Pub Date : 2024-08-15DOI: 10.1038/s44220-024-00292-4
Lisa M. Shitomi-Jones, Clare Dolman, Ian Jones, George Kirov, Valentina Escott-Price, Sophie E. Legge, Arianna Di Florio
Although the relationship between perimenopause and changes in mood has been well established, knowledge of risk of a broad spectrum of psychiatric disorders associated with reproductive aging is limited. Here we investigate whether the perimenopause (that is, the years around the final menstrual period (FMP)) is associated with increased risk of developing psychiatric disorders compared with the late reproductive stage. Information on menopausal timing and psychiatric history was obtained from nurse-administered interviews and online questionnaires from 128,294 female participants within UK Biobank. Incidence rates of psychiatric disorders during the perimenopause (4 years surrounding the FMP) were compared with the reference premenopausal period (6–10 years before the FMP). The rates were calculated for major depressive disorder (MDD), mania, schizophrenia spectrum disorders and other diagnoses. Overall, of 128,294 participants, 753 (0.59%) reported their first onset of a psychiatric disorder during the late reproductive stage (incidence rate 1.53 per 1,000 person-years) and 1,133 (0.88%) during the perimenopause (incidence rate 2.33 per 1,000 person-years). Compared with the reference reproductive period, incidence rates of psychiatric disorders significantly increased during the perimenopause (incidence rate ratio (RR) of 1.52, 95% confidence interval (CI) 1.39–1.67) and decreased back down to that observed in the premenopausal period in the postmenopause (RR of 1.09 (95% CI 0.98–1.21)). The effect was primarily driven by increased incidence rates of MDD, with an incidence RR of 1.30 (95% CI 1.16–1.45). However, the largest effect size at perimenopause was observed for mania (RR of 2.12 (95% CI 1.30–3.52)). No association was found between perimenopause and incidence rates of schizophrenia spectrum disorders (RR of 0.95 (95% CI 0.48–1.88)). In conclusion, perimenopause was associated with an increased risk of developing MDD and mania. No association was found between perimenopause and first onsets of schizophrenia spectrum disorders. The authors investigate first onsets of psychiatric disorders during perimenopause, finding higher incidence rates of major depressive disorder and mania.
{"title":"Exploration of first onsets of mania, schizophrenia spectrum disorders and major depressive disorder in perimenopause","authors":"Lisa M. Shitomi-Jones, Clare Dolman, Ian Jones, George Kirov, Valentina Escott-Price, Sophie E. Legge, Arianna Di Florio","doi":"10.1038/s44220-024-00292-4","DOIUrl":"10.1038/s44220-024-00292-4","url":null,"abstract":"Although the relationship between perimenopause and changes in mood has been well established, knowledge of risk of a broad spectrum of psychiatric disorders associated with reproductive aging is limited. Here we investigate whether the perimenopause (that is, the years around the final menstrual period (FMP)) is associated with increased risk of developing psychiatric disorders compared with the late reproductive stage. Information on menopausal timing and psychiatric history was obtained from nurse-administered interviews and online questionnaires from 128,294 female participants within UK Biobank. Incidence rates of psychiatric disorders during the perimenopause (4 years surrounding the FMP) were compared with the reference premenopausal period (6–10 years before the FMP). The rates were calculated for major depressive disorder (MDD), mania, schizophrenia spectrum disorders and other diagnoses. Overall, of 128,294 participants, 753 (0.59%) reported their first onset of a psychiatric disorder during the late reproductive stage (incidence rate 1.53 per 1,000 person-years) and 1,133 (0.88%) during the perimenopause (incidence rate 2.33 per 1,000 person-years). Compared with the reference reproductive period, incidence rates of psychiatric disorders significantly increased during the perimenopause (incidence rate ratio (RR) of 1.52, 95% confidence interval (CI) 1.39–1.67) and decreased back down to that observed in the premenopausal period in the postmenopause (RR of 1.09 (95% CI 0.98–1.21)). The effect was primarily driven by increased incidence rates of MDD, with an incidence RR of 1.30 (95% CI 1.16–1.45). However, the largest effect size at perimenopause was observed for mania (RR of 2.12 (95% CI 1.30–3.52)). No association was found between perimenopause and incidence rates of schizophrenia spectrum disorders (RR of 0.95 (95% CI 0.48–1.88)). In conclusion, perimenopause was associated with an increased risk of developing MDD and mania. No association was found between perimenopause and first onsets of schizophrenia spectrum disorders. The authors investigate first onsets of psychiatric disorders during perimenopause, finding higher incidence rates of major depressive disorder and mania.","PeriodicalId":74247,"journal":{"name":"Nature mental health","volume":"2 10","pages":"1161-1168"},"PeriodicalIF":0.0,"publicationDate":"2024-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s44220-024-00292-4.