabolites. These findings support the anti-inflammatory properties of lithium. Furthermore, since quinolinic acid has neurotoxic features via the glutamatergic pathway, they also strengthen the assumption that the clinical drug response might be associated with biochemical processes. The relationship between the lithium response and the measurements of the tryptophan to the kynurenine pathway is of clinical relevance and may potentially bring advantages towards a personalized medicine approach to bipolar disorder that allows for the selection of the most effective mood-stabilizing drug.

Methylomic Biomarkers of Lithium Response in Bipolar Disorder: A Proof of Transferability Study

Response to lithium (Li) is highly variable in bipolar disorders (BD) and no clinical or biological predictors of long-term response have been validated to date. Using a genome-wide methylomic approach (SeqCapEpi), we previously identified seven differentially methylated regions (DMRs) that discriminated good from non-responders (prophylactic response phenotype defined using the “Alda” scale). This study is a proof of transferability from bench to bedside of this epigenetic signature. For this purpose, we used Methylation Specific High-Resolution Melting (MS-HRM), a PCR based method that can be implemented in any medical laboratory at low cost and with minimal equipment. In 23 individuals with BD, MS-HRM measures of three out of seven DMRs were technically feasible and consistencies between SeqCapEpi and MS-HRM-measures were moderate to high. In an extended sample of individuals with BD (n = 70), the three MS-HRM-measured DMRs mainly predicted nonresponse, with AUC between 0.70–0.80 according to different definitions of the phenotype (Alda- or machine-learning-based definitions). Classification tree analyses further suggested that the MS-HRM-measured DMRs correctly classified up to 84% of individuals as good or non-responders. This study suggested that epigenetic biomarkers, identified in a retrospective sample, accurately discriminate non-responders from responders to Li and may be transferrable to routine practice.

The crossroads of digital phenotyping

The term 'Digital Phenotyping' has started to appear with increasing regularity in medical research, especially within psychiatry. This aims to bring together digital traces (e.g., from smartphones), medical data (e.g., electronic health records), and lived experiences (e.g., daily activity, location, social contact), to better monitor, intervene, and diagnose various psychiatric conditions. However, is this notion any different from digital traces or the quantified self? While digital phenotyping has the potential to transform and revolutionize medicine as we know it; there are a number of challenges that must be addressed if research is to blossom. At present, these issues include; (1) methodological issues, for example, the lack of clear theoretical links between digital markers (e.g., battery life, interactions with smartphones) and condition relapses, (2) the current tools being employed, where they typically have a number of security or privacy issues, and are invasive by nature, (3) analytical methods and approaches, where I question whether research should start in larger-scale epidemiological scale or in smaller (and potentially highly vulnerable) patient populations as is the current norm, (4) the current lack of security and privacy regulation adherence of apps used, and finally, (5) how do such technologies become integrated into various healthcare systems? This aims to provide deep insight into how the Digital Phenotyping could provide huge promise if we critically reflect now and gather clinical insights with a number of other disciplines such as epidemiology, computer- and the social sciences to move forward.

Clinical pharmacology: Current innovations and future challenges

Clinical pharmacology is the study of drugs in humans, from first-in-human studies to randomized controlled trials (RCTs) and benefit-risk ratio assessment in large populations. The objective of this review is to present the recent innovations that may revolutionize the development of drugs in the future. On behalf of the French Society of Pharmacology and Therapeutics, we provide recommendations to address those future challenges in clinical pharmacology. Whatever the future will be, robust preliminary data on drug mechanism of action and rigorous study design will remain crucial prior to the start of pharmacological studies in human. At the present time, RCTs remains the gold standard to evaluate the efficacy of human drugs, although alternative designs (pragmatic trials, platform trials, etc.) are emerging. Innovations in healthy volunteers' studies and the contribution of new technologies such as artificial intelligence, machine learning and internet-based trials have the potential to improve drug development. In the field of precision medicine, new disease phenotypes and endotypes will probably help to identify new pharmacological targets, responders to therapies and patients at risk for drug adverse events. In such a moving landscape, the development of translational research through academic and private partnership, transparent sharing of clinical trial data and enhanced interactions between drug experts, patients and the general public are priority areas for action.

