Nipple and areola lesions: review of dermoscopy and reflectance confocal microscopy features
E. Cinotti D. Galluccio L. Tognetti C. Habougit A. M. Manganoni M. Venturini J.L. Perrot P. Rubegni
First published: 05 June 2019 https://doi.org/10.1111/jdv.15727
This article has been accepted for publication and undergone full peer review but has not been through the copyediting, typesetting, pagination and proofreading process, which may lead to differences between this version and the Version of Record. Please cite this article as doi: 10.1111/jdv.15727
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Abstract
The differential diagnosis of nipple and areola complex (NAC) lesions encompasses a large spectrum of conditions from benign tumors to inflammatory diseases that could be challenging to recognize on clinical ground. While melanoma (MM) of the NAC is exceedingly rare, benign lesions are more frequent but could be difficult to distinguish from MM. Besides MM, other malignant tumors can affect this area and in particular Paget's disease (PD). For clinically doubtful lesions, biopsy is required, with possible functional and aesthetic consequences in this sensitive area. Dermoscopy and reflectance confocal microscopy (RCM) are widely used techniques for the diagnosis of many skin lesions, but their use for NAC lesions is not well established. The objective of this study was to evaluate current literature on these imaging techniques for NAC lesions. We searched in Medline, PubMed and Cochrane database all studies up to November 2018 dealing with dermoscopy, RCM and this special site. We found that the most described malignant tumor was PD and that only two primary MMs of the NAC have been reported with these imaging techniques. Although there are few data on diagnostic accuracy of non‐invasive imaging techniques for NAC lesions, it seems that dermoscopy and RCM can add relevant information to be integrated with clinical examination for the diagnosis of NAC lesions and in particular for the differential diagnosis of PD and eczema.
E. Cinotti D. Galluccio L. Tognetti C. Habougit A. M. Manganoni M. Venturini J.L. Perrot P. Rubegni
First published: 05 June 2019 https://doi.org/10.1111/jdv.15727
This article has been accepted for publication and undergone full peer review but has not been through the copyediting, typesetting, pagination and proofreading process, which may lead to differences between this version and the Version of Record. Please cite this article as doi: 10.1111/jdv.15727
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Abstract
The differential diagnosis of nipple and areola complex (NAC) lesions encompasses a large spectrum of conditions from benign tumors to inflammatory diseases that could be challenging to recognize on clinical ground. While melanoma (MM) of the NAC is exceedingly rare, benign lesions are more frequent but could be difficult to distinguish from MM. Besides MM, other malignant tumors can affect this area and in particular Paget's disease (PD). For clinically doubtful lesions, biopsy is required, with possible functional and aesthetic consequences in this sensitive area. Dermoscopy and reflectance confocal microscopy (RCM) are widely used techniques for the diagnosis of many skin lesions, but their use for NAC lesions is not well established. The objective of this study was to evaluate current literature on these imaging techniques for NAC lesions. We searched in Medline, PubMed and Cochrane database all studies up to November 2018 dealing with dermoscopy, RCM and this special site. We found that the most described malignant tumor was PD and that only two primary MMs of the NAC have been reported with these imaging techniques. Although there are few data on diagnostic accuracy of non‐invasive imaging techniques for NAC lesions, it seems that dermoscopy and RCM can add relevant information to be integrated with clinical examination for the diagnosis of NAC lesions and in particular for the differential diagnosis of PD and eczema.
Gastrointestinal symptoms, gastrointestinal bleeding, and the role of diet in patients with autoimmune blistering disease: A survey of the International Pemphigus and Pemphigoid Foundation
D Kneiber E H Kowalski K Kridin M L Yale S A Grando K T. Amber
First published: 06 June 2019 https://doi.org/10.1111/jdv.15731
This article has been accepted for publication and undergone full peer review but has not been through the copyediting, typesetting, pagination and proofreading process, which may lead to differences between this version and the Version of Record. Please cite this article as doi: 10.1111/jdv.15731
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Abstract
Background
Autoimmune blistering diseases are a group of severe mucocutaneous conditions that typically require the use of prolonged corticosteroids and immunosuppression. Properly managing associated comorbidities is an integral part of these patients' care. The frequency of gastrointestinal symptoms, particularly gastrointestinal bleeding in these patients is not known. Likewise, the effect of diet on disease is unknown.
Objective
To determine the incidence of gastrointestinal comorbidities and the role of diet in patients with autoimmune blistering disease.
Methods
We distributed an e‐survey to patients with autoimmune blistering disease utilizing the International Pemphigus and Pemphigoid foundation's listserv. The incidence of gastrointestinal symptoms and gastrointestinal bleeding; foods avoided by patients and those noted to be beneficial in their disease. Historical incidences in the general population were used as controls.
Results
200 responses were collected. 30.3% of patients experienced gastroesophageal reflux following treatment of their autoimmune blistering disease, with 51.7% utilizing some form of gastrointestinal symptomatic treatment. The incidence of gastrointestinal bleeding following an autoimmune blistering diagnosis was 2.1%, which remained significant despite correction for non‐steroidal anti‐inflammatory use (NSAID), but not corticosteroid use. 65.2% of patients reported dietary limitations because of their autoimmune blistering disease. Significant intolerances after correction for multiple comparisons included alcohol, citrus, and spicy foods. Greater than 10% of patients reported improvements in their disease with vegetables and dairy.
Conclusions
Gastrointestinal comorbidities are common in patients with autoimmune blistering diseases, with gastrointestinal bleeding occurring in 2.1% of patients following a diagnosis of autoimmune blistering disease. While further work is needed to determine the relative risk of routine gastrointestinal prophylaxis in this population, gastrointestinal bleeding prophylaxis should be considered in patients receiving corticosteroids, particularly those taking NSAIDs. Dietary limitations are additionally frequent in this population. Patients should be cautious of alcohol, citrus, and spicy foods.
D Kneiber E H Kowalski K Kridin M L Yale S A Grando K T. Amber
First published: 06 June 2019 https://doi.org/10.1111/jdv.15731
This article has been accepted for publication and undergone full peer review but has not been through the copyediting, typesetting, pagination and proofreading process, which may lead to differences between this version and the Version of Record. Please cite this article as doi: 10.1111/jdv.15731
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Abstract
Background
Autoimmune blistering diseases are a group of severe mucocutaneous conditions that typically require the use of prolonged corticosteroids and immunosuppression. Properly managing associated comorbidities is an integral part of these patients' care. The frequency of gastrointestinal symptoms, particularly gastrointestinal bleeding in these patients is not known. Likewise, the effect of diet on disease is unknown.
Objective
To determine the incidence of gastrointestinal comorbidities and the role of diet in patients with autoimmune blistering disease.
Methods
We distributed an e‐survey to patients with autoimmune blistering disease utilizing the International Pemphigus and Pemphigoid foundation's listserv. The incidence of gastrointestinal symptoms and gastrointestinal bleeding; foods avoided by patients and those noted to be beneficial in their disease. Historical incidences in the general population were used as controls.
Results
200 responses were collected. 30.3% of patients experienced gastroesophageal reflux following treatment of their autoimmune blistering disease, with 51.7% utilizing some form of gastrointestinal symptomatic treatment. The incidence of gastrointestinal bleeding following an autoimmune blistering diagnosis was 2.1%, which remained significant despite correction for non‐steroidal anti‐inflammatory use (NSAID), but not corticosteroid use. 65.2% of patients reported dietary limitations because of their autoimmune blistering disease. Significant intolerances after correction for multiple comparisons included alcohol, citrus, and spicy foods. Greater than 10% of patients reported improvements in their disease with vegetables and dairy.
Conclusions
Gastrointestinal comorbidities are common in patients with autoimmune blistering diseases, with gastrointestinal bleeding occurring in 2.1% of patients following a diagnosis of autoimmune blistering disease. While further work is needed to determine the relative risk of routine gastrointestinal prophylaxis in this population, gastrointestinal bleeding prophylaxis should be considered in patients receiving corticosteroids, particularly those taking NSAIDs. Dietary limitations are additionally frequent in this population. Patients should be cautious of alcohol, citrus, and spicy foods.
Female patients are less satisfied with biological treatment for psoriasis and experience more side effects than male patients. Results from the prospective BioCAPTURE registry
L.S. van der Schoot J.M.P.A. van den Reek J.M.M. Groenewoud M.E. Otero M.D. Njoo P.M. Ossenkoppele J.M. Mommers M.I.A. Koetsier M.A.M. Berends W.P. Arnold … See all authors
First published: 08 June 2019 https://doi.org/10.1111/jdv.15733
This article has been accepted for publication and undergone full peer review but has not been through the copyediting, typesetting, pagination and proofreading process, which may lead to differences between this version and the Version of Record. Please cite this article as doi: 10.1111/jdv.15733
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Abstract
Background
Female sex has been reported as a predictor for treatment discontinuation with biological therapies for psoriasis, although reasons remain unclear. It can be hypothesized that lower satisfaction with biological treatment in women might add to the lower drug survival rates.
Objectives
To identify possible differences in satisfaction with biological treatment between female and male patients using the Treatment Satisfaction Questionnaire for Medication (TSQM).
Methods
Data of psoriasis patients treated with biologics were obtained from the prospective, multicentre, daily‐practice BioCAPTURE registry. Longitudinal TSQM data were analyzed by linear mixed models. Relevant patient characteristics were incorporated as possible confounding factors. Post hoc analysis of adverse events was performed in order to investigate differences between sexes.
Results
We included 315 patients with 396 corresponding treatment episodes (137 adalimumab, 90 etanercept, 137 ustekinumab, 24 secukinumab, 8 infliximab). Almost forty percent of the patients were female. Females had significantly lower baseline PASI scores (p=0.01). Longitudinal analyses demonstrated lower TSQM scores for 'side effects' (p=0.05) and 'global satisfaction' (p=0.01) in female patients compared to male patients over one year of treatment. Females reported more relevant adverse events in the context of biologic treatment compared to males (rate ratio 1.79; p<0.001), with more fungal (rate ratio 2.20; p=0.001) and herpes simplex infections (rate ratio 3.25; p=0.005).
Conclusions
This study provides a prospective, longitudinal analysis of treatment satisfaction with biologics in female and male patients with psoriasis. Women were slightly less satisfied with treatment regarding side effects and global satisfaction. Differences in treatment satisfaction and side effects might add to the fact that women discontinue biological treatments more often.
L.S. van der Schoot J.M.P.A. van den Reek J.M.M. Groenewoud M.E. Otero M.D. Njoo P.M. Ossenkoppele J.M. Mommers M.I.A. Koetsier M.A.M. Berends W.P. Arnold … See all authors
First published: 08 June 2019 https://doi.org/10.1111/jdv.15733
This article has been accepted for publication and undergone full peer review but has not been through the copyediting, typesetting, pagination and proofreading process, which may lead to differences between this version and the Version of Record. Please cite this article as doi: 10.1111/jdv.15733
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Abstract
Background
Female sex has been reported as a predictor for treatment discontinuation with biological therapies for psoriasis, although reasons remain unclear. It can be hypothesized that lower satisfaction with biological treatment in women might add to the lower drug survival rates.
Objectives
To identify possible differences in satisfaction with biological treatment between female and male patients using the Treatment Satisfaction Questionnaire for Medication (TSQM).
Methods
Data of psoriasis patients treated with biologics were obtained from the prospective, multicentre, daily‐practice BioCAPTURE registry. Longitudinal TSQM data were analyzed by linear mixed models. Relevant patient characteristics were incorporated as possible confounding factors. Post hoc analysis of adverse events was performed in order to investigate differences between sexes.
Results
We included 315 patients with 396 corresponding treatment episodes (137 adalimumab, 90 etanercept, 137 ustekinumab, 24 secukinumab, 8 infliximab). Almost forty percent of the patients were female. Females had significantly lower baseline PASI scores (p=0.01). Longitudinal analyses demonstrated lower TSQM scores for 'side effects' (p=0.05) and 'global satisfaction' (p=0.01) in female patients compared to male patients over one year of treatment. Females reported more relevant adverse events in the context of biologic treatment compared to males (rate ratio 1.79; p<0.001), with more fungal (rate ratio 2.20; p=0.001) and herpes simplex infections (rate ratio 3.25; p=0.005).
Conclusions
This study provides a prospective, longitudinal analysis of treatment satisfaction with biologics in female and male patients with psoriasis. Women were slightly less satisfied with treatment regarding side effects and global satisfaction. Differences in treatment satisfaction and side effects might add to the fact that women discontinue biological treatments more often.
Strategies to reduce stigma related to visible chronic skin diseases A systematic review
J Topp V Andrees N Weinberger I Schäfer R Sommer U Mrowietz C Luck‐Sikorski M Augustin
First published: 08 June 2019 https://doi.org/10.1111/jdv.15734
This article has been accepted for publication and undergone full peer review but has not been through the copyediting, typesetting, pagination and proofreading process, which may lead to differences between this version and the Version of Record. Please cite this article as doi:10.1111/jdv.15734
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Abstract
Many patients with a visible chronic skin disease experience discrimination and stigmatisation. This results in psychosocial impairments in addition to the burden of disease and emphasises the urgency to implement effective stigma‐reduction strategies.
To synthesis what is known globally about effective interventions to reduce stigma associated with visible chronic skin diseases, a systematic review was conducted.
Four electronic databases were searched until May 2018. Studies evaluating interventions to reduce stigmatisation in patients with visible chronic skin diseases and applying at least one stigma‐related outcome measure were included. Data was extracted on study design, country, study population, outcome measures, and main findings. Results were subsequently synthesised in a narrative review. CASP critical appraisal tools were used to assess study quality.
Nineteen studies were included in the review. Study design was very heterogeneous and study quality rather poor. Thirteen studies addresses patients with leprosy in low‐ and middle income countries and one study each targeted patients with onychomycosis, leg ulcer, facial disfigurement, atopic dermatitis, vitiligo, and alopecia. Evaluated interventions were mainly multi‐faceted incorporating more than one type of intervention. While 10 studies focused on the reduction of self‐stigma and 4 on the reduction of public stigma, another 5 studies aimed at reducing both.
The present review revealed a lack of high quality studies on effective approaches to reduce stigmatisation of patients with visible chronic skin diseases. Development and evaluation of intervention formats to adequately address stigma is essential to promote patients' health and well‐being.
J Topp V Andrees N Weinberger I Schäfer R Sommer U Mrowietz C Luck‐Sikorski M Augustin
First published: 08 June 2019 https://doi.org/10.1111/jdv.15734
This article has been accepted for publication and undergone full peer review but has not been through the copyediting, typesetting, pagination and proofreading process, which may lead to differences between this version and the Version of Record. Please cite this article as doi:10.1111/jdv.15734
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Abstract
Many patients with a visible chronic skin disease experience discrimination and stigmatisation. This results in psychosocial impairments in addition to the burden of disease and emphasises the urgency to implement effective stigma‐reduction strategies.
To synthesis what is known globally about effective interventions to reduce stigma associated with visible chronic skin diseases, a systematic review was conducted.
Four electronic databases were searched until May 2018. Studies evaluating interventions to reduce stigmatisation in patients with visible chronic skin diseases and applying at least one stigma‐related outcome measure were included. Data was extracted on study design, country, study population, outcome measures, and main findings. Results were subsequently synthesised in a narrative review. CASP critical appraisal tools were used to assess study quality.
Nineteen studies were included in the review. Study design was very heterogeneous and study quality rather poor. Thirteen studies addresses patients with leprosy in low‐ and middle income countries and one study each targeted patients with onychomycosis, leg ulcer, facial disfigurement, atopic dermatitis, vitiligo, and alopecia. Evaluated interventions were mainly multi‐faceted incorporating more than one type of intervention. While 10 studies focused on the reduction of self‐stigma and 4 on the reduction of public stigma, another 5 studies aimed at reducing both.
The present review revealed a lack of high quality studies on effective approaches to reduce stigmatisation of patients with visible chronic skin diseases. Development and evaluation of intervention formats to adequately address stigma is essential to promote patients' health and well‐being.
Wells syndrome and chronic spontaneous urticaria: report of four cases successfully treated with omalizumab
I. Ogueta J. Spertino G. Deza S. Alcantara Luna V. Zaragoza Ninet R.M. Pujol A.M. Giménez‐Arnau
First published: 20 May 2019 https://doi.org/10.1111/jdv.15683
I. Ogueta J. Spertino G. Deza S. Alcantara Luna V. Zaragoza Ninet R.M. Pujol A.M. Giménez‐Arnau
First published: 20 May 2019 https://doi.org/10.1111/jdv.15683
Wells syndrome (eosinophilic cellulitis) is an uncommon condition of unknown etiology. The presentation usually involves a mildly pruritic or tender cellulitis-like eruption with typical histologic features characterized by edema, flame figures, and a marked infiltrate of eosinophils in the dermis.
