Associations between soluble urokinase plasminogen activator receptor (suPAR) concentration and psychiatric disorders - A systematic review and meta-analysis.

BACKGROUND: There is some evidence of an association between inflammation in the pathogenesis of mental disorders. Soluble urokinase plasminogen activator receptor (suPAR) is a biomarker of chronic inflammation, which provides a more stable index of systemic inflammation than more widely used biomarkers. This review aims to synthesise studies that measured suPAR concentrations in individuals with a psychiatric disorder, to determine if these concentrations are altered in comparison to healthy participants. METHOD: Comprehensive literature searches from inception to October 2023 were conducted of five relevant databases (PubMed, Web of Science, Embase, Scopus, APA PsychInfo). Random-effects meta-analyses were performed to compare the standardised mean difference of blood suPAR levels (i.e. plasma or serum) for individuals with any psychiatric disorder relative to controls. Separate meta-analyses of suPAR levels were conducted for individuals with schizophrenia or other psychotic disorder and depressive disorder. Risk of bias was assessed using the Newcastle Ottawa Scale. Post-hoc sensitivity analyses included excluding studies at high risk of bias, and analyses of studies that measured suPAR concentrations either in serum or in plasma separately. RESULTS: The literature search identified 149 records. Ten full-text studies were screened for eligibility and 9 studies were included for review. Primary analyses revealed no significant difference in suPAR levels between individuals with any psychiatric disorder compared to controls (k = 7, SMD = 0.42, 95 % CI [-0.20, 1.04]). However, those with depressive disorder had elevated suPAR levels relative to controls (k = 3, SMD = 0.61, 95 % CI [0.34, 0.87]). Similarly, secondary analyses showed no evidence of a significant difference in suPAR levels in individuals with any psychiatric disorder when studies at high risk of bias were excluded (k = 6, SMD = 0.54, 95 % CI [-0.14, 1.22]), but elevated suPAR concentrations for those with schizophrenia or other psychotic disorder were found (k = 3, SMD = 0.98, 95 % CI [0.39, 1.58]). Furthermore, studies that analysed plasma suPAR concentrations found elevated plasma suPAR levels in individuals with any psychiatric disorder relative to controls (k = 5, SMD = 0.84, 95 % CI [0.38, 1.29]), while studies measuring serum suPAR levels in any psychiatric disorder did not find a difference (k = 2, SMD = -0.61, 95 % CI [-1.27, 0.04]). For plasma, elevated suPAR concentrations were also identified for those with schizophrenia or other psychotic disorder (k = 3, SMD = 0.98, 95 % CI [0.39, 1.58]). DISCUSSION: When studies measuring either only serum or only plasma suPAR were considered, no significant difference in suPAR levels were observed between psychiatric disorder groups, although significantly elevated suPAR levels were detected in those with moderate to severe depressive disorder. However, plasma suPAR levels were significantly elevated in those with any psychiatric disorder relative to controls, while no difference in serum samples was found. A similar finding was reported for schizophrenia or other psychotic disorder. The plasma findings suggest that chronic inflammatory dysregulation may contribute to the pathology of schizophrenia and depressive disorder. Future longitudinal studies are required to fully elucidate the role of this marker in the psychopathology of these disorders.

Pregnancy and birth complications and long-term maternal mental health outcomes: A systematic review and meta-analysis.

