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Habets PC, Thomas RM, Milaneschi Y, Jansen R, Pool R, Peyrot WJ, Penninx BWJH, Meijer OC, van Wingen GA, Vinkers CH. Multimodal Data Integration Advances Longitudinal Prediction of the Naturalistic Course of Depression and Reveals a Multimodal Signature of Remission During 2-Year Follow-up. Biol Psychiatry 2023; 94:948-958. [PMID: 37330166 DOI: 10.1016/j.biopsych.2023.05.024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/04/2022] [Revised: 05/11/2023] [Accepted: 05/30/2023] [Indexed: 06/19/2023]
Abstract
BACKGROUND The ability to predict the disease course of individuals with major depressive disorder (MDD) is essential for optimal treatment planning. Here, we used a data-driven machine learning approach to assess the predictive value of different sets of biological data (whole-blood proteomics, lipid metabolomics, transcriptomics, genetics), both separately and added to clinical baseline variables, for the longitudinal prediction of 2-year remission status in MDD at the individual-subject level. METHODS Prediction models were trained and cross-validated in a sample of 643 patients with current MDD (2-year remission n = 325) and subsequently tested for performance in 161 individuals with MDD (2-year remission n = 82). RESULTS Proteomics data showed the best unimodal data predictions (area under the receiver operating characteristic curve = 0.68). Adding proteomic to clinical data at baseline significantly improved 2-year MDD remission predictions (area under the receiver operating characteristic curve = 0.63 vs. 0.78, p = .013), while the addition of other omics data to clinical data did not yield significantly improved model performance. Feature importance and enrichment analysis revealed that proteomic analytes were involved in inflammatory response and lipid metabolism, with fibrinogen levels showing the highest variable importance, followed by symptom severity. Machine learning models outperformed psychiatrists' ability to predict 2-year remission status (balanced accuracy = 71% vs. 55%). CONCLUSIONS This study showed the added predictive value of combining proteomic data, but not other omics data, with clinical data for the prediction of 2-year remission status in MDD. Our results reveal a novel multimodal signature of 2-year MDD remission status that shows clinical potential for individual MDD disease course predictions from baseline measurements.
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Affiliation(s)
- Philippe C Habets
- Department of Anatomy & Neurosciences, Amsterdam University Medical Center, Vrije Universiteit, Amsterdam, the Netherlands; Department of Psychiatry, Amsterdam Neuroscience, Amsterdam University Medical Center, Vrije Universiteit, Amsterdam, the Netherlands; Department of Internal Medicine, section Endocrinology, Leiden University Medical Center, Leiden, the Netherlands.
| | - Rajat M Thomas
- Department of Anatomy & Neurosciences, Amsterdam University Medical Center, Vrije Universiteit, Amsterdam, the Netherlands
| | - Yuri Milaneschi
- Department of Anatomy & Neurosciences, Amsterdam University Medical Center, Vrije Universiteit, Amsterdam, the Netherlands; Department of Psychiatry, Amsterdam Neuroscience, Amsterdam University Medical Center, Vrije Universiteit, Amsterdam, the Netherlands
| | - Rick Jansen
- Department of Anatomy & Neurosciences, Amsterdam University Medical Center, Vrije Universiteit, Amsterdam, the Netherlands; Department of Psychiatry, Amsterdam Neuroscience, Amsterdam University Medical Center, Vrije Universiteit, Amsterdam, the Netherlands
| | - Rene Pool
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Neuroscience Campus Amsterdam, Amsterdam, the Netherlands
| | - Wouter J Peyrot
- Department of Psychiatry, Amsterdam Neuroscience, Amsterdam University Medical Center, Vrije Universiteit, Amsterdam, the Netherlands; Department of Complex Traits Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit, Amsterdam, the Netherlands
| | - Brenda W J H Penninx
- Department of Psychiatry, Amsterdam Neuroscience, Amsterdam University Medical Center, Vrije Universiteit, Amsterdam, the Netherlands
| | - Onno C Meijer
- Department of Internal Medicine, section Endocrinology, Leiden University Medical Center, Leiden, the Netherlands
| | - Guido A van Wingen