Original Article
Prolonged overall survival following metastasectomy in stage IV melanoma
M.L. Elias S. Behbahani S. Maddukuri A.M. John R.A. Schwartz W.C. Lambert
First published: 09 May 2019 https://doi.org/10.1111/jdv.15667
Conflicts of interest: The authors report no conflicts of interest.
Funding sources: None.
Presented as an Oral Presentation at the 2019 American Academy of Dermatology (AAD) meeting in Washington D.C. on March 1, 2019.
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Abstract
Background/Objectives
Current literature supports mixed conclusions regarding the outcomes of metastasectomy in Stage IV melanoma. The objective of this national study was to determine the associations of non‐primary site surgery with overall survival (OS) in Stage IV melanoma.
Methods
The National Cancer Database (NCDB) was queried for all Stage IV melanoma cases diagnosed from 2004 to 2015. Cases missing treatment/staging data or undergoing palliative treatment were excluded (remaining n = 14 034). Patients were separated into 'metastasectomy' (n = 4214, 30.0%) and 'non‐metastasectomy' (n = 9820, 70.0%) cohorts. Survival outcomes were analysed using Kaplan–Meier and Cox proportional hazards regressions.
Results
On univariate analysis, patients with Stage IV melanoma undergoing metastasectomy (median survival: 15.67 month) had greater overall survival compared with those not receiving non‐primary surgery (median survival: 7.13 month; 5‐year OS 13.2% vs. 5.6%, P < 0.001). M1a patients that underwent non‐primary metastasectomy (median survival: 46.36 month) showed greater survival than those that did not (median survival: 15.31 month; P < 0.001). Metastasectomy was undertaken more frequently for cutaneous (M1a) metastasis compared with non‐M1a metastasis (34.6% vs. 28.4%, P < 0.001). Of those receiving metastasectomy, 20.3% also received primary site resection, 33.6% radiation, 26.5% chemotherapy and 31.5% immunotherapy. Controlling for covariates on Cox proportional hazard analysis, all metastasectomy patients demonstrated longer survival [Hazard Ratio = 0.519, P < 0.001; CI 95% (0.495–0.545)] as well as when analysing solely M1a metastasectomy patients [Hazard Ratio = 0.546, P < 0.001; CI 95% (0.456–0.653)], lung (M1b) metastasectomy patients [Hazard Ratio = 0.389, P < 0.001; CI 95% (0.328–0.462)] and visceral (M1c) metastasectomy patients [Hazard Ratio = 0.474, P < 0.001; CI 95% (0.434–0.517)].
Conclusion
Metastasectomy for Stage IV melanoma is independently associated with improved OS in metastatic cases involving the skin, lung and visceral organs.
Prolonged overall survival following metastasectomy in stage IV melanoma
M.L. Elias S. Behbahani S. Maddukuri A.M. John R.A. Schwartz W.C. Lambert
First published: 09 May 2019 https://doi.org/10.1111/jdv.15667
Conflicts of interest: The authors report no conflicts of interest.
Funding sources: None.
Presented as an Oral Presentation at the 2019 American Academy of Dermatology (AAD) meeting in Washington D.C. on March 1, 2019.
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Abstract
Background/Objectives
Current literature supports mixed conclusions regarding the outcomes of metastasectomy in Stage IV melanoma. The objective of this national study was to determine the associations of non‐primary site surgery with overall survival (OS) in Stage IV melanoma.
Methods
The National Cancer Database (NCDB) was queried for all Stage IV melanoma cases diagnosed from 2004 to 2015. Cases missing treatment/staging data or undergoing palliative treatment were excluded (remaining n = 14 034). Patients were separated into 'metastasectomy' (n = 4214, 30.0%) and 'non‐metastasectomy' (n = 9820, 70.0%) cohorts. Survival outcomes were analysed using Kaplan–Meier and Cox proportional hazards regressions.
Results
On univariate analysis, patients with Stage IV melanoma undergoing metastasectomy (median survival: 15.67 month) had greater overall survival compared with those not receiving non‐primary surgery (median survival: 7.13 month; 5‐year OS 13.2% vs. 5.6%, P < 0.001). M1a patients that underwent non‐primary metastasectomy (median survival: 46.36 month) showed greater survival than those that did not (median survival: 15.31 month; P < 0.001). Metastasectomy was undertaken more frequently for cutaneous (M1a) metastasis compared with non‐M1a metastasis (34.6% vs. 28.4%, P < 0.001). Of those receiving metastasectomy, 20.3% also received primary site resection, 33.6% radiation, 26.5% chemotherapy and 31.5% immunotherapy. Controlling for covariates on Cox proportional hazard analysis, all metastasectomy patients demonstrated longer survival [Hazard Ratio = 0.519, P < 0.001; CI 95% (0.495–0.545)] as well as when analysing solely M1a metastasectomy patients [Hazard Ratio = 0.546, P < 0.001; CI 95% (0.456–0.653)], lung (M1b) metastasectomy patients [Hazard Ratio = 0.389, P < 0.001; CI 95% (0.328–0.462)] and visceral (M1c) metastasectomy patients [Hazard Ratio = 0.474, P < 0.001; CI 95% (0.434–0.517)].
Conclusion
Metastasectomy for Stage IV melanoma is independently associated with improved OS in metastatic cases involving the skin, lung and visceral organs.
Letter to the Editor
The integration of dermoscopy and reflectance confocal microscopy improves the diagnosis of lentigo maligna
E. Cinotti D. Fiorani B. Labeille S. Gonzalez S. Debarbieux M. Agozzino M. Ardigò F. Lacarrubba F. Farnetani C. Carrera G. Cevenini F. Le Duff L. Tognetti G. Pellacani P. Rubegni J.L. Perrot
First published: 10 May 2019 https://doi.org/10.1111/jdv.15669
The integration of dermoscopy and reflectance confocal microscopy improves the diagnosis of lentigo maligna
E. Cinotti D. Fiorani B. Labeille S. Gonzalez S. Debarbieux M. Agozzino M. Ardigò F. Lacarrubba F. Farnetani C. Carrera G. Cevenini F. Le Duff L. Tognetti G. Pellacani P. Rubegni J.L. Perrot
First published: 10 May 2019 https://doi.org/10.1111/jdv.15669
Original Article
Low Drosha protein expression in cutaneous T‐cell lymphoma is associated with worse disease outcome
T. Gambichler K. Salveridou L. Schmitz H.U. Käfferlein T. Brüning E. Stockfleth M. Sand K. Lang
First published: 04 May 2019 https://doi.org/10.1111/jdv.15652
Conflicts of interest None declared.
Funding sources None.
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Abstract
Background
Dysregulation of microRNAs (miRNAs) key regulators may contribute to the pathogenesis of malignancies. miRNA machinery genes such Dicer and Drosha have been reported to be biomarkers in different cancer types.
Objectives
We aimed to evaluate Drosha and Dicer protein expression in cutaneous T‐cell lymphoma (CTCL).
Methods
We performed Drosha and Dicer immunohistochemistry in 45 patients with mycosis fungoides and subtypes. Drosha and Dicer expression scores were correlated with clinical parameters including disease‐specific death (DSD), stage of disease and different laboratory data. Uni‐ and multivariate statistics were performed.
Results
On univariate analysis, elevated serum LDH and low Drosha expression were significantly associated with advanced stage (P = 0.032 and 0.0062, respectively) and lymphoma‐specific death (LSD; P = 0.017 and P = 0.005, respectively). Moreover, elevated circulating CD4+/CD26− lymphocytes were significantly associated with advanced stage (P = 0.032) and DSD (P = 0.0098). On multivariate analysis, low Drosha expression remained in the logistic regression model as significant independent predictor for advanced disease stages [P = 0.013; odds ratio: 5 (confidence interval) CI 1.3–19.3]. Moreover, low Drosha expression (P = 0.026) and elevated LDH (P = 0.025) remained as significant independent predictors for DSD with odds ratios of 13.5 (CI 1.3–134.4 and 8.7 CI 1.3–57.2, respectively).
Conclusions
Low Drosha expression is an independent predictor for advanced stage as well as LSD in CTCL patients indicating a tumour suppressor gene function of Drosha in this disorder.
Low Drosha protein expression in cutaneous T‐cell lymphoma is associated with worse disease outcome
T. Gambichler K. Salveridou L. Schmitz H.U. Käfferlein T. Brüning E. Stockfleth M. Sand K. Lang
First published: 04 May 2019 https://doi.org/10.1111/jdv.15652
Conflicts of interest None declared.
Funding sources None.
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Abstract
Background
Dysregulation of microRNAs (miRNAs) key regulators may contribute to the pathogenesis of malignancies. miRNA machinery genes such Dicer and Drosha have been reported to be biomarkers in different cancer types.
Objectives
We aimed to evaluate Drosha and Dicer protein expression in cutaneous T‐cell lymphoma (CTCL).
Methods
We performed Drosha and Dicer immunohistochemistry in 45 patients with mycosis fungoides and subtypes. Drosha and Dicer expression scores were correlated with clinical parameters including disease‐specific death (DSD), stage of disease and different laboratory data. Uni‐ and multivariate statistics were performed.
Results
On univariate analysis, elevated serum LDH and low Drosha expression were significantly associated with advanced stage (P = 0.032 and 0.0062, respectively) and lymphoma‐specific death (LSD; P = 0.017 and P = 0.005, respectively). Moreover, elevated circulating CD4+/CD26− lymphocytes were significantly associated with advanced stage (P = 0.032) and DSD (P = 0.0098). On multivariate analysis, low Drosha expression remained in the logistic regression model as significant independent predictor for advanced disease stages [P = 0.013; odds ratio: 5 (confidence interval) CI 1.3–19.3]. Moreover, low Drosha expression (P = 0.026) and elevated LDH (P = 0.025) remained as significant independent predictors for DSD with odds ratios of 13.5 (CI 1.3–134.4 and 8.7 CI 1.3–57.2, respectively).
Conclusions
Low Drosha expression is an independent predictor for advanced stage as well as LSD in CTCL patients indicating a tumour suppressor gene function of Drosha in this disorder.
Original Article
Atopic eczema: burden of disease and individual suffering – results from a large EU study in adults
J. Ring A. Zink B.W.M. Arents I.A. Seitz U. Mensing M.C. Schielein N. Wettemann G. de Carlo A. Fink‐Wagner
First published: 19 April 2019 https://doi.org/10.1111/jdv.15634 Cited by: 1
Conflicts of interest None declared.
Funding source See acknowledgement.
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Abstract
Background
Atopic eczema (AE, atopic dermatitis) is one of the most common non‐communicable inflammatory skin diseases affecting 1–5% of the adult population in Europe with marked impairment in quality of life. In spite of great progress in understanding the pathophysiology of disturbed skin barrier and immune deviation, AE still represents a problem in daily clinical practice. Furthermore, the true impact of AE on individual suffering is often not recognized.
Objectives
With a large European study, we wanted to provide insights into the actual suffering and individual burden of disease in adult patients with AE.
Methods
A total of 1189 adult patients (18–87 years, 56% female) with moderate to severe AE were recruited in nine European countries by dermatologists or allergists together with the help of patient organizations. A computer‐assisted telephone interview was performed by experienced interviewers between October 2017 and March 2018. The following instruments were used to assess severity or measure quality of life: Patient‐Oriented Eczema Measure (POEM), Dermatology Life Quality Index (DLQI), Hospital Anxiety and Depression Scale (HADS‐D) and a newly developed Atopic Eczema Score of Emotional Consequences (AESEC). Patients were also asked to self‐assess the severity of their disease.
Results
Despite current treatment, 45% of participants still had actual moderate to very severe AE in POEM. Due to their skin disease, 57% missed at least 1 day of work in the preceding year. DLQI showed moderate to extremely large impairment in 55%. According to HADS‐D, 10% scored on or above the threshold of eight points with signs of depressive symptoms. Assessed with AESEC, 57% were emotionally burdened with feelings such as 'trying to hide the eczema', 'feeling guilty about eczema', having 'problems with intimacy' and more. Of persons actually suffering from severe AE, 88% stated that their AE at least partly compromised their ability to face life.
Conclusions
This real‐life study shows that adults with a moderate to severe form of AE are suffering more than what would be deemed acceptable. There is a need for increased awareness of this problem among healthcare professionals, policymakers and the general public to support research in the development of new and more effective treatments and provide access to better and affordable health care for affected patients.
Atopic eczema: burden of disease and individual suffering – results from a large EU study in adults
J. Ring A. Zink B.W.M. Arents I.A. Seitz U. Mensing M.C. Schielein N. Wettemann G. de Carlo A. Fink‐Wagner
First published: 19 April 2019 https://doi.org/10.1111/jdv.15634 Cited by: 1
Conflicts of interest None declared.
Funding source See acknowledgement.
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Abstract
Background
Atopic eczema (AE, atopic dermatitis) is one of the most common non‐communicable inflammatory skin diseases affecting 1–5% of the adult population in Europe with marked impairment in quality of life. In spite of great progress in understanding the pathophysiology of disturbed skin barrier and immune deviation, AE still represents a problem in daily clinical practice. Furthermore, the true impact of AE on individual suffering is often not recognized.
Objectives
With a large European study, we wanted to provide insights into the actual suffering and individual burden of disease in adult patients with AE.
Methods
A total of 1189 adult patients (18–87 years, 56% female) with moderate to severe AE were recruited in nine European countries by dermatologists or allergists together with the help of patient organizations. A computer‐assisted telephone interview was performed by experienced interviewers between October 2017 and March 2018. The following instruments were used to assess severity or measure quality of life: Patient‐Oriented Eczema Measure (POEM), Dermatology Life Quality Index (DLQI), Hospital Anxiety and Depression Scale (HADS‐D) and a newly developed Atopic Eczema Score of Emotional Consequences (AESEC). Patients were also asked to self‐assess the severity of their disease.
Results
Despite current treatment, 45% of participants still had actual moderate to very severe AE in POEM. Due to their skin disease, 57% missed at least 1 day of work in the preceding year. DLQI showed moderate to extremely large impairment in 55%. According to HADS‐D, 10% scored on or above the threshold of eight points with signs of depressive symptoms. Assessed with AESEC, 57% were emotionally burdened with feelings such as 'trying to hide the eczema', 'feeling guilty about eczema', having 'problems with intimacy' and more. Of persons actually suffering from severe AE, 88% stated that their AE at least partly compromised their ability to face life.
Conclusions
This real‐life study shows that adults with a moderate to severe form of AE are suffering more than what would be deemed acceptable. There is a need for increased awareness of this problem among healthcare professionals, policymakers and the general public to support research in the development of new and more effective treatments and provide access to better and affordable health care for affected patients.
Original Article
The association between the socioeconomic status and anxiety–depression comorbidity in patients with psoriasis: a nationwide population‐based study
D. Tzur Bitan I. Krieger D. Comaneshter A.D. Cohen D. Feingold
First published: 04 May 2019 https://doi.org/10.1111/jdv.15651
Conflicts of interest Prof. Arnon Cohen received research grants from Janssen, Novartis, AbbVie, Janssen and Sanofi. Prof. Arnon Cohen served as a consultant, advisor or speaker to AbbVie, Amgen, Boehringer Ingelheim, Dexcel pharma, Janssen, Kamedis, Lilly, Neopharm, Novartis, Perrigo, Pfizer, Rafa, Samsung Bioepis, Sanofi, Sirbal and Taro.
Funding sources The authors have no funding sources to declare.
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Abstract
Background
Numerous studies have indicated that comorbid anxiety and depression are associated with a more severe course of illness. Yet generally, the study of the effect of psoriasis on patients' mental health has considered anxiety and depression to be separate states.
Objective
To measure the association between psoriasis and anxiety, depression and anxiety–depression co‐occurrence among patients according to their socioeconomic statuses (SES).
Methods
A nationwide population‐based study of psoriasis patients and age and gender frequency‐matched controls (n = 255 862) was designed. Diagnostic data were obtained from Clalit Health Services, the largest managed care organization in Israel. This database was established using continuous real‐time input from healthcare providers, pharmacies, medical care facilities and administrative computerized operating systems.
Results
After controlling for demographic and clinical variables, psoriasis was associated with anxiety (OR 1.11, 95% CI 1.01–1.23, P < 0.05), depression (OR 1.17, 95% CI 1.08–1.26, P < 0.001), and anxiety and depression co‐occurrence (OR 1.32, 95% CI 1.21–1.45, P < 0.001) among patients with low SES, yet was associated only with anxiety (OR 1.15 95% CI 1.04–1.27, P < 0.001) but not depression or comorbid anxiety–depression among patients with high SES. Survival analyses indicated that between the ages of 40 and 60, the cumulative probability of psoriasis patients with low SES to suffer from anxiety, depression and their co‐occurrence inclined more sharply with age as compared to psoriasis patients with high SES.