BACKGROUND: Few studies have examined the associations between pregnancy and birth complications and long-term (>12 months) maternal mental health outcomes. OBJECTIVES: To review the published literature on pregnancy and birth complications and long-term maternal mental health outcomes. SEARCH STRATEGY: Systematic search of Cumulative Index to Nursing and Allied Health Literature (CINAHL), Excerpta Medica Database (Embase), PsycInfo®, PubMed® and Web of Science from inception until August 2022. SELECTION CRITERIA: Three reviewers independently reviewed titles, abstracts and full texts. DATA COLLECTION AND ANALYSIS: Two reviewers independently extracted data and appraised study quality. Random-effects meta-analyses were used to calculate pooled estimates. The Meta-analyses of Observational Studies in Epidemiology (MOOSE) guidelines were followed. The protocol was prospectively registered on the International Prospective Register of Systematic Reviews (PROSPERO: CRD42022359017). MAIN RESULTS: Of the 16 310 articles identified, 33 studies were included (3 973 631 participants). Termination of pregnancy was associated with depression (pooled adjusted odds ratio, aOR 1.49, 95% CI 1.20-1.83) and anxiety disorder (pooled aOR 1.43, 95% CI 1.20-1.71). Miscarriage was associated with depression (pooled aOR 1.97, 95% CI 1.38-2.82) and anxiety disorder (pooled aOR 1.24, 95% CI 1.11-1.39). Sensitivity analyses excluding early pregnancy loss and termination reported similar results. Preterm birth was associated with depression (pooled aOR 1.37, 95% CI 1.32-1.42), anxiety disorder (pooled aOR 0.97, 95% CI 0.41-2.27) and post-traumatic stress disorder (PTSD) (pooled aOR 1.75, 95% CI 0.52-5.89). Caesarean section was not significantly associated with PTSD (pooled aOR 2.51, 95% CI 0.75-8.37). There were few studies on other mental disorders and therefore it was not possible to perform meta-analyses. CONCLUSIONS: Exposure to complications during pregnancy and birth increases the odds of long-term depression, anxiety disorder and PTSD.

Detecting goals of care conversations in clinical notes with active learning.

OBJECTIVE: Goals of care (GOC) discussions are an increasingly used quality metric in serious illness care and research. Wide variation in documentation practices within the Electronic Health Record (EHR) presents challenges for reliable measurement of GOC discussions. Novel natural language processing approaches are needed to capture GOC discussions documented in real-world samples of seriously ill hospitalized patients' EHR notes, a corpus with a very low event prevalence. METHODS: To automatically detect sentences documenting GOC discussions outside of dedicated GOC note types, we proposed an ensemble of classifiers aggregating the predictions of rule-based, feature-based, and three transformers-based classifiers. We trained our classifier on 600 manually annotated EHR notes among patients with serious illnesses. Our corpus exhibited an extremely imbalanced ratio between sentences discussing GOC and sentences that do not. This ratio challenges standard supervision methods to train a classifier. Therefore, we trained our classifier with active learning. RESULTS: Using active learning, we reduced the annotation cost to fine-tune our ensemble by 70% while improving its performance in our test set of 176 EHR notes, with 0.557 F1-score for sentence classification and 0.629 for note classification. CONCLUSION: When classifying notes, with a true positive rate of 72% (13/18) and false positive rate of 8% (13/158), our performance may be sufficient for deploying our classifier in the EHR to facilitate bedside clinicians' access to GOC conversations documented outside of dedicated notes types, without overburdening clinicians with false positives. Improvements are needed before using it to enrich trial populations or as an outcome measure.

Methods and Annotated Data Sets Used to Predict the Gender and Age of Twitter Users: Scoping Review.

BACKGROUND: Patient health data collected from a variety of nontraditional resources, commonly referred to as real-world data, can be a key information source for health and social science research. Social media platforms, such as Twitter (Twitter, Inc), offer vast amounts of real-world data. An important aspect of incorporating social media data in scientific research is identifying the demographic characteristics of the users who posted those data. Age and gender are considered key demographics for assessing the representativeness of the sample and enable researchers to study subgroups and disparities effectively. However, deciphering the age and gender of social media users poses challenges. OBJECTIVE: This scoping review aims to summarize the existing literature on the prediction of the age and gender of Twitter users and provide an overview of the methods used. METHODS: We searched 15 electronic databases and carried out reference checking to identify relevant studies that met our inclusion criteria: studies that predicted the age or gender of Twitter users using computational methods. The screening process was performed independently by 2 researchers to ensure the accuracy and reliability of the included studies. RESULTS: Of the initial 684 studies retrieved, 74 (10.8%) studies met our inclusion criteria. Among these 74 studies, 42 (57%) focused on predicting gender, 8 (11%) focused on predicting age, and 24 (32%) predicted a combination of both age and gender. Gender prediction was predominantly approached as a binary classification task, with the reported performance of the methods ranging from 0.58 to 0.96 F1-score or 0.51 to 0.97 accuracy. Age prediction approaches varied in terms of classification groups, with a higher range of reported performance, ranging from 0.31 to 0.94 F1-score or 0.43 to 0.86 accuracy. The heterogeneous nature of the studies and the reporting of dissimilar performance metrics made it challenging to quantitatively synthesize results and draw definitive conclusions. CONCLUSIONS: Our review found that although automated methods for predicting the age and gender of Twitter users have evolved to incorporate techniques such as deep neural networks, a significant proportion of the attempts rely on traditional machine learning methods, suggesting that there is potential to improve the performance of these tasks by using more advanced methods. Gender prediction has generally achieved a higher reported performance than age prediction. However, the lack of standardized reporting of performance metrics or standard annotated corpora to evaluate the methods used hinders any meaningful comparison of the approaches. Potential biases stemming from the collection and labeling of data used in the studies was identified as a problem, emphasizing the need for careful consideration and mitigation of biases in future studies. This scoping review provides valuable insights into the methods used for predicting the age and gender of Twitter users, along with the challenges and considerations associated with these methods.