- Department of Anatomy & Neurosciences, Amsterdam University Medical Center, Vrije Universiteit, Amsterdam, the Netherlands
| | - Christiaan H Vinkers
- Department of Anatomy & Neurosciences, Amsterdam University Medical Center, Vrije Universiteit, Amsterdam, the Netherlands; Department of Psychiatry, Amsterdam Neuroscience, Amsterdam University Medical Center, Vrije Universiteit, Amsterdam, the Netherlands
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Luykx JJ, Gerritse F, Habets PC, Vinkers CH. The performance of ChatGPT in generating answers to clinical questions in psychiatry: a two-layer assessment. World Psychiatry 2023; 22:479-480. [PMID: 37713576 PMCID: PMC10503909 DOI: 10.1002/wps.21145] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 05/30/2023] [Indexed: 09/17/2023] Open
Affiliation(s)
- Jurjen J Luykx
- Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Maastricht University Medical Centre, Maastricht, The Netherlands
- Department of Psychiatry, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands
- Outpatient Second Opinion Clinic, GGNet Mental Health, Warnsveld, The Netherlands
| | - Frank Gerritse
- Department of Psychiatry, Tergooi MC, Hilversum, The Netherlands
| | - Philippe C Habets
- Department of Psychiatry and Anatomy & Neurosciences, Amsterdam University Medical Center, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Department of Internal Medicine, Leiden University Medical Center, Leiden, The Netherlands
| | - Christiaan H Vinkers
- Department of Psychiatry and Anatomy & Neurosciences, Amsterdam University Medical Center, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Public Health, Mental Health Program and Amsterdam Neuroscience, Mood, Anxiety, Psychosis, Sleep & Stress Program, Amsterdam, The Netherlands
- GGZ inGeest Mental Health Care, Amsterdam, The Netherlands
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Habets PC, Kalafatakis K, Dzyubachyk O, van der Werff SJ, Keo A, Thakrar J, Mahfouz A, Pereira AM, Russell GM, Lightman SL, Meijer OC. Transcriptional and cell type profiles of cortical brain regions showing ultradian cortisol rhythm dependent responses to emotional face stimulation. Neurobiol Stress 2023; 22:100514. [PMID: 36660181 PMCID: PMC9842700 DOI: 10.1016/j.ynstr.2023.100514] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2022] [Revised: 01/02/2023] [Accepted: 01/02/2023] [Indexed: 01/05/2023] Open
Abstract
The characteristic endogenous circadian rhythm of plasma glucocorticoid concentrations is made up from an underlying ultradian pulsatile secretory pattern. Recent evidence has indicated that this ultradian cortisol pulsatility is crucial for normal emotional response in man. In this study, we investigate the anatomical transcriptional and cell type signature of brain regions sensitive to a loss of ultradian rhythmicity in the context of emotional processing. We combine human cell type and transcriptomic atlas data of high spatial resolution with functional magnetic resonance imaging (fMRI) data. We show that the loss of cortisol ultradian rhythm alters emotional processing response in cortical brain areas that are characterized by transcriptional and cellular profiles of GABAergic function. We find that two previously identified key components of rapid non-genomic GC signaling - the ANXA1 gene and retrograde endocannabinoid signaling - show most significant differential expression (q = 3.99e-10) and enrichment (fold enrichment = 5.56, q = 9.09e-4). Our results further indicate that specific cell types, including a specific NPY-expressing GABAergic neuronal cell type, and specific G protein signaling cascades underly the cerebral effects of a loss of ultradian cortisol rhythm. Our results provide a biological mechanistic underpinning of our fMRI findings, indicating specific cell types and cascades as a target for manipulation in future experimental studies.
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Affiliation(s)
- Philippe C. Habets
- Leiden University Medical Center, Department of Medicine, Division of Endocrinology, 2300 RC Leiden, the Netherlands,Amsterdam University Medical Centre, Department of Psychiatry, Department of Anatomy and Neurosciences, 1081 HZ, Amsterdam, the Netherlands,Corresponding author. Leiden University Medical Center, Department of Medicine, Division of Endocrinology, 2300 RC Leiden, the Netherlands.