Conclusions
As psoriasis patients with low SES are prone to suffer from more severe courses of anxiety and depression, the choice of treatment of psoriasis should address the SES as well as the underlying psychiatric disease.
The association between the socioeconomic status and anxiety–depression comorbidity in patients with psoriasis: a nationwide population‐based study
D. Tzur Bitan I. Krieger D. Comaneshter A.D. Cohen D. Feingold
First published: 04 May 2019 https://doi.org/10.1111/jdv.15651
Conflicts of interest Prof. Arnon Cohen received research grants from Janssen, Novartis, AbbVie, Janssen and Sanofi. Prof. Arnon Cohen served as a consultant, advisor or speaker to AbbVie, Amgen, Boehringer Ingelheim, Dexcel pharma, Janssen, Kamedis, Lilly, Neopharm, Novartis, Perrigo, Pfizer, Rafa, Samsung Bioepis, Sanofi, Sirbal and Taro.
Funding sources The authors have no funding sources to declare.
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Abstract
Background
Numerous studies have indicated that comorbid anxiety and depression are associated with a more severe course of illness. Yet generally, the study of the effect of psoriasis on patients' mental health has considered anxiety and depression to be separate states.
Objective
To measure the association between psoriasis and anxiety, depression and anxiety–depression co‐occurrence among patients according to their socioeconomic statuses (SES).
Methods
A nationwide population‐based study of psoriasis patients and age and gender frequency‐matched controls (n = 255 862) was designed. Diagnostic data were obtained from Clalit Health Services, the largest managed care organization in Israel. This database was established using continuous real‐time input from healthcare providers, pharmacies, medical care facilities and administrative computerized operating systems.
Results
After controlling for demographic and clinical variables, psoriasis was associated with anxiety (OR 1.11, 95% CI 1.01–1.23, P < 0.05), depression (OR 1.17, 95% CI 1.08–1.26, P < 0.001), and anxiety and depression co‐occurrence (OR 1.32, 95% CI 1.21–1.45, P < 0.001) among patients with low SES, yet was associated only with anxiety (OR 1.15 95% CI 1.04–1.27, P < 0.001) but not depression or comorbid anxiety–depression among patients with high SES. Survival analyses indicated that between the ages of 40 and 60, the cumulative probability of psoriasis patients with low SES to suffer from anxiety, depression and their co‐occurrence inclined more sharply with age as compared to psoriasis patients with high SES.
Conclusions
As psoriasis patients with low SES are prone to suffer from more severe courses of anxiety and depression, the choice of treatment of psoriasis should address the SES as well as the underlying psychiatric disease.
Short Report
Use of dose–exposure–response relationships in Phase 2 and Phase 3 guselkumab studies to optimize dose selection in psoriasis
M. Lebwohl R.G. Langley Y. Zhu H. Zhou M. Song Y.K. Shen K. Parnell Lafferty K. Reich
First published: 11 May 2019 https://doi.org/10.1111/jdv.15668
Conflicts of interest M. Lebwohl is an employee of Mount Sinai, which receives research funds from: AbbVie, Boehringer Ingelheim, Celgene, Eli Lilly, Incyte, Janssen/Johnson & Johnson, LEO Pharma, Medimmune/Astra Zeneca, Novartis, Pfizer, Sciderm, UCB, Valeant and ViDac. Dr. Lebwohl is a consultant for Allergan, Aqua, Arcutis, Boehringer‐Ingelheim, LEO Pharma, Menlo and Promius. R. G. Langley has served as principal investigator for and is on the scientific advisory board or served as a speaker for AbbVie, Amgen, Boehringer, Celgene, Eli Lilly, Ingelheim, Janssen, LEO Pharma, Merck, Novartis and Pfizer. Y. Zhu, H. Zhou, M. Song, Y. K. Shen and K. Parnell Lafferty are all employees of Janssen and own stock in Johnson & Johnson, of which Janssen is a subsidiary. K. Reich has served as an advisor and/or paid speaker for and/or participated in clinical trials sponsored by AbbVie, Affibody, Almirall, Amgen, Biogen, Boehringer Ingelheim, Celgene, Centocor/Janssen Covagen, Eli Lilly, Forward Pharma, Fresenius Medical Care, GlaxoSmithKline, Janssen‐Cilag, Kyowa Kirin, LEO Pharma, Medac, Merck Sharp & Dohme, Novartis, Miltenyi Biotec, Ocean Pharma, Pfizer, Regeneron, Samsung Bioepis, Sanofi, Takeda, UCB, Valeant and Xenoport.
Funding sources This study was funded by Janssen Research & Development, LLC.
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Abstract
Background
Guselkumab is an anti‐interleukin‐23 monoclonal antibody for the treatment of moderate‐to‐severe psoriasis.
Objective
To evaluate the association between dose–response and exposure–response of guselkumab in Phase 2 and Phase 3 studies to optimize dose selection.
Methods
Serum guselkumab concentrations in Phase 2 and Phase 3 studies (VOYAGE 1 and VOYAGE 2) were measured using a validated immunoassay. Efficacy assessments included Physician's Global Assessment (PGA), Investigator's Global Assessment (IGA) and Psoriasis Area and Severity Index (PASI).
Results
In Phase 2, a positive dose–response relationship was observed for PASI and PGA (5‐mg through 100‐mg dose regimens). Exposure–response analysis showed that patients with steady‐state trough serum guselkumab concentrations ≥0.67 μg/mL achieved the highest levels of efficacy (PGA 0/1: 90.0%; PGA 0: 70.0%). The guselkumab 100‐mg every 8‐week (q8w) dose regimen, safe and well‐tolerated in Phase 2, provided the highest serum guselkumab concentrations among all regimens studied and was selected for Phase 3. In Phase 3, 72.5% of patients achieved guselkumab concentrations ≥0.67 μg/mL at week 28, the level associated with the highest clinical responses in Phase 2, with patients achieving response rates of IGA 0/1: 91.2%, IGA 0: 55.3%, PASI 90: 83.8% and PASI 100: 49.1% at week 28.
Conclusion
The 100‐mg guselkumab q8w dose regimen, based on the dose–exposure–response relationship from the Phase 2 study, produced the target serum concentration associated with high‐level efficacy in the majority of patients in Phase 3. Phase 3 data further confirmed that guselkumab 100mg q8w is the optimum dosing regimen for treating patients with moderate‐to‐severe psoriasis.
Use of dose–exposure–response relationships in Phase 2 and Phase 3 guselkumab studies to optimize dose selection in psoriasis
M. Lebwohl R.G. Langley Y. Zhu H. Zhou M. Song Y.K. Shen K. Parnell Lafferty K. Reich
First published: 11 May 2019 https://doi.org/10.1111/jdv.15668
Conflicts of interest M. Lebwohl is an employee of Mount Sinai, which receives research funds from: AbbVie, Boehringer Ingelheim, Celgene, Eli Lilly, Incyte, Janssen/Johnson & Johnson, LEO Pharma, Medimmune/Astra Zeneca, Novartis, Pfizer, Sciderm, UCB, Valeant and ViDac. Dr. Lebwohl is a consultant for Allergan, Aqua, Arcutis, Boehringer‐Ingelheim, LEO Pharma, Menlo and Promius. R. G. Langley has served as principal investigator for and is on the scientific advisory board or served as a speaker for AbbVie, Amgen, Boehringer, Celgene, Eli Lilly, Ingelheim, Janssen, LEO Pharma, Merck, Novartis and Pfizer. Y. Zhu, H. Zhou, M. Song, Y. K. Shen and K. Parnell Lafferty are all employees of Janssen and own stock in Johnson & Johnson, of which Janssen is a subsidiary. K. Reich has served as an advisor and/or paid speaker for and/or participated in clinical trials sponsored by AbbVie, Affibody, Almirall, Amgen, Biogen, Boehringer Ingelheim, Celgene, Centocor/Janssen Covagen, Eli Lilly, Forward Pharma, Fresenius Medical Care, GlaxoSmithKline, Janssen‐Cilag, Kyowa Kirin, LEO Pharma, Medac, Merck Sharp & Dohme, Novartis, Miltenyi Biotec, Ocean Pharma, Pfizer, Regeneron, Samsung Bioepis, Sanofi, Takeda, UCB, Valeant and Xenoport.
Funding sources This study was funded by Janssen Research & Development, LLC.
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Abstract
Background
Guselkumab is an anti‐interleukin‐23 monoclonal antibody for the treatment of moderate‐to‐severe psoriasis.
Objective
To evaluate the association between dose–response and exposure–response of guselkumab in Phase 2 and Phase 3 studies to optimize dose selection.
Methods
Serum guselkumab concentrations in Phase 2 and Phase 3 studies (VOYAGE 1 and VOYAGE 2) were measured using a validated immunoassay. Efficacy assessments included Physician's Global Assessment (PGA), Investigator's Global Assessment (IGA) and Psoriasis Area and Severity Index (PASI).
Results
In Phase 2, a positive dose–response relationship was observed for PASI and PGA (5‐mg through 100‐mg dose regimens). Exposure–response analysis showed that patients with steady‐state trough serum guselkumab concentrations ≥0.67 μg/mL achieved the highest levels of efficacy (PGA 0/1: 90.0%; PGA 0: 70.0%). The guselkumab 100‐mg every 8‐week (q8w) dose regimen, safe and well‐tolerated in Phase 2, provided the highest serum guselkumab concentrations among all regimens studied and was selected for Phase 3. In Phase 3, 72.5% of patients achieved guselkumab concentrations ≥0.67 μg/mL at week 28, the level associated with the highest clinical responses in Phase 2, with patients achieving response rates of IGA 0/1: 91.2%, IGA 0: 55.3%, PASI 90: 83.8% and PASI 100: 49.1% at week 28.
Conclusion
The 100‐mg guselkumab q8w dose regimen, based on the dose–exposure–response relationship from the Phase 2 study, produced the target serum concentration associated with high‐level efficacy in the majority of patients in Phase 3. Phase 3 data further confirmed that guselkumab 100mg q8w is the optimum dosing regimen for treating patients with moderate‐to‐severe psoriasis.
Letter to the Editor
Prostaglandin analogue for eyebrow loss in frontal fibrosing alopecia: a case report
A. Murad W.F. Bergfeld
First published: 22 May 2019 https://doi.org/10.1111/jdv.15704
Prostaglandin analogue for eyebrow loss in frontal fibrosing alopecia: a case report
A. Murad W.F. Bergfeld
First published: 22 May 2019 https://doi.org/10.1111/jdv.15704
Letter to the Editor
Disease burden and prescription patterns treating dermatophytosis in North India: salient findings from an online survey of 1041 dermatologists
T. Narang A. Bishnoi S. Dogra T.D. Singh R. Mahajan K. Kavita
First published: 18 May 2019 https://doi.org/10.1111/jdv.15686
Disease burden and prescription patterns treating dermatophytosis in North India: salient findings from an online survey of 1041 dermatologists
T. Narang A. Bishnoi S. Dogra T.D. Singh R. Mahajan K. Kavita
First published: 18 May 2019 https://doi.org/10.1111/jdv.15686
Original Article Open Access
Associations of pigmentary and naevus phenotype with melanoma risk in two populations with comparable ancestry but contrasting levels of ambient sun exposure
A.E. Cust M. Drummond D.T. Bishop L. Azizi H. Schmid M.A. Jenkins J.L. Hopper B.K. Armstrong J.F. Aitken R.F. Kefford G.G. Giles F. Demenais A.M. Goldstein J.H. Barrett … See all authors
First published: 13 May 2019 https://doi.org/10.1111/jdv.15680
Funding sources This work was supported by the National Health and Medical Research Council of Australia (NHMRC) (project grants 566946, 107359, 211172 and program grant number 402761); the Cancer Council New South Wales (project grant 77/00, 06/10), the Cancer Council Victoria and the Cancer Council Queensland (project grant 371); the US National Institutes of Health (NIH RO1 grant CA83115 to Genomel (www.genomel.org)); Cancer Research UK (Project Grant C8216/A6129 and Programme awards C588/A4994 and C588/A10589); and in part by the Intramural Research Program of the National Cancer Institute, the National Institutes of Health, the Division of Cancer Epidemiology and Genetics. AE Cust received Career Development Fellowships from the National Health and Medical Research Council of Australia (NHMRC; #1147843) and Cancer Institute NSW (15/CDF/1‐14).
Conflicts of interest The authors declare that they have no conflict of interest.
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Abstract
Background
People at high risk of developing melanoma are usually identified by pigmentary and naevus phenotypes.
Objective
We examined whether associations of these phenotypes with melanoma risk differed by ambient sun exposure or participant characteristics in two population‐based, case–control studies with comparable ancestry but different ambient sun exposure.
Methods
Data were analysed from 616 cases and 496 controls from the Australian Melanoma Family Study and 2012 cases and 504 controls from the Leeds (UK) case–control study. Questionnaire, interview and dermatological skin examination data were collected using the same measurement protocols. Relative risks were estimated as odds ratios using unconditional logistic regression, adjusted for potential confounders.
Results
Hair and skin colour were the strongest pigmentary phenotype risk factors. All associations of pigmentary phenotype with melanoma risk were similar across countries. The median number of clinically assessed naevi was approximately three times higher in Australia than Leeds, but the relative risks for melanoma associated with each additional common or dysplastic naevus were higher for Leeds than Australia, especially for naevi on the upper and lower limbs. Higher naevus counts on the head and neck were associated with a stronger relative risk for melanoma for women than men. The two countries had similar relative risks for melanoma based on self‐reported naevus density categories, but personal perceptions of naevus number differed by country. There was no consistent evidence of interactions between phenotypes on risk.
Conclusions
Classifying people at high risk of melanoma based on their number of naevi should ideally take into account their country of residence, type of counts (clinical or self‐reported), body site on which the naevus counts are measured and sex. The presence of naevi may be a stronger indicator of a genetic predisposition in the UK than in Australia based on less opportunity for sun exposure to influence naevus development.
Introduction
Melanoma rates have been increasing,1 despite being a largely preventable disease.2 People at high risk of developing melanoma are often identified by pigmentary and naevus phenotypes. Melanocytic naevi predominantly originate in childhood, and their development is influenced by sun exposure and genetic factors.3-7 A person's number of naevi may change over time with age and sun exposure,5, 8-11 which could contribute to different magnitudes of association between naevi and melanoma risk by region, age or sex.
Although pigmentary and naevus phenotypes are established risk factors for melanoma, the magnitude of these associations may differ by geographical region, participant characteristics and study methodology.12-15 One previous clinic‐based study with 300 cases and 325 controls suggested that atypical naevi were a stronger risk factor for melanoma in the United Kingdom (UK) than in Australia9; however, few population‐based data are available. We also have limited understanding of possible interactions between different risk factors.12, 13
To address these knowledge gaps and overcome limitations of previous studies, we examined the association of pigmentary and naevus phenotype with melanoma risk in two large, population‐based case–control studies under the auspices of the melanoma genetics consortium (GenoMEL, www.genomel.org). The studies were conducted using the same measurement protocols implemented as far as feasible in an identical manner and conducted in populations with similar ethnic backgrounds (Australia and the UK) but very different ambient sun exposure.
Materials and methods
Study samples
The Australian Melanoma Family Study was a multi‐centre, population‐based, case–control family study of invasive cutaneous melanoma diagnosed between ages 18–39 years. The study design, recruitment, data collection and participant characteristics have been described.16 Recruitment of case (n = 629) and control (n = 535) participants was locally coordinated in Sydney, Melbourne, and Brisbane, Australia. Cases were identified from population‐based state cancer registries, diagnosed between 1st July 2000 and 31st December 2002 at ages 18–39 years with incident, histopathologically confirmed, first primary invasive cutaneous melanoma. Participation was 76% of those contactable. Population controls were aged between 18–39 years at the time of approach and had no history of invasive or in situ melanoma. They were selected from the electoral roll (registration to vote is compulsory for Australians aged ≥18 years) and were frequency‐matched to cases by city, age and sex. Participation was 42% of those contactable. In addition, spouse/partner or friend controls were recruited through nomination by a case. They had to be at least 18 years of age and have no history of invasive or in situ melanoma; there were no other age, sex or residency restrictions. A spouse or friend was nominated as a potential control subject by 59% of cases, and participation was 80% of those nominated.
