Staging the bipolar disorders: Are early stages too early a stage for intervention?
Notice bibliographique
Résumé
In his Review, Parker questions the usefulness of a staging approach for bipolar disorders (BD) given the state of knowledge and warns of the risk of overdiagnosis and overtreatment of early stage presentations. Parker raises the point that childhood anxiety and sleep disorders are common, making it difficult to ascertain when these presentations represent antecedents to BD and that by identifying individuals at increased risk, unnecessary worry and possible stigma may ensue. Although concerns about going beyond the evidence are valid and core to the values of medicine to do no harm, a developmental approach to understanding the evolution of BD has been extremely informative, advancing both clinical practice and research. The Canadian Flourish longitudinal offspring study started in 19971 in direct response to questions from BD parents about the risk of illness in their children. At the time, there was insufficient data to inform accurate individual risk prediction together with an appreciation that, given the substantial genetic and phenotypic heterogeneity, the risk would vary significantly between families and among individuals within families. An unexpected finding from the Flourish offspring study was the elevated rate of childhood anxiety and sleep disorders in high risk children compared with children of well parents. The high-risk children came from mostly intact, middle-class families, with only one BD parent (i.e., the other parent had no lifetime history of mental illness). With the longer observation of more children over the peak risk period, we found evidence of an increased risk of major mood disorder of about 2.5-fold in high-risk offspring with, compared to those without, childhood anxiety and sleep disorders.2 This finding has since been independently replicated. In contradiction to concerns raised by Parker, parents found it helpful and reassuring to understand that the risk of their child(ren) developing BD was much lower than anticipated and that families could be signposted to low-intensity support when first indicated—which likely explains the low (under 10%) attrition over decades of observational research. Several groups around the world have invested in longitudinal studies of children at familial risk of mood disorders, and as a result, the approaches and precision of individualized risk prediction have advanced; taking into account heterogeneity, and being more honest about statistical uncertainty of predictions, largely related to sample sizes.3, 4 One concern raised by Parker is the possibility that variables used to assess the risk of BD may have non causal or indirect relationships with the onset of illness. While understanding causal mechanisms is important and may inform future directions in prevention and treatment, it is not necessary that variables used to assess risk be strictly causal in nature. In practice, the causal variable may be difficult to measure for various reasons; however, a proxy variable, confounded by the truly causal variable, will still contribute useful information in assessing future risk. Further, by taking a developmental approach to mapping psychopathology in children at confirmed familial risk, we have been able to develop a refined conceptual framework to advance progress in both clinical practice and research (Figure 1).5 The staging approach has provided evidence that while not all high-risk children who develop BD will manifest each and every clinical stage, a progressive forward sequence may be the most parsimonious trajectory. This developmental framework will advance studies of multi-level risk factors (epigenetic, neurobiologic, psychologic, sociologic) associated with illness onset and inform the timing and nature of prevention opportunities (i.e., parenting and family support, psychoeducation, psychosocial interventions). Interestingly, the model also considers differential trajectories and prevention needs based on familial BD subtypes: that is the developmental trajectory of episodic lithium-responsive BD compared with that of psychotic spectrum lithium-nonresponsive BD. Although we agree with Parker's concern regarding going beyond the evidence and possible misuse of the staging approach, having reliable data to inform more precise risk prediction, refined study designs to better understand BD onset, and developmentally tailored prevention targets is vital to improving outcomes—as seen in other areas of medicine such as cancer and cardiovascular care. Furthermore, understanding better one's own health risks can empower and motivate families and individuals to engage in self-management by reducing modifiable risk exposures (i.e., substance use), making healthy lifestyle choices (i.e., diet, exercise, and sleep), and strengthening resilience (i.e., healthy socioemotional coping, family functioning). In addition, identifying among children at familial risk those who might benefit most from closer monitoring and low-intensity prevention may improve outcomes. In fact, we branded our offspring research Flourish to emphasize that the majority of children at familial risk will not develop BD and highlight the importance of developmentally appropriate, common sense, low-intensity nonstigmatizing prevention with the potential for benefits that extend lifelong and possibly intergenerationally. As with most relatively novel approaches, we believe that staging models and risk calculators would benefit from further refinement and assessment in larger cohorts; however, this should not preclude their adoption in evidence-informed practice. Data sharing is not applicable to this article as no new data were created or analyzed in this study.
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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; les deux têtes enseignantes s’accordent sur ce qui est montré ici.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».