Not missing at random: Missing data are associated with clinical status and trajectories in an electronic monitoring longitudinal study of bipolar disorder.

There is limited information on the association between participants' clinical status or trajectories and missing data in electronic monitoring studies of bipolar disorder (BD). We collected self-ratings scales and sensor data in 145 adults with BD. Using a new metric, Missing Data Ratio (MDR), we assessed missing self-rating data and sensor data monitoring activity and sleep. Missing data were lowest for participants in the midst of a depressive episode, intermediate for participants with subsyndromal symptoms, and highest for participants who were euthymic. Over a mean ± SD follow-up of 246 ± 181 days, missing data remained unchanged for participants whose clinical status did not change throughout the study (i.e., those who entered the study in a depressive episode and did not improve, or those who entered the study euthymic and remained euthymic). Conversely, when participants' clinical status changed during the study (e.g., those who entered the study euthymic and experienced the occurrence of a depressive episode), missing data for self-rating scales increased, but not for sensor data. Overall missing data were associated with participants' clinical status and its changes, suggesting that these are not missing at random.

Phenotypic clustering of bipolar disorder supports stratification by lithium responsiveness over diagnostic subtypes.

INTRODUCTION: The aim of this study was to determine whether the clinical profiles of bipolar disorder (BD) patients could be differentiated more clearly using the existing classification by diagnostic subtype or by lithium treatment responsiveness. METHODS: We included adult patients with BD-I or II (N = 477 across four sites) who were treated with lithium as their principal mood stabilizer for at least 1 year. Treatment responsiveness was defined using the dichotomized Alda score. We performed hierarchical clustering on phenotypes defined by 40 features, covering demographics, clinical course, family history, suicide behaviour, and comorbid conditions. We then measured the amount of information that inferred clusters carried about (A) BD subtype and (B) lithium responsiveness using adjusted mutual information (AMI) scores. Detailed phenotypic profiles across clusters were then evaluated with univariate comparisons. RESULTS: Two clusters were identified (n = 56 and n = 421), which captured significantly more information about lithium responsiveness (AMI range: 0.033 to 0.133) than BD subtype (AMI: 0.004 to 0.011). The smaller cluster had disproportionately more lithium responders (n = 47 [83.8%]) when compared to the larger cluster (103 [24.4%]; p = 0.006). CONCLUSIONS: Phenotypes derived from detailed clinical data may carry more information about lithium responsiveness than the current classification of diagnostic subtype. These findings support lithium responsiveness as a valid approach to stratification in clinical samples.

A pilot study examining the impact of lithium treatment and responsiveness on mnemonic discrimination in bipolar disorder.

INTRODUCTION: Mnemonic discrimination (MD), the ability to discriminate new stimuli from similar memories, putatively involves dentate gyrus pattern separation. Since lithium may normalize dentate gyrus functioning in lithium-responsive bipolar disorder (BD), we hypothesized that lithium treatment would be associated with better MD in lithium-responsive BD patients. METHODS: BD patients (N = 69; NResponders = 16 [23 %]) performed the Continuous Visual Memory Test (CVMT), which requires discriminating between novel and previously seen images. Before testing, all patients had prophylactic lithium responsiveness assessed over ≥1 year of therapy (with the Alda Score), although only thirty-eight patients were actively prescribed lithium at time of testing (55 %; 12/16 responders, 26/53 nonresponders). We then used computational modelling to extract patient-specific MD indices. Linear models were used to test how (A) lithium treatment, (B) lithium responsiveness via the continuous Alda score, and (C) their interaction, affected MD. RESULTS: Superior MD performance was associated with lithium treatment exclusively in lithium-responsive patients (Lithium x AldaScore ß = 0.257 [SE 0.078], p = 0.002). Consistent with prior literature, increased age was associated with worse MD (ß = -0.03 [SE 0.01], p = 0.005). LIMITATIONS: Secondary pilot analysis of retrospectively collected data in a cross-sectional design limits generalizability. CONCLUSION: Our study is the first to examine MD performance in BD. Lithium is associated with better MD performance only in lithium responders, potentially due to lithium's effects on dentate gyrus granule cell excitability. Our results may influence the development of behavioural probes for dentate gyrus neuronal hyperexcitability in BD.