A Comparison of Different Approaches to Clinical Phenotyping of Lithium Response: A Proof of Principle Study Employing Genetic Variants of Three Candidate Circadian Genes

Optimal classification of the response to lithium (Li) is crucial in genetic and biomarker research. This proof of concept study aims at exploring whether different approaches to phenotyping the response to Li may influence the likelihood of detecting associations between the response and genetic markers. We operationalized Li response phenotypes using the Retrospective Assessment of Response to Lithium Scale (i.e., the Alda scale) in a sample of 164 cases with bipolar disorder (BD). Three phenotypes were defined using the established approaches, whilst two phenotypes were generated by machine learning algorithms. We examined whether these five different Li response phenotypes showed different levels of statistically significant associations with polymorphisms of three candidate circadian genes (RORA, TIMELESS and PPARGC1A), which were selected for this study because they were plausibly linked with the response to Li. The three original and two revised Alda ratings showed low levels of discordance (misclassification rates: 8–12%). However, the significance of associations with circadian genes differed when examining previously recommended categorical and continuous phenotypes versus machine-learning derived phenotypes. Findings using machine learning approaches identified more putative signals of the Li response. Established approaches to Li response phenotyping are easy to use but may lead to a significant loss of data (excluding partial responders) due to recent attempts to improve the reliability of the original rating system. While machine learning approaches require additional modeling to generate Li response phenotypes, they may offer a more nuanced approach, which, in turn, would enhance the probability of identifying significant signals in genetic studies.

Effect of lithium on circadian rhythm in bipolar disorder: A systematic review and meta-analysis

OBJECTIVES

Circadian rhythm disruption is commonly reported in patients with bipolar disorder. Lithium has been suggested to have effects on the circadian clock, the biological basis of the circadian rhythm. The objective of the current review was to review systematically the existing studies on the effect of lithium on circadian rhythm in patients with bipolar disorder.

METHODS

We systematically searched the scientific literature up to September 2020 for experimental or observational studies which measured circadian rhythm in bipolar patients taking lithium (in comparison with placebo or other active treatments) and carried out a meta-analysis. Circadian rest-activity was our primary outcome, but we also collected data about sleep quality and chronotype (Morningness-Eveningness). The protocol was registered in PROSPERO (CRD42018109790).

RESULTS

Four observational studies (n = 668) and one experimental study (n = 29) were included. Results from the meta-analysis suggest a potential association between lithium and shifts towards morningness (standardized mean difference [SMD]: 0.42, 95% confidence interval [CI]: -0.05 to 0.90). One cohort study with 21 days of follow-up found that patients treated with lithium had significantly larger amplitude (0.68, 0.01 to 1.36) when compared to anticonvulsants.

CONCLUSION

This review highlights the insufficient evidence to inform us about the effect of lithium on circadian rhythm. However, we found that chronotype can be a potential target for further exploration of biomarkers or biosignatures of lithium treatment in patients with bipolar disorder. Further studies with prospective and longitudinal study design, adopting actigraphy to monitor daily circadian rest-activity changes are needed.

Mini review: Recent advances on epigenetic effects of lithium

Lithium (Li) remains the first line long-term treatment of bipolar disorders notwithstanding a high inter-individual variability of response. Significant research effort has been undertaken to understand the molecular mechanisms underlying Li cellular and clinical effects in order to identify predictive biomarkers of response. Li response has been shown to be partly heritable, however mechanisms that do not rely on DNA variants could also be involved. In recent years, modulation of epigenetic marks in relation with the level of Li response has appeared increasingly plausible. Recent results in this field of research have provided new insights into the molecular processes involved in Li effects. In this review, we examined the literature investigating the involvement of three epigenetic mechanisms (DNA methylation, noncoding RNAs and histone modifications) in Li clinical efficacy in bipolar disorder.