| | - Konstantinos Kalafatakis
- Henry Wellcome Laboratories of Integrative Neuroscience and Endocrinology, Bristol Medical School, University of Bristol, BS1 3NY, Bristol, United Kingdom,Institute of Health Science Education, Barts and the London School of Medicine & Dentistry, Queen Mary University of London Malta Campus, VCT 2520, Victoria Gozo, Malta
| | - Oleh Dzyubachyk
- Department of Radiology, Division of Medical Image Processing, Leiden University Medical Center, 2333 ZA, Leiden, the Netherlands,Leiden University Medical Center, Department of Cell and Chemical Biology, Section Electron Microscopy, 2300 RC, Leiden, the Netherlands
| | - Steven J.A. van der Werff
- Department of Psychiatry, Leiden University Medical Center LUMC, Leiden, the Netherlands,Leiden Institute for Brain and Cognition, Leiden, the Netherlands
| | - Arlin Keo
- Leiden Computational Biology Center, Leiden University Medical Center, Leiden, the Netherlands,Delft Bioinformatics Lab, Delft University of Technology, Delft, the Netherlands
| | - Jamini Thakrar
- Henry Wellcome Laboratories of Integrative Neuroscience and Endocrinology, Bristol Medical School, University of Bristol, BS1 3NY, Bristol, United Kingdom
| | - Ahmed Mahfouz
- Leiden Computational Biology Center, Leiden University Medical Center, Leiden, the Netherlands,Delft Bioinformatics Lab, Delft University of Technology, Delft, the Netherlands,Department of Human Genetics, Leiden University Medical Center, Leiden, the Netherlands
| | - Alberto M. Pereira
- Leiden University Medical Center, Department of Medicine, Division of Endocrinology, 2300 RC Leiden, the Netherlands,Department of Endocrinology & Metabolism, Amsterdam University Medical Center, Amsterdam, the Netherlands
| | - Georgina M. Russell
- Henry Wellcome Laboratories of Integrative Neuroscience and Endocrinology, Bristol Medical School, University of Bristol, BS1 3NY, Bristol, United Kingdom
| | - Stafford L. Lightman
- Henry Wellcome Laboratories of Integrative Neuroscience and Endocrinology, Bristol Medical School, University of Bristol, BS1 3NY, Bristol, United Kingdom
| | - Onno C. Meijer
- Leiden University Medical Center, Department of Medicine, Division of Endocrinology, 2300 RC Leiden, the Netherlands,Leiden Institute for Brain and Cognition, Leiden, the Netherlands
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Habets PC, van IJzendoorn DG, Vinkers CH, Härmark L, de Vries LC, Otte WM. Development and validation of a machine-learning algorithm to predict the relevance of scientific articles within the field of teratology. Reprod Toxicol 2022; 113:150-154. [PMID: 36067870 DOI: 10.1016/j.reprotox.2022.09.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2022] [Revised: 08/29/2022] [Accepted: 09/01/2022] [Indexed: 10/14/2022]
Abstract
The Dutch Teratology Information Service Lareb counsels healthcare professionals and patients about medication use during pregnancy and lactation. To keep the evidence up to date, employees perform a standardized weekly PubMed query where relevant literature is identified manually. We aimed to develop an accurate machine-learning algorithm to predict the relevance of PubMed entries, thereby reducing the labor-intensive task of manually screening the articles. We fine-tuned a pre-trained natural language processing transformer model to identify relevant entries. We split 15,540 labeled entries into case-control-balanced train, validation, and test datasets. Additionally, we externally validated the model prospectively with 1288 labeled entries obtained from weekly queries after developing the model. This dataset was also independently labeled by a team of six experienced human raters to evaluate our model's performance. The validation of our machine learning model on the retrospectively collected outheld dataset obtained an area under the sensitivity-versus-specificity curve of 89.3 % (CI: 88.2- 90.4). In the prospective external validation of the model, our model classified relevant literature with a sensitivity versus specificity curve area of 87.4 % (CI: 85.0-89.8). Our model achieved a higher sensitivity than the human raters' team without sacrificing too much specificity. The team of human raters showed weak to moderate levels of agreement in their article classifications (kappa range 0.40-0.64). The human selection of the latest relevant literature is indispensable to keep the teratology information up to date. We show that automatic preselection of relevant abstracts using machine learning is possible without sacrificing the selection performance.