MetaboLights: open data repository for metabolomics.

MetaboLights is a global database for metabolomics studies including the raw experimental data and the associated metadata. The database is cross-species and cross-technique and covers metabolite structures and their reference spectra as well as their biological roles and locations where available. MetaboLights is the recommended metabolomics repository for a number of leading journals and ELIXIR, the European infrastructure for life science information. In this article, we describe the continued growth and diversity of submissions and the significant developments in recent years. In particular, we highlight MetaboLights Labs, our new Galaxy Project instance with repository-scale standardized workflows, and how data public on MetaboLights are being reused by the community. Metabolomics resources and data are available under the EMBL-EBI's Terms of Use at https://www.ebi.ac.uk/metabolights and under Apache 2.0 at https://github.com/EBI-Metabolights.

Open access repository-scale propagated nearest neighbor suspect spectral library for untargeted metabolomics.

Despite the increasing availability of tandem mass spectrometry (MS/MS) community spectral libraries for untargeted metabolomics over the past decade, the majority of acquired MS/MS spectra remain uninterpreted. To further aid in interpreting unannotated spectra, we created a nearest neighbor suspect spectral library, consisting of 87,916 annotated MS/MS spectra derived from hundreds of millions of MS/MS spectra originating from published untargeted metabolomics experiments. Entries in this library, or "suspects," were derived from unannotated spectra that could be linked in a molecular network to an annotated spectrum. Annotations were propagated to unknowns based on structural relationships to reference molecules using MS/MS-based spectrum alignment. We demonstrate the broad relevance of the nearest neighbor suspect spectral library through representative examples of propagation-based annotation of acylcarnitines, bacterial and plant natural products, and drug metabolism. Our results also highlight how the library can help to better understand an Alzheimer's brain phenotype. The nearest neighbor suspect spectral library is openly available for download or for data analysis through the GNPS platform to help investigators hypothesize candidate structures for unknown MS/MS spectra in untargeted metabolomics data.

Differential characteristics of bipolar I and II disorders: a retrospective, cross-sectional evaluation of clinical features, illness course, and response to treatment.

BACKGROUND: The distinction between bipolar I and bipolar II disorder and its treatment implications have been a matter of ongoing debate. The aim of this study was to examine differences between patients with bipolar I and II disorders with particular emphasis on the early phases of the disorders. METHODS: 808 subjects diagnosed with bipolar I (N = 587) or bipolar II disorder (N = 221) according to DSM-IV criteria were recruited between April 1994 and March 2022 from tertiary-level mood disorder clinics. Sociodemographic and clinical variables concerning psychiatric and medical comorbidities, family history, illness course, suicidal behavior, and response to treatment were compared between the bipolar disorder types. RESULTS: Bipolar II disorder patients were more frequently women, older, married or widowed. Bipolar II disorder was associated with later "bipolar" presentation, higher age at first (hypo)mania and treatment, less frequent referral after a single episode, and more episodes before lithium treatment. A higher proportion of first-degree relatives of bipolar II patients were affected by major depression and anxiety disorders. The course of bipolar II disorder was typically characterized by depressive onset, early depressive episodes, multiple depressive recurrences, and depressive predominant polarity; less often by (hypo)mania or (hypo)mania-depression cycles at onset or during the early course. The lifetime clinical course was more frequently rated as chronic fluctuating than episodic. More patients with bipolar II disorder had a history of rapid cycling and/or high number of episodes. Mood stabilizers and antipsychotics were prescribed less frequently during the early course of bipolar II disorder, while antidepressants were more common. We found no differences in global functioning, lifetime suicide attempts, family history of suicide, age at onset of mood disorders and depressive episodes, and lithium response. CONCLUSIONS: Differences between bipolar I and II disorders are not limited to the severity of (hypo)manic syndromes but include patterns of clinical course and family history. Caution in the use of potentially mood-destabilizing agents is warranted during the early course of bipolar II disorder.