Deficient LEF1 expression is associated with lithium resistance and hyperexcitability in neurons derived from bipolar disorder patients

Bipolar disorder (BD) is a psychiatric condition characterized by depressive and manic episodes that affect 2% of the world population. The first-line long-term treatment for mood stabilization is lithium (Li). Induced pluripotent stem cell modeling of BD using hippocampal dentate gyrus-like neurons derived from Li-responsive (LR) and Li-non-responsive (NR) patients previously showed neuronal hyperexcitability. Li treatment reversed hyperexcitability only on the LR neurons. In this study we searched for specific targets of Li resistance in NR neurons and found that the activity of Wnt/β-catenin signaling pathway was severely affected, with a significant decrease in expression of LEF1. Li targets the Wnt/β-catenin signaling pathway by inhibiting GSK-3β and releasing β-catenin that forms a nuclear complex with TCF/LEF1, activating the Wnt/β-catenin transcription program. Therefore, we propose that downregulation of LEF1 may account for Li resistance in NR neurons. Our results show that valproic acid (VPA), a drug used to treat NR patients that also acts downstream of GSK-3β, upregulated LEF1 and Wnt/β-catenin gene targets, increased transcriptional activity of complex β-catenin/TCF/LEF1, and reduced excitability in NR neurons. In addition, decreasing LEF1 expression in control neurons using shLEF1 caused hyperexcitability, confirming that the impact of VPA on excitability in NR neurons was connected to changes in LEF1 and in the Wnt/β-catenin pathway. Our results suggest that LEF1 may be a useful target for the discovery of new drugs for BD treatment.

The Genetics of Response to and Side Effects of Lithium Treatment in Bipolar Disorder: Future Research Perspectives

Although the mood stabilizer lithium is a first-line treatment in bipolar disorder, a substantial number of patients do not benefit from it and experience side effects. No clinical tool is available for predicting lithium response or the occurrence of side effects in everyday clinical practice. Multiple genetic research efforts have been performed in this field because lithium response and side effects are considered to be multifactorial endophenotypes. Available results from linkage and segregation, candidate-gene, and genome-wide association studies indicate a role of genetic factors in determining response and side effects. For example, candidate-gene studies often report GSK3β, brain-derived neurotrophic factor, and SLC6A4 as being involved in lithium response, and the latest genome-wide association study found a genome-wide significant association of treatment response with a locus on chromosome 21 coding for two long non-coding RNAs. Although research results are promising, they are limited mainly by a lack of replicability and, despite the collaboration of consortia, insufficient sample sizes. The need for larger sample sizes and "multi-omics" approaches is apparent, and such approaches are crucial for choosing the best treatment options for patients with bipolar disorder. In this article, we delineate the mechanisms of action of lithium and summarize the results of genetic research on lithium response and side effects.

Can actigraphy be used to define lithium response dimensions in bipolar disorders?

BACKGROUND

Actigraphy is commonly used in case-control studies to explore sleep-wake patterns and circadian rhythmicity in bipolar disorders (BD). However, there is limited ecological research regarding actigraphy parameters associated with response to lithium (Li_Resp).

METHODS

Outpatients with BD-I (n=70) and BD-II (n=20) who were all prescribed prophylactic Li undertook 21 consecutive days of actigraphy recording. The Retrospective Assessment of Response to Lithium Scale (also referred as the Alda scale) was rated on a 0-10 continuum. We used principal component analysis (PCA) to summarize interrelationships among clinical and actigraphic variables and Li_Resp.

RESULTS

PCA demonstrated the existence of a Li_Resp dimension (accounting for >20% explained variance) characterized by 5 markers of circadian timing and rhythmicity. Replication of the PCA, using the resampling procedure, confirmed this model was robust for the BD-I but not for BD-II (which showed weaker associations between Li_Resp and sleep variables). These circadian rhythm markers identified by PCA correctly classified 64% (95% Confidence Intervals: 52-76%; p<0.03) of all BD cases as Li responders or non-responders.

LIMITATIONS

Although we attempted to minimize risk of statistical error, the small BD-II subsample may have undermined the ability of PCA to identify a robust Li_Resp dimension for this subtype.

CONCLUSIONS

Our findings are compatible with circadian models of BD and with putative mechanisms of action of Li. If confirmed in prospective studies, the study offers support for use of actigraphy as a relevant method for real time objective monitoring of Li_Resp, with few concerns regarding reliability and validity.