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Affiliation(s)
| | | | | | - Linda Härmark
- Netherlands Pharmacovigilance Centre Lareb, 's-Hertogenbosch, the Netherlands
| | - Loes C de Vries
- Netherlands Pharmacovigilance Centre Lareb, 's-Hertogenbosch, the Netherlands
| | - Willem M Otte
- DeepDoc Academy, Rotterdam, the Netherlands; Department of Child Neurology, UMC Utrecht Brain Center, University Medical Center (UMC) Utrecht, Utrecht, the Netherlands
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Bonapersona, Born FJ, Bakvis P, Branje S, Elzinga B, Evers A, van Eysden M, Fernandez G, Habets PC, Hartman CA, Hermans EJ, Meeus W, van Middendorp H, Nelemans S, Oei NY, Oldehinkel AJ, Roelofs K, de Rooij SR, Smeets T, Tollenaar MS, Joëls M, Vinkers CH. The STRESS-NL database: A resource for human acute stress studies across the Netherlands. Psychoneuroendocrinology 2022; 141:105735. [PMID: 35447495 DOI: 10.1016/j.psyneuen.2022.105735] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/02/2021] [Revised: 01/10/2022] [Accepted: 03/17/2022] [Indexed: 10/18/2022]
Abstract
Stress initiates a cascade of (neuro)biological, physiological, and behavioral changes, allowing us to respond to a challenging environment. The human response to acute stress can be studied in detail in controlled settings, usually in a laboratory environment. To this end, many studies employ acute stress paradigms to probe stress-related outcomes in healthy and patient populations. Though valuable, these studies in themselves often have relatively limited sample sizes. We established a data-sharing and collaborative interdisciplinary initiative, the STRESS-NL database, which combines (neuro)biological, physiological, and behavioral data across many acute stress studies in order to accelerate our understanding of the human acute stress response in health and disease (www.stressdatabase.eu). Researchers in the stress field from 12 Dutch research groups of 6 Dutch universities created a database to achieve an accurate inventory of (neuro)biological, physiological, and behavioral data from laboratory-based human studies that used acute stress tests. Currently, the STRESS-NL database consists of information on 5529 individual participants (2281 females and 3348 males, age range 6-99 years, mean age 27.7 ± 16 years) stemming from 57 experiments described in 42 independent studies. Studies often did not use the same stress paradigm; outcomes were different and measured at different time points. All studies currently included in the database assessed cortisol levels before, during and after experimental stress, but cortisol measurement will not be a strict requirement for future study inclusion. Here, we report on the creation of the STRESS-NL database and infrastructure to illustrate the potential of accumulating and combining existing data to allow meta-analytical, proof-of-principle analyses. The STRESS-NL database creates a framework that enables human stress research to take new avenues in explorative and hypothesis-driven data analyses with high statistical power. Future steps could be to incorporate new studies beyond the borders of the Netherlands; or build similar databases for experimental stress studies in rodents. In our view, there are major scientific benefits in initiating and maintaining such international efforts.
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Affiliation(s)
- Bonapersona
- Department of Translational Neuroscience, University Medical Center Utrecht Brain Center, Utrecht University,Utrecht, The Netherlands
| | - F J Born
- Department of Translational Neuroscience, University Medical Center Utrecht Brain Center, Utrecht University,Utrecht, The Netherlands; Charité University, Berlin,Germany
| | - P Bakvis
- Clinical Psychology unit, Institute of Psychology and Leiden Institute for Brain and Cognition, Leiden University,The Netherlands; SEIN, Epilepsy Institute in the Netherlands,Heemstede,The Netherlands
| | - S Branje
- Department of Youth & Family, Utrecht University,Utrecht,The Netherlands
| | - B Elzinga
- Clinical Psychology unit, Institute of Psychology and Leiden Institute for Brain and Cognition, Leiden University,The Netherlands
| | - Awm Evers
- Health, Medical & Neuropsychology unit, Institute of Psychology and Leiden Institute for Brain and Cognition, Leiden University, The Netherlands
| | - M van Eysden
- Department of Translational Neuroscience, University Medical Center Utrecht Brain Center, Utrecht University,Utrecht, The Netherlands
| | - G Fernandez
- Donders Institute for Brain, Cognition, and Behaviour, Radboud University Medical Center,Nijmegen,The Netherlands