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142435933","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-14DOI: 10.1038/s44220-024-00294-2
Dirk H. M. Pelt, Philippe C. Habets, Christiaan H. Vinkers, Lannie Ligthart, Catharina E. M. van Beijsterveldt, René Pool, Meike Bartels
Effective personalized well-being interventions require the ability to predict who will thrive or not, and the understanding of underlying mechanisms. Here, using longitudinal data of a large population cohort (the Netherlands Twin Register, collected 1991–2022), we aim to build machine learning prediction models for adult well-being from the exposome and genome, and identify the most predictive factors (N between 702 and 5874). The specific exposome was captured by parent and self-reports of psychosocial factors from childhood to adulthood, the genome was described by polygenic scores, and the general exposome was captured by linkage of participants’ postal codes to objective, registry-based exposures. Not the genome (R2 = −0.007 [−0.026–0.010]), but the general exposome (R2 = 0.047 [0.015–0.076]) and especially the specific exposome (R2 = 0.702 [0.637–0.753]) were predictive of well-being in an independent test set. Adding the genome (P = 0.334) and general exposome (P = 0.695) independently or jointly (P = 0.029) beyond the specific exposome did not improve prediction. Risk/protective factors such as optimism, personality, social support and neighborhood housing characteristics were most predictive. Our findings highlight the importance of longitudinal monitoring and promises of different data modalities for well-being prediction. Machine learning prediction models for adult well-being were built on longitudinal data from the Netherlands Twin Register population cohort. The exposome, but not the genome, predicted well-being in adulthood, with key factors including optimism, personality, social support and neighborhood housing characteristics.
{"title":"Building machine learning prediction models for well-being using predictors from the exposome and genome in a population cohort","authors":"Dirk H. M. Pelt, Philippe C. Habets, Christiaan H. Vinkers, Lannie Ligthart, Catharina E. M. van Beijsterveldt, René Pool, Meike Bartels","doi":"10.1038/s44220-024-00294-2","DOIUrl":"10.1038/s44220-024-00294-2","url":null,"abstract":"Effective personalized well-being interventions require the ability to predict who will thrive or not, and the understanding of underlying mechanisms. Here, using longitudinal data of a large population cohort (the Netherlands Twin Register, collected 1991–2022), we aim to build machine learning prediction models for adult well-being from the exposome and genome, and identify the most predictive factors (N between 702 and 5874). The specific exposome was captured by parent and self-reports of psychosocial factors from childhood to adulthood, the genome was described by polygenic scores, and the general exposome was captured by linkage of participants’ postal codes to objective, registry-based exposures. Not the genome (R2 = −0.007 [−0.026–0.010]), but the general exposome (R2 = 0.047 [0.015–0.076]) and especially the specific exposome (R2 = 0.702 [0.637–0.753]) were predictive of well-being in an independent test set. Adding the genome (P = 0.334) and general exposome (P = 0.695) independently or jointly (P = 0.029) beyond the specific exposome did not improve prediction. Risk/protective factors such as optimism, personality, social support and neighborhood housing characteristics were most predictive. Our findings highlight the importance of longitudinal monitoring and promises of different data modalities for well-being prediction. Machine learning prediction models for adult well-being were built on longitudinal data from the Netherlands Twin Register population cohort. The exposome, but not the genome, predicted well-being in adulthood, with key factors including optimism, personality, social support and neighborhood housing characteristics.","PeriodicalId":74247,"journal":{"name":"Nature mental health","volume":"2 10","pages":"1217-1230"},"PeriodicalIF":0.0,"publicationDate":"2024-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142435952","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-09DOI: 10.1038/s44220-024-00303-4
