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More and more clinical studies include potentially sensitive human proteomics or metabolomics datasets, but bioinformatics resources for managing the access to these data are not yet available. This commentary discusses current best practices and future perspectives for the responsible handling of clinical proteomics and metabolomics data.

Little is known about the impact of insulin resistance on bipolar disorder. To examine the relationships between insulin resistance, type 2 diabetes and clinical course and treatment outcomes in bipolar disorder. We measured fasting glucose and insulin in 121 adults with bipolar disorder. We diagnosed type 2 diabetes and determined insulin resistance. The National Institute of Mental Health Life Chart was used to record the course of bipolar disorder and the Alda scale to establish response to prophylactic lithium treatment. Patients with bipolar disorder and type 2 diabetes or insulin resistance had three times higher odds of a chronic course of bipolar disorder compared with euglycaemic patients (50% and 48.7% respectively v. 27.3%, odds ratio (OR) = 3.07, P = 0.007), three times higher odds of rapid cycling (38.5% and 39.5% respectively v. 18.2%, OR = 3.13, P = 0.012) and were more likely to be refractory to lithium treatment (36.8% and 36.7% respectively v. 3.2%, OR = 8.40, P<0.0001). All associations remained significant after controlling for antipsychotic exposure and body mass index in sensitivity analyses. Comorbid insulin resistance may be an important factor in resistance to treatment in bipolar disorder.

We describe here the agreed upon first development steps and priority objectives of a community engagement effort to address current challenges in quality assurance (QA) and quality control (QC) in untargeted metabolomic studies. This has included (1) a QA and QC questionnaire responded to by the metabolomics community in 2015 which recommended education of the metabolomics community, development of appropriate standard reference materials and providing incentives for laboratories to apply QA and QC; (2) a 2-day ‘Think Tank on Quality Assurance and Quality Control for Untargeted Metabolomic Studies’ held at the National Cancer Institute’s Shady Grove Campus and (3) establishment of the Metabolomics Quality Assurance and Quality Control Consortium (mQACC) to drive forward developments in a coordinated manner.

Public untargeted metabolomics data is a growing resource for metabolite and phenotype discovery; however, accessing and utilizing these data across repositories pose significant challenges. Therefore, here we develop pan-repository universal identifiers and harmonized cross-repository metadata. This ecosystem facilitates discovery by integrating diverse data sources from public repositories including MetaboLights, Metabolomics Workbench, and GNPS/MassIVE. Our approach simplified data handling and unlocks previously inaccessible reanalysis workflows, fostering unmatched research opportunities. Public untargeted metabolomics data hold great promise for discovery but are difficult to access across repositories. Here, the authors develop universal identifiers and harmonized metadata to integrate major databases, enabling streamlined analysis and expanded research possibilities.

Metabolomics encompasses the systematic identification and quantification of all metabolic products in the human body. This field could provide clinicians with novel sets of diagnostic biomarkers for disease states in addition to quantifying treatment response to medications at an individualized level. This literature review aims to highlight the technology underpinning metabolic profiling, identify potential applications of metabolomics in clinical practice, and discuss the translational challenges that the field faces. We searched PubMed, MEDLINE, and EMBASE for primary and secondary research articles regarding clinical applications of metabolomics. Metabolic profiling can be performed using mass spectrometry and nuclear magnetic resonance-based techniques using a variety of biological samples. This is carried out in vivo or in vitro following careful sample collection, preparation, and analysis. The potential clinical applications constitute disruptive innovations in their respective specialities, particularly oncology and metabolic medicine. Outstanding issues currently preventing widespread clinical use are scalability of data interpretation, standardization of sample handling practice, and e-infrastructure. Routine utilization of metabolomics at a patient and population level will constitute an integral part of future healthcare provision.

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. This Perspective discusses available software tools for lipidomics data analysis and provides a web-based Lipidomics Tools Guide to help guide the choice of these tools, organized by the major tasks for lipidomics research.