The Association between Threatened Miscarriage and Autism Spectrum Disorder and Attention-Deficit/Hyperactivity Disorder in Offspring by Age 14 Years.

OBJECTIVE: To examine the association between threatened miscarriage, and neurodevelopmental disorders, including autism spectrum disorder (ASD) and attention-deficit/hyperactivity disorder (ADHD) in offspring by age 14 years. METHODS: We used data from the Millennium Cohort Study, a nationally representative longitudinal study of children born in the UK. Data on threatened miscarriage and potential confounders were maternal-reported and collected at 9 months postpartum. Data on ASD and ADHD were based on maternal-reported doctor diagnoses and collected when children were aged 5, 7, 11 and 14 years. A diagnosis of ASD or ADHD was assumed if parents reported ASD or ADHD at age 5, 7, 11 or 14 years. Crude and adjusted logistic regression examined threatened miscarriage and ASD and ADHD relationship, adjusting for several sociodemographic, maternal and lifestyle factors. RESULTS: A total of 18,294 singleton babies were included at baseline, and 1,104 (6.0%) women experienced a threatened miscarriage during their pregnancy. Adjusted results suggested an association between threatened miscarriage and ASD (OR: 1.55, 95% CI 1.15, 2.08), and ADHD (OR: 1.51, 95% CI 1.09, 2.10) by age 14 years. E-values for threatened miscarriage and ASD were 2.47, while the lower limits of the 95% CI were 1.57. E-values for threatened miscarriage and ADHD were 2.39, while the corresponding lower limits of the 95% CI were 1.40. CONCLUSION: Threatened miscarriage was associated with an increased likelihood of ASD and ADHD by the age of 14 years, however, residual confounding cannot be ruled out. Placental pathology may be a potential mechanism for the observed associations.

Health activism, vaccine, and mpox discourse: BERTopic based mixed-method analyses of tweets from sexual minority men and gender diverse (SMMGD) individuals in the U.S.

Objectives: To synthesize discussions among sexual minority men and gender diverse (SMMGD) individuals on mpox, given limited representation of SMMGD voices in existing mpox literature. Methods: BERTopic (a topic modeling technique) was employed with human validations to analyze mpox-related tweets (n = 8,688; October 2020-September 2022) from 2,326 self-identified SMMGD individuals in the U.S.; followed by content analysis and geographic analysis. Results: BERTopic identified 11 topics: health activism (29.81%); mpox vaccination (25.81%) and adverse events (0.98%); sarcasm, jokes, emotional expressions (14.04%); COVID-19 and mpox (7.32%); government/public health response (6.12%); mpox symptoms (2.74%); case reports (2.21%); puns on the virus' naming (i.e., monkeypox; 0.86%); media publicity (0.68%); mpox in children (0.67%). Mpox health activism negatively correlated with LGB social climate index at U.S. state level, ρ = -0.322, p = 0.031. Conclusions: SMMGD discussions on mpox encompassed utilitarian (e.g., vaccine access, case reports, mpox symptoms) and emotionally-charged themes-advocating against homophobia, misinformation, and stigma. Mpox health activism was more prevalent in states with lower LGB social acceptance. Public Health Implications: Findings illuminate SMMGD engagement with mpox discourse, underscoring the need for more inclusive health communication strategies in infectious disease outbreaks to control associated stigma.

Mpox Discourse on Twitter by Sexual Minority Men and Gender-Diverse Individuals: Infodemiological Study Using BERTopic.