Ye Ella Tian, James H. Cole, Edward T. Bullmore, Andrew Zalesky
Depression and anxiety are prevalent in people with a chronic physical illness. Increasing evidence suggests that co-occurring physical and mental illness is associated with shared biological pathways. However, little is known about the brain’s role in mediating links between physical and mental health. Here, using multimodal brain imaging and organ-specific physiological markers from the UK Biobank, we establish prospective associations between the baseline health of seven organs including cardiovascular, pulmonary, musculoskeletal, immune, renal, hepatic and metabolic systems, and mental health outcomes at 4–14 years’ follow-up, focusing on depression and anxiety. We reveal multiple pathways, mediated by the brain, through which poor organ health may lead to poor mental health. We identify lifestyle and environmental factors, including exercise, sedentary behavior, diet, sleep quality, smoking, alcohol intake, education and socioeconomic status that influence mental health through their selective impact on the physiology of specific organ systems and brain structure. Our work reveals the interplay between brain, body and lifestyle, and their collective influence on mental health. Pathways elucidated here may inform behavioral interventions to mitigate or prevent the synergistic co-occurrence of physical and mental disorders. In a large-scale UK Biobank study of multimodal brain imaging and physiological markers, the authors find brain-mediated patterns of organ function and lifestyle pathways that are predictive of specific mental health outcomes.
{"title":"Brain, lifestyle and environmental pathways linking physical and mental health","authors":"Ye Ella Tian, James H. Cole, Edward T. Bullmore, Andrew Zalesky","doi":"10.1038/s44220-024-00303-4","DOIUrl":"10.1038/s44220-024-00303-4","url":null,"abstract":"Depression and anxiety are prevalent in people with a chronic physical illness. Increasing evidence suggests that co-occurring physical and mental illness is associated with shared biological pathways. However, little is known about the brain’s role in mediating links between physical and mental health. Here, using multimodal brain imaging and organ-specific physiological markers from the UK Biobank, we establish prospective associations between the baseline health of seven organs including cardiovascular, pulmonary, musculoskeletal, immune, renal, hepatic and metabolic systems, and mental health outcomes at 4–14 years’ follow-up, focusing on depression and anxiety. We reveal multiple pathways, mediated by the brain, through which poor organ health may lead to poor mental health. We identify lifestyle and environmental factors, including exercise, sedentary behavior, diet, sleep quality, smoking, alcohol intake, education and socioeconomic status that influence mental health through their selective impact on the physiology of specific organ systems and brain structure. Our work reveals the interplay between brain, body and lifestyle, and their collective influence on mental health. Pathways elucidated here may inform behavioral interventions to mitigate or prevent the synergistic co-occurrence of physical and mental disorders. In a large-scale UK Biobank study of multimodal brain imaging and physiological markers, the authors find brain-mediated patterns of organ function and lifestyle pathways that are predictive of specific mental health outcomes.","PeriodicalId":74247,"journal":{"name":"Nature mental health","volume":"2 10","pages":"1250-1261"},"PeriodicalIF":0.0,"publicationDate":"2024-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141922104","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-08DOI: 10.1038/s44220-024-00291-5
Sarah H. Sperry, Anastasia K. Yocum, Melvin G. McInnis
