Does genetic risk modify the effect of skin screening on melanoma detection rates?


Journal

The British journal of dermatology
ISSN: 1365-2133
Titre abrégé: Br J Dermatol
Pays: England
ID NLM: 0004041

Informations de publication

Date de publication:
08 Sep 2023
Historique:
received: 01 06 2023
revised: 28 08 2023
accepted: 06 09 2023
medline: 8 9 2023
pubmed: 8 9 2023
entrez: 8 9 2023
Statut: aheadofprint

Résumé

Skin screening is associated with higher melanoma detection rates, a potential indicator of overdiagnosis, but it remains possible that this effect is due to confounding by genetic risk. To compare melanoma incidence among screened vs. unscreened participants within tertiles of genetic risk. We investigated melanoma incidence in the QSkin Study, a prospective cohort study which for this analysis comprised 15,283 participants aged 40-69 years with genotype data and no prior history of melanoma. We calculated a polygenic score (PGS) for melanoma. We first calculated age-standardised rate (ASR) of melanoma within PGS tertiles, and then measured the association between skin examination and melanoma detection by calculating the hazard ratio (HR) and 95% confidence interval (95% CI), overall and within PGS tertiles. Melanoma incidence increased with PGS (ASR/100000/yr) tertile 1: 442; tertile 2: 519; tertile 3: 871). We found that the hazard ratios for all melanomas (i.e. in situ and invasive) associated with skin examination differed slightly across PGS tertiles (age- and sex-adjusted tertile 1 HR 1.88, 95% CI 1.26-2.81; tertile 2 HR 1.70, 95% CI 1.20-2.41; tertile 3 HR 1.96, 95% CI 1.43-2.70; fully adjusted tertile 1 HR 1.14, 95% CI 0.74-1.75; tertile 2 HR 1.21, 95% CI 0.82-1.78; tertile 3 HR 1.41, 95% CI 1.00-1.98) but these differences were not statistically significant. Hazard ratios for in situ melanoma associated with skin examination were similar across PGS tertiles. For invasive melanomas, the point estimates appeared highest in PGS tertile 3 in both minimally adjusted (age, sex) and fully adjusted models, however these apparent differences were also not statistically significant. Genetic risk predicts subsequent melanoma incidence, and is weakly associated with screening behaviour, but does not explain the higher rate of melanoma detection between screened and unscreened people.

Sections du résumé

BACKGROUND BACKGROUND
Skin screening is associated with higher melanoma detection rates, a potential indicator of overdiagnosis, but it remains possible that this effect is due to confounding by genetic risk.
OBJECTIVES OBJECTIVE
To compare melanoma incidence among screened vs. unscreened participants within tertiles of genetic risk.
METHODS METHODS
We investigated melanoma incidence in the QSkin Study, a prospective cohort study which for this analysis comprised 15,283 participants aged 40-69 years with genotype data and no prior history of melanoma. We calculated a polygenic score (PGS) for melanoma. We first calculated age-standardised rate (ASR) of melanoma within PGS tertiles, and then measured the association between skin examination and melanoma detection by calculating the hazard ratio (HR) and 95% confidence interval (95% CI), overall and within PGS tertiles.
RESULTS RESULTS
Melanoma incidence increased with PGS (ASR/100000/yr) tertile 1: 442; tertile 2: 519; tertile 3: 871). We found that the hazard ratios for all melanomas (i.e. in situ and invasive) associated with skin examination differed slightly across PGS tertiles (age- and sex-adjusted tertile 1 HR 1.88, 95% CI 1.26-2.81; tertile 2 HR 1.70, 95% CI 1.20-2.41; tertile 3 HR 1.96, 95% CI 1.43-2.70; fully adjusted tertile 1 HR 1.14, 95% CI 0.74-1.75; tertile 2 HR 1.21, 95% CI 0.82-1.78; tertile 3 HR 1.41, 95% CI 1.00-1.98) but these differences were not statistically significant. Hazard ratios for in situ melanoma associated with skin examination were similar across PGS tertiles. For invasive melanomas, the point estimates appeared highest in PGS tertile 3 in both minimally adjusted (age, sex) and fully adjusted models, however these apparent differences were also not statistically significant.
CONCLUSIONS CONCLUSIONS
Genetic risk predicts subsequent melanoma incidence, and is weakly associated with screening behaviour, but does not explain the higher rate of melanoma detection between screened and unscreened people.

Identifiants

pubmed: 37681503
pii: 7264084
doi: 10.1093/bjd/ljad333
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Commentaires et corrections

Type : CommentIn

Informations de copyright

© The Author(s) 2023. Published by Oxford University Press on behalf of British Association of Dermatologists. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Auteurs

Nirmala Pandeya (N)

Departments of Population Health and Computational Biology, QIMR Berghofer Medical Research Institute, Queensland, Australia.
Faculty of Medicine, University of Queensland, Queensland, Australia.

Jean Claude Dusingize (JC)

Departments of Population Health and Computational Biology, QIMR Berghofer Medical Research Institute, Queensland, Australia.

Catherine M Olsen (CM)

Departments of Population Health and Computational Biology, QIMR Berghofer Medical Research Institute, Queensland, Australia.
Faculty of Medicine, University of Queensland, Queensland, Australia.

Stuart MacGregor (S)

Departments of Population Health and Computational Biology, QIMR Berghofer Medical Research Institute, Queensland, Australia.

Rachel E Neale (RE)

Departments of Population Health and Computational Biology, QIMR Berghofer Medical Research Institute, Queensland, Australia.
Faculty of Medicine, University of Queensland, Queensland, Australia.

Matthew H Law (MH)

Departments of Population Health and Computational Biology, QIMR Berghofer Medical Research Institute, Queensland, Australia.
Faculty of Health, Queensland University of Technology, Queensland, Australia.
School of Biomedical Sciences, University of Queensland, Queensland, Australia.

David C Whiteman (DC)

Departments of Population Health and Computational Biology, QIMR Berghofer Medical Research Institute, Queensland, Australia.
Faculty of Medicine, University of Queensland, Queensland, Australia.

Classifications MeSH