| | - P C Habets
- Amsterdam UMC location Vrije Universiteit Amsterdam, Psychiatry,DeBoelelaan 1117, Amsterdam,The Netherlands; Amsterdam Neurosciences, Mood, Anxiety, Psychosis, Stress, and Sleep (MAPSS),Amsterdam, The Netherlands
| | - C A Hartman
- Department of Psychiatry and Interdisciplinary Center Psychopathology and Emotion Regulation, University of Groningen, University Medical Center Groningen,Groningen,The Netherlands
| | - E J Hermans
- Donders Institute for Brain, Cognition, and Behaviour, Radboud University Medical Center,Nijmegen,The Netherlands
| | - W Meeus
- Department of Youth & Family, Utrecht University,Utrecht,The Netherlands
| | - H van Middendorp
- Health, Medical & Neuropsychology unit, Institute of Psychology and Leiden Institute for Brain and Cognition, Leiden University, The Netherlands
| | - S Nelemans
- Department of Youth & Family, Utrecht University,Utrecht,The Netherlands
| | - N Y Oei
- Amsterdam Brain and Cognition (ABC), University of Amsterdam,Amsterdam,The Netherlands; Department of Developmental Psychology, Addiction Development and Psychopathology(ADAPT)-Lab, University of Amsterdam, Amsterdam, The Netherlands, University of Amsterdam,Amsterdam,The Netherlands
| | - A J Oldehinkel
- Department of Psychiatry and Interdisciplinary Center Psychopathology and Emotion Regulation, University of Groningen, University Medical Center Groningen,Groningen,The Netherlands
| | - K Roelofs
- Radboud University Nijmegen: Donders Institute for Brain Cognition and Behaviour and Behavioural Science Institute
| | - S R de Rooij
- Department of Epidemiology and Data Science, University of Amsterdam, Amsterdam UMC,Amsterdam,The Netherlands
| | - T Smeets
- Department of Medical and Clinical Psychology, Center of Research on Psychological disorders and Somatic diseases (CoRPS), Tilburg School of Social and Behavioral Sciences, Tilburg University,Tilburg,The Netherlands
| | - M S Tollenaar
- Clinical Psychology unit, Institute of Psychology and Leiden Institute for Brain and Cognition, Leiden University,The Netherlands
| | - M Joëls
- University of Groningen, University Medical Center Groningen,Groningen,The Netherlands
| | - C H Vinkers
- Amsterdam UMC location Vrije Universiteit Amsterdam, Psychiatry,DeBoelelaan 1117, Amsterdam,The Netherlands; Amsterdam Neurosciences, Mood, Anxiety, Psychosis, Stress, and Sleep (MAPSS),Amsterdam, The Netherlands.
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Koning ASCAM, Habets PC, Bogaards M, Kroon J, van Santen HM, de Bont JM, Meijer OC. Mineralocorticoid receptor status in the human brain after dexamethasone treatment: a single case study. Endocr Connect 2022; 11:EC-21-0425.R1. [PMID: 35148274 PMCID: PMC8942311 DOI: 10.1530/ec-21-0425] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/26/2022] [Accepted: 02/11/2022] [Indexed: 11/08/2022]
Abstract
BACKGROUND Synthetic glucocorticoids like dexamethasone can cause severe neuropsychiatric effects. They preferentially bind to the glucocorticoid receptor (GR) over the mineralocorticoid receptor (MR). High dosages result in strong GR activation but likely also result in lower MR activation based on GR-mediated negative feedback on cortisol levels. Therefore, reduced MR activity may contribute to dexamethasone-induced neuropsychiatric symptoms. OBJECTIVE In this single case study, we evaluate whether dexamethasone leads to reduced MR activation in the human brain. Brain tissue of an 8-year-old brain tumor patient was used, who suffered chronically from dexamethasone-induced neuropsychiatric symptoms and deceased only hours after a high dose of dexamethasone. MAIN OUTCOME MEASURES The efficacy of dexamethasone to induce MR activity was determined in HEK293T cells using a reporter construct. Subcellular localization of GR and MR was assessed in paraffin-embedded hippocampal tissue from the patient and two controls. In hippocampal tissue from the patient and eight controls, mRNA of MR/GR target genes was measured. RESULTS In vitro, dexamethasone stimulated MR with low efficacy and low potency. Immunofluorescence showed the presence of both GR and MR in the hippocampal cell nuclei after dexamethasone exposure. The putative MR target gene JDP2 was consistently expressed at relatively low levels in the dexamethasone-treated brain samples. Gene expression showed substantial variation in MR/GR target gene expression in two different hippocampus tissue blocks from the same patient. CONCLUSIONS Dexamethasone may induce MR nuclear translocation in the human brain. Conclusions on in vivo effects on gene expression in the brain await the availability of more tissue of dexamethasone-treated patients.