The Leeds case–control study recruited population‐based incident histopathologically confirmed invasive melanoma cases (n = 2184), aged between 18–82 years, and living in a geographically defined area of Yorkshire and the Northern region of the UK.17, 18 The cases were identified through clinicians, pathology registers and the cancer registry to ensure maximal ascertainment (67% participation). Between September 2000 and June 2003, all people with invasive melanoma were invited to participate. From July 2003 to September 2011, only cases with Breslow thickness ≥0.75 mm were invited, in order to enrich the cohort to observe clinical outcomes. Population‐ascertained controls were identified by the cases' family doctors as not having cancer and were randomly invited from individuals who were matched by sex and age (55% participation, 513 recruited).
Approval for the study was obtained from the ethics committees of the coordinating centres in Australia and Leeds and the cancer registries. All participants provided written‐informed consent.
Self‐reported pigmentary phenotype and naevus counts
Participants completed a questionnaire in which they reported skin colour, eye colour, natural hair colour at age 18, freckling as a child and adult,19 ability to tan, propensity to sunburn, usual tanning and sunburn response to prolonged or repeated exposure of skin to sunlight in summer, number of naevi covering their body (described pictorially as none, few, some, many) and were asked to have someone count the number of all moles on their back (using picture guides).16
We created a pigmentation score using factor analysis: this contained skin colour, eye colour, childhood freckling and skin phototype (see Data S1).
Clinical assessment of pigmentary and naevus phenotype for a subset of participants
Clinical assessment was conducted by research nurses in the UK and by dermatology trainees in Australia. Assessors were jointly trained on the study protocol including recognizing and counting naevi according to international guidelines,20 and annual refresher courses were conducted jointly. Melanocytic naevi were defined as brown‐to‐black pigmented macules or papules which were reasonably well defined and darker in colour than the surrounding skin. Dysplastic (atypical) naevi were defined as having a macular component in at least one area in addition to at least three of the following: (i) ill‐defined border, (ii) size ≥5 mm, (iii) variegated colour, (iv) uneven outline and (v) erythema.20 Participants removed their clothing except for underpants and bra. Separate counts were made for melanocytic naevi of 2‐<5 mm and ≥5 mm, and clinically atypical naevi on different body sites but excluding scalp, breasts, buttocks and genitals. Naevi <2 mm were not counted to minimize confusion with freckles and lentigines.
Clinical assessment of naevi was completed for the first 1022 cases, and all population controls in Leeds, and by 73% of cases, 55% of population controls, and 67% of spouse or friend controls in Australia. Natural hair colour at age 18 and eye colour were also recorded using wig hair swatches and eye photographs for comparison.
Self‐reported personal sun exposure
Comprehensive data on sun exposure throughout life were collected by telephone interview, with the aid of a residence calendar. Questions referred to the frequency of sunburn and time spent outdoors between 9 am and 5 pm separately for weekdays, weekends and holidays in warmer months and in cooler months.18, 21
Statistical analysis
We excluded: relatives, participants who did not complete a questionnaire, Australian controls who were ≥45 years at interview (since all Australian cases were diagnosed <40 years), and participants missing either three or more key pigmentary phenotype variables, self‐report naevus density, summer holiday sun exposure or painful sunburn variables. The analysis dataset included 1112 participants (616 cases, 496 controls) from Australia and 2516 participants (2012 cases, 504 controls) from Leeds. Analyses of the associations between naevi and melanoma risk excluded participants with missing pigmentation score or hair colour, and analyses of the associations between clinically assessed naevus phenotype and melanoma risk were restricted to participants who had a clinical skin examination. Australian population controls (n = 237) and spouse/friend controls (n = 259) were combined into one control group for analysis, as done previously.16
Relative risks (RR) for melanoma were estimated as odds ratios (OR) and 95% confidence intervals (CI), using unconditional logistic regression. Minimally adjusted models included age (continuous), sex and city of recruitment (in Australia). Analyses of pigmentary phenotype were further adjusted for self‐reported naevus density, summer holiday sun exposure hours and painful sunburns; and analyses of naevus phenotype were further adjusted for pigmentation score and hair colour. We estimated the OR per standard deviation adjusted for age (5‐yr groups) and sex (OPERA method22) as a way of comparing the predictive strength of naevus number across Leeds and Australia while accounting for the countries' different naevus, age and sex distributions.
To test whether the pigmentary and naevus phenotype associations with melanoma differed between Leeds and Australia, or by other factors, we added to the models a product term between the phenotype variable and country (or other factor), fitted as a one degree‐of‐freedom ordinal variable to test for interaction in the trend effect. Data were analysed using SAS version 9.4 (SAS Institute, Cary NC), and statistical significance was inferred at two‐sided P <0.05. We reported the study according to STROBE guidelines for observational studies.
Results
Characteristics
Demographic characteristics are shown in Table 1. Both studies had a majority of female participants. They had a similar proportion of participants with self‐reported European ethnicity, but the Australian study had a higher proportion of Eastern European ethnic background. Excluding Eastern Europeans from our analyses did not materially impact results.
Table 1. Characteristics of cases and controls in the Australian Melanoma Family Study and Leeds case–control study
Characteristic
Australia
N (%)
Leeds
N (%)
Total, cases and controls 1112 2516
Cases 616 (55) 2,012 (80)
Controls 496 (45) 504 (20)
Sex
Female 663 (60) 1,438 (57)
Male 449 (40) 1,078 (43)
Age at diagnosis/interview (years) †
18–29 291 (26) 105 (4)
30–39 733 (66) 287 (11)
40–49 88 (8) 445 (18)
50–69 0 (0) 1,335 (53)
≥70 0 (0) 344 (14)
Ethnic background ‡
English 676 (61) 2,340 (93)
Scottish, Irish, Welsh 54 (5) 120 (5)
Other Northern European 49 (4) 14 (1)
Southern European 12 (1) 6 (0)
Eastern European 251 (23) 4 (0)
Mixed/Other European 20 (2) 28 (1)
Non‐European 49 (4) 0 (0)
Missing 1 4
†Leeds cases and controls were unselected for age at diagnosis. In Australia, all cases were <40 years at diagnosis and all population controls were <40 years when ascertained; cases and controls could be up to age 44 years at interview for this analysis.
‡Self‐reported.
Pigmentary phenotype and melanoma risk
The associations of self‐reported and clinically assessed pigmentary phenotype factors with melanoma risk were similar across countries (Table 2). Pigmentation score was associated with an approximately twofold increased melanoma risk for the highest vs. lowest tertile. Both studies observed a threefold to fourfold increased risk of melanoma for those with red hair, and a twofold increased risk for those with fair or blonde hair, compared to those with dark brown or black hair. Very fair skin more than doubled risk compared with having olive or brown skin. The results remained consistent in the analyses adjusted for other risk factors and excluding naevus count density from the multivariable models had minimal impact on the risk estimates.
Table 2. Association of pigmentary phenotype with melanoma risk in the Australian Melanoma Family Study and Leeds case–control study
Pigmentary phenotype† Australia (N = 1112) Leeds (N = 2516) P‐int¶
Case
N (%)
Control
N (%)
OR (95% CI)‡ Adjusted OR (95% CI)§
Case
N (%)
Control
N (%)
OR (95% CI)‡ Adjusted OR (95% CI)§
Hair colour
Dark brown/black 160 (26) 200 (40) 1.00 1.00 822 (41) 288 (57) 1.00 1.00 0.37
Light brown 244 (40) 208 (42) 1.41 (1.05, 1.88) 1.26 (0.93, 1.70) 537 (27) 119 (24) 1.62 (1.27, 2.06) 1.61 (1.25, 2.06)
Fair or Blonde 142 (23) 64 (13) 2.64 (1.82, 3.84) 2.38 (1.61, 3.51) 398 (20) 68 (13) 2.05 (1.54, 2.75) 1.99 (1.48, 2.69)
Red 70 (11) 24 (5) 3.41 (2.03, 5.75) 4.21 (2.44, 7.27) 253 (13) 29 (6) 3.10 (2.06, 4.66) 3.01 (1.99, 4.57)
Eye colour
Brown or Black 111 (18) 119 (24) 1.00 1.00 322 (16) 94 (19) 1.00 1.00 0.62
Green or Hazel 224 (36) 167 (34) 1.52 (1.08, 2.13) 1.35 (0.95, 1.93) 605 (30) 182 (36) 0.98 (0.73, 1.30) 0.97 (0.72, 1.30)
Blue or Grey 279 (45) 206 (42) 1.57 (1.13, 2.17) 1.49 (1.06, 2.09) 1075 (54) 228 (45) 1.40 (1.07, 1.84) 1.40 (1.06, 1.85)
Skin colour
Olive or Brown 62 (10) 92 (19) 1.00 1.00 149 (7) 60 (12) 1.00 1.00 0.30
Fair 430 (70) 336 (68) 1.92 (1.33, 2.76) 1.75 (1.20, 2.55) 1342 (67) 365 (72) 1.51 (1.09, 2.08) 1.48 (1.06, 2.08)
Very fair 119 (19) 67 (14) 2.33 (1.48, 3.66) 2.29 (1.42, 3.68) 519 (26) 79 (16) 2.63 (1.79, 3.86) 2.54 (1.71, 3.79)
Freckles in childhood
None 116 (19) 127 (26) 1.00 1.00 543 (28) 187 (37) 1.00 1.00 0.28
Very few 185 (30) 163 (33) 1.17 (0.83, 1.64) 1.17 (0.82, 1.66) 571 (29) 131 (26) 1.46 (1.13, 1.89) 1.33 (1.02, 1.73)
Few/Some 244 (40) 156 (32) 1.56 (1.12, 2.18) 1.69 (1.19, 2.39) 698 (35) 159 (32) 1.48 (1.15, 1.89) 1.35 (1.05, 1.75)
Many 69 (11) 48 (10) 1.41 (0.89, 2.24) 1.55 (0.95, 2.52) 161 (8) 26 (5) 2.11 (1.34, 3.31) 2.06 (1.30, 3.29)
Freckles in adulthood
None 160 (26) 156 (32) 1.00 1.00 654 (33) 209 (42) 1.00 1.00 0.38
Very few 238 (39) 200 (40) 1.07 (0.79, 1.45) 1.03 (0.75, 1.40) 630 (31) 132 (26) 1.51 (1.17, 1.93) 1.38 (1.07, 1.78)
Few/Some 175 (28) 119 (24) 1.25 (0.89, 1.75) 1.24 (0.87, 1.77) 609 (30) 144 (29) 1.35 (1.05, 1.73) 1.22 (0.94, 1.58)
Many 42 (7) 20 (4) 1.62 (0.89, 2.95) 1.67 (0.89, 3.13) 108 (5) 18 (4) 1.87 (1.10, 3.18) 1.83 (1.06, 3.16)
General skin reaction to sun (skin phototype)
Sometimes/Never Burns 290 (47) 292 (59) 1.00 1.00 1276 (64) 375 (75) 1.00 1.00 0.88
Usually/Always Burns 322 (53) 200 (41) 1.55 (1.21, 1.99) 1.60 (1.23, 2.09) 726 (36) 127 (25) 1.69 (1.36, 2.11) 1.62 (1.28, 2.04)
Ability to tan from repeated exposure
Moderate/Deep Tan 359 (59) 331 (67) 1.00 1.00 542 (53) 336 (67) 1.00 1.00 0.13
Mild/No Tan 254 (41) 162 (33) 1.32 (1.02, 1.70) 1.33 (1.01, 1.74) 484 (47) 163 (33) 1.85 (1.48, 2.33) 1.88 (1.48, 2.39)
Propensity to sunburn
Mild/No burn 222 (36) 242 (49) 1.00 1.00 606 (59) 343 (69) 1.00 1.00 0.44
Severe/Painful burn 392 (64) 252 (51) 1.68 (1.31, 2.16) 1.69 (1.30, 2.20) 418 (41) 157 (31) 1.51 (1.20, 1.89) 1.47 (1.16, 1.86)
Clinically assessed hair colour
Black/Brown 176 (39) 172 (57) 1.00 1.00 424 (68) 217 (84) 1.00 1.00 0.55
Fair/Blonde 195 (43) 109 (36) 1.59 (1.15, 2.20) 1.48 (1.05, 2.08) 102 (16) 25 (10) 2.09 (1.31, 3.34) 2.14 (1.31, 3.49)
Red 82 (18) 21 (7) 3.27 (1.90, 5.60) 3.58 (2.05, 6.27) 93 (15) 16 (6) 2.90 (1.66, 5.07) 3.19 (1.79, 5.66)
Clinically assessed eye colour
Brown 87 (19) 68 (23) 1.00 1.00 101 (11) 77 (15) 1.00 1.00 0.44
Green/Hazel 132 (29) 94 (31) 1.11 (0.72, 1.71) 1.06 (0.68, 1.65) 332 (35) 195 (39) 1.35 (0.95, 1.91) 1.31 (0.91, 1.88)
Blue/Grey 231 (51) 140 (46) 1.44 (0.97, 2.14) 1.43 (0.95, 2.16) 515 (54) 229 (46) 1.85 (1.31, 2.59) 1.83 (1.29, 2.60)
Pigmentation score ††
Tertile 1 120 (20) 153 (31) 1.00 1.00 483 (25) 190 (38) 1.00 1.00 0.32
Tertile 2 188 (31) 145 (30) 1.55 (1.10, 2.18) 1.57 (1.11, 2.22) 629 (32) 170 (34) 1.40 (1.10, 1.80) 1.36 (1.06, 1.75)
Tertile 3 296 (49) 188 (39) 1.83 (1.32, 2.52) 1.92 (1.38, 2.67) 839 (43) 141 (28) 2.40 (1.86, 3.10) 2.17 (1.67, 2.82)
CI, confidence interval; OR, odds ratio.
Missing data for each variable (N for Australia, N for Leeds): hair colour (0, 2), eye colour (6, 10), skin colour (6, 2), freckling child (4, 40), freckling adult (2, 12), skin reaction to sun (8, 12), skin often exposed (6, 991), skin exposed (4, 992), clinically assessed hair colour (357, 1639), clinically assessed eye colour (360, 1067) and pigmentation score (22, 64).
†Phenotype variables were based on self‐report unless specified as clinically assessed.
‡Minimally adjusted models adjusted for age (continuous), sex and city of recruitment in Australia.
§Further adjusted for self‐reported naevus density, summer holiday sun exposure hours per day and painful sunburns to 40 years of age.
¶P‐value for the interaction between pigmentary phenotype variable and country (Australia/Leeds) using minimally adjusted models.
††Based on factor analysis (see Supplementary online material). Tertile cut‐points were based on the combined Australia/Leeds control distributions.
Naevus phenotype and melanoma risk
Cases had a significantly higher median number of naevi than controls, and Australian participants had more naevi than Leeds participants, even after accounting for the different age distributions (Fig. 1). These differences were apparent for both self‐reported naevi (Table 3) and clinically assessed naevi (Table 4). Participants' perceptions of their own naevus density, when compared to clinical counts, differed by country and disease status (Fig. 2). For example, Leeds control participants who self‐reported 'many' naevi had a median of 34 naevi ≥2 mm diameter (interquartile range (IQR) 24–56), which was similar to Australian control participants who self‐reported 'none' (median 35, IQR 11–58). Nevertheless, the relative risks for melanoma associated with a higher self‐reported naevus density category were similar for Australia and Leeds (Table 3). Compared with those who self‐reported no naevi, those with 'some' naevi had an approximately threefold higher risk, and those with 'many' naevi had an approximately fivefold increased risk.
image
Figure 1
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The distribution of clinically measured whole‐body naevus counts ≥2 mm for Australian cases, Australian controls, Leeds cases, Leeds controls, stratified by age ≤40, >40 years. The x‐axis represents the number of clinically assessed naevi, and the y‐axis represents the proportion of participants.
