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Enregistrement W1976367452 · doi:10.1111/jgs.12960

Metabolic Syndrome, Executive Dysfunction, and Late‐Onset Depression: Just a Matter of White Matter?

2014· letter· en· W1976367452 sur OpenAlexaboutno aff
Giovanni Viscogliosi, P. Andreozzi, Licia Manzon, Evaristo Ettorre, Mauro Cacciafesta

Notice bibliographique

RevueJournal of the American Geriatrics Society · 2014
Typeletter
Langueen
DomaineMedicine
ThématiqueDementia and Cognitive Impairment Research
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésMedicineDepression (economics)HyperintensityMetabolic syndromeMood disordersCognitionMoodCognitive declinePsychiatryComorbidityExecutive dysfunctionInternal medicineClinical psychologyDementiaAnxietyObesityDiseaseNeuropsychologyMagnetic resonance imaging

Résumé

récupéré en direct d'OpenAlex

To the Editor: Depression and cognitive disorders often overlap in older individuals, bringing with them a heavy burden of disability. The causal direction of such association has been widely questioned.1-3 Both conditions appear to be tightly associated with cardiovascular and metabolic disorders.1, 2, 4-8 Metabolic syndrome (MetS), a cluster of cardio-metabolic risk factors comprising peripheral inflammation and insulin resistance, is risky for the aging brain4-8 and has been associated with white matter damage. MetS may promote cognitive and depressive disorders independently of the extent of brain vascular damage.4 There is a lack of consensus as to whether white matter hyperintensities (WMHs) may modulate the association between cognitive decline and depression. Depression is often the earliest sign of an approaching cognitive disorder, and symptoms of cognitive loss may characterize the course of mood disorders.1-3 The goal of the current study was to explore whether subjects with late-onset depression had poorer global and domain-specific cognitive functioning than controls, whether MetS was positively associated with more depressive symptoms, and whether such an association was independent of WMH severity. Thirty subjects with current major depression and 15 age- and sex-matched controls (age 78.1 ± 9.2, 29 women) were selected from among community-dwelling individuals referred for evaluation of cardiovascular risk factors. Major depression was diagnosed according to the Structured Diagnostic Interview of the Diagnostic and Statistic Manual of Mental Disorders, Fourth Edition, for depression. Subjects in both groups were consecutively enrolled if they did not have any of the following conditions: dementia; mood disorder with onset before aged 60; cerebrovascular or coronary artery disease, diabetes mellitus, cancer. Standard blood analyses, high-sensitivity C-reactive protein (hsCRP), and fasting insulin were assessed. Insulin resistance was estimated using homeostasis model assessment of insulin resistance (HOMA-IR). The National Cholesterol Education Program Adult Treatment Panel-III definition of MetS was used. All subjects underwent brain magnetic resonance imaging (1.5 Tesla). Deep and periventricular WMHs were rated using the Fazekas scale on a 4-point scale, as described elsewhere.9 Depressive symptoms were rated using the 15-item Geriatric Depression Scale (GDS). Cognitive function was assessed using the Montreal Cognitive Assessment (MoCA), whose details are described elsewhere.10 Statistical analyses were performed using SPSS (version 17.0 for Windows, SPSS, Inc., Chicago, IL). Multivariable regression models were constructed to identify independent predictors of higher GDS scores, controlling for age, sex, and education. Hs-CRP values were normalized using log10 transformation. Statistical significance was set for two-sided P-values <.05. Participants with depression were more likely to have MetS (82.3% vs 40.0%; P < .001), higher HOMA-IR (P = .004) and hsCRP (P = .006), more-severe deep and periventricular WMHs (P < .001), and lower visuospatial and executive MoCA subscale scores (P = .006) than controls. Average MoCA total scores were 24.1 ± 3.3 for cases and 24.5 ± 3.7 for controls (P = .715). Table 1 depicts the multivariable analyses. The greater number of depressive symptoms was no longer associated with poorer visuospatial and executive function after adjusting for WMH severity. Deep but not periventricular WMH severity was positively associated with depressive symptoms. Individuals with late-onset depression, even in the absence of overt cognitive impairment, had poorer executive and visuospatial function and more-severe WMHs than controls. Higher GDS scores were not associated with poorer visuospatial and executive function after adjustment for WMHs severity. Deep WMHs predicted a greater number of depressive symptoms. Cardio-metabolic risk factors lead in time to deep white matter changes, which may promote depression and loss of cognitive function as the result of the cross-talk between arterial and brain aging.4 The findings of the current study support the hypothesis that depression with onset in late life is associated with deterioration in domain-specific cognition because of their shared association with brain vascular injury. MetS mostly affects older adults, and it independently accelerates arterial aging and affects the risk of depression and cognitive disorders.4, 5 MetS predicts depression severity independent of WMHs, as well as blood glucose, inflammatory levels, and insulin resistance, indicating that MetS accelerates brain aging in addition to its association with WMHs. Further investigations are needed to better characterize the mechanisms through which cardio-metabolic disorders promote brain dysfunction in old age; longitudinal studies will reveal whether the early targeting of such risk factors may reduce the prevalence of depression and cognitive disorders at a population level. Conflict of Interest: The editor in chief has reviewed the conflict of interest checklist provided by the authors and has determined that the authors have no financial or any other kind of personal conflicts with this paper. Author Contributions: Giovanni Viscogliosi reviewed the current medical literature, performed the statistical analyses, and wrote the letter. Evaristo Ettorre, Licia Manzon, and Paola Andreozzi collected participant data and performed the neuropsychological studies. Mauro Cacciafesta conceived of the letter. Sponsor's Role: 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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,002
score de la tête « metaresearch » (Gemma)0,009
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Commentaire · Signal consensuel: Commentaire
Score de désaccord entre enseignants0,004
Score d'incertitude au seuil0,013

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0020,009
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0020,000
Bibliométrie0,0010,001
Études des sciences et des technologies0,0010,001
Communication savante0,0020,002
Science ouverte0,0020,000
Intégrité de la recherche0,0040,005
Charge utile insuffisante (le modèle a refusé de juger)0,0040,001

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.

Tête enseignante Opus0,012
Tête enseignante GPT0,272
Écart entre enseignants0,261 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreCommentaire

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations6
Publié2014
Routes d'admission1
Résumé présentoui

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Même revueJournal of the American Geriatrics Society→Même sujetDementia and Cognitive Impairment Research→Travaux en français237 207→