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Enregistrement W2124736441 · doi:10.1186/1742-4933-3-1

A Need to Study the Immune Status of Frail Older Adults

2006· article· en· W2124736441 sur OpenAlexaff
Steven C. Castle, Koichi Uyemura, Tamàs Fülöp, Katsuiku Hirokawa, Takashi Makinodan

Notice bibliographique

RevueImmunity & Ageing · 2006
Typearticle
Langueen
DomaineNeuroscience
ThématiqueTryptophan and brain disorders
Établissements canadiensUniversité de Sherbrooke
Organismes subventionnairesnon disponible
Mots-clésGerontologyMedicineDiseaseImmune systemImmunosenescenceImmunologyInternal medicine

Résumé

récupéré en direct d'OpenAlex

The frail older adult subpopulation, which is growing at a rapid rate, contributes significantly to the increasing global healthcare cost [1]. Many frail older adults are immunologically compromised, and, therefore, succumb to common infections in spite of the availability of antibiotics and other therapeutic measures. It is also not known how repeated or recurrent infections affect the progression of the underlying chronic disease. Thus, there is an urgent need to understand the nature of the diminished immune capacity of frail older adults, for it could provide new insight into developing effective intervention modalities. Unfortunately, very little research has been done to describe and elucidate the immune deficits of the frail older adults. In the past, researchers have been discouraged to study this subpopulation because of its complexity in terms of disease category and disease burden. In contrast, much is known of the immune status of the healthy older adult subpopulation, for this subpopulation has been investigated extensively over the past three decades. Thus, it is known that T cell-dependent immune functions decline with age [2], and associated with the decline are structural changes in T cells [3]. However, a review of more than 200 scientific articles that evaluated healthy older adults, who were selected on a set of rigorous criteria as defined by the SENEIUR Protocol [4], showed that the magnitude of decline in T cell-dependent immune functions with age is modest [5], relative to that of the aging mouse model [6]. More recently, Sehl and Yates [7] analyzed changes in various physiologic functions with age from 469 studies involving more than 54,000 healthy and frail older adults. The expansive review included 43 immunologic studies of 372 individuals. They found that the mean annual rate of decline with age in immune functions is greater than that of other physiologic functions that were assessed. The authors concluded that the deterioration in immune function in older adults is due not only to aging, but also the presence of chronic disease. This review also underscores the need to evaluate the immune status of frail older adults with chronic diseases. Recently, the influence of chronic disease on T cell immunity as been investigated [8], using the Cumulative Illness Rating Scale (CIRS) [9]. CIRS is an instrument that measures disease burden in individuals with various chronic diseases, but with no evidence of acute deterioration or infection. The CIRS instrument was originally developed in 1968 and is acknowledged as a user-friendly, comprehensive review of medical problems of 14 organ systems [9]. It is based on a 0 to 4 rating of each organ system. The scale has been validated in older adults living in long-term care facilities and congregate apartments in the community and has demonstrated better validity in predicting healthcare outcomes than functional measures [10]. T cell immunity was based on phytohemagglutinin (PHA)-induced proliferation and production of immunosuppressive interleukin (IL)-10 and immunoenhancing IL-12. The study showed that decrease in T cell proliferation, increase in production of IL-10 and decrease in production of IL-12 are linearly correlated with increase in chronic disease burden (i.e., increased CIRS score), but not with increase in chronologic age, between 51 to 95 years. The demonstration that reduced immunity in older adults is correlated with chronic disease burden, but not with chronologic age, suggests that chronic disease burden markedly enhances the reduction in immunity of older adults caused by biologic aging. Others have suggested chronic infections, caused by especially cytomegalovirus, and also by Helicobacter pylori, Mycobacterium tuberculosis, Chlamydia pneumoniae, Herpes viruses, Epstein-Barr virus, and Hepatitis viruses, could play a role on progression of chronic disease, especially atherosclerosis, and impaired immunity [11-15]. While CIRS does not directly address prior viral infections, if these infections do impact on progression of disease it would be predicted that there would be a correlation between CIRS and evidence of chronic infection. Chronic infections, increased levels of inflammatory mediators, disease progression and frailty have a very complex association, and, furthermore, an unclear temporal relationship. Therefore, at this stage of progress, it would be appropriate and timely, to study the immune status of frail older adults using an instrument, such as the CIRS, to categorize frail older adults according to specific chronic disease and disease burden. The immune status of frail older adults in each category could then be assessed, and immunosuppressive factors produced by specific disease that are present in the microenvironment of immune cells could be identified and their impact on immunity mitigated. Consequently, the compromised immune status of frail older adults could be boosted to that of healthy older adults, thereby improving their innate and adaptive immunologic defense mechanisms to infections and response to vaccination. This should significantly increase their resistance to infectious and other immunocompromised-related diseases, possibly slow the progression of chronic diseases, and, therefore, contribute to the goal of reducing the global healthcare cost.

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)0,014
score de la tête « metaresearch » (Gemma)0,020
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: aucune
GenreSignal candidat: Commentaire · Signal consensuel: aucune
Score de désaccord entre enseignants0,014
Score d'incertitude au seuil0,075

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0140,020
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0030,001
Bibliométrie0,0010,001
Études des sciences et des technologies0,0010,001
Communication savante0,0020,006
Science ouverte0,0020,002
Intégrité de la recherche0,0040,004
Charge utile insuffisante (le modèle a refusé de juger)0,0050,002

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 Opus0,018
Tête enseignante GPT0,265
Écart entre enseignants0,247 · 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'étudeSans objet
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

Citations18
Publié2006
Routes d'admission1
Résumé présentoui

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