Table 3. Association of self‐reported naevus phenotype with melanoma risk in the Australian Melanoma Family Study and Leeds case–control study
Naevi Australia (N = 1093) Leeds (N = 2479) P‐int§
Case
N (%) or median (IQR)
Control
N (%) or median (IQR)
OR (95% CI)† Adjusted OR (95% CI)‡
Case
N (%) or median (IQR)
Control
N (%) or median (IQR)
OR (95% CI)† Adjusted OR (95% CI)‡
Self‐reported naevi
None 21 (3) 40 (8) 1.00 1.00 176 (9) 97 (19) 1.00 1.00 0.44
Few 172 (28) 237 (49) 1.30 (0.73, 2.32) 1.42 (0.78, 2.59) 771 (39) 252 (50) 1.67 (1.26, 2.23) 1.75 (1.30, 2.35)
Some 264 (44) 158 (33) 3.05 (1.71, 5.45) 3.44 (1.88, 6.29) 718 (37) 118 (24) 3.29 (2.37, 4.56) 3.54 (2.53, 4.96)
Many 147 (24) 51 (10) 5.17 (2.75, 9.72) 5.71 (2.96, 11.02) 296 (15) 34 (7) 4.67 (3.00, 7.29) 4.82 (3.07, 7.58)
Self‐reported naevi on the back ¶
Quartiles
Quartile 1 (AMFS: 0–3; Leeds: 0–0) 96 (17) 136 (29) 1.00 1.00 300 (16) 123 (26) 1.00 1.00 0.82
Quartile 2 (AMFS: 4–8; Leeds: 1–3) 113 (19) 107 (23) 1.46 (0.99, 2.14) 1.52 (1.03, 2.26) 356 (19) 118 (25) 1.22 (0.91, 1.64) 1.23 (0.91, 1.67)
Quartile 3 (AMFS: 9–19; Leeds: 4–10) 150 (26) 112 (24) 1.75 (1.21, 2.53) 1.85 (1.26, 2.71) 536 (29) 125 (27) 1.70 (1.27, 2.27) 1.81 (1.34, 2.43)
Quartile 4 (AMFS: >=20; Leeds: >=11) 221 (38) 114 (24) 2.63 (1.85, 3.76) 2.79 (1.93, 4.03) 677 (36) 104 (22) 2.49 (1.83, 3.39) 2.71 (1.98, 3.71)
Continuous variables
Median (IQR) & OR per 1 naevi†† 13 (6, 29) 8 (3, 19) 1.02 (1.01, 1.03) 1.02 (1.01, 1.03) 6 (2, 17) 3 (0, 10) 1.02 (1.01, 1.03) 1.02 (1.01, 1.03) 0.41
OR per adjusted SD increase in naevi‡‡ 1.47 (1.27, 1.71) 1.50 (1.29, 1.74) 1.38 (1.19, 1.60) 1.42 (1.22, 1.65) 0.37
OR, odds ratio; CI, confidence interval; IQR, interquartile range; SD, standard deviation.
Missing data for each variable (N for Australia, N for Leeds): naevus density (3, 17), naevi on back (44, 140).
†Minimally adjusted models adjusted for age (continuous), sex, and city of recruitment in Australia.
‡Further adjusted for pigmentation score and hair colour.
§P‐value for the interaction between naevus phenotype and country (Australia/Leeds) using minimally adjusted models.
¶Quartile cut‐points were based on the country‐specific control distributions.
††OR per 1‐unit increase in naevus count modelled as a continuous variable.
‡‡OR per adjusted standard deviation, stratified by country (Australia/Leeds) and adjusted for age (5‐year groups) and sex, using the OPERA method.22
Table 4. Association of clinically assessed naevus phenotype with melanoma risk in the Australian Melanoma Family Study and Leeds case–control study
Naevi Australia (N = 740) Leeds (N = 1450) P‐int§
Case
N (%) or median (IQR)
Control
N (%) or median (IQR)
OR (95% CI)† Adjusted OR (95% CI)‡
Case
N (%) or median (IQR)
Control
N (%) or median (IQR)
OR (95% CI)† Adjusted OR (95% CI)‡
Naevi on the whole body ≥ 2 mm
Categories
0–15 6 (1) 36 (12) 1.00 1.00 193 (20) 258 (52) 1.00 1.00 0.97¶
16–40 20 (5) 56 (19) 2.08 (0.74, 5.85) 1.44 (0.50, 4.19) 291 (31) 163 (33) 2.39 (1.82, 3.14) 2.48 (1.88, 3.29)
41–60 33 (7) 42 (14) 5.02 (1.83, 13.76) 4.97 (1.79, 13.80) 148 (16) 40 (8) 4.91 (3.27, 7.37) 5.29 (3.49, 8.02)
61–80 22 (5) 28 (9) 5.11 (1.75, 14.96) 4.75 (1.60, 14.13) 81 (9) 21 (4) 5.14 (3.05, 8.66) 5.40 (3.17, 9.20)
81–100 21 (5) 17 (6) 9.06 (2.93, 27.98) 6.83 (2.15, 21.71) 66 (7) 5 (1) 17.47 (6.86, 44.47) 16.44 (6.42, 42.08)
101–200 119 (27) 70 (24) 12.65 (4.86, 32.90) 10.91 (4.15, 28.70) 136 (14) 12 (2) 15.05 (8.03, 28.20) 14.84 (7.87, 28.01)
≥201 222 (50) 48 (16) 35.98 (13.65, 94.82) 31.36 (11.75, 83.67) 36 (4) 0 (0) n/a n/a
Quartiles
Q1 (AMFS: 0–29; Leeds: 0–7) 19 (4) 73 (25) 1.00 1.00 75 (8) 134 (27) 1.00 1.00 0.12
Q2 (AMFS: 30–69; Leeds: 8–15) 56 (13) 76 (26) 3.29 (1.73, 6.25) 4.15 (2.10, 8.18) 118 (12) 124 (25) 1.68 (1.15, 2.46) 1.90 (1.28, 2.82)
Q3 (AMFS: 70–155; Leeds: 16–29) 94 (21) 73 (25) 6.45 (3.42, 12.18) 7.36 (3.76, 14.42) 185 (19) 121 (24) 2.72 (1.88, 3.93) 3.03 (2.07, 4.43)
Q4 (AMFS: >155; Leeds: >29) 274 (62) 75 (25) 20.10 (10.68, 37.83) 22.79 (11.65, 44.57) 573 (60) 120 (24) 8.37 (5.85, 11.98) 9.33 (6.43, 13.54)
Continuous variables
Median (IQR) & OR per 1 naevi ††
Whole‐body naevi 201 (106, 308) 68 (30, 157) 1.01 (1.01, 1.01) 1.01 (1.01, 1.01) 40 (19, 81) 15 (7, 29) 1.03 (1.02, 1.03) 1.03 (1.02, 1.03) <.0001
Head and neck naevi 14 (6, 24) 6 (2, 13) 1.06 (1.04, 1.08) 1.07 (1.05, 1.09) 3 (1, 6) 1 (1, 3) 1.15 (1.11, 1.20) 1.16 (1.11, 1.20) 0.0010
Trunk naevi 45 (23, 74) 18 (7, 43) 1.02 (1.02, 1.03) 1.02 (1.02, 1.03) 10 (4, 24) 5 (2, 11) 1.04 (1.03, 1.05) 1.04 (1.03, 1.06) 0.0003
Upper limbs naevi 76 (36, 121) 27 (12, 57) 1.02 (1.01, 1.02) 1.02 (1.01, 1.02) 11 (4, 22) 3 (1, 8) 1.09 (1.07, 1.11) 1.09 (1.07, 1.11) <.0001
Lower limbs naevi 53 (26, 91) 16 (5, 41) 1.02 (1.01, 1.02) 1.02 (1.01, 1.02) 10 (4, 25) 3 (1, 7) 1.07 (1.05, 1.08) 1.06 (1.05, 1.08) <.0001
OR per adjusted SD increase in naevi ‡‡
Whole‐body naevi 2.62 (2.08, 3.30) 2.60 (2.06, 3.29) 3.09 (2.50, 3.81) 3.07 (2.48, 3.81) 0.20
Head and neck naevi 1.84 (1.50, 2.25) 1.99 (1.60, 2.47) 1.60 (1.39, 1.84) 1.62 (1.41, 1.87) 0.16
Trunk naevi 2.26 (1.82, 2.79) 2.36 (1.89, 2.94) 1.93 (1.63, 2.29) 2.05 (1.71, 2.44) 0.29
Upper limbs naevi 2.50 (2.00, 3.14) 2.58 (2.04, 3.27) 3.18 (2.56, 3.96) 3.20 (2.56, 3.98) 0.06
Lower limbs naevi 2.45 (1.93, 3.12) 2.26 (1.77, 2.89) 3.39 (2.64, 4.34) 3.21 (2.50, 4.11) 0.04
Dysplastic naevi
Categories
0 245 (55) 229 (77) 1.00 1.00 689 (72) 458 (92) 1.00 1.00 0.04
1 48 (11) 34 (11) 1.41 (0.86, 2.32) 1.34 (0.80, 2.24) 124 (13) 28 (6) 2.80 (1.82, 4.31) 2.71 (1.75, 4.19)
≥2 150 (34) 34 (11) 3.90 (2.54, 5.99) 4.06 (2.61, 6.30) 138 (15) 13 (3) 6.44 (3.57, 11.61) 6.03 (3.33, 10.92)
Continuous
OR per 1 dysplastic naevi 1.20 (1.11, 1.30) 1.20 (1.11, 1.30) 1.78 (1.47, 2.17) 1.74 (1.43, 2.11) <.0001
Naevi >5 mm
Categories
0 51 (12) 92 (34) 1.00 1.00 405 (46) 324 (68) 1.00 1.00 0.50
1–2 65 (15) 72 (27) 1.68 (1.02, 2.75) 1.72 (1.04, 2.85) 313 (35) 126 (26) 1.91 (1.48, 2.46) 1.83 (1.41, 2.37)
>2 306 (73) 104 (39) 5.46 (3.55, 8.40) 5.02 (3.23, 7.79) 164 (19) 30 (6) 4.25 (2.80, 6.46) 3.91 (2.56, 5.98)
Continuous
Whole‐body OR per 1 naevi >5 mm†† 9 (2, 22) 2 (0, 5) 1.06 (1.04, 1.08) 1.06 (1.04, 1.07) 1 (0, 2) 0 (0, 1) 1.28 (1.20, 1.38) 1.26 (1.18, 1.36) <.0001
Whole‐body OR per adjusted SD increase in naevi >5 mm‡‡ 2.01 (1.55, 2.60) 1.87 (1.43, 2.43) 1.86 (1.56, 2.23) 1.79 (1.49, 2.14) 0.93
CI, confidence interval; IQR, interquartile range; OR, odds ratio; SD, standard deviation.
Data were missing for participants who did not have a clinical skin examination (353 in Australia, 876 in Leeds). In addition, data were missing for Leeds for trunk (1), upper limbs (1) and lower limbs (7).
†Models adjusted for age (continuous), sex and city of recruitment in Australia.
‡Further adjusted for pigmentation score and hair colour.
§P‐value for the interaction between naevus phenotype and population (Australia/Leeds) using minimally adjusted models.
¶P‐value based on model excluding the top category
††OR per 1‐unit increase in naevus count modelled as a continuous variable.
‡‡OR per adjusted standard deviation, stratified by country (Australia/Leeds) and adjusted for age (5‐year groups) and sex, using the OPERA method.22
image
Figure 2
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Comparison of self‐reported and clinically measured naevus counts (≥2 mm) in the Australian Melanoma Family Study and Leeds case–control study. The bar graph plots the median clinically measured naevus counts (y‐axis) according to self‐reported naevus density category (none, few, some, many) (x‐axis), separately for cases and controls in Australia and Leeds.
Risk of melanoma increased sharply with increasing number of clinically assessed naevi (Table 4). The top category of >200 naevi could only be assessed in the Australian sample as 50% and 16% of Australian cases and controls, respectively, were in this category, compared with 4% and 0% of Leeds cases and controls (5% and 0% of Leeds cases and controls ≤40 years; Table S2). For both countries, fewer naevi occurred on the head and neck than on other body sites, but the OR for melanoma per additional naevus was higher for head and neck naevi. Based on the ORs per adjusted standard deviation increase in naevi, the upper and lower limbs were the body sites that were most predictive of melanoma risk for Leeds, and the upper and lower limbs and the trunk for Australia. The number of clinically assessed common naevi analysed on a continuous scale, and the presence and number of clinically assessed dysplastic naevi, was each associated with greater relative risks for melanoma in Leeds than in Australia (P‐interaction <0.05). This pattern of a higher relative risk of melanoma in Leeds was consistent for common naevi on different body sites when modelled as an OR per 1 naevus increase, and for naevi on the upper and lower limbs when modelled as an OR per adjusted standard deviation.
Since the Australian study recruited only participants aged <40 years, we further examined the naevi results for the Leeds sample in age‐stratified analysis (≤40, >40 years; Table S2). Leeds' participants aged ≤40 years had higher numbers of naevi than those aged > 40 years; the median total body count was 53 and 23 naevi for younger cases and controls, respectively, and 35 and 15 naevi for older cases and controls, respectively. This increase was more noticeable on the trunk, with 17 and 10 naevi for younger cases and controls, respectively, and 9 and 5 naevi for older cases and controls, respectively. Nevertheless, similar ORs between naevus counts and melanoma risk were observed for the Leeds study when stratified by age (≤40 years, >40 years) and there was no evidence of interaction by age (all P‐interaction values were ≥0.15; Table S2). In analyses stratified by sex (Table S3), we found that higher naevus counts on the head and neck were associated with a stronger relative risk for melanoma for women than men, and this was consistent across countries: in Australia the OR per adjusted SD increase in naevi was 2.19 (95% CI 1.63, 2.95) for women and 1.59 (95% CI 1.20, 2.11) for men (P‐interaction = 0.03), and in Leeds was 1.92 (1.57, 2.35) for women and 1.29 (1.06, 1.57) for men (P‐interaction =0.01).
Table S4 shows the association of naevi with melanoma risk, stratified by pigmentation score and hair colour. In Leeds, there was evidence of a stronger association between clinically assessed naevi and melanoma risk for participants with a sun‐sensitive phenotype, but the opposite was observed in Australia, whereby self‐reported naevi were a stronger risk factor for those with a sun‐resistant phenotype. Stratified by hair colour, the association of melanoma risk with clinically assessed naevi (Table S4) and sun‐sensitive pigmentation phenotype (Table S5) appeared stronger for participants with red hair, although the confidence intervals were wide. There was no evidence for interactions of dysplastic naevi with common naevi or hair colour on melanoma risk (data not shown).
Discussion
The findings from these two population‐based case–control studies, using the same measurement protocols and harmonized data, allow a direct comparison of the magnitude of associations of pigmentary and naevus phenotype with melanoma risk in two countries with similar ethnic background but vastly different ambient sun exposure.
The emergence of naevi is thought to be under strong genetic control, whereas sun exposure influences the mean number of naevi.7 As naevus measurement and training protocols were essentially the same across our Leeds and Australian studies and the samples had a similar genetic background,23 we can reasonably assume that the observed large (approximately threefold, age‐adjusted) differences in clinically assessed number of naevi are due to higher sun exposure in Australia than the UK. Similarly, the proportion of participants with one or more dysplastic naevi or with large naevi was also higher in Australia than Leeds. Bataille and colleagues' smaller, clinic‐based, cross‐country comparison of naevi recruited between 1989–1993 found about twofold greater number of common and dysplastic naevi in Australia than the UK.9
A potential limitation of our analysis was the different age structure between studies. There are limited prospective data on naevus counts over time, but it is thought that number of naevi may change with age or cohort effects, and we observed higher numbers of naevi for Leeds' participants aged ≤40 years than for those aged >40 years. We addressed this in several ways. Firstly, we adjusted all analyses for age. Secondly, we conducted sensitivity analyses stratified by age group (≤40, >40 years); this still showed 3.8 times higher common naevus counts ≥2 mm for Australian cases and 3 times higher for Australian controls, and that the associations of naevi with melanoma risk was similar for younger and older age groups in Leeds. Finally, we also estimated the OR for melanoma per adjusted standard deviation of naevus counts as a way of comparing the predictive strength of this risk factor across the two countries while accounting for the different naevus, age and sex distributions.22 Another limitation was the potential bias from the targeted selection of thicker melanomas in the later years of recruitment in the Leeds group. People with thicker melanomas tended to have fewer naevi, but this is also confounded with age, as older people were more likely to have thicker melanomas and fewer naevi.
Interestingly, participants' perceptions of their own naevus density (using the self‐reported naevus categories), when compared to clinical counts, differed by country and disease status. This indicates that people may report their own naevus phenotype based on how it compares with the 'norm' for their peers. Thus, self‐reported naevus density categories should not be used to infer the same absolute naevus counts across different populations.