Clinical care for bipolar disorder (BD) has a narrow focus on prevention and remission of episodes with pre-/posttreatment reductions in symptom severity as the ‘gold standard’ for outcomes in clinical trials and measurement-based care strategies. Here the study aim was to provide an innovative method for measuring outcomes in BD that has clinical utility and can stratify individuals with BD based on mood instability. The 603 participants comprised those with a BD (n = 385), other or nonaffective disorder (n = 71) or no psychiatric history (n = 147) enrolled in an longitudinal cohort for at least 10 years that collects patient-reported outcome measures (PROMs) assessing depression, (hypo)mania, anxiety and functioning every 2 months. Mood instability was calculated as the intraindividual s.d. of PROMs over 1-year rolling windows and stratified into low, moderate and high thresholds. Individuals with BD had significantly higher 1-year rolling s.d. for depression, (hypo)mania and anxiety compared with psychiatric comparisons (small–moderate effects) and healthy controls (large effects). A significantly greater proportion of scores for those with BD fell into the moderate (depression 50.6%; anxiety 36.5%; and (hypo)mania 52.1%) and high thresholds (depression 9.4%; anxiety 6.1%; and (hypo)mania 10.1%) compared with psychiatric comparisons (moderate 32.3–42.9% and high 2.6–6.6%) and healthy controls (moderate 11.5–31.7% and high 0.4–5.8%). Being in the high or moderate threshold predicted worse mental health functioning (small to large effects). Mood instability, as measured in commonly used PROMs, characterized the course of illness over time, correlated with functional outcomes and significantly differentiated those with BD from healthy controls and psychiatric comparisons. The results suggest a paradigm shift in monitoring outcomes in BD, by measuring intraindividual s.d. as a primary outcome index. This study introduces a method to measure outcomes in bipolar disorder by quantifying mood instability over time.
{"title":"Mood instability metrics to stratify individuals and measure outcomes in bipolar disorder","authors":"Sarah H. Sperry, Anastasia K. Yocum, Melvin G. McInnis","doi":"10.1038/s44220-024-00291-5","DOIUrl":"10.1038/s44220-024-00291-5","url":null,"abstract":"Clinical care for bipolar disorder (BD) has a narrow focus on prevention and remission of episodes with pre-/posttreatment reductions in symptom severity as the ‘gold standard’ for outcomes in clinical trials and measurement-based care strategies. Here the study aim was to provide an innovative method for measuring outcomes in BD that has clinical utility and can stratify individuals with BD based on mood instability. The 603 participants comprised those with a BD (n = 385), other or nonaffective disorder (n = 71) or no psychiatric history (n = 147) enrolled in an longitudinal cohort for at least 10 years that collects patient-reported outcome measures (PROMs) assessing depression, (hypo)mania, anxiety and functioning every 2 months. Mood instability was calculated as the intraindividual s.d. of PROMs over 1-year rolling windows and stratified into low, moderate and high thresholds. Individuals with BD had significantly higher 1-year rolling s.d. for depression, (hypo)mania and anxiety compared with psychiatric comparisons (small–moderate effects) and healthy controls (large effects). A significantly greater proportion of scores for those with BD fell into the moderate (depression 50.6%; anxiety 36.5%; and (hypo)mania 52.1%) and high thresholds (depression 9.4%; anxiety 6.1%; and (hypo)mania 10.1%) compared with psychiatric comparisons (moderate 32.3–42.9% and high 2.6–6.6%) and healthy controls (moderate 11.5–31.7% and high 0.4–5.8%). Being in the high or moderate threshold predicted worse mental health functioning (small to large effects). Mood instability, as measured in commonly used PROMs, characterized the course of illness over time, correlated with functional outcomes and significantly differentiated those with BD from healthy controls and psychiatric comparisons. The results suggest a paradigm shift in monitoring outcomes in BD, by measuring intraindividual s.d. as a primary outcome index. This study introduces a method to measure outcomes in bipolar disorder by quantifying mood instability over time.","PeriodicalId":74247,"journal":{"name":"Nature mental health","volume":"2 9","pages":"1111-1119"},"PeriodicalIF":0.0,"publicationDate":"2024-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141925464","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-08DOI: 10.1038/s44220-024-00305-2
Much of psychiatry, psychology and mental health broadly has been dependent on the notion that people are predominantly similar or simply by neglecting diversity. Yet there are powerful influences related to one’s national or country identity, race and ethnicity, community and cultural heritage that speak to a far more complex and dynamic reality. Reflecting on these factors in the context of research is not only a challenge but a profound opportunity to spur future work and to improve care and treatment for individuals.