The Impact of Lithium on Brain Function in Bipolar Disorder: An Updated Review of Functional Magnetic Resonance Imaging Studies

Lithium remains a gold standard treatment for bipolar disorder (BD), and functional magnetic resonance imaging (fMRI) studies have contributed to clarifying its impact on neural circuitries in affected individuals. However, the specific neurobiological mechanisms through which lithium exerts its effects on brain function are not fully understood. In this review, we aimed to summarize the results of recent fMRI studies evaluating the impact of lithium on brain functional activity and connectivity in patients diagnosed with BD. We performed a literature search of available sources found in the PubMed database reported in English since 2016, when the last available review on this topic was published. Five fMRI studies in resting-state condition and six studies performed during the execution of emotional tasks met the inclusion criteria. Overall, the available evidence supports normalizing effects of lithium on brain activity and connectivity. Most of these studies reported a normalization in prefrontal regions and interconnected areas involved in emotion regulation and processing, regardless of the task employed. Importantly, lithium treatment showed distinct patterns of activity/connectivity changes compared with other treatments. Finally, lithium modulation of neural circuitries was found to be associated with clinical improvement in BD. These results are consistent with the hypothesis that selective abnormalities in neural circuitries supporting emotion processing and regulation improve during lithium treatment in BD. However, the heterogeneity of the examined studies regarding study design, sample selection, and analysis methods might limit the generalizability of the findings and lead to difficulties in comparing the results. Therefore, in future studies, larger cohorts and homogeneous experimental tasks are needed to further corroborate these findings.

Real-time mobile monitoring of bipolar disorder: a review of evidence and future directions

Rapidly accumulating data from mobile assessments are facilitating our ability to track patterns of emotions, behaviors, biologic rhythms, and their contextual influences in real time. These approaches have been widely applied to study the core features, traits, changes in states, and the impact of treatments in bipolar disorder (BD). This paper reviews recent evidence on the application of both passive and active mobile technologies to gain insight into the role of the circadian system and patterns of sleep and motor activity in people with BD. Findings of more than two dozen studies converge in demonstrating a broad range of sleep disturbances, particularly longer duration and variability of sleep patterns, lower average and greater variability of motor activity, and a shift to later peak activity and sleep midpoint, indicative of greater evening orientation among people with BD. The strong associations across the domains tapped by real-time monitoring suggest that future research should shift focus on sleep, physical/motor activity, or circadian patterns to identify common biologic pathways that influence their interrelations. The development of novel data-driven functional analytic tools has enabled the derivation of individualized multilevel dynamic representations of rhythms of multiple homeostatic regulatory systems. These multimodal tools can inform clinical research through identifying heterogeneity of the manifestations of BD and provide more objective indices of treatment response in real-world settings. Collaborative efforts with common protocols for the application of multimodal sensor technology will facilitate our ability to gain deeper insight into mechanisms and multisystem dynamics, as well as environmental, physiologic, and genetic correlates of BD.

Resting State Functional Connectivity Biomarkers of Treatment Response in Mood Disorders: A Review

There are currently no validated treatment biomarkers in psychiatry. Resting State Functional Connectivity (RSFC) is a popular method for investigating the neural correlates of mood disorders, but the breadth of the field makes it difficult to assess progress toward treatment response biomarkers. In this review, we followed general PRISMA guidelines to evaluate the evidence base for mood disorder treatment biomarkers across diagnoses, brain network models, and treatment modalities. We hypothesized that no treatment biomarker would be validated across these domains or with independent datasets. Results are organized, interpreted, and discussed in the context of four popular analytic techniques: (1) reference region (seed-based) analysis, (2) independent component analysis, (3) graph theory analysis, and (4) other methods. Cortico-limbic connectivity is implicated across studies, but there is no single biomarker that spans analyses or that has been replicated in multiple independent datasets. We discuss RSFC limitations and future directions in biomarker development.

Socio-demographic and clinical predictors of outcome to long-term treatment with lithium in bipolar disorders: a systematic review of the contemporary literature and recommendations from the ISBD/IGSLI Task Force on treatment with lithium

Objective

To identify possible socio-demographic and clinical factors associated with Good Outcome (GO) as compared with Poor Outcome (PO) in adult patients diagnosed with Bipolar Disorder (BD) who received long-term treatment with lithium.

Methods

A comprehensive search of major electronic databases was performed to identify relevant studies that included adults patients (18 years or older) with a diagnosis of BD and reported sociodemographic and/or clinical variables associated with treatment response and/or with illness outcome during long-term treatment to lithium (> = 6 months). The quality of the studies was scored using the Quality Assessment Tool for Observational Cohort and Cross-Sectional Studies from the National Institute of Health.