Guiding the choice of informatics software and tools for lipidomics research applications.

Progress in mass spectrometry lipidomics has led to a rapid proliferation of studies across biology and biomedicine. These generate extremely large raw datasets requiring sophisticated solutions to support automated data processing. To address this, numerous software tools have been developed and tailored for specific tasks. However, for researchers, deciding which approach best suits their application relies on ad hoc testing, which is inefficient and time consuming. Here we first review the data processing pipeline, summarizing the scope of available tools. Next, to support researchers, LIPID MAPS provides an interactive online portal listing open-access tools with a graphical user interface. This guides users towards appropriate solutions within major areas in data processing, including (1) lipid-oriented databases, (2) mass spectrometry data repositories, (3) analysis of targeted lipidomics datasets, (4) lipid identification and (5) quantification from untargeted lipidomics datasets, (6) statistical analysis and visualization, and (7) data integration solutions. Detailed descriptions of functions and requirements are provided to guide customized data analysis workflows.

Cultural Differences in Psychological Distress and Illness Perceptions Amongst People Living With Cardiac Inherited Diseases.

Cardiac Inherited diseases (CID) and minority ethnic status are both associated with anxiety and depression. This study aimed to investigate differences in patient experiences of CID between ethnic groups in New Zealand (NZ) in order to inform psychosocial interventions and promote health equity. A cross-sectional survey was administered to a NZ CID database. One-hundred and fifty-two (152) NZ Europeans, 19 Maori, and two Pasifika participated. Maori and Pasifika peoples reported significantly greater symptom perceptions, shorter timeline perceptions, higher perceived risk of severe symptoms, and were less likely to attribute the cause of their CID to hereditary factors than NZ Europeans. Maori and Pasifika also reported more anxiety and distress, although both groups reported beneficial medication perceptions and high medication adherence. Differences could not be attributed to clinical or other demographic variables. The use of screening tools and development of culturally appropriate interventions may help reduce both distress and health inequities.

Perspectives and experiences of Maori and Pasifika peoples living with cardiac inherited disease: a qualitative study.

OBJECTIVE: Cardiac inherited diseases can have considerable psychosocial effects, including lifestyle limitations, anxiety and depression. Most research to date on patient experiences of CID has been conducted with people from Western cultures, yet culture can shape patient views and experiences of health. The aim of this research was to explore the experiences and perspectives of Maori and Pasifika living with a cardiac inherited disease (CID). METHODS AND MEASURES: Semi-structured interviews were conducted with 14 Maori and 14 Pasifika patients living with a cardiac inherited disease and seven of their family members, using Talanoa and Kaupapa Maori methodologies. Themes from the interviews were identified using interpretative phenomenological analysis. RESULTS: Three common themes were identified as important in shaping participants' perceptions and experiences of CID: (1) difficulty in understanding the disease as separate from symptoms, (2) considering ancestors and future generations and (3) the role of spirituality and religion. CONCLUSION: This study highlights a gap between indigenous patients' understanding of CID and the western biomedical approach. Patients' understanding and treatment behaviours depend on symptoms, familial ties and spirituality. The findings support the need for transparency and culturally appropriate practices in healthcare. Considering these aspects may help to reduce health inequities for these populations.

Treating Insulin Resistance With Metformin as a Strategy to Improve Clinical Outcomes in Treatment-Resistant Bipolar Depression (the TRIO-BD Study): A Randomized, Quadruple-Masked, Placebo-Controlled Clinical Trial.