{"title":"Country and culture, mental health in context","authors":"","doi":"10.1038/s44220-024-00305-2","DOIUrl":"10.1038/s44220-024-00305-2","url":null,"abstract":"Much of psychiatry, psychology and mental health broadly has been dependent on the notion that people are predominantly similar or simply by neglecting diversity. Yet there are powerful influences related to one’s national or country identity, race and ethnicity, community and cultural heritage that speak to a far more complex and dynamic reality. Reflecting on these factors in the context of research is not only a challenge but a profound opportunity to spur future work and to improve care and treatment for individuals.","PeriodicalId":74247,"journal":{"name":"Nature mental health","volume":"2 8","pages":"877-878"},"PeriodicalIF":0.0,"publicationDate":"2024-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s44220-024-00305-2.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141929489","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-05DOI: 10.1038/s44220-024-00298-y
Gustavo Deco, Yonatan Sanz Perl, Samuel Johnson, Niamh Bourke, Robin L. Carhart-Harris, Morten L. Kringelbach
Effective interventions for neuropsychiatric disorders may work by rebalancing the brain’s functional hierarchical organization. Here we directly investigated the effects of two different serotonergic pharmacological interventions on functional brain hierarchy in major depressive disorder in a two-arm double-blind phase II randomized controlled trial comparing psilocybin therapy (22 patients) with escitalopram (20 patients). Patients with major depressive disorder received either 2 × 25 mg of oral psilocybin, three weeks apart, plus six weeks of daily placebo (‘psilocybin arm’) or 2 × 1 mg of oral psilocybin, three weeks apart, plus six weeks of daily escitalopram (10–20 mg; ‘escitalopram arm’). Resting-state functional magnetic resonance imaging scans were acquired at baseline and three weeks after the second psilocybin dose ( NCT03429075 ). The brain mechanisms were captured by generative effective connectivity, estimated from whole-brain modeling of resting state for each session and patient. Hierarchy was determined for each of these sessions using measures of directedness and trophic levels on the effective connectivity, which captures cycle structure, stability and percolation. The results showed that the two pharmacological interventions created significantly different hierarchical reconfigurations of whole-brain dynamics with differential, opposite statistical effect responses. Furthermore, the use of machine learning revealed significant differential reorganization of brain hierarchy before and after the two treatments. Machine learning was also able to predict treatment response with an accuracy of 0.85 ± 0.04. Overall, the results demonstrate that psilocybin and escitalopram work in different ways for rebalancing brain dynamics in depression. This suggests the hypothesis that neuropsychiatric disorders could be closely linked to the breakdown in regions orchestrating brain dynamics from the top of the hierarchy. Psilocybin and escitalopram create significantly different reconfigurations in the global functional hierarchy of brain dynamics with opposite statistical effect responses in people with major depressive disorder.