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Affiliation(s)
- Anne-Sophie C A M Koning
- Division of Endocrinology, Department of Medicine, Leiden University Medical Center, Leiden, The Netherlands
- Einthoven Laboratory for Experimental Vascular Medicine, Leiden University Medical Center, Leiden, The Netherlands
- Correspondence should be addressed to A-S C A M Koning or O C Meijer: or
| | - Philippe C Habets
- Division of Endocrinology, Department of Medicine, Leiden University Medical Center, Leiden, The Netherlands
- Einthoven Laboratory for Experimental Vascular Medicine, Leiden University Medical Center, Leiden, The Netherlands
| | - Marit Bogaards
- Division of Endocrinology, Department of Medicine, Leiden University Medical Center, Leiden, The Netherlands
- Einthoven Laboratory for Experimental Vascular Medicine, Leiden University Medical Center, Leiden, The Netherlands
| | - Jan Kroon
- Division of Endocrinology, Department of Medicine, Leiden University Medical Center, Leiden, The Netherlands
- Einthoven Laboratory for Experimental Vascular Medicine, Leiden University Medical Center, Leiden, The Netherlands
| | - Hanneke M van Santen
- Department of Pediatric Endocrinology, Wilhelmina Children’s Hospital, University Medical Center Utrecht, Utrecht, The Netherlands
- Department of Pediatric Neuro-Oncology, Prinses Máxima Centrum, Utrecht, The Netherlands
| | - Judith M de Bont
- Department of Pediatric Neuro-Oncology, Prinses Máxima Centrum, Utrecht, The Netherlands
| | - Onno C Meijer
- Division of Endocrinology, Department of Medicine, Leiden University Medical Center, Leiden, The Netherlands
- Einthoven Laboratory for Experimental Vascular Medicine, Leiden University Medical Center, Leiden, The Netherlands
- Correspondence should be addressed to A-S C A M Koning or O C Meijer: or
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Otte WM, Vinkers CH, Habets PC, van IJzendoorn DGP, Tijdink JK. Analysis of 567,758 randomized controlled trials published over 30 years reveals trends in phrases used to discuss results that do not reach statistical significance. PLoS Biol 2022; 20:e3001562. [PMID: 35180228 PMCID: PMC8893613 DOI: 10.1371/journal.pbio.3001562] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2021] [Revised: 03/03/2022] [Accepted: 01/31/2022] [Indexed: 11/19/2022] Open
Abstract
The power of language to modify the reader's perception of interpreting biomedical results cannot be underestimated. Misreporting and misinterpretation are pressing problems in randomized controlled trials (RCT) output. This may be partially related to the statistical significance paradigm used in clinical trials centered around a P value below 0.05 cutoff. Strict use of this P value may lead to strategies of clinical researchers to describe their clinical results with P values approaching but not reaching the threshold to be "almost significant." The question is how phrases expressing nonsignificant results have been reported in RCTs over the past 30 years. To this end, we conducted a quantitative analysis of English full texts containing 567,758 RCTs recorded in PubMed between 1990 and 2020 (81.5% of all published RCTs in PubMed). We determined the exact presence of 505 predefined phrases denoting results that approach but do not cross the line of formal statistical significance (P < 0.05). We modeled temporal trends in phrase data with Bayesian linear regression. Evidence for temporal change was obtained through Bayes factor (BF) analysis. In a randomly sampled subset, the associated P values were manually extracted. We identified 61,741 phrases in 49,134 RCTs indicating almost significant results (8.65%; 95% confidence interval (CI): 8.58% to 8.73%). The overall prevalence of these phrases remained stable over time, with the most prevalent phrases being "marginally significant" (in 7,735 RCTs), "all but significant" (7,015), "a nonsignificant trend" (3,442), "failed to reach statistical significance" (2,578), and "a strong trend" (1,700). The strongest evidence for an increased temporal prevalence was found for "a numerical trend," "a positive trend," "an increasing trend," and "nominally significant." In contrast, the phrases "all but significant," "approaches statistical significance," "did not quite reach statistical significance," "difference was apparent," "failed to reach statistical significance," and "not quite significant" decreased over time. In a random sampled subset of 29,000 phrases, the manually identified and corresponding 11,926 P values, 68,1% ranged between 0.05 and 0.15 (CI: 67. to 69.0; median 0.06). Our results show that RCT reports regularly contain specific phrases describing marginally nonsignificant results to report P values close to but above the dominant 0.05 cutoff. The fact that the prevalence of the phrases remained stable over time indicates that this practice of broadly interpreting P values close to a predefined threshold remains prevalent. To enhance responsible and transparent interpretation of RCT results, researchers, clinicians, reviewers, and editors may reduce the focus on formal statistical significance thresholds and stimulate reporting of P values with corresponding effect sizes and CIs and focus on the clinical relevance of the statistical difference found in RCTs.