Objective: Therapeutic options are limited for treatment-resistant bipolar depression (TRBD). Insulin resistance (IR) confers increased risk for TRBD. We investigated metformin, an insulin sensitizer, to reverse IR and improve clinical outcomes in TRBD.Methods: Using a random-assignment (1:1), intent-to-treat, 2-site, quadruple-masked, parallel-group (metformin to 2,000 mg/d or placebo) clinical trial design, patients with DSM-5 bipolar disorder (BD) type I or II and IR received study medication for 26 weeks (February 2016 to October 2019). The primary outcome was the change in depression rating scores (Montgomery-Asberg Depression Rating Scale [MADRS]) at 14 weeks between those who no longer met IR criteria (converters) and those who still did (non-converters). Additional outcomes included scores on the Global Assessment of Functioning (GAF); the Clinical Global Impressions Scale, Bipolar Disorders version (CGI-BP); and the Hamilton Anxiety Rating Scale (HAM-A) and maintenance of improved outcomes up to 26 weeks.Results: Forty-five BD patients were randomized to metformin (n = 20) or placebo (n = 25), and at 14 weeks or later, 11 subjects no longer met IR criteria (n = 10 with metformin, n = 1 with placebo; P = .0009). These converters experienced significant improvements in MADRS (P values ranged from .031 to .008) and GAF (P values ranged from .045 to .008) scores compared to non-converters beginning at week 6, sustained to week 26. HAM-A (P = .022 at week 14 and .019 at week 26) and CGI-BP change scores (P = .046 at 26 weeks) significantly favored converters over non-converters. Effect sizes were large for the MADRS and GAF (Cohen d > 1 at 14 and 26 weeks) and large for the HAM-A and CGI-BP at 26 weeks. Transient gastrointestinal side effects occurred under both treatment conditions.Conclusions: Pending replication, this early study suggests that reversal of IR by metformin offers a path out of TRBD. Further characterization of metformin converters with TRBD will prove informative.Trial Registration: ClinicalTrials.gov identifier: NCT02519543.

The Enzyme Portal: an integrative tool for enzyme information and analysis.

Enzymes play essential roles in all life processes and are used extensively in the biomedical and biotechnological fields. However, enzyme-related information is spread across multiple resources making its retrieval time-consuming. In response to this challenge, the Enzyme Portal has been established to facilitate enzyme research, by providing a freely available hub where researchers can easily find and explore enzyme-related information. It integrates relevant enzyme data for a wide range of species from various resources such as UniProtKB, PDBe and ChEMBL. Here, we describe what type of enzyme-related data the Enzyme Portal provides, how the information is organized and, by show-casing two potential use cases, how to access and retrieve it.

A new machine learning-derived screening measure for differentiating bipolar from unipolar mood disorders.

BACKGROUND: While there are several accepted screening measures for identifying those with a bipolar disorder, variations in overall classification rates argue for the pursuit of a more discriminating measure. Extant measures, as well as the DSM-5, rate each diagnostic criterion as having equivalent weighting values; an approach which may compromise diagnostic assignment if symptoms vary considerably in their diagnostic sensitivity. We therefore sought to develop a new measure and examine whether a weighted rating scale was superior to one assigning equivalent weightings to each item. METHODS: An international sample of 165 bipolar patients and a comparison sample of 29 unipolar patients completed a measure assessing 96 putative manic/hypomanic symptoms. A previous machine learning analysis had identified the twenty most discriminating items. In this study, analysis was undertaken involving only the ten most discriminating items. RESULTS: Whether items were scored as each having equivalent value or as weighted by their machine learning-generated values, classificatory accuracy was extremely high (in the order of 96%). Analyses also identified optimal cut-off scores. High classificatory accuracy was also obtained when scores for separate bipolar I and bipolar II groups were compared with scores from the unipolar group. LIMITATIONS: The sample consisted of comparatively few unipolar patients. CONCLUSIONS: The ten-item set allows a new measure for researchers to evaluate, while the items should assist clinician assessment as to whether a patient has a bipolar or unipolar mood disorder.

Canadian Network for Mood and Anxiety Treatments (CANMAT) and International Society for Bipolar Disorders (ISBD) recommendations for the management of patients with bipolar disorder with mixed presentations.