{"title":"Different hierarchical reconfigurations in the brain by psilocybin and escitalopram for depression","authors":"Gustavo Deco, Yonatan Sanz Perl, Samuel Johnson, Niamh Bourke, Robin L. Carhart-Harris, Morten L. Kringelbach","doi":"10.1038/s44220-024-00298-y","DOIUrl":"10.1038/s44220-024-00298-y","url":null,"abstract":"Effective interventions for neuropsychiatric disorders may work by rebalancing the brain’s functional hierarchical organization. Here we directly investigated the effects of two different serotonergic pharmacological interventions on functional brain hierarchy in major depressive disorder in a two-arm double-blind phase II randomized controlled trial comparing psilocybin therapy (22 patients) with escitalopram (20 patients). Patients with major depressive disorder received either 2 × 25 mg of oral psilocybin, three weeks apart, plus six weeks of daily placebo (‘psilocybin arm’) or 2 × 1 mg of oral psilocybin, three weeks apart, plus six weeks of daily escitalopram (10–20 mg; ‘escitalopram arm’). Resting-state functional magnetic resonance imaging scans were acquired at baseline and three weeks after the second psilocybin dose ( NCT03429075 ). The brain mechanisms were captured by generative effective connectivity, estimated from whole-brain modeling of resting state for each session and patient. Hierarchy was determined for each of these sessions using measures of directedness and trophic levels on the effective connectivity, which captures cycle structure, stability and percolation. The results showed that the two pharmacological interventions created significantly different hierarchical reconfigurations of whole-brain dynamics with differential, opposite statistical effect responses. Furthermore, the use of machine learning revealed significant differential reorganization of brain hierarchy before and after the two treatments. Machine learning was also able to predict treatment response with an accuracy of 0.85 ± 0.04. Overall, the results demonstrate that psilocybin and escitalopram work in different ways for rebalancing brain dynamics in depression. This suggests the hypothesis that neuropsychiatric disorders could be closely linked to the breakdown in regions orchestrating brain dynamics from the top of the hierarchy. Psilocybin and escitalopram create significantly different reconfigurations in the global functional hierarchy of brain dynamics with opposite statistical effect responses in people with major depressive disorder.","PeriodicalId":74247,"journal":{"name":"Nature mental health","volume":"2 9","pages":"1096-1110"},"PeriodicalIF":0.0,"publicationDate":"2024-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s44220-024-00298-y.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142160312","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-01DOI: 10.1038/s44220-024-00285-3
Elena Toffol, Markus Stracke, Neele Harlos, Stefanie Lambrecht, Florian Brandt, Sören Friedrich, Sonja Kennard, Lasse Wenzel, Giovanni de Girolamo, Kristin Gilbert, Corinna Reck, Kathleen Otto, Ricarda Steinmayr, Babette Renneberg, Jean L. Paul, Anne A. E. Thorup, Christina Schwenck, Anna-Lena Zietlow, Linda Wirthwein, Hanna Christiansen
Children of parents with a mental illness (COPMI) are at risk of adverse outcomes, as well as of developing a mental illness themselves. Recognition of modifiable risk factors, along with targeted initiatives and interventions have the potential to improve their and their families’ strengths and resilience, and thus effectively interrupt this vicious circle of the transgenerational transmission of mental disorders. Although several international projects have been funded and implemented, their planning, implementation and translation are not free from problems and downsides, and the use of measures specifically targeting COPMI is not yet part of regular clinical practice. Here we illustrate four European projects targeting family mental health, addressing the main problems encountered and the principal focuses for future directions, as learned from live discussions between project team members, participating patients/parents and other stakeholders. Our goal was to summarize those as lessons learned and make them available to the public and research community. Children of parents with a mental illness are at risk of adverse mental health outcomes. This Perspective discusses lessons learned from the European projects targeting family mental health and, on the basis of identified problems and barriers, provides recommendations to guide the development of future projects and facilitate successful implementation of their results.