Results

Following review, 34 publications (from 31 independent datasets) were eligible for inclusion in this review. Most of them (n = 25) used a retrospective design. Only 11 studies were graded as good or borderline good quality. Forty-three potential predictors of outcome to lithium were identified. Four factors were associated with PO to lithium: alcohol use disorder; personality disorders; higher lifetime number of hospital admissions and rapid cycling pattern. Two factors were associated with GO in patients treated with lithium: good social support and episodic evolution of BD. However, when the synthesis of findings was limited to the highest (good or borderline good) quality studies (11 studies), only higher lifetime number of hospitalization admissions remained associated with PO to lithium and no associations remained for GO to lithium.

Conclusion

Despite decades of research on lithium and its clinical use, besides lifetime number of hospital admissions, no factor being consistently associated with GO or PO to lithium was identified. Hence, there remains a substantial gap in our understanding of predictors of outcome of lithium treatment indicating there is a need of high quality research on large representative samples.

An evidence map of actigraphy studies exploring longitudinal associations between rest-activity rhythms and course and outcome of bipolar disorders

BACKGROUND

Evidence mapping is a structured approach used to synthesize the state-of-the-art in an emerging field of research when systematic reviews or meta-analyses are deemed inappropriate. We employed this strategy to summarise knowledge regarding longitudinal ecological monitoring of rest-activity rhythms (RAR) and disease modifiers, course of illness, treatment response or outcome in bipolar disorders (BD).

STRUCTURE

We had two key aims: (1) to determine the number and type of actigraphy studies of in BD that explored data regarding: outcome over time (e.g. relapse/recurrence according to polarity, or recovery/remission), treatment response or illness trajectories and (2) to examine the range of actigraphy metrics that can be used to estimate disruptions of RAR and describe which individual circadian rhythm or sleep-wake cycle parameters are most consistently associated with outcome over time in BD. The mapping process incorporated four steps: clarifying the project focus, describing boundaries and 'coordinates' for mapping, searching the literature and producing a brief synopsis with summary charts of the key outputs. Twenty-seven independent studies (reported in 29 publications) were eligible for inclusion in the map. Most were small-scale, with the median sample size being 15 per study and median duration of actigraphy being about 7 days (range 1-210). Interestingly, 17 studies comprised wholly or partly of inpatients (63%). The available evidence indicated that a discrete number of RAR metrics are more consistently associated with transition between different phases of BD and/or may be predictive of longitudinal course of illness or treatment response. The metrics that show the most frequent associations represent markers of the amount, timing, or variability of RAR rather than the sleep quality metrics that are frequently targeted in contemporary studies of BD.

CONCLUSIONS

Despite 50 years of research, use of actigraphy to assess RAR in longitudinal studies and examination of these metrics and treatment response, course and outcome of BD is under-investigated. This is in marked contrast to the extensive literature on case-control or cross-sectional studies of actigraphy, especially typical sleep analysis metrics in BD. However, given the encouraging findings on putative RAR markers, we recommend increased study of putative circadian phenotypes of BD.

Lithium - past, present, future

OBJECTIVES

A narrative review of past, present, and future of lithium use in psychiatry.

METHODS

The most important references on the topic were reviewed with special emphasis on the author's works.

RESULTS

The history of medical and psychiatric use of lithium dates back to more than one and a half-century ago. However, modern psychiatric history began with the publication of John Cade, in 1949, showing a therapeutic effect of lithium in mania. Currently, lithium is a drug of choice as a mood-stabilizer for the maintenance treatment of the bipolar disorder. The second most important use of lithium is probably augmentation of antidepressants in treatment-resistant depression. In addition to its mood-stabilizing properties, lithium exerts anti-suicidal, immunomodulatory, and neuroprotective action. The drug may protect against dementia and some promising effects of lithium in neurodegenerative disorders have been observed.