OBJECTIVES: The 2018 Canadian Network for Mood and Anxiety Treatments (CANMAT) and International Society for Bipolar Disorders (ISBD) guidelines provided clinicians with pragmatic treatment recommendations for bipolar disorder (BD). While these guidelines included commentary on how mixed features may direct treatment selection, specific recommendations were not provided-a critical gap which the current update aims to address. METHOD: Overview of research regarding mixed presentations in BD, with treatment recommendations developed using a modified CANMAT/ISBD rating methodology. Limitations are discussed, including the dearth of high-quality data and reliance on expert opinion. RESULTS: No agents met threshold for first-line treatment of DSM-5 manic or depressive episodes with mixed features. For mania + mixed features second-line treatment options include asenapine, cariprazine, divalproex, and aripiprazole. In depression + mixed features, cariprazine and lurasidone are recommended as second-line options. For DSM-IV defined mixed episodes, with a longer history of research, asenapine and aripiprazole are first-line, and olanzapine (monotherapy or combination), carbamazepine, and divalproex are second-line. Research on maintenance treatments following a DSM-5 mixed presentation is extremely limited, with third-line recommendations based on expert opinion. For maintenance treatment following a DSM-IV mixed episode, quetiapine (monotherapy or combination) is first-line, and lithium and olanzapine identified as second-line options. CONCLUSION: The CANMAT and ISBD groups hope these guidelines provide valuable support for clinicians providing care to patients experiencing mixed presentations, as well as further influence investment in research to improve diagnosis and treatment of this common and complex clinical state.

ISA API: An open platform for interoperable life science experimental metadata.

BACKGROUND: The Investigation/Study/Assay (ISA) Metadata Framework is an established and widely used set of open source community specifications and software tools for enabling discovery, exchange, and publication of metadata from experiments in the life sciences. The original ISA software suite provided a set of user-facing Java tools for creating and manipulating the information structured in ISA-Tab-a now widely used tabular format. To make the ISA framework more accessible to machines and enable programmatic manipulation of experiment metadata, the JSON serialization ISA-JSON was developed. RESULTS: In this work, we present the ISA API, a Python library for the creation, editing, parsing, and validating of ISA-Tab and ISA-JSON formats by using a common data model engineered as Python object classes. We describe the ISA API feature set, early adopters, and its growing user community. CONCLUSIONS: The ISA API provides users with rich programmatic metadata-handling functionality to support automation, a common interface, and an interoperable medium between the 2 ISA formats, as well as with other life science data formats required for depositing data in public databases.

Progress towards an OECD reporting framework for transcriptomics and metabolomics in regulatory toxicology.

Omics methodologies are widely used in toxicological research to understand modes and mechanisms of toxicity. Increasingly, these methodologies are being applied to questions of regulatory interest such as molecular point-of-departure derivation and chemical grouping/read-across. Despite its value, widespread regulatory acceptance of omics data has not yet occurred. Barriers to the routine application of omics data in regulatory decision making have been: 1) lack of transparency for data processing methods used to convert raw data into an interpretable list of observations; and 2) lack of standardization in reporting to ensure that omics data, associated metadata and the methodologies used to generate results are available for review by stakeholders, including regulators. Thus, in 2017, the Organisation for Economic Co-operation and Development (OECD) Extended Advisory Group on Molecular Screening and Toxicogenomics (EAGMST) launched a project to develop guidance for the reporting of omics data aimed at fostering further regulatory use. Here, we report on the ongoing development of the first formal reporting framework describing the processing and analysis of both transcriptomic and metabolomic data for regulatory toxicology. We introduce the modular structure, content, harmonization and strategy for trialling this reporting framework prior to its publication by the OECD.

Interactions of Carbohydrate Intake and Physical Activity with Regulatory Genes Affecting Glycaemia: A Food4Me Study Analysis.

