The impact of Type 2 diabetes in Parkinson’s disease
Notice bibliographique
Résumé
Abstract Importance Type 2 diabetes (T2DM) is an established risk factor for developing Parkinson’s disease (PD) but its effect on disease progression is not well understood. Objective To examine the effects of co-morbid T2DM on Parkinson’s disease progression and quality of life. Design We analysed data from the Tracking Parkinson’s study, a large multi-centre prospective study in the UK. Participants The study included 1930 adults with recent onset PD, recruited between February 2012 and May 2014, and followed up regularly thereafter. Exposure A diagnosis of pre-existing T2DM was based on self-report at baseline. After controlling for confounders, an evaluation of how T2DM affects PD was performed by comparing symptom severity scores; and analyses using multivariable mixed models was used to determine the effects of T2DM on Parkinson’s disease progression. Main Outcomes and Measures The impact of T2DM on Parkinson’s disease severity was derived from scores collected using the Movement Disorders Society Unified Parkinson’s Disease Rating Scale (MDS-UPDRS), Non-Motor Symptoms Scale (NMSS), Montreal Cognitive Assessment (MoCA), Questionnaire for impulsive-compulsive disorders in PD (QUIP), Leeds Anxiety and Depression Scale (LADS), and Schwab and England ADL scale. Results We identified 167 (8.7%) patients with PD and T2DM (PD+T2DM) and 1763 (91.3%) with PD without T2DM (PD). Patients with T2DM had more severe motor symptoms, as assessed by MDS-UPDS III 25.8 (0.9) vs 22.5 (0.3) p=0.002, had significantly faster motor symptom progression over time (p=0.012), and T2DM was an independent predictor for the development of substantial gait impairment (HR 1.55, CI 1.07-2.23, p=0.020). Patients were more likely to have loss of independence (OR 2.08, CI 1.34-3.25, p=0.001); and depression (OR 1.62, CI 1.10-2.39, p=0.015), and developed worsening mood (p=0.041) over time compared to the PD group. T2DM was also an independent predictor for the development mild cognitive impairment (HR 1.7, CI 1.24-2.51, p=0.002) over time Conclusions and relevance T2DM is associated with faster disease progression in PD, highlighting an interaction between these two diseases. As it is a potentially modifiable, metabolic state, with multiple peripheral and central targets for intervention, it may represent a target for ameliorating parkinsonian symptoms, and progression to disability and dementia. Key points Question What is the impact of Type 2 diabetes on Parkinson’s disease progression? Findings In this prospective study of 1930 patients with recent onset PD, T2DM is an independent risk factor for more severe motor features, non-motor symptoms, and poorer quality of life; and importantly is associated with faster motor and non-motor symptom progression, and increases the risk of developing cognitive impairment. Meaning T2DM is identified as a new factor that alters Parkinson’s disease progression. T2DM predicts both motor and non-motor symptom progression in PD and is associated with poorer quality of life, highlighting an interaction of two chronic disease states. This highlights a particular need for improved treatment in this subgroup of patients with Parkinson’s.
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 | 0,002 | 0,007 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| 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; 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 ».