{"title":"Lessons on targeting family mental health and improving outcomes for children of parents with a mental illness","authors":"Elena Toffol, Markus Stracke, Neele Harlos, Stefanie Lambrecht, Florian Brandt, Sören Friedrich, Sonja Kennard, Lasse Wenzel, Giovanni de Girolamo, Kristin Gilbert, Corinna Reck, Kathleen Otto, Ricarda Steinmayr, Babette Renneberg, Jean L. Paul, Anne A. E. Thorup, Christina Schwenck, Anna-Lena Zietlow, Linda Wirthwein, Hanna Christiansen","doi":"10.1038/s44220-024-00285-3","DOIUrl":"10.1038/s44220-024-00285-3","url":null,"abstract":"Children of parents with a mental illness (COPMI) are at risk of adverse outcomes, as well as of developing a mental illness themselves. Recognition of modifiable risk factors, along with targeted initiatives and interventions have the potential to improve their and their families’ strengths and resilience, and thus effectively interrupt this vicious circle of the transgenerational transmission of mental disorders. Although several international projects have been funded and implemented, their planning, implementation and translation are not free from problems and downsides, and the use of measures specifically targeting COPMI is not yet part of regular clinical practice. Here we illustrate four European projects targeting family mental health, addressing the main problems encountered and the principal focuses for future directions, as learned from live discussions between project team members, participating patients/parents and other stakeholders. Our goal was to summarize those as lessons learned and make them available to the public and research community. Children of parents with a mental illness are at risk of adverse mental health outcomes. This Perspective discusses lessons learned from the European projects targeting family mental health and, on the basis of identified problems and barriers, provides recommendations to guide the development of future projects and facilitate successful implementation of their results.","PeriodicalId":74247,"journal":{"name":"Nature mental health","volume":"2 8","pages":"893-900"},"PeriodicalIF":0.0,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141968610","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-30DOI: 10.1038/s44220-024-00284-4
N. J. Ermers, G. E. H. I. Franssen, F. E. Scheepers, N. van Sambeek, S. M. van Geelen
In mental health care, large differences in perspective between individuals with psychosis and professionals are an everyday reality. Such discrepancies become apparent in the substantial number of patients judged to lack illness insight. This Perspective argues that ‘illness insight’ typically refers to patient conformity to medical views rather than denoting true understanding into their condition. We outline limitations of the current conceptualization of illness insight (‘clinical insight’) and discuss an alternative, narrative understanding, drawing on literature from various academic disciplines. After addressing definitional ambiguities, etiological complexities and methodological inconsistencies inherent to the current understanding, this paper highlights several normative, cultural and ethical issues surrounding clinical insight. A narrative approach allows patients to find more meaningful explanations that resonate better with the complexity of their experiences and tackles other problems identified with clinical insight. We argue that narrative insight is inherently co-constructed, emphasizing the shared meaning-making process between individuals with psychosis and professionals. This Perspective seeks to identify the limitations of the ‘clinical insight’ construct discussing an alternative narrative approach for individuals with psychosis.
{"title":"From diagnostic conformity to co-narration of self-insight in mental health care","authors":"N. J. Ermers, G. E. H. I. Franssen, F. E. Scheepers, N. van Sambeek, S. M. van Geelen","doi":"10.1038/s44220-024-00284-4","DOIUrl":"10.1038/s44220-024-00284-4","url":null,"abstract":"In mental health care, large differences in perspective between individuals with psychosis and professionals are an everyday reality. Such discrepancies become apparent in the substantial number of patients judged to lack illness insight. This Perspective argues that ‘illness insight’ typically refers to patient conformity to medical views rather than denoting true understanding into their