CONCLUSION

Given the clinical and biological properties of lithium, this drug is presently greatly underutilized in mood disorders. Therefore, the efforts should be undertaken for challenging a skepticism about the use of lithium and optimizing its long-term administration. In such a way, more patients with mood disorders can become the beneficiaries of lithium's therapeutic action. KEY POINTS Lithium is a drug of choice as a mood-stabiliser for the maintenance treatment of bipolar disorder. Augmentation of antidepressants by lithium is one of the best strategies in treatment-resistant depression. Lithium exerts anti-suicidal, immunomodulatory, and neuroprotective action and may protect against dementia. Despite the evidence for the efficacy and added favourable properties, lithium is greatly underutilised in mood disorders. Challenging a scepticism about the use of lithium and optimising its long-term administration can make more patients with mood disorders the beneficiaries of lithium's therapeutic action.

Big data in severe mental illness: the role of electronic monitoring tools and metabolomics

There is an increasing interest in the development of effective early detection and intervention strategies in severe mental illness (SMI). Ideally, these efforts should lead to the delineation of accurate staging models of SMI enabling personalized interventions. It is plausible that big data approaches will be instrumental in describing the developmental trajectories of SMI by facilitating the incorporation of data from multiple sources, including those pertaining to the biological make-up of affected subjects. In this review, we first aimed to offer a perspective on how big data are helping the delineation of personalized approaches in SMI, and, second, to offer a quantitative synthesis of big data approaches in metabolomics of SMI. We finally described future directions of this research area.

Digital Phenotyping in Bipolar Disorder: Which Integration with Clinical Endophenotypes and Biomarkers?

Bipolar disorder (BD) is a complex neurobiological disorder characterized by a pathologic mood swing. Digital phenotyping, defined as the 'moment-by-moment quantification of the individual-level human phenotype in its own environment', represents a new approach aimed at measuring the human behavior and may theoretically enhance clinicians' capability in early identification, diagnosis, and management of any mental health conditions, including BD. Moreover, a digital phenotyping approach may easily introduce and allow clinicians to perform a more personalized and patient-tailored diagnostic and therapeutic approach, in line with the framework of precision psychiatry. The aim of the present paper is to investigate the role of digital phenotyping in BD. Despite scarce literature published so far, extremely heterogeneous methodological strategies, and limitations, digital phenotyping may represent a grounding research and clinical field in BD, by owning the potentialities to quickly identify, diagnose, longitudinally monitor, and evaluating clinical response and remission to psychotropic drugs. Finally, digital phenotyping might potentially constitute a possible predictive marker for mood disorders.

Neuroanatomic and Functional Neuroimaging Findings

The search for brain morphology findings that could explain behavioral disorders has gone through a long path in the history of psychiatry. With the advance of brain imaging technology, studies have been able to identify brain morphology and neural circuits associated with the pathophysiology of mental illnesses, such as bipolar disorders (BD). Promising results have also shown the potential of neuroimaging findings in the identification of outcome predictors and response to treatment among patients with BD. In this chapter, we present brain imaging structural and functional findings associated with BD, as well as their hypothesized relationship with the pathophysiological aspects of that condition and their potential clinical applications.

Translating big data to better treatment in bipolar disorder - a manifesto for coordinated action

Bipolar disorder (BD) is a major healthcare and socio-economic challenge. Despite its substantial burden on society, the research activity in BD is much smaller than its economic impact appears to demand. There is a consensus that the accurate identification of the underlying pathophysiology for BD is fundamental to realize major health benefits through better treatment and preventive regimens. However, to achieve these goals requires coordinated action and innovative approaches to boost the discovery of the neurobiological underpinnings of BD, and rapid translation of research findings into development and testing of better and more specific treatments. To this end, we here propose that only a large-scale coordinated action can be successful in integrating international big-data approaches with real-world clinical interventions. This could be achieved through the creation of a Global Bipolar Disorder Foundation, which could bring government, industry and philanthropy together in common cause. A global initiative for BD research would come at a highly opportune time given the seminal advances promised for our understanding of the genetic and brain basis of the disease and the obvious areas of unmet clinical need. Such an endeavour would embrace the principles of open science and see the strong involvement of user groups and integration of dissemination and public involvement with the research programs. We believe the time is right for a step change in our approach to understanding, treating and even preventing BD effectively.