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Affiliation(s)
- Willem M. Otte
- Biomedical MR Imaging and Spectroscopy, Center for Image Sciences, University Medical Center Utrecht, Utrecht, the Netherlands
- Department of Child Neurology, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Christiaan H. Vinkers
- Department of Psychiatry, Department of Anatomy and Neurosciences, Amsterdam UMC, Amsterdam, the Netherlands
| | - Philippe C. Habets
- Department of Psychiatry, Department of Anatomy and Neurosciences, Amsterdam UMC, Amsterdam, the Netherlands
| | - David G. P. van IJzendoorn
- Department of Pathology, Stanford University School of Medicine, Stanford, California, United States of America
| | - Joeri K. Tijdink
- Department of Ethics, Law and Humanities, Amsterdam UMC, Amsterdam, the Netherlands
- Department of Philosophy, Vrije Universiteit, Amsterdam, the Netherlands
- * E-mail:
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Habets PC, Mclain C, Meijer OC. Brain areas affected by intranasal oxytocin show higher oxytocin receptor expression. Eur J Neurosci 2021; 54:6374-6381. [PMID: 34498316 PMCID: PMC9291869 DOI: 10.1111/ejn.15447] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2021] [Revised: 08/18/2021] [Accepted: 08/31/2021] [Indexed: 12/17/2022]
Abstract
Neuroimaging studies suggest that intranasal oxytocin (IN‐OXT) may modulate emotional and social processes by altering neural activity patterns. The extent of brain penetration after IN‐OXT is unclear, and it is currently unknown whether IN‐OXT can directly bind central oxytocin receptors (OXTRs). We investigated oxytocin pathway gene expression in regions affected by IN‐OXT on task‐based fMRI. We found that OXTR is more highly expressed in affected than unaffected subcortical regions; this effect did not vary by task type or sex. Cortical results revealed higher OXTR expression in regions affected by IN‐OXT in emotional processing tasks and in male‐only data. No significant differences were found in expression of the closely related vasopressin receptors. Our findings suggest that the mechanism by which IN‐OXT may alter brain functionality involves direct activation of central OXTRs.
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Affiliation(s)
- Philippe C Habets
- Division of Endocrinology, Department of Internal Medicine, Leiden University Medical Center, Leiden, The Netherlands.,Department of Psychiatry, Department of Anatomy and Neurosciences, Amsterdam University Medical Centre, Amsterdam, The Netherlands
| | - Christabel Mclain
- Division of Endocrinology, Department of Internal Medicine, Leiden University Medical Center, Leiden, The Netherlands
| | - Onno C Meijer
- Division of Endocrinology, Department of Internal Medicine, Leiden University Medical Center, Leiden, The Netherlands
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Habets PC. [Screening for asymptomatic Chlamydia trachomatis infection: cost-effectiveness favorable at a minimum prevalence rate of 3% or more]. Ned Tijdschr Geneeskd 2001; 145:499-501. [PMID: 11268916] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 02/19/2023]
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