condition. We outline limitations of the current conceptualization of illness insight (‘clinical insight’) and discuss an alternative, narrative understanding, drawing on literature from various academic disciplines. After addressing definitional ambiguities, etiological complexities and methodological inconsistencies inherent to the current understanding, this paper highlights several normative, cultural and ethical issues surrounding clinical insight. A narrative approach allows patients to find more meaningful explanations that resonate better with the complexity of their experiences and tackles other problems identified with clinical insight. We argue that narrative insight is inherently co-constructed, emphasizing the shared meaning-making process between individuals with psychosis and professionals. This Perspective seeks to identify the limitations of the ‘clinical insight’ construct discussing an alternative narrative approach for individuals with psychosis.","PeriodicalId":74247,"journal":{"name":"Nature mental health","volume":"2 8","pages":"883-892"},"PeriodicalIF":0.0,"publicationDate":"2024-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141968609","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-26DOI: 10.1038/s44220-024-00301-6
Cynthia H. Y. Fu, Mathilde Antoniades, Guray Erus, Jose A. Garcia, Yong Fan, Danilo Arnone, Stephen R. Arnott, Taolin Chen, Ki Sueng Choi, Cherise Chin Fatt, Benicio N. Frey, Vibe G. Frokjaer, Melanie Ganz, Beata R. Godlewska, Stefanie Hassel, Keith Ho, Andrew M. McIntosh, Kun Qin, Susan Rotzinger, Matthew D. Sacchet, Jonathan Savitz, Haochang Shou, Ashish Singh, Aleks Stolicyn, Irina Strigo, Stephen C. Strother, Duygu Tosun, Teresa A. Victor, Dongtao Wei, Toby Wise, Roland Zahn, Ian M. Anderson, W. Edward Craighead, J. F. William Deakin, Boadie W. Dunlop, Rebecca Elliott, Qiyong Gong, Ian H. Gotlib, Catherine J. Harmer, Sidney H. Kennedy, Gitte M. Knudsen, Helen S. Mayberg, Martin P. Paulus, Jiang Qiu, Madhukar H. Trivedi, Heather C. Whalley, Chao-Gan Yan, Allan H. Young, Christos Davatzikos
{"title":"Publisher Correction: Neuroanatomical dimensions in medication-free individuals with major depressive disorder and treatment response to SSRI antidepressant medications or placebo","authors":"Cynthia H. Y. Fu, Mathilde Antoniades, Guray Erus, Jose A. Garcia, Yong Fan, Danilo Arnone, Stephen R. Arnott, Taolin Chen, Ki Sueng Choi, Cherise Chin Fatt, Benicio N. Frey, Vibe G. Frokjaer, Melanie Ganz, Beata R. Godlewska, Stefanie Hassel, Keith Ho, Andrew M. McIntosh, Kun Qin, Susan Rotzinger, Matthew D. Sacchet, Jonathan Savitz, Haochang Shou, Ashish Singh, Aleks Stolicyn, Irina Strigo, Stephen C. Strother, Duygu Tosun, Teresa A. Victor, Dongtao Wei, Toby Wise, Roland Zahn, Ian M. Anderson, W. Edward Craighead, J. F. William Deakin, Boadie W. Dunlop, Rebecca Elliott, Qiyong Gong, Ian H. Gotlib, Catherine J. Harmer, Sidney H. Kennedy, Gitte M. Knudsen, Helen S. Mayberg, Martin P. Paulus, Jiang Qiu, Madhukar H. Trivedi, Heather C. Whalley, Chao-Gan Yan, Allan H. Young, Christos Davatzikos","doi":"10.1038/s44220-024-00301-6","DOIUrl":"10.1038/s44220-024-00301-6","url":null,"abstract":"","PeriodicalId":74247,"journal":{"name":"Nature mental health","volume":"2 9","pages":"1120-1120"},"PeriodicalIF":0.0,"publicationDate":"2024-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s44220-024-00301-6.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142160336","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-22DOI: 10.1038/s44220-024-00283-5
David J. Grelotti, Jessica Montoya, Violaine Delorme-Walker, Jennifer Iudicello, Ronald Ellis, Igor Grant, Scott Letendre, Maria Cecilia Garibaldi Marcondes, Mariana Cherner
{"title":"On the issue of treating HIV in people with syndemic mental-health and substance-use disorders","authors":"David J. Grelotti, Jessica Montoya, Violaine Delorme-Walker, Jennifer Iudicello, Ronald Ellis, Igor Grant, Scott Letendre, Maria Cecilia Garibaldi Marcondes, Mariana Cherner","doi":"10.1038/s44220-024-00283-5","DOIUrl":"10.1038/s44220-024-00283-5","url":null,"abstract":"","PeriodicalId":74247,"journal":{"name":"Nature mental health","volume":"2 8","pages":"879-880"},"PeriodicalIF":0.0,"publicationDate":"2024-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141815354","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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