Orphan G Protein Coupled Receptors in Affective Disorders

G protein coupled receptors (GPCRs) are the main mediators of signal transduction in the central nervous system. Therefore, it is not surprising that many GPCRs have long been investigated for their role in the development of anxiety and mood disorders, as well as in the mechanism of action of antidepressant therapies. Importantly, the endogenous ligands for a large group of GPCRs have not yet been identified and are therefore known as orphan GPCRs (oGPCRs). Nonetheless, growing evidence from animal studies, together with genome wide association studies (GWAS) and post-mortem transcriptomic analysis in patients, pointed at many oGPCRs as potential pharmacological targets. Among these discoveries, we summarize in this review how emotional behaviors are modulated by the following oGPCRs: ADGRB2 (BAI2), ADGRG1 (GPR56), GPR3, GPR26, GPR37, GPR50, GPR52, GPR61, GPR62, GPR88, GPR135, GPR158, and GPRC5B.

Can network analysis shed light on predictors of lithium response in bipolar I disorder?

OBJECTIVE

To undertake a large-scale clinical study of predictors of lithium (Li) response in bipolar I disorder (BD-I) and apply contemporary multivariate approaches to account for inter-relationships between putative predictors.

METHODS

We used network analysis to estimate the number and strength of connections between potential predictors of good Li response (measured by a new scoring algorithm for the Retrospective Assessment of Response to Lithium Scale) in 900 individuals with BD-I recruited to the Consortium of Lithium Genetics.

RESULTS

After accounting for co-associations between potential predictors, the most important factors associated with the good Li response phenotype were panic disorder, manic predominant polarity, manic first episode, age at onset between 15-32 years and family history of BD. Factors most strongly linked to poor outcome were comorbid obsessive-compulsive disorder, alcohol and/or substance misuse, and/or psychosis (symptoms or syndromes).

CONCLUSIONS

Network analysis can offer important additional insights to prospective studies of predictors of Li treatment outcomes. It appears to especially help in further clarifying the role of family history of BD (i.e. its direct and indirect associations) and highlighting the positive and negative associations of different subtypes of anxiety disorders with Li response, particularly the little-known negative association between Li response and obsessive-compulsive disorder.

Challenges and Future Prospects of Precision Medicine in Psychiatry

Precision medicine is increasingly recognized as a promising approach to improve disease treatment, taking into consideration the individual clinical and biological characteristics shared by specific subgroups of patients. In specific fields such as oncology and hematology, precision medicine has already started to be implemented in the clinical setting and molecular testing is routinely used to select treatments with higher efficacy and reduced adverse effects. The application of precision medicine in psychiatry is still in its early phases. However, there are already examples of predictive models based on clinical data or combinations of clinical, neuroimaging and biological data. While the power of single clinical predictors would remain inadequate if analyzed only with traditional statistical approaches, these predictors are now increasingly used to impute machine learning models that can have adequate accuracy even in the presence of relatively small sample size. These models have started to be applied to disentangle relevant clinical questions that could lead to a more effective management of psychiatric disorders, such as prediction of response to the mood stabilizer lithium, resistance to antidepressants in major depressive disorder or stratification of the risk and outcome prediction in schizophrenia. In this narrative review, we summarized the most important findings in precision medicine in psychiatry based on studies that constructed machine learning models using clinical, neuroimaging and/or biological data. Limitations and barriers to the implementation of precision psychiatry in the clinical setting, as well as possible solutions and future perspectives, will be presented.

mHealth in affective disorders: hype or hope? A focused narrative review

The constant growth and widespread availability of mobile technologies (i.e. smartphones and wearables) over the last decades have been a subject of intense interest and research in the affective disorders (AD) field. The potential of mHealth for collecting a new kind of passive and active information while providing cost-effective and tailored interventions have raised many hopes. However, until now, despite some encouraging results, research in the field has not been translated to reach real-world clinical settings or to develop additional evidence-based mHealth tools for people suffering from AD. Meanwhile, commercial untested apps and wearables are already being increasingly used and adopted by patients for the self-management of their illnesses. Hence, there is a latent need and demand from service users to integrate mHealth in their care, which the field cannot yet fulfil. In this article, through a focused narrative review, we discuss the evidence available for the use, validity and efficacy of mHealth tools in AD. Challenges in the academic field hampering the advancement of these technologies and its implementation into clinical practice are discussed. Lastly, we propose a framework to overcome these issues, which may facilitate mHealth solutions reaching service users.