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Enregistrement W4415759098 · doi:10.1111/ijd.70133

Effects of <scp>GLP</scp> ‐1 Receptor Agonists on Hair Loss and Regrowth: A Systematic Review

2025· review· en· W4415759098 sur OpenAlexaff
Miranda K. Branyiczky, Megan S. Lowe, Eric McMullen, Jeffrey Donovan, Touraj Khosravi‐Hafshejani

Notice bibliographique

RevueInternational Journal of Dermatology · 2025
Typereview
Langueen
DomaineMedicine
ThématiqueHair Growth and Disorders
Établissements canadiensUniversity of British ColumbiaQueen's UniversityUniversity of TorontoMcMaster University
Organismes subventionnairesnon disponible
Mots-clésPharmacovigilanceLiraglutideHair lossAdverse effectOdds ratioRetrospective cohort studyCohort study

Résumé

récupéré en direct d'OpenAlex

Glucagon-like peptide-1 receptor agonists (GLP-1RAs) are increasingly used for metabolic disorders, including type 2 diabetes mellitus (T2DM) and obesity. Reports to date have documented hair loss as an adverse event, while cases of hair regrowth have also been described [1]. This review evaluates evidence linking GLP-1RA use to hair outcomes. Following PRISMA guidelines, Embase and MEDLINE were searched (from inception to July 2025) using keywords specific to GLP-1RAs, “alopecia,” and “hair regrowth” (PROSPERO: CRD420251104356). Of 485 records identified, 10 full-text studies were included, comprising three retrospective cohort studies, four retrospective pharmacovigilance database studies, one living benefit-harm modeling study, one case–control study, and one case report. Risk of bias was assessed using Joanna Briggs critical appraisal tools. Hair loss type was mostly unspecified; however, 82 patients had centrifugal cicatricial alopecia (CCCA), 20 had androgenic alopecia (AGA), and 10 had telogen effluvium. Pharmacovigilance and modeling analyses examined a total of 920,890 adverse events, while cohort studies involved 382 patients. GLP-1RAs investigated included exenatide (27.1%, 170,880/630,671), dulaglutide (23.4%, 147,624/630,671), liraglutide (21.4%, 135,178/630,671), semaglutide (11.3%, 71,528/630,671), and tirzepatide (9.9%, 62,196/630,671). Of 626,894 patients on GLP-1RA for whom hair outcomes were reported, 9933 (1.6%) experienced hair loss, whereas 52 (0.008%) reported hair regrowth. Latency to hair loss or gender differences in alopecia events were not well described in studies. Clinical outcomes are summarized in Table 1. Multiple pharmacovigilance analysis studies identified a weak association between GLP-1RA use and hair loss, most commonly with semaglutide (reporting odds ratio (ROR), 1.24–2.46), followed by tirzepatide (ROR, 0.83–1.73) and liraglutide (ROR, 0.61–1.53). One disproportionality analysis of the US Food and Drug Administration's Adverse Event Reporting System (FAERS) (2022–2023 exclusively) revealed a positive signal for alopecia with semaglutide (ROR, 2.46) and tirzepatide (ROR, 1.73), suggesting that rates may exceed background reporting levels [2]. A review of several pharmacovigilance databases (FAERS, Vigibase, Eudravigilance, Database of Adverse Event Notifications (DAEN)) found that GLP-1RAs were associated with greater reporting of hair loss compared with other antidiabetic medications; however, disproportionality analyses were negative [3]. Hair regrowth was observed in select cases, and was variably defined as improvement in density, symptom resolution, or hair loss stabilization. A 57-year-old male with insulin resistance, obesity, and AGA experienced hair regrowth and normalized insulin resistance parameters within 6 months of initiating tirzepatide [1]. In 81 patients with CCCA, longer GLP-1RA duration and improved metabolic parameters (e.g., reduced HbA1c) were associated with scalp symptom improvement and/or hair regrowth in 58.0% of patients classified as “GLP-1RA responders”; however, explicit hair regrowth data were not reported; therefore, the number experiencing true hair gains is unknown [4]. Conversely, a cohort study of 283 GLP-1RA users visiting a hospital-based dermatology clinic for any dermatologic diagnosis found mixed outcomes: 32 patients (11.3%) had new-onset or worsening hair loss, while three patients (1.1%) with preexisting hair loss had resolution/stabilization following treatment [5]. Several mechanisms may contribute to the relationship between GLP-1RA use and hair loss or regrowth (Figure 1). Hormonal changes (particularly involving insulin and insulin-like growth factor), rapid weight loss, and the psychosocial stress of managing chronic disease may influence androgen production or the hair follicle cycle, precipitating AGA or telogen effluvium. Changes in dietary intake and potential nutritional deficiencies (i.e., vitamin D) secondary to altered satiety could potentially also contribute. Semaglutide and tirzepatide had the strongest associations with hair loss (ROR values > 1); however, without direct comparison, it is unclear whether their risk is greater than that of other GLP-1RAs. Several types of hair loss (e.g., CCCA, AGA) may improve with glycemic control. Causality cannot be inferred, and study limitations include the predominance of pharmacovigilance data, where reporting bias or differential GLP-1RA prescribing patterns may confound results. Further larger cohort studies are required to determine if certain hair loss disorders may benefit or worsen from GLP-1RA therapy. M.K.B., M.S.L., E.M. and T.K.-H. have no conflicts of interest to declare. J.D. has received honoraria from Pfizer and Vichy, has participated on advisory boards at Pfizer for payment, receives royalties from UpToDate, participates on the Board of Directors for the Scarring Alopecia Foundation, and is the active director of the Evidence Based Hair Fellowship Training Program. The data underlying this study are available in the article and in the supplemental material at https://data.mendeley.com/datasets/k7pydjb66f/1.

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)0,006
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Revue systématique · Signal consensuel: Revue systématique
GenreSignal candidat: Synthèse · Signal consensuel: Synthèse
Score de désaccord entre enseignants0,165
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,006
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0040,001
Bibliométrie0,0010,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,001
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 Opus0,010
Tête enseignante GPT0,320
Écart entre enseignants0,311 · 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.

Devis d'étudeRevue systématique
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

Citations4
Publié2025
Routes d'admission1
Résumé présentoui

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