BACKGROUND: The mpox outbreak resulted in 32,063 cases and 58 deaths in the United States and 95,912 cases worldwide from May 2022 to March 2024 according to the US Centers for Disease Control and Prevention (CDC). Like other disease outbreaks (eg, HIV) with perceived community associations, mpox can create the risk of stigma, exacerbate homophobia, and potentially hinder health care access and social equity. However, the existing literature on mpox has limited representation of the perspective of sexual minority men and gender-diverse (SMMGD) individuals. OBJECTIVE: To fill this gap, this study aimed to synthesize themes of discussions among SMMGD individuals and listen to SMMGD voices for identifying problems in current public health communication surrounding mpox to improve inclusivity, equity, and justice. METHODS: We analyzed mpox-related posts (N=8688) posted between October 2020 and September 2022 by 2326 users who self-identified on Twitter/X as SMMGD and were geolocated in the United States. We applied BERTopic (a topic-modeling technique) on the tweets, validated the machine-generated topics through human labeling and annotations, and conducted content analysis of the tweets in each topic. Geographic analysis was performed on the size of the most prominent topic across US states in relation to the University of California, Los Angeles (UCLA) lesbian, gay, and bisexual (LGB) social climate index. RESULTS: BERTopic identified 11 topics, which annotators labeled as mpox health activism (n=2590, 29.81%), mpox vaccination (n=2242, 25.81%), and adverse events (n=85, 0.98%); sarcasm, jokes, and emotional expressions (n=1220, 14.04%); COVID-19 and mpox (n=636, 7.32%); government or public health response (n=532, 6.12%); mpox symptoms (n=238, 2.74%); case reports (n=192, 2.21%); puns on the naming of the virus (ie, mpox; n=75, 0.86%); media publicity (n=59, 0.68%); and mpox in children (n=58, 0.67%). Spearman rank correlation indicated significant negative correlation (ρ=-0.322, P=.03) between the topic size of health activism and the UCLA LGB social climate index at the US state level. CONCLUSIONS: Discussions among SMMGD individuals on mpox encompass both utilitarian (eg, vaccine access, case reports, and mpox symptoms) and emotionally charged (ie, promoting awareness, advocating against homophobia, misinformation/disinformation, and health stigma) themes. Mpox health activism is more prevalent in US states with lower LGB social acceptance, suggesting a resilient communicative pattern among SMMGD individuals in the face of public health oppression. Our method for social listening could facilitate future public health efforts, providing a cost-effective way to capture the perspective of impacted populations. This study illuminates SMMGD engagement with the mpox discourse, underscoring the need for more inclusive public health programming. Findings also highlight the social impact of mpox: health stigma. Our findings could inform interventions to optimize the delivery of informational and tangible health resources leveraging computational mixed-method analyses (eg, BERTopic) and big data.

Risk of Psychosis Among Individuals Who Have Presented to Hospital With Self-harm: A Prospective Nationwide Register Study in Sweden.

BACKGROUND AND HYPOTHESIS: Recent research showed that young people who presented to hospital with self-harm in Finland had a significantly elevated risk of later psychosis. We investigated the prospective relationship between hospital presentation for self-harm and risk of psychosis in an unprecedentedly large national Swedish cohort. STUDY DESIGN: We used inpatient and outpatient healthcare registers to identify all individuals born between 1981 and 1993 who were alive and living in Sweden on their 12th birthday and who presented to hospital one or more times with self-harm. We compared them with a matched cohort, followed up for up to 20 years, and compared the cumulative incidence of psychotic disorders. Furthermore, we examined whether the strength of the relationship between hospital presentation for self-harm and later psychosis changed over time by examining for cohort effects. STUDY RESULTS: In total, 28 908 (2.0%) individuals presented to hospital with self-harm without prior psychosis diagnosis during the follow-up. For individuals who presented to hospital with self-harm, the cumulative incidence of diagnosed psychosis was 20.7% at 20 years follow-up (hazard radio = 13.9, 95% CI 13.3-14.6, P-value <5 × 10-308). There was no evidence of a dilution of the effect over time: while the incidence of hospital self-harm presentation increased, this did not result in an attenuation over time of the strength of the relationship between hospital self-harm presentation and subsequent psychosis. CONCLUSIONS: Individuals who present to hospital with self-harm in their teens and 20s represent an important risk group for psychosis prediction and prevention.