A meta‐analysis of 49 studies13 estimated that the RR for melanoma was 1.02 (95% CI 1.01–1.02) for each additional common naevus, and for people with ≥1 atypical naevi the summary RR was 3.63 (95% CI 2.85–4.62) compared to no atypical naevi. These summary estimates fall in the middle of the estimates for Australia and Leeds; based on absolute counts measured clinically, the relative risk of melanoma 'per naevus' was greater in Leeds than in Australia. However, the relative risks for melanoma were similar for the two countries when using the self‐reported naevus categories because the reference group reflected different absolute numbers of naevi in Leeds and Australia. The higher relative risk for melanoma 'per naevus' (based on clinical counts) in Leeds indicate that naevi may be a stronger indicator of a genetic predisposition in the UK based on less opportunity for sun exposure to influence naevus development. A previous pooled analysis found that relative risks for melanoma were fairly similar across latitudes and age groups analysed using study‐specific quantiles.24
Calculating the population attributable fraction (PAF)13 from our study indicates that 64% of cases in Australia and 16% of cases in Leeds were attributable to having >100 naevi. Olsen and colleagues' meta‐analysis13 concluded that patients with ≥25 common naevi and/or ≥1 atypical naevi should be managed as high risk since almost half of melanomas occurred in this group.13 In our study, 97% of melanoma cases and 81% of controls from Australia, and 70% of cases and 34% of controls from Leeds met this high‐risk criteria. It may not be practicable or cost‐effective to apply the same high‐risk naevus count criteria to different countries, and it is important to also take into account other risk factors.25
The upper and lower limbs were the body sites that were most predictive of melanoma risk for Leeds, and for Australia the most predictive sites were the upper and lower limbs and the trunk, based on the ORs per adjusted standard deviation22 increase in naevi. We observed that higher naevus counts on the head and neck were associated with a stronger relative risk for melanoma for women than men, whereas Ribero and colleagues found that men had a higher relative risk for melanoma associated with naevi on the legs, arms and head and neck.26
Our relative risk estimates for the associations of pigmentary phenotype factors with melanoma risk for Australia and Leeds were consistent with a previous meta‐analysis.12 Based on our findings, the population attributable fraction for red hair colour was 9% in Australia and Leeds, and for very fair skin was 11% and 16%, respectively. The PAFs calculated in the meta‐analysis from weighted averages across the studies were 10% for red hair and 10% for very fair skin.12
Some studies have observed super‐multiplicative joint effects of naevi and red hair colour on melanoma risk.8, 27 There was some suggestion of similar effect modification in our study between naevi and hair colour or pigmentation score, but the findings were not always consistent. Our results suggest that, in most cases, pigmentary and naevus risk factors act independently of each other.
In conclusion, hair and skin colour were the strongest pigmentary phenotype risk factors, and all associations of pigmentary phenotype with melanoma risk were similar across countries. On average, Australians have about three times as many naevi as those living in the UK, which contributes to Australia's higher burden of melanoma. The magnitude of associations for naevus phenotype with melanoma risk was similar for both populations when based on self‐reported measures but differed when based on clinically assessed number of naevi. Personal perceptions of naevus number also differed by country. Self‐reported naevus count density is a consistent and strong risk factor across populations and is suitable for stratifying levels of melanoma risk; however, caution is needed when meta‐analysing data from different countries or when inferring absolute naevus counts from these categories. Classifying people at high risk of melanoma based on their number of naevi should ideally take into account their country of residence, type of counts (clinical or self‐reported), body site on which the naevus counts are measured and sex.
Acknowledgements
We gratefully acknowledge all of the participants, the work and dedication of the research coordinators, interviewers, examiners and data management staff. Emma Northwood assisted with the harmonization of data across the studies. For the Australian Melanoma Family Study, this included Judith Maskiell, Jackie Arbuckle, Steven Columbus, Michaela Lang, Helen Rodais, Caroline Ellis (The University of Melbourne, Melbourne, Australia); Carol El Hayek, Lynne Morgan, Joanne Roland, Emma Tyler, Jodi Barton, Caroline Watts, Lesley Porter (Westmead Institute for Medical Research, The University of Sydney, Sydney, Australia); Jodie Jetann, Megan Ferguson, Michelle Hillcoat, Kellie Holland, Pamela Saunders, Joan Roberts and Sheree Tait (Viertel Centre for Research in Cancer Control, Cancer Council Queensland, Brisbane, Australia); Anil Kurien, Clare Patterson, Caroline Thoo, Sally de Zwaan, Angelo Sklavos, Shobhan Manoharan, Jenny Cahill and Sarah Brennand (skin examiners). In the Leeds Melanoma Study, recruitment was facilitated by the UK National Cancer Research Network. Patricia Mack and Kate Gamble collected data for the studies. Paul King carried out data entry. We are extremely grateful to Birute Karpavicius, Susan Leake, Susan Haynes, Elaine Fitzgibbon, and the many clinicians and research staff who assisted with recruiting participants to the studies, and to the pathologists who assisted with the melanoma samples. David Espinoza, Serigne Lo, Yu‐mei Chang, Caro Badcock and May Chan provided assistance with data and/or statistical analysis.
Supporting Information
Filename Description
jdv15680-sup-0001-Supinfo.docxWord document, 46.1 KB
Data S1 Creation of a pigmentation score using factor analysis.
Table S1 A. Spearman rank correlations between pigmentary phenotype variables. B. Factor analysis loadings, derived from controls, for creation of a pigmentation score variable including hair colour. C. Subsequent factor analysis excluding hair colour. One factor was retained (pigmentation score), which explained 42% of the variance.
Table S2 Association of clinically‐assessed naevus phenotype with melanoma risk in the Leeds case‐control study, stratified by age ≤ 40, >40 years.
Table S3 Association of naevus phenotype with melanoma risk in the Australian Melanoma Family Study and Leeds case‐control study, stratified by sex.
Table S4 Associations of naevus phenotype with melanoma risk in the Australian Melanoma Family Study and Leeds case‐control study, stratified by pigmentation score and hair colour.
Table S5 Association of pigmentation score with melanoma risk in the Australian Melanoma Family Study and Leeds case‐control study, stratified by hair colour.
Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.
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Associations of pigmentary and naevus phenotype with melanoma risk in two populations with comparable ancestry but contrasting levels of ambient sun exposure
A.E. Cust M. Drummond D.T. Bishop L. Azizi H. Schmid M.A. Jenkins J.L. Hopper B.K. Armstrong J.F. Aitken R.F. Kefford G.G. Giles F. Demenais A.M. Goldstein J.H. Barrett … See all authors
First published: 13 May 2019 https://doi.org/10.1111/jdv.15680
Funding sources This work was supported by the National Health and Medical Research Council of Australia (NHMRC) (project grants 566946, 107359, 211172 and program grant number 402761); the Cancer Council New South Wales (project grant 77/00, 06/10), the Cancer Council Victoria and the Cancer Council Queensland (project grant 371); the US National Institutes of Health (NIH RO1 grant CA83115 to Genomel (www.genomel.org)); Cancer Research UK (Project Grant C8216/A6129 and Programme awards C588/A4994 and C588/A10589); and in part by the Intramural Research Program of the National Cancer Institute, the National Institutes of Health, the Division of Cancer Epidemiology and Genetics. AE Cust received Career Development Fellowships from the National Health and Medical Research Council of Australia (NHMRC; #1147843) and Cancer Institute NSW (15/CDF/1‐14).
Conflicts of interest The authors declare that they have no conflict of interest.
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Abstract
Background
People at high risk of developing melanoma are usually identified by pigmentary and naevus phenotypes.
Objective
We examined whether associations of these phenotypes with melanoma risk differed by ambient sun exposure or participant characteristics in two population‐based, case–control studies with comparable ancestry but different ambient sun exposure.
Methods
Data were analysed from 616 cases and 496 controls from the Australian Melanoma Family Study and 2012 cases and 504 controls from the Leeds (UK) case–control study. Questionnaire, interview and dermatological skin examination data were collected using the same measurement protocols. Relative risks were estimated as odds ratios using unconditional logistic regression, adjusted for potential confounders.
Results
Hair and skin colour were the strongest pigmentary phenotype risk factors. All associations of pigmentary phenotype with melanoma risk were similar across countries. The median number of clinically assessed naevi was approximately three times higher in Australia than Leeds, but the relative risks for melanoma associated with each additional common or dysplastic naevus were higher for Leeds than Australia, especially for naevi on the upper and lower limbs. Higher naevus counts on the head and neck were associated with a stronger relative risk for melanoma for women than men. The two countries had similar relative risks for melanoma based on self‐reported naevus density categories, but personal perceptions of naevus number differed by country. There was no consistent evidence of interactions between phenotypes on risk.
Conclusions
Classifying people at high risk of melanoma based on their number of naevi should ideally take into account their country of residence, type of counts (clinical or self‐reported), body site on which the naevus counts are measured and sex. The presence of naevi may be a stronger indicator of a genetic predisposition in the UK than in Australia based on less opportunity for sun exposure to influence naevus development.
Introduction
Melanoma rates have been increasing,1 despite being a largely preventable disease.2 People at high risk of developing melanoma are often identified by pigmentary and naevus phenotypes. Melanocytic naevi predominantly originate in childhood, and their development is influenced by sun exposure and genetic factors.3-7 A person's number of naevi may change over time with age and sun exposure,5, 8-11 which could contribute to different magnitudes of association between naevi and melanoma risk by region, age or sex.
Although pigmentary and naevus phenotypes are established risk factors for melanoma, the magnitude of these associations may differ by geographical region, participant characteristics and study methodology.12-15 One previous clinic‐based study with 300 cases and 325 controls suggested that atypical naevi were a stronger risk factor for melanoma in the United Kingdom (UK) than in Australia9; however, few population‐based data are available. We also have limited understanding of possible interactions between different risk factors.12, 13
To address these knowledge gaps and overcome limitations of previous studies, we examined the association of pigmentary and naevus phenotype with melanoma risk in two large, population‐based case–control studies under the auspices of the melanoma genetics consortium (GenoMEL, www.genomel.org). The studies were conducted using the same measurement protocols implemented as far as feasible in an identical manner and conducted in populations with similar ethnic backgrounds (Australia and the UK) but very different ambient sun exposure.
Materials and methods
Study samples
The Australian Melanoma Family Study was a multi‐centre, population‐based, case–control family study of invasive cutaneous melanoma diagnosed between ages 18–39 years. The study design, recruitment, data collection and participant characteristics have been described.16 Recruitment of case (n = 629) and control (n = 535) participants was locally coordinated in Sydney, Melbourne, and Brisbane, Australia. Cases were identified from population‐based state cancer registries, diagnosed between 1st July 2000 and 31st December 2002 at ages 18–39 years with incident, histopathologically confirmed, first primary invasive cutaneous melanoma. Participation was 76% of those contactable. Population controls were aged between 18–39 years at the time of approach and had no history of invasive or in situ melanoma. They were selected from the electoral roll (registration to vote is compulsory for Australians aged ≥18 years) and were frequency‐matched to cases by city, age and sex. Participation was 42% of those contactable. In addition, spouse/partner or friend controls were recruited through nomination by a case. They had to be at least 18 years of age and have no history of invasive or in situ melanoma; there were no other age, sex or residency restrictions. A spouse or friend was nominated as a potential control subject by 59% of cases, and participation was 80% of those nominated.
The Leeds case–control study recruited population‐based incident histopathologically confirmed invasive melanoma cases (n = 2184), aged between 18–82 years, and living in a geographically defined area of Yorkshire and the Northern region of the UK.17, 18 The cases were identified through clinicians, pathology registers and the cancer registry to ensure maximal ascertainment (67% participation). Between September 2000 and June 2003, all people with invasive melanoma were invited to participate. From July 2003 to September 2011, only cases with Breslow thickness ≥0.75 mm were invited, in order to enrich the cohort to observe clinical outcomes. Population‐ascertained controls were identified by the cases' family doctors as not having cancer and were randomly invited from individuals who were matched by sex and age (55% participation, 513 recruited).
Approval for the study was obtained from the ethics committees of the coordinating centres in Australia and Leeds and the cancer registries. All participants provided written‐informed consent.
Self‐reported pigmentary phenotype and naevus counts
Participants completed a questionnaire in which they reported skin colour, eye colour, natural hair colour at age 18, freckling as a child and adult,19 ability to tan, propensity to sunburn, usual tanning and sunburn response to prolonged or repeated exposure of skin to sunlight in summer, number of naevi covering their body (described pictorially as none, few, some, many) and were asked to have someone count the number of all moles on their back (using picture guides).16
We created a pigmentation score using factor analysis: this contained skin colour, eye colour, childhood freckling and skin phototype (see Data S1).
Clinical assessment of pigmentary and naevus phenotype for a subset of participants
Clinical assessment was conducted by research nurses in the UK and by dermatology trainees in Australia. Assessors were jointly trained on the study protocol including recognizing and counting naevi according to international guidelines,20 and annual refresher courses were conducted jointly. Melanocytic naevi were defined as brown‐to‐black pigmented macules or papules which were reasonably well defined and darker in colour than the surrounding skin. Dysplastic (atypical) naevi were defined as having a macular component in at least one area in addition to at least three of the following: (i) ill‐defined border, (ii) size ≥5 mm, (iii) variegated colour, (iv) uneven outline and (v) erythema.20 Participants removed their clothing except for underpants and bra. Separate counts were made for melanocytic naevi of 2‐<5 mm and ≥5 mm, and clinically atypical naevi on different body sites but excluding scalp, breasts, buttocks and genitals. Naevi <2 mm were not counted to minimize confusion with freckles and lentigines.
Clinical assessment of naevi was completed for the first 1022 cases, and all population controls in Leeds, and by 73% of cases, 55% of population controls, and 67% of spouse or friend controls in Australia. Natural hair colour at age 18 and eye colour were also recorded using wig hair swatches and eye photographs for comparison.
Self‐reported personal sun exposure
Comprehensive data on sun exposure throughout life were collected by telephone interview, with the aid of a residence calendar. Questions referred to the frequency of sunburn and time spent outdoors between 9 am and 5 pm separately for weekdays, weekends and holidays in warmer months and in cooler months.18, 21
Statistical analysis
We excluded: relatives, participants who did not complete a questionnaire, Australian controls who were ≥45 years at interview (since all Australian cases were diagnosed <40 years), and participants missing either three or more key pigmentary phenotype variables, self‐report naevus density, summer holiday sun exposure or painful sunburn variables. The analysis dataset included 1112 participants (616 cases, 496 controls) from Australia and 2516 participants (2012 cases, 504 controls) from Leeds. Analyses of the associations between naevi and melanoma risk excluded participants with missing pigmentation score or hair colour, and analyses of the associations between clinically assessed naevus phenotype and melanoma risk were restricted to participants who had a clinical skin examination. Australian population controls (n = 237) and spouse/friend controls (n = 259) were combined into one control group for analysis, as done previously.16
Relative risks (RR) for melanoma were estimated as odds ratios (OR) and 95% confidence intervals (CI), using unconditional logistic regression. Minimally adjusted models included age (continuous), sex and city of recruitment (in Australia). Analyses of pigmentary phenotype were further adjusted for self‐reported naevus density, summer holiday sun exposure hours and painful sunburns; and analyses of naevus phenotype were further adjusted for pigmentation score and hair colour. We estimated the OR per standard deviation adjusted for age (5‐yr groups) and sex (OPERA method22) as a way of comparing the predictive strength of naevus number across Leeds and Australia while accounting for the countries' different naevus, age and sex distributions.
To test whether the pigmentary and naevus phenotype associations with melanoma differed between Leeds and Australia, or by other factors, we added to the models a product term between the phenotype variable and country (or other factor), fitted as a one degree‐of‐freedom ordinal variable to test for interaction in the trend effect. Data were analysed using SAS version 9.4 (SAS Institute, Cary NC), and statistical significance was inferred at two‐sided P <0.05. We reported the study according to STROBE guidelines for observational studies.
Results
Characteristics
Demographic characteristics are shown in Table 1. Both studies had a majority of female participants. They had a similar proportion of participants with self‐reported European ethnicity, but the Australian study had a higher proportion of Eastern European ethnic background. Excluding Eastern Europeans from our analyses did not materially impact results.
Table 1. Characteristics of cases and controls in the Australian Melanoma Family Study and Leeds case–control study
Characteristic
Australia
N (%)
Leeds
N (%)
Total, cases and controls 1112 2516
Cases 616 (55) 2,012 (80)
Controls 496 (45) 504 (20)
Sex
Female 663 (60) 1,438 (57)
Male 449 (40) 1,078 (43)
Age at diagnosis/interview (years) †
18–29 291 (26) 105 (4)
30–39 733 (66) 287 (11)
40–49 88 (8) 445 (18)
50–69 0 (0) 1,335 (53)
≥70 0 (0) 344 (14)
Ethnic background ‡
English 676 (61) 2,340 (93)
Scottish, Irish, Welsh 54 (5) 120 (5)
Other Northern European 49 (4) 14 (1)
Southern European 12 (1) 6 (0)
Eastern European 251 (23) 4 (0)
Mixed/Other European 20 (2) 28 (1)
Non‐European 49 (4) 0 (0)
Missing 1 4
†Leeds cases and controls were unselected for age at diagnosis. In Australia, all cases were <40 years at diagnosis and all population controls were <40 years when ascertained; cases and controls could be up to age 44 years at interview for this analysis.
