Natural history of nonketotic hyperglycinemia in 65 patients
Notice bibliographique
Résumé
Abstract Alzheimer’s disease is a multifactorial disorder with a heterogeneous patient population. Comorbidities such as hypertension, hypercholesterolemia and diabetes are known contributors to the disease progression. Indeed, therapies targeting these disorders have been shown efficient in dementia prevention. However, their mechanistic contribution to Alzheimer’s pathology and neurodegeneration has not been fully clarified. In the current study, we used CSF samples from a memory clinic cohort of 90 patients without diagnosed hypertension, hypercholesterolemia, or diabetes nor other neurodegenerative disorder, to investigate 13 molecular markers representing key mechanisms underlying Alzheimer’s pathogenesis. Levels were compared between clinical groups of subjective cognitive decline, mild cognitive impairment, and Alzheimer’s disease. Associations between markers and groups of markers were analyzed by linear regression. Two-step cluster analysis was used to determine patient clusters. Two key markers were further analyzed by immunofluorescence staining in hippocampus from control and AD individuals without hypertension, hypercholesterolemia nor diabetes. CSF angiotensinogen, thioredoxin-1, and interleukin-15 were the biomarkers with the most prominent associations with Alzheimer’s pathology, synaptic and axonal damage. Synaptosomal-associated protein 25 kDa and neurofilament light chain were increased in mild cognitive impairment and Alzheimer cases. When we grouped biomarkers by biological function, we found that inflammatory and survival components were associated with Alzheimer’s pathology, synaptic dysfunction and axonal damage. Moreover, a vascular/metabolic component was associated with synaptic dysfunction. In data-driven analysis, two patient clusters were identified; Older participants with increased CSF markers of oxidative stress, vascular pathology and neuroinflammation were assigned to cluster 1, that was also smaller and characterized by increased synaptic and axonal damage, compared to individuals in cluster 2. Clinical groups were evenly distributed between the clusters. Analysis of post-mortem hippocampal tissue, showed that, compared to controls, angiotensinogen staining was higher in Alzheimer’s disease and was also found to co-localize with phosphorylated-tau. In a population free of common comorbidities, we could still find associations between Alzheimer’s disease biomarkers and markers of pathways associated with increased risk for Alzheimer’s disease (i.e., neuroinflammation, vascular function, oxidative stress and cholesterol homeostasis), suggesting that these pathways are contributing to Alzheimer’s disease mechanisms even in absence of clinically diagnosed comorbidities. The identification of distinct biomarker-driven endophenotypes of cognitive disorder patients, further highlights the biological heterogeneity of Alzheimer’s disease and the importance of developing tailored prevention and treatment strategies.
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 | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 ».