INTRODUCTION: Carbohydrate intake and physical activity are related to glucose homeostasis, both being influenced by individual genetic makeup. However, the interactions between these 2 factors, as affected by genetics, on glycaemia have been scarcely reported. OBJECTIVE: We focused on analysing the interplay between carbohydrate intake and physical activity levels on blood glucose, taking into account a genetic risk score (GRS), based on SNPs related to glucose/energy metabolism. METHODS: A total of 1,271 individuals from the Food4Me cohort, who completed the nutritional intervention, were evaluated at baseline. We collected dietary information by using an online-validated food frequency questionnaire, a questionnaire on physical activity, blood biochemistry by analysis of dried blood spots, and by analysis of selected SNPs. Fifteen out of 31 SNPs, with recognized participation in carbohydrate/energy metabolism, were included in the component analyses. The GRS included risk alleles involved in the control of glycaemia or energy-yielding processes. RESULTS: Data concerning anthropometric, clinical, metabolic, dietary intake, physical activity, and genetics related to blood glucose levels showed expected trends in European individuals of comparable sex and age, being categorized by lifestyle, BMI, and energy/carbohydrate intakes, in this Food4Me population. Blood glucose was inversely associated with physical activity level (ß = -0.041, p = 0.013) and positively correlated with the GRS values (ß = 0.015, p = 0.047). Interestingly, an interaction affecting glycaemia, concerning physical activity level with carbohydrate intake, was found (ß = -0.060, p = 0.033), which also significantly depended on the genetic background (GRS). CONCLUSIONS: The relationships of carbohydrate intake and physical activity are important in understanding glucose homeostasis, where a role for the genetic background should be ascribed.

Differentiating mania/hypomania from happiness using a machine learning analytic approach.

BACKGROUND: This study aimed to improve the accuracy of bipolar disorder diagnoses by identifying symptoms that help to distinguish mania/hypomania in bipolar disorders from general 'happiness' in those with unipolar depression. METHODS: An international sample of 165 bipolar and 29 unipolar depression patients (as diagnosed by their clinician) were recruited. All participants were required to rate a set of 96 symptoms with regards to whether they typified their experiences of manic/hypomanic states (for bipolar patients) or when they were 'happy' (unipolar patients). A machine learning paradigm (prediction rule ensembles; PREs) was used to derive rule ensembles that identified which of the 94 non-psychotic symptoms and their combinations best predicted clinically-allocated diagnoses. RESULTS: The PREs were highly accurate at predicting clinician bipolar and unipolar diagnoses (92% and 91% respectively). A total of 20 items were identified from the analyses, which were all highly discriminating across the two conditions. When compared to a classificatory approach insensitive to the weightings of the items, the ensembles were of comparable accuracy in their discriminatory capacity despite the unbalanced sample. This illustrates the potential for PREs to supersede traditional classificatory approaches. LIMITATIONS: There were considerably less unipolar than bipolar patients in the sample, which limited the overall accuracy of the PREs. CONCLUSIONS: The consideration of symptoms outlined in this study should assist clinicians in distinguishing between bipolar and unipolar disorders. Future research will seek to further refine and validate these symptoms in a larger and more balanced sample.

Categorical differentiation of the unipolar and bipolar disorders.

There has been a longstanding debate as to whether the bipolar disorders differ categorically or dimensionally, with some dimensional or spectrum models including unipolar depressive disorders within a bipolar spectrum model. We analysed manic/hypomanic symptom data in samples of clinically diagnosed bipolar I, bipolar II and unipolar patients, employing latent class analyses to determine if separate classes could be identified. Mixture analyses were also undertaken to determine if a unimodal, bimodal or a trimodal pattern was present. For both a refined 15-item set and an extended 30-item set of manic/hypomanic symptoms, our latent class analyses favoured three-class solutions, while mixture analyses identified trimodal distributions of scores. Findings argue for a categorical distinction between unipolar and bipolar disorders, as well as between bipolar I and bipolar II disorders. Future research should aim to consolidate these results in larger samples, particularly given that the size of the unipolar group in this study was a salient limitation.