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Enregistrement W4405558304 · doi:10.1111/ijd.17614

Alopecia areata and the risk of insulin resistance: a systematic review and meta‐analysis

2024· review· en· W4405558304 sur OpenAlexaffabout
Eric McMullen, Parsa Abdi, Shanti Mehta, Dea Metko, Jeffrey Donovan

Notice bibliographique

RevueInternational Journal of Dermatology · 2024
Typereview
Langueen
DomaineMedicine
ThématiqueHair Growth and Disorders
Établissements canadiensHamilton Health SciencesUniversity of British ColumbiaMemorial University of NewfoundlandUniversity of Toronto
Organismes subventionnairesnon disponible
Mots-clésMedicineAlopecia areataInsulin resistanceMeta-analysisDermatologyMEDLINEInternal medicineInsulin

Résumé

récupéré en direct d'OpenAlex

The pathogenesis of alopecia areata (AA) remains incompletely understood. Prior research identified an increased odds of metabolic syndrome, hyperinsulinemia, and dyslipidemia in patients with AA.1 In this systematic review and meta-analysis, we aimed to investigate whether insulin resistance was more common in patients with AA compared with controls based on full multivariate analysis. Medline and Embase databases were searched from inception to April 3, 2024, using variations of “alopecia areata” and “insulin resistance,” adhering to PRISMA reporting guidelines.2 PROSPERO protocol was registered (CRD42024532125). Clinical studies that measured the homeostasis model assessment of insulin resistance (HOMA-IR), along with other metabolic biomarkers including insulin, fasting blood glucose (FBG), C-peptide, and body mass index (BMI) in patients with AA and control subjects were included. Non-English, non-published works, review articles, and literature including patients with known metabolic disorders (e.g., diabetes mellitus) were excluded. Statistical analyses were conducted using Review Manager v5.4.1 (Cochrane Collaboration). Continuous outcomes were assessed using the standardized mean difference (SMD) with 95% confidence intervals employing a random-effects model. Heterogeneity among studies was evaluated using the I2 statistic, and publication bias was assessed via funnel plot analysis. Risk of bias across included studies was assessed using the Newcastle–Ottawa Scale (NOS) for nonrandomized studies. The systematic search yielded 799 unique results, of which five met the inclusion criteria for analysis (Fig. 1). A total of 249 patients with AA (mean age 32.7 ± 10.56, 45.8% female), and 187 controls (mean age 32.1 ± 8.7, 47.1% female) were included. The specific AA subtypes included multifocal (62.1%), universalis (18.2%), totalis (11.1%), ophiasis (6.6%), and diffuse (2.0%). Compared with controls, patients with AA had higher HOMA-IR values (SMD = 0.62; 95% CI, 0.20–0.88; P = 0.004), (Fig. 1) higher fasting insulin values (SMD = 0.54; 95% CI, 0.16–0.93; P = 0.006), (Fig. 2), higher FBG (SMD = 0.33; 95% CI, 0.13–0.53; P = 0.001), and higher C-peptide (SMD = 0.67; 95% CI, 0.40–0.95; P < 0.00001). BMI did not differ significantly between groups (SMD = 0.01; 95% CI, −0.18 to 0.20; P = 0.89). Risk of bias assessment was overall heterogeneous with two studies scoring “good,” two studies scoring “fair,” and one study scoring “poor”. Further research is needed to understand the precise role of insulin resistance in the pathogenesis of AA and whether specific treatments that target insulin resistance will impact AA-specific patient outcomes and associated comorbidities. AA and insulin resistance may share similar immunopathogenic mechanisms, including elevated cytokines (e.g., IFN-γ, TNF-α, and IL-1) and disrupted insulin receptor signaling.3 The potential contribution of insulin resistance to AA pathogenesis continues to be investigated. Adiponectin, an adipose tissue-circulating adipokine has effects in promoting insulin sensitivity. Patients with AA have shown lower serum levels of adiponectin than healthy controls, illustrating a potential mechanism for AA pathogenesis.4 Prior genome-wide association studies have suggested a shared genetic foundation between AA and metabolic dysregulation, identifying common genes such as ERBB3, PTPN22, and CTLA4 in both conditions.5 Further research is also needed to better understand which subset of patients with AA are at highest risk for insulin resistance. These patients may benefit from measurement of hemoglobin A1c, FBG, and fasting insulin in addition to their standard blood testing panels. At present, it seems reasonable to consider these tests in patients with known risk factors such as family history, obesity, hypertension, hypercholesterolemia, current heart disease, sleep apnea, smoking history, and known liver disease. Consultation with primary care and/or endocrinology will be important for patients with AA with confirmed insulin resistance. Lifestyle modifications are an important first-line treatment strategy, with pharmacotherapies added to the treatment plan when these lifestyle changes are not successful.

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 distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,001
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Méta-analyse · Signal consensuel: aucune
GenreSignal candidat: Synthèse · Signal consensuel: Synthèse
Score de désaccord entre enseignants0,589
Score d'incertitude au seuil0,435

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0060,002
Bibliométrie0,0010,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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.

Tête enseignante Opus0,029
Tête enseignante GPT0,355
Écart entre enseignants0,326 · 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 tête enseignante, pas un consensus.

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

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

En bref

Citations0
Publié2024
Routes d'admission2
Résumé présentoui

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