Literature DB >> 20143394

Estimating the attributable fraction for melanoma: a meta-analysis of pigmentary characteristics and freckling.

Catherine M Olsen1, Heidi J Carroll, David C Whiteman.   

Abstract

Epidemiologic research has demonstrated convincingly that certain pigmentary characteristics are associated with increased relative risks of melanoma; however there has been no comprehensive review to rank these characteristics in order of their importance on a population level. We conducted a systematic review of the literature and meta-analysis to quantify the contribution of pigmentary characteristics to melanoma, estimated by the population-attributable fraction (PAF). Eligible studies were those that permitted quantitative assessment of the association between histologically confirmed melanoma and hair colour, eye colour, skin phototype and presence of freckling; we identified 66 such studies using citation databases, followed by manual review of retrieved references. We calculated summary relative risks using weighted averages of the log RR, taking into account random effects, and used these to estimate the PAF. The pooled RRs for pigmentary characteristics were: 2.64 for red/red-blond, 2.0 for blond and 1.46 for light brown hair colour (vs. dark); 1.57 for blue/blue-grey and 1.51 for green/grey/hazel eye colour (vs. dark); 2.27, 1.99 and 1.35 for skin phototypes I, II and III respectively (vs. IV); and 1.99 for presence of freckling. The highest PAFs were observed for skin phototypes 1/II (0.27), presence of freckling (0.23), and blond hair colour (0.23). For eye colour, the PAF for blue/blue-grey eye colour was higher than for green/grey/hazel eye colour (0.18 vs. 0.13). The PAF of melanoma associated with red hair colour was 0.10. These estimates of melanoma burden attributable to pigmentary characteristics provide a basis for designing prevention strategies for melanoma.

Entities:  

Mesh:

Year:  2010        PMID: 20143394     DOI: 10.1002/ijc.25243

Source DB:  PubMed          Journal:  Int J Cancer        ISSN: 0020-7136            Impact factor:   7.396


  22 in total

Review 1.  Epidemiological and genetic factors underlying melanoma development in Italy.

Authors:  Giuseppe Palmieri; Maria Colombino; Milena Casula; Mario Budroni; Antonella Manca; Maria Cristina Sini; Amelia Lissia; Ignazio Stanganelli; Paolo A Ascierto; Antonio Cossu
Journal:  Melanoma Manag       Date:  2015-05-18

2.  MelaNostrum: a consensus questionnaire of standardized epidemiologic and clinical variables for melanoma risk assessment by the melanostrum consortium.

Authors:  Alexander J Stratigos; Maria Concetta Fargnoli; Arcangela De Nicolo; Ketty Peris; Susana Puig; Efthymia Soura; Chiara Menin; Donato Calista; Paola Ghiorzo; Mario Mandala; Daniela Massi; Monica Rodolfo; Laura Del Regno; Irene Stefanaki; Helen Gogas; Veronique Bataille; Margaret A Tucker; David Whiteman; Eduardo Nagore; Maria Teresa Landi
Journal:  J Eur Acad Dermatol Venereol       Date:  2018-09-14       Impact factor: 6.166

Review 3.  The melanomas: a synthesis of epidemiological, clinical, histopathological, genetic, and biological aspects, supporting distinct subtypes, causal pathways, and cells of origin.

Authors:  David C Whiteman; William J Pavan; Boris C Bastian
Journal:  Pigment Cell Melanoma Res       Date:  2011-08-16       Impact factor: 4.693

4.  Skin surveillance intentions among family members of patients with melanoma.

Authors:  Elliot J Coups; Sharon L Manne; Paul B Jacobsen; Michael E Ming; Carolyn J Heckman; Stuart R Lessin
Journal:  BMC Public Health       Date:  2011-11-14       Impact factor: 3.295

Review 5.  Melanoma Risk and Melanocyte Biology.

Authors:  Julie U Bertrand; Eirikur Steingrimsson; Fanélie Jouenne; Brigitte Bressac-de Paillerets; Lionel Larue
Journal:  Acta Derm Venereol       Date:  2020-06-03       Impact factor: 3.875

6.  A Possible Association between Melanoma and Prostate Cancer. Results from a Case-Control-Study.

Authors:  Alina Goldenberg; Shang I Brian Jiang; Philip R Cohen
Journal:  Cancers (Basel)       Date:  2015-04-15       Impact factor: 6.639

7.  Reverse Engineering Applied to Red Human Hair Pheomelanin Reveals Redox-Buffering as a Pro-Oxidant Mechanism.

Authors:  Eunkyoung Kim; Lucia Panzella; Raffaella Micillo; William E Bentley; Alessandra Napolitano; Gregory F Payne
Journal:  Sci Rep       Date:  2015-12-16       Impact factor: 4.379

Review 8.  "Fifty Shades" of Black and Red or How Carboxyl Groups Fine Tune Eumelanin and Pheomelanin Properties.

Authors:  Raffaella Micillo; Lucia Panzella; Kenzo Koike; Giuseppe Monfrecola; Alessandra Napolitano; Marco d'Ischia
Journal:  Int J Mol Sci       Date:  2016-05-17       Impact factor: 5.923

9.  Association Between Race/Ethnicity and Survival of Melanoma Patients in the United States Over 3 Decades: A Secondary Analysis of SEER Data.

Authors:  Melissa Ward-Peterson; Juan M Acuña; Mohammed K Alkhalifah; Abdulrahman M Nasiri; Elharith S Al-Akeel; Talal M Alkhaldi; Sakhr A Dawari; Sami A Aldaham
Journal:  Medicine (Baltimore)       Date:  2016-04       Impact factor: 1.889

Review 10.  Current Data on Risk Factor Estimates Does Not Explain the Difference in Rates of Melanoma between Hispanics and Non-Hispanic Whites.

Authors:  Sonia Kamath; Kimberly A Miller; Myles G Cockburn
Journal:  J Skin Cancer       Date:  2016-03-22
View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.