‡Self‐reported.
Pigmentary phenotype and melanoma risk
The associations of self‐reported and clinically assessed pigmentary phenotype factors with melanoma risk were similar across countries (Table 2). Pigmentation score was associated with an approximately twofold increased melanoma risk for the highest vs. lowest tertile. Both studies observed a threefold to fourfold increased risk of melanoma for those with red hair, and a twofold increased risk for those with fair or blonde hair, compared to those with dark brown or black hair. Very fair skin more than doubled risk compared with having olive or brown skin. The results remained consistent in the analyses adjusted for other risk factors and excluding naevus count density from the multivariable models had minimal impact on the risk estimates.
Table 2. Association of pigmentary phenotype with melanoma risk in the Australian Melanoma Family Study and Leeds case–control study
Pigmentary phenotype† Australia (N = 1112) Leeds (N = 2516) P‐int¶
Case
N (%)
Control
N (%)
OR (95% CI)‡ Adjusted OR (95% CI)§
Case
N (%)
Control
N (%)
OR (95% CI)‡ Adjusted OR (95% CI)§
Hair colour
Dark brown/black 160 (26) 200 (40) 1.00 1.00 822 (41) 288 (57) 1.00 1.00 0.37
Light brown 244 (40) 208 (42) 1.41 (1.05, 1.88) 1.26 (0.93, 1.70) 537 (27) 119 (24) 1.62 (1.27, 2.06) 1.61 (1.25, 2.06)
Fair or Blonde 142 (23) 64 (13) 2.64 (1.82, 3.84) 2.38 (1.61, 3.51) 398 (20) 68 (13) 2.05 (1.54, 2.75) 1.99 (1.48, 2.69)
Red 70 (11) 24 (5) 3.41 (2.03, 5.75) 4.21 (2.44, 7.27) 253 (13) 29 (6) 3.10 (2.06, 4.66) 3.01 (1.99, 4.57)
Eye colour
Brown or Black 111 (18) 119 (24) 1.00 1.00 322 (16) 94 (19) 1.00 1.00 0.62
Green or Hazel 224 (36) 167 (34) 1.52 (1.08, 2.13) 1.35 (0.95, 1.93) 605 (30) 182 (36) 0.98 (0.73, 1.30) 0.97 (0.72, 1.30)
Blue or Grey 279 (45) 206 (42) 1.57 (1.13, 2.17) 1.49 (1.06, 2.09) 1075 (54) 228 (45) 1.40 (1.07, 1.84) 1.40 (1.06, 1.85)
Skin colour
Olive or Brown 62 (10) 92 (19) 1.00 1.00 149 (7) 60 (12) 1.00 1.00 0.30
Fair 430 (70) 336 (68) 1.92 (1.33, 2.76) 1.75 (1.20, 2.55) 1342 (67) 365 (72) 1.51 (1.09, 2.08) 1.48 (1.06, 2.08)
Very fair 119 (19) 67 (14) 2.33 (1.48, 3.66) 2.29 (1.42, 3.68) 519 (26) 79 (16) 2.63 (1.79, 3.86) 2.54 (1.71, 3.79)
Freckles in childhood
None 116 (19) 127 (26) 1.00 1.00 543 (28) 187 (37) 1.00 1.00 0.28
Very few 185 (30) 163 (33) 1.17 (0.83, 1.64) 1.17 (0.82, 1.66) 571 (29) 131 (26) 1.46 (1.13, 1.89) 1.33 (1.02, 1.73)
Few/Some 244 (40) 156 (32) 1.56 (1.12, 2.18) 1.69 (1.19, 2.39) 698 (35) 159 (32) 1.48 (1.15, 1.89) 1.35 (1.05, 1.75)
Many 69 (11) 48 (10) 1.41 (0.89, 2.24) 1.55 (0.95, 2.52) 161 (8) 26 (5) 2.11 (1.34, 3.31) 2.06 (1.30, 3.29)
Freckles in adulthood
None 160 (26) 156 (32) 1.00 1.00 654 (33) 209 (42) 1.00 1.00 0.38
Very few 238 (39) 200 (40) 1.07 (0.79, 1.45) 1.03 (0.75, 1.40) 630 (31) 132 (26) 1.51 (1.17, 1.93) 1.38 (1.07, 1.78)
Few/Some 175 (28) 119 (24) 1.25 (0.89, 1.75) 1.24 (0.87, 1.77) 609 (30) 144 (29) 1.35 (1.05, 1.73) 1.22 (0.94, 1.58)
Many 42 (7) 20 (4) 1.62 (0.89, 2.95) 1.67 (0.89, 3.13) 108 (5) 18 (4) 1.87 (1.10, 3.18) 1.83 (1.06, 3.16)
General skin reaction to sun (skin phototype)
Sometimes/Never Burns 290 (47) 292 (59) 1.00 1.00 1276 (64) 375 (75) 1.00 1.00 0.88
Usually/Always Burns 322 (53) 200 (41) 1.55 (1.21, 1.99) 1.60 (1.23, 2.09) 726 (36) 127 (25) 1.69 (1.36, 2.11) 1.62 (1.28, 2.04)
Ability to tan from repeated exposure
Moderate/Deep Tan 359 (59) 331 (67) 1.00 1.00 542 (53) 336 (67) 1.00 1.00 0.13
Mild/No Tan 254 (41) 162 (33) 1.32 (1.02, 1.70) 1.33 (1.01, 1.74) 484 (47) 163 (33) 1.85 (1.48, 2.33) 1.88 (1.48, 2.39)
Propensity to sunburn
Mild/No burn 222 (36) 242 (49) 1.00 1.00 606 (59) 343 (69) 1.00 1.00 0.44
Severe/Painful burn 392 (64) 252 (51) 1.68 (1.31, 2.16) 1.69 (1.30, 2.20) 418 (41) 157 (31) 1.51 (1.20, 1.89) 1.47 (1.16, 1.86)
Clinically assessed hair colour
Black/Brown 176 (39) 172 (57) 1.00 1.00 424 (68) 217 (84) 1.00 1.00 0.55
Fair/Blonde 195 (43) 109 (36) 1.59 (1.15, 2.20) 1.48 (1.05, 2.08) 102 (16) 25 (10) 2.09 (1.31, 3.34) 2.14 (1.31, 3.49)
Red 82 (18) 21 (7) 3.27 (1.90, 5.60) 3.58 (2.05, 6.27) 93 (15) 16 (6) 2.90 (1.66, 5.07) 3.19 (1.79, 5.66)
Clinically assessed eye colour
Brown 87 (19) 68 (23) 1.00 1.00 101 (11) 77 (15) 1.00 1.00 0.44
Green/Hazel 132 (29) 94 (31) 1.11 (0.72, 1.71) 1.06 (0.68, 1.65) 332 (35) 195 (39) 1.35 (0.95, 1.91) 1.31 (0.91, 1.88)
Blue/Grey 231 (51) 140 (46) 1.44 (0.97, 2.14) 1.43 (0.95, 2.16) 515 (54) 229 (46) 1.85 (1.31, 2.59) 1.83 (1.29, 2.60)
Pigmentation score ††
Tertile 1 120 (20) 153 (31) 1.00 1.00 483 (25) 190 (38) 1.00 1.00 0.32
Tertile 2 188 (31) 145 (30) 1.55 (1.10, 2.18) 1.57 (1.11, 2.22) 629 (32) 170 (34) 1.40 (1.10, 1.80) 1.36 (1.06, 1.75)
Tertile 3 296 (49) 188 (39) 1.83 (1.32, 2.52) 1.92 (1.38, 2.67) 839 (43) 141 (28) 2.40 (1.86, 3.10) 2.17 (1.67, 2.82)
CI, confidence interval; OR, odds ratio.
Missing data for each variable (N for Australia, N for Leeds): hair colour (0, 2), eye colour (6, 10), skin colour (6, 2), freckling child (4, 40), freckling adult (2, 12), skin reaction to sun (8, 12), skin often exposed (6, 991), skin exposed (4, 992), clinically assessed hair colour (357, 1639), clinically assessed eye colour (360, 1067) and pigmentation score (22, 64).
†Phenotype variables were based on self‐report unless specified as clinically assessed.
‡Minimally adjusted models adjusted for age (continuous), sex and city of recruitment in Australia.
§Further adjusted for self‐reported naevus density, summer holiday sun exposure hours per day and painful sunburns to 40 years of age.
¶P‐value for the interaction between pigmentary phenotype variable and country (Australia/Leeds) using minimally adjusted models.
††Based on factor analysis (see Supplementary online material). Tertile cut‐points were based on the combined Australia/Leeds control distributions.
Naevus phenotype and melanoma risk
Cases had a significantly higher median number of naevi than controls, and Australian participants had more naevi than Leeds participants, even after accounting for the different age distributions (Fig. 1). These differences were apparent for both self‐reported naevi (Table 3) and clinically assessed naevi (Table 4). Participants' perceptions of their own naevus density, when compared to clinical counts, differed by country and disease status (Fig. 2). For example, Leeds control participants who self‐reported 'many' naevi had a median of 34 naevi ≥2 mm diameter (interquartile range (IQR) 24–56), which was similar to Australian control participants who self‐reported 'none' (median 35, IQR 11–58). Nevertheless, the relative risks for melanoma associated with a higher self‐reported naevus density category were similar for Australia and Leeds (Table 3). Compared with those who self‐reported no naevi, those with 'some' naevi had an approximately threefold higher risk, and those with 'many' naevi had an approximately fivefold increased risk.
image
Figure 1
Open in figure viewerPowerPoint
The distribution of clinically measured whole‐body naevus counts ≥2 mm for Australian cases, Australian controls, Leeds cases, Leeds controls, stratified by age ≤40, >40 years. The x‐axis represents the number of clinically assessed naevi, and the y‐axis represents the proportion of participants.
Table 3. Association of self‐reported naevus phenotype with melanoma risk in the Australian Melanoma Family Study and Leeds case–control study
Naevi Australia (N = 1093) Leeds (N = 2479) P‐int§
Case
N (%) or median (IQR)
Control
N (%) or median (IQR)
OR (95% CI)† Adjusted OR (95% CI)‡
Case
N (%) or median (IQR)
Control
N (%) or median (IQR)
OR (95% CI)† Adjusted OR (95% CI)‡
Self‐reported naevi
None 21 (3) 40 (8) 1.00 1.00 176 (9) 97 (19) 1.00 1.00 0.44
Few 172 (28) 237 (49) 1.30 (0.73, 2.32) 1.42 (0.78, 2.59) 771 (39) 252 (50) 1.67 (1.26, 2.23) 1.75 (1.30, 2.35)
Some 264 (44) 158 (33) 3.05 (1.71, 5.45) 3.44 (1.88, 6.29) 718 (37) 118 (24) 3.29 (2.37, 4.56) 3.54 (2.53, 4.96)
Many 147 (24) 51 (10) 5.17 (2.75, 9.72) 5.71 (2.96, 11.02) 296 (15) 34 (7) 4.67 (3.00, 7.29) 4.82 (3.07, 7.58)
Self‐reported naevi on the back ¶
Quartiles
Quartile 1 (AMFS: 0–3; Leeds: 0–0) 96 (17) 136 (29) 1.00 1.00 300 (16) 123 (26) 1.00 1.00 0.82
Quartile 2 (AMFS: 4–8; Leeds: 1–3) 113 (19) 107 (23) 1.46 (0.99, 2.14) 1.52 (1.03, 2.26) 356 (19) 118 (25) 1.22 (0.91, 1.64) 1.23 (0.91, 1.67)
Quartile 3 (AMFS: 9–19; Leeds: 4–10) 150 (26) 112 (24) 1.75 (1.21, 2.53) 1.85 (1.26, 2.71) 536 (29) 125 (27) 1.70 (1.27, 2.27) 1.81 (1.34, 2.43)
Quartile 4 (AMFS: >=20; Leeds: >=11) 221 (38) 114 (24) 2.63 (1.85, 3.76) 2.79 (1.93, 4.03) 677 (36) 104 (22) 2.49 (1.83, 3.39) 2.71 (1.98, 3.71)
Continuous variables
Median (IQR) & OR per 1 naevi†† 13 (6, 29) 8 (3, 19) 1.02 (1.01, 1.03) 1.02 (1.01, 1.03) 6 (2, 17) 3 (0, 10) 1.02 (1.01, 1.03) 1.02 (1.01, 1.03) 0.41
OR per adjusted SD increase in naevi‡‡ 1.47 (1.27, 1.71) 1.50 (1.29, 1.74) 1.38 (1.19, 1.60) 1.42 (1.22, 1.65) 0.37
OR, odds ratio; CI, confidence interval; IQR, interquartile range; SD, standard deviation.
Missing data for each variable (N for Australia, N for Leeds): naevus density (3, 17), naevi on back (44, 140).
†Minimally adjusted models adjusted for age (continuous), sex, and city of recruitment in Australia.
‡Further adjusted for pigmentation score and hair colour.
§P‐value for the interaction between naevus phenotype and country (Australia/Leeds) using minimally adjusted models.
¶Quartile cut‐points were based on the country‐specific control distributions.
††OR per 1‐unit increase in naevus count modelled as a continuous variable.
