Unveiling mild behavioural impairment in Parkinson’s disease: insights from a systematic review
Notice bibliographique
Résumé
Editorial to accompany: Mild Behavioural Impairment in Parkinson’s Disease: A Systematic Review [ 6]. Non-motor symptoms, particularly behavioural changes, can be identified and categorized as Mild Behavioural Impairment (MBI). The reported prevalence of MBI in PD can range from 20% to 84.1%. The inconsistency in diagnostic criteria and assessment tools for MBI poses a significant challenge. Parkinson’s disease (PD) is globally recognised for its cardinal motor symptoms—tremor, rigidity and bradykinesia. However, an equally significant but often under-appreciated aspect of PD is its non-motor symptoms, particularly behavioural changes categorised as mild behavioural impairment (MBI). A recent systematic review by Yu et al. [6] titled ‘Mild Behavioural Impairment in Parkinson’s Disease: A Systematic Review’ sheds light on the prevalence, characteristics, and implications of MBI in individuals with PD (PwP). This editorial aims to highlight the key findings of this review, discuss current urgent issues in MBI research, and emphasise the necessity of integrating MBI recognition into clinical practice. Yu et al. [6] conducted a comprehensive analysis of nine studies from five distinct research institutions, focusing on the prevalence and characteristics of MBI in PwP. The review revealed considerable variability in the reported prevalence of MBI, ranging from 20% to 84.1%. This variation was primarily attributed to differences in diagnostic criteria and assessment tools used across studies. For instance, some studies utilized the mild behavioural impairment checklist (MBI-C) with varying cut-off scores, while others relied on the Neuropsychiatric Inventory Questionnaire or the International Society to Advance Alzheimer’s Research and Treatment–Alzheimer’s Association criteria. A significant finding from Yu et al. [6] is the association between MBI and impaired cognitive function in PwP. Patients with MBI generally exhibited diminished cognitive performance, as indicated by lower scores on the Mini–Mental State Examination or Montreal Cognitive Assessment. Importantly, no substantial differences were observed in age, disease duration, or motor symptom severity between PwP with and without MBI, suggesting that MBI could serve as an early indicator of cognitive decline independent of these factors. The review also highlighted that affective dysregulation and impulse dyscontrol are the most prevalent MBI subdomains in PwP. Yu et al. [6] found that AD and ID were primary contributors to MBI, whereas abnormal perception and social inappropriateness were less common. This pattern underscores the need for clinicians to pay particular attention to mood disturbances and impulse control issues in the early stages of PD. While the systematic review by Yu et al. [6] provides valuable insights, it also brings to the forefront several urgent issues in the field of MBI research that warrant attention, which are as follows: 1. Lack of Standardised Diagnostic Criteria. The inconsistency in diagnostic criteria and assessment tools for MBI poses a significant challenge. The variability in prevalence rates across studies underscores the urgent need for standardisation. Without uniform diagnostic protocols, comparing results and drawing definitive conclusions about MBI in PD becomes difficult. As noted by Ismail et al. [3], the development of standardised tools like the MBI-C is crucial for consistent assessment and research comparability. 2. Under-diagnosis in Clinical Practice. MBI remains under-diagnosed in clinical settings, often overshadowed by the focus on motor symptoms. The subtlety and variability of MBI symptoms lead to them being overlooked or misattributed to normal aging or stress-related changes [4]. This under-recognition can result in missed opportunities for early intervention, potentially allowing cognitive decline to progress unchecked. 3. Overlap with Medication Side Effects. An important issue is the challenge in distinguishing MBI symptoms from side effects of dopaminergic treatments. Dopamine agonists, commonly used in PD management, can exacerbate behavioural symptoms like impulse control disorders [5]. This overlap complicates the clinical picture and necessitates careful assessment to ensure appropriate treatment strategies. 4. Need for Longitudinal Studies. Yu et al. [6] highlight the paucity of longitudinal studies focusing on MBI in PD. The lack of long-term data limits our understanding of how MBI symptoms develop and progress over time, as well as their potential role as predictors for PD-related dementia or increased dependency. Addressing this gap is crucial for developing effective interventions and improving patient outcomes. The urgent need for longitudinal studies cannot be overstated. Researchers should prioritise studies that track the progression of MBI over time to better understand its relationship with cognitive decline and disease progression. Standardising diagnostic criteria and assessment methods will enhance the comparability of research findings. 5. Multidisciplinary Approach and Policy Implications. Implementing a team-based approach involving neurologists, psychiatrists, psychologists and other specialists can enhance patient care by addressing the complex interplay of motor and non-motor symptoms [1]. Additionally, advocating for policies that support research funding and resource allocation for MBI can facilitate advancements in this field. The findings of Yu et al. [6] underscore the critical need for early detection of MBI in PwP. Identifying behavioural symptoms early provides an opportunity for timely interventions that may slow cognitive decline and improve quality of life. Clinicians should be vigilant in monitoring for signs of AD and ID, given their prominence in MBI among PwP. Incorporating comprehensive tools like the MBI-C into routine clinical evaluations can enhance the detection of MBI. Standardising the use of these tools will improve the consistency of diagnoses and facilitate better patient management. A personalised approach to treatment that considers the individual’s neuropsychiatric profile and disease stage is essential. While pharmacological interventions may be beneficial for certain symptoms, non-pharmacological strategies such as cognitive-behavioural therapy can also play a vital role [2]. Tailoring interventions can address the specific needs of each patient, potentially improving adherence to treatment and overall outcomes. There is a pressing need for healthcare systems and policymakers to support initiatives aimed at improving MBI recognition and management. Allocating resources for clinician training, patient education, and research funding is essential to advance this field. In summary, the systematic review by Yu et al. [6] provides valuable insights into the prevalence and characteristics of MBI in Parkinson’s disease. Recognising MBI as a significant component of PD is crucial for improving patient outcomes. Early identification and management of behavioural symptoms can enhance quality of life, reduce caregiver burden, and potentially slow cognitive decline. By integrating MBI assessments into clinical practice and addressing the urgent issues in research, we can move towards a more holistic and effective approach to Parkinson’s disease management. None. None.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,007 | 0,022 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,009 | 0,007 |
| Bibliométrie | 0,006 | 0,007 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,003 | 0,002 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 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; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».