Despite an increasing prevalence of adults living with a CHD, little is known about the psychosocial impact of CHD. We sought to investigate the relative impact of disease severity and patients' perceptions about their condition on depression, anxiety, and quality of life over a period of a year. A total of 110 patients aged over 16 years completed an initial questionnaire containing measures for anxiety, depression, quality of life, and illness perceptions when they attended the Adult Congenital Heart Disease Clinic. Cardiologists rated the patients' disease severity and illness course. A year later, patients were invited to complete the same measures. Regression analyses were performed to determine the relative impact of illness perceptions and disease severity on psychological outcomes a year later. At baseline, 23% of the study population had depressive symptoms and 30% had elevated trait anxiety. After controlling for associations with disease-related variables, illness perceptions explained 28% of the variance in depression, 40% anxiety, and 27% overall quality of life at baseline. Baseline illness perceptions bivariately predicted quality of life, cardiac anxiety, and depression 1 year later, and regression analyses controlling for other factors showed that they were significant predictors of outcomes 1 year later. Symptoms of depression and anxiety are common among adults with CHD. Patients' illness perceptions are related to psychological outcomes, especially cross-sectionally. Future research could investigate whether an intervention to discuss patients' perceptions about their CHD can improve mental health and quality of life.

BackgroundThe 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.Methods808 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.ResultsBipolar 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.ConclusionsDifferences 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.

ObjectiveThe cardiac inherited disease (CID) population has suboptimal adherence to long-term β-blocker therapy, which is known to be a risk for sudden cardiac death. This study aimed to identify the clinical and psychosocial variables associated with non-adherence in this population.Methods130 individuals (aged 16–81 years, median: 54) from the New Zealand Cardiac Inherited Disease Registry taking β-blockers participated: 65 (50%) long QT syndrome, 42 (32%) hypertrophic cardiomyopathy and 23 (18%) other. Participants completed one questionnaire recording self-reported adherence, anxiety, depression, confidence in taking medication, illness perceptions and medication beliefs. Demographic and clinical variables were taken from the registry.Results21 participants (16%) were classed as non-adherent. Bivariate analysis showed that self-reported adherence was worse in those who were younger (p<0.001), had a channelopathy not cardiomyopathy (p<0.01), reported lower confidence in taking β-blockers (p<0.001), had high concerns (p<0.05) and low necessity beliefs about their β-blocker (p<0.001), a poorer understanding of their CID (p<0.01), and lower treatment control beliefs (p<0.01). These variables accounted for 37% of the variance in adherence in a linear regression model. Stronger beliefs around medication necessity and higher confidence in their ability to take their medication predicted β-blocker adherence.ConclusionsFactors associated with β-blocker non-adherence in patients with CID include young age, having a channelopathy, negative medication beliefs, low confidence in taking medication and poor illness perceptions. These findings present an opportunity to develop targeted interventions to improve adherence.

Anticipating clinical transitions in bipolar disorder (BD) is essential for the development of clinically actionable predictions. Our aim was to determine what is the earliest indicator of the onset of depressive symptoms in BD. We hypothesized that changes in activity would be the earliest indicator of future depressive symptoms. The study was a prospective, observational, contactless study. Participants were 127 outpatients with a primary diagnosis of BD, followed up for 12.6 (5.7) [(mean (SD)] months. They wore a smart ring continuously, which monitored their daily activity and sleep parameters. Participants were also asked to complete weekly self-ratings using the Patient Health Questionnaire (PHQ-9) and Altman Self-Rating Mania Scale (ASRS) scales. Primary outcome measures were depressive symptom onset detection metrics (i.e., accuracy, sensitivity, and specificity); and detection delay (in days), compared between self-rating scales and wearable data. Depressive symptoms were labeled as two or more consecutive weeks of total PHQ-9 > 10, and data-driven symptom onsets were detected using time-frequency spectral derivative spike detection (TF-SD 2 ). Our results showed that day-to-day variability in the number of steps anticipated the onset of depressive symptoms 7.0 (9.0) (median (IQR)) days before they occurred, significantly earlier than the early prediction window provided by deep sleep duration (median (IQR), 4.0 (5.0) days; p  <.05). Taken together, our results demonstrate that changes in activity were the earliest indicator of depressive symptoms in participants with BD. Transition to dynamic representations of behavioral phenomena in psychiatry may facilitate episode forecasting and individualized preventive interventions.