‡‡OR per adjusted standard deviation, stratified by country (Australia/Leeds) and adjusted for age (5‐year groups) and sex, using the OPERA method.22
Table 4. Association of clinically assessed naevus phenotype with melanoma risk in the Australian Melanoma Family Study and Leeds case–control study
Naevi Australia (N = 740) Leeds (N = 1450) P‐int§
Case
N (%) or median (IQR)
Control
N (%) or median (IQR)
OR (95% CI)† Adjusted OR (95% CI)‡
Case
N (%) or median (IQR)
Control
N (%) or median (IQR)
OR (95% CI)† Adjusted OR (95% CI)‡
Naevi on the whole body ≥ 2 mm
Categories
0–15 6 (1) 36 (12) 1.00 1.00 193 (20) 258 (52) 1.00 1.00 0.97¶
16–40 20 (5) 56 (19) 2.08 (0.74, 5.85) 1.44 (0.50, 4.19) 291 (31) 163 (33) 2.39 (1.82, 3.14) 2.48 (1.88, 3.29)
41–60 33 (7) 42 (14) 5.02 (1.83, 13.76) 4.97 (1.79, 13.80) 148 (16) 40 (8) 4.91 (3.27, 7.37) 5.29 (3.49, 8.02)
61–80 22 (5) 28 (9) 5.11 (1.75, 14.96) 4.75 (1.60, 14.13) 81 (9) 21 (4) 5.14 (3.05, 8.66) 5.40 (3.17, 9.20)
81–100 21 (5) 17 (6) 9.06 (2.93, 27.98) 6.83 (2.15, 21.71) 66 (7) 5 (1) 17.47 (6.86, 44.47) 16.44 (6.42, 42.08)
101–200 119 (27) 70 (24) 12.65 (4.86, 32.90) 10.91 (4.15, 28.70) 136 (14) 12 (2) 15.05 (8.03, 28.20) 14.84 (7.87, 28.01)
≥201 222 (50) 48 (16) 35.98 (13.65, 94.82) 31.36 (11.75, 83.67) 36 (4) 0 (0) n/a n/a
Quartiles
Q1 (AMFS: 0–29; Leeds: 0–7) 19 (4) 73 (25) 1.00 1.00 75 (8) 134 (27) 1.00 1.00 0.12
Q2 (AMFS: 30–69; Leeds: 8–15) 56 (13) 76 (26) 3.29 (1.73, 6.25) 4.15 (2.10, 8.18) 118 (12) 124 (25) 1.68 (1.15, 2.46) 1.90 (1.28, 2.82)
Q3 (AMFS: 70–155; Leeds: 16–29) 94 (21) 73 (25) 6.45 (3.42, 12.18) 7.36 (3.76, 14.42) 185 (19) 121 (24) 2.72 (1.88, 3.93) 3.03 (2.07, 4.43)
Q4 (AMFS: >155; Leeds: >29) 274 (62) 75 (25) 20.10 (10.68, 37.83) 22.79 (11.65, 44.57) 573 (60) 120 (24) 8.37 (5.85, 11.98) 9.33 (6.43, 13.54)
Continuous variables
Median (IQR) & OR per 1 naevi ††
Whole‐body naevi 201 (106, 308) 68 (30, 157) 1.01 (1.01, 1.01) 1.01 (1.01, 1.01) 40 (19, 81) 15 (7, 29) 1.03 (1.02, 1.03) 1.03 (1.02, 1.03) <.0001
Head and neck naevi 14 (6, 24) 6 (2, 13) 1.06 (1.04, 1.08) 1.07 (1.05, 1.09) 3 (1, 6) 1 (1, 3) 1.15 (1.11, 1.20) 1.16 (1.11, 1.20) 0.0010
Trunk naevi 45 (23, 74) 18 (7, 43) 1.02 (1.02, 1.03) 1.02 (1.02, 1.03) 10 (4, 24) 5 (2, 11) 1.04 (1.03, 1.05) 1.04 (1.03, 1.06) 0.0003
Upper limbs naevi 76 (36, 121) 27 (12, 57) 1.02 (1.01, 1.02) 1.02 (1.01, 1.02) 11 (4, 22) 3 (1, 8) 1.09 (1.07, 1.11) 1.09 (1.07, 1.11) <.0001
Lower limbs naevi 53 (26, 91) 16 (5, 41) 1.02 (1.01, 1.02) 1.02 (1.01, 1.02) 10 (4, 25) 3 (1, 7) 1.07 (1.05, 1.08) 1.06 (1.05, 1.08) <.0001
OR per adjusted SD increase in naevi ‡‡
Whole‐body naevi 2.62 (2.08, 3.30) 2.60 (2.06, 3.29) 3.09 (2.50, 3.81) 3.07 (2.48, 3.81) 0.20
Head and neck naevi 1.84 (1.50, 2.25) 1.99 (1.60, 2.47) 1.60 (1.39, 1.84) 1.62 (1.41, 1.87) 0.16
Trunk naevi 2.26 (1.82, 2.79) 2.36 (1.89, 2.94) 1.93 (1.63, 2.29) 2.05 (1.71, 2.44) 0.29
Upper limbs naevi 2.50 (2.00, 3.14) 2.58 (2.04, 3.27) 3.18 (2.56, 3.96) 3.20 (2.56, 3.98) 0.06
Lower limbs naevi 2.45 (1.93, 3.12) 2.26 (1.77, 2.89) 3.39 (2.64, 4.34) 3.21 (2.50, 4.11) 0.04
Dysplastic naevi
Categories
0 245 (55) 229 (77) 1.00 1.00 689 (72) 458 (92) 1.00 1.00 0.04
1 48 (11) 34 (11) 1.41 (0.86, 2.32) 1.34 (0.80, 2.24) 124 (13) 28 (6) 2.80 (1.82, 4.31) 2.71 (1.75, 4.19)
≥2 150 (34) 34 (11) 3.90 (2.54, 5.99) 4.06 (2.61, 6.30) 138 (15) 13 (3) 6.44 (3.57, 11.61) 6.03 (3.33, 10.92)
Continuous
OR per 1 dysplastic naevi 1.20 (1.11, 1.30) 1.20 (1.11, 1.30) 1.78 (1.47, 2.17) 1.74 (1.43, 2.11) <.0001
Naevi >5 mm
Categories
0 51 (12) 92 (34) 1.00 1.00 405 (46) 324 (68) 1.00 1.00 0.50
1–2 65 (15) 72 (27) 1.68 (1.02, 2.75) 1.72 (1.04, 2.85) 313 (35) 126 (26) 1.91 (1.48, 2.46) 1.83 (1.41, 2.37)
>2 306 (73) 104 (39) 5.46 (3.55, 8.40) 5.02 (3.23, 7.79) 164 (19) 30 (6) 4.25 (2.80, 6.46) 3.91 (2.56, 5.98)
Continuous
Whole‐body OR per 1 naevi >5 mm†† 9 (2, 22) 2 (0, 5) 1.06 (1.04, 1.08) 1.06 (1.04, 1.07) 1 (0, 2) 0 (0, 1) 1.28 (1.20, 1.38) 1.26 (1.18, 1.36) <.0001
Whole‐body OR per adjusted SD increase in naevi >5 mm‡‡ 2.01 (1.55, 2.60) 1.87 (1.43, 2.43) 1.86 (1.56, 2.23) 1.79 (1.49, 2.14) 0.93
CI, confidence interval; IQR, interquartile range; OR, odds ratio; SD, standard deviation.
Data were missing for participants who did not have a clinical skin examination (353 in Australia, 876 in Leeds). In addition, data were missing for Leeds for trunk (1), upper limbs (1) and lower limbs (7).
†Models adjusted for age (continuous), sex and city of recruitment in Australia.
‡Further adjusted for pigmentation score and hair colour.
§P‐value for the interaction between naevus phenotype and population (Australia/Leeds) using minimally adjusted models.
¶P‐value based on model excluding the top category
††OR per 1‐unit increase in naevus count modelled as a continuous variable.
‡‡OR per adjusted standard deviation, stratified by country (Australia/Leeds) and adjusted for age (5‐year groups) and sex, using the OPERA method.22
image
Figure 2
Open in figure viewerPowerPoint
Comparison of self‐reported and clinically measured naevus counts (≥2 mm) in the Australian Melanoma Family Study and Leeds case–control study. The bar graph plots the median clinically measured naevus counts (y‐axis) according to self‐reported naevus density category (none, few, some, many) (x‐axis), separately for cases and controls in Australia and Leeds.
Risk of melanoma increased sharply with increasing number of clinically assessed naevi (Table 4). The top category of >200 naevi could only be assessed in the Australian sample as 50% and 16% of Australian cases and controls, respectively, were in this category, compared with 4% and 0% of Leeds cases and controls (5% and 0% of Leeds cases and controls ≤40 years; Table S2). For both countries, fewer naevi occurred on the head and neck than on other body sites, but the OR for melanoma per additional naevus was higher for head and neck naevi. Based on the ORs per adjusted standard deviation increase in naevi, the upper and lower limbs were the body sites that were most predictive of melanoma risk for Leeds, and the upper and lower limbs and the trunk for Australia. The number of clinically assessed common naevi analysed on a continuous scale, and the presence and number of clinically assessed dysplastic naevi, was each associated with greater relative risks for melanoma in Leeds than in Australia (P‐interaction <0.05). This pattern of a higher relative risk of melanoma in Leeds was consistent for common naevi on different body sites when modelled as an OR per 1 naevus increase, and for naevi on the upper and lower limbs when modelled as an OR per adjusted standard deviation.
Since the Australian study recruited only participants aged <40 years, we further examined the naevi results for the Leeds sample in age‐stratified analysis (≤40, >40 years; Table S2). Leeds' participants aged ≤40 years had higher numbers of naevi than those aged > 40 years; the median total body count was 53 and 23 naevi for younger cases and controls, respectively, and 35 and 15 naevi for older cases and controls, respectively. This increase was more noticeable on the trunk, with 17 and 10 naevi for younger cases and controls, respectively, and 9 and 5 naevi for older cases and controls, respectively. Nevertheless, similar ORs between naevus counts and melanoma risk were observed for the Leeds study when stratified by age (≤40 years, >40 years) and there was no evidence of interaction by age (all P‐interaction values were ≥0.15; Table S2). In analyses stratified by sex (Table S3), we found that higher naevus counts on the head and neck were associated with a stronger relative risk for melanoma for women than men, and this was consistent across countries: in Australia the OR per adjusted SD increase in naevi was 2.19 (95% CI 1.63, 2.95) for women and 1.59 (95% CI 1.20, 2.11) for men (P‐interaction = 0.03), and in Leeds was 1.92 (1.57, 2.35) for women and 1.29 (1.06, 1.57) for men (P‐interaction =0.01).
Table S4 shows the association of naevi with melanoma risk, stratified by pigmentation score and hair colour. In Leeds, there was evidence of a stronger association between clinically assessed naevi and melanoma risk for participants with a sun‐sensitive phenotype, but the opposite was observed in Australia, whereby self‐reported naevi were a stronger risk factor for those with a sun‐resistant phenotype. Stratified by hair colour, the association of melanoma risk with clinically assessed naevi (Table S4) and sun‐sensitive pigmentation phenotype (Table S5) appeared stronger for participants with red hair, although the confidence intervals were wide. There was no evidence for interactions of dysplastic naevi with common naevi or hair colour on melanoma risk (data not shown).
Discussion
The findings from these two population‐based case–control studies, using the same measurement protocols and harmonized data, allow a direct comparison of the magnitude of associations of pigmentary and naevus phenotype with melanoma risk in two countries with similar ethnic background but vastly different ambient sun exposure.
The emergence of naevi is thought to be under strong genetic control, whereas sun exposure influences the mean number of naevi.7 As naevus measurement and training protocols were essentially the same across our Leeds and Australian studies and the samples had a similar genetic background,23 we can reasonably assume that the observed large (approximately threefold, age‐adjusted) differences in clinically assessed number of naevi are due to higher sun exposure in Australia than the UK. Similarly, the proportion of participants with one or more dysplastic naevi or with large naevi was also higher in Australia than Leeds. Bataille and colleagues' smaller, clinic‐based, cross‐country comparison of naevi recruited between 1989–1993 found about twofold greater number of common and dysplastic naevi in Australia than the UK.9
A potential limitation of our analysis was the different age structure between studies. There are limited prospective data on naevus counts over time, but it is thought that number of naevi may change with age or cohort effects, and we observed higher numbers of naevi for Leeds' participants aged ≤40 years than for those aged >40 years. We addressed this in several ways. Firstly, we adjusted all analyses for age. Secondly, we conducted sensitivity analyses stratified by age group (≤40, >40 years); this still showed 3.8 times higher common naevus counts ≥2 mm for Australian cases and 3 times higher for Australian controls, and that the associations of naevi with melanoma risk was similar for younger and older age groups in Leeds. Finally, we also estimated the OR for melanoma per adjusted standard deviation of naevus counts as a way of comparing the predictive strength of this risk factor across the two countries while accounting for the different naevus, age and sex distributions.22 Another limitation was the potential bias from the targeted selection of thicker melanomas in the later years of recruitment in the Leeds group. People with thicker melanomas tended to have fewer naevi, but this is also confounded with age, as older people were more likely to have thicker melanomas and fewer naevi.
Interestingly, participants' perceptions of their own naevus density (using the self‐reported naevus categories), when compared to clinical counts, differed by country and disease status. This indicates that people may report their own naevus phenotype based on how it compares with the 'norm' for their peers. Thus, self‐reported naevus density categories should not be used to infer the same absolute naevus counts across different populations.
A meta‐analysis of 49 studies13 estimated that the RR for melanoma was 1.02 (95% CI 1.01–1.02) for each additional common naevus, and for people with ≥1 atypical naevi the summary RR was 3.63 (95% CI 2.85–4.62) compared to no atypical naevi. These summary estimates fall in the middle of the estimates for Australia and Leeds; based on absolute counts measured clinically, the relative risk of melanoma 'per naevus' was greater in Leeds than in Australia. However, the relative risks for melanoma were similar for the two countries when using the self‐reported naevus categories because the reference group reflected different absolute numbers of naevi in Leeds and Australia. The higher relative risk for melanoma 'per naevus' (based on clinical counts) in Leeds indicate that naevi may be a stronger indicator of a genetic predisposition in the UK based on less opportunity for sun exposure to influence naevus development. A previous pooled analysis found that relative risks for melanoma were fairly similar across latitudes and age groups analysed using study‐specific quantiles.24
Calculating the population attributable fraction (PAF)13 from our study indicates that 64% of cases in Australia and 16% of cases in Leeds were attributable to having >100 naevi. Olsen and colleagues' meta‐analysis13 concluded that patients with ≥25 common naevi and/or ≥1 atypical naevi should be managed as high risk since almost half of melanomas occurred in this group.13 In our study, 97% of melanoma cases and 81% of controls from Australia, and 70% of cases and 34% of controls from Leeds met this high‐risk criteria. It may not be practicable or cost‐effective to apply the same high‐risk naevus count criteria to different countries, and it is important to also take into account other risk factors.25
The upper and lower limbs were the body sites that were most predictive of melanoma risk for Leeds, and for Australia the most predictive sites were the upper and lower limbs and the trunk, based on the ORs per adjusted standard deviation22 increase in naevi. We observed that higher naevus counts on the head and neck were associated with a stronger relative risk for melanoma for women than men, whereas Ribero and colleagues found that men had a higher relative risk for melanoma associated with naevi on the legs, arms and head and neck.26
Our relative risk estimates for the associations of pigmentary phenotype factors with melanoma risk for Australia and Leeds were consistent with a previous meta‐analysis.12 Based on our findings, the population attributable fraction for red hair colour was 9% in Australia and Leeds, and for very fair skin was 11% and 16%, respectively. The PAFs calculated in the meta‐analysis from weighted averages across the studies were 10% for red hair and 10% for very fair skin.12
Some studies have observed super‐multiplicative joint effects of naevi and red hair colour on melanoma risk.8, 27 There was some suggestion of similar effect modification in our study between naevi and hair colour or pigmentation score, but the findings were not always consistent. Our results suggest that, in most cases, pigmentary and naevus risk factors act independently of each other.
In conclusion, hair and skin colour were the strongest pigmentary phenotype risk factors, and all associations of pigmentary phenotype with melanoma risk were similar across countries. On average, Australians have about three times as many naevi as those living in the UK, which contributes to Australia's higher burden of melanoma. The magnitude of associations for naevus phenotype with melanoma risk was similar for both populations when based on self‐reported measures but differed when based on clinically assessed number of naevi. Personal perceptions of naevus number also differed by country. Self‐reported naevus count density is a consistent and strong risk factor across populations and is suitable for stratifying levels of melanoma risk; however, caution is needed when meta‐analysing data from different countries or when inferring absolute naevus counts from these categories. Classifying people at high risk of melanoma based on their number of naevi should ideally take into account their country of residence, type of counts (clinical or self‐reported), body site on which the naevus counts are measured and sex.
Acknowledgements
We gratefully acknowledge all of the participants, the work and dedication of the research coordinators, interviewers, examiners and data management staff. Emma Northwood assisted with the harmonization of data across the studies. For the Australian Melanoma Family Study, this included Judith Maskiell, Jackie Arbuckle, Steven Columbus, Michaela Lang, Helen Rodais, Caroline Ellis (The University of Melbourne, Melbourne, Australia); Carol El Hayek, Lynne Morgan, Joanne Roland, Emma Tyler, Jodi Barton, Caroline Watts, Lesley Porter (Westmead Institute for Medical Research, The University of Sydney, Sydney, Australia); Jodie Jetann, Megan Ferguson, Michelle Hillcoat, Kellie Holland, Pamela Saunders, Joan Roberts and Sheree Tait (Viertel Centre for Research in Cancer Control, Cancer Council Queensland, Brisbane, Australia); Anil Kurien, Clare Patterson, Caroline Thoo, Sally de Zwaan, Angelo Sklavos, Shobhan Manoharan, Jenny Cahill and Sarah Brennand (skin examiners). In the Leeds Melanoma Study, recruitment was facilitated by the UK National Cancer Research Network. Patricia Mack and Kate Gamble collected data for the studies. Paul King carried out data entry. We are extremely grateful to Birute Karpavicius, Susan Leake, Susan Haynes, Elaine Fitzgibbon, and the many clinicians and research staff who assisted with recruiting participants to the studies, and to the pathologists who assisted with the melanoma samples. David Espinoza, Serigne Lo, Yu‐mei Chang, Caro Badcock and May Chan provided assistance with data and/or statistical analysis.
Supporting Information
Filename Description
jdv15680-sup-0001-Supinfo.docxWord document, 46.1 KB
Data S1 Creation of a pigmentation score using factor analysis.
Table S1 A. Spearman rank correlations between pigmentary phenotype variables. B. Factor analysis loadings, derived from controls, for creation of a pigmentation score variable including hair colour. C. Subsequent factor analysis excluding hair colour. One factor was retained (pigmentation score), which explained 42% of the variance.
Table S2 Association of clinically‐assessed naevus phenotype with melanoma risk in the Leeds case‐control study, stratified by age ≤ 40, >40 years.
Table S3 Association of naevus phenotype with melanoma risk in the Australian Melanoma Family Study and Leeds case‐control study, stratified by sex.
Table S4 Associations of naevus phenotype with melanoma risk in the Australian Melanoma Family Study and Leeds case‐control study, stratified by pigmentation score and hair colour.
Table S5 Association of pigmentation score with melanoma risk in the Australian Melanoma Family Study and Leeds case‐control study, stratified by hair colour.
Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.
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Alexandros Sfakianakis
Anapafseos 5 . Agios Nikolaos
Crete.Greece.72100
2841026182
Anapafseos 5 . Agios Nikolaos
Crete.Greece.72100
2841026182
6948891480
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