This paper is in the following e-collection/theme issue:

Skin Cancer and Melanoma Prevention (110) Cancer Epidemiology, Cancer Surveillance and Infodemiology (68) Epidemiology in Dermatology (18) Epidemiology (137) Descriptive Epidemiology and Population Size Estimates (87) Smoking Cessation (521) Obesity and Nutrition as Public Health Problem (281) Alcohol Abuse, Alcoholism Prevention and Recovery (286)

Published on in Vol 4, No 2 (2021): Jul-Dec

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/31275, first published .
Global Melanoma Correlations With Obesity, Smoking, and Alcohol Consumption

Global Melanoma Correlations With Obesity, Smoking, and Alcohol Consumption

Global Melanoma Correlations With Obesity, Smoking, and Alcohol Consumption

Authors of this article:

Nisha Batta1 Author Orcid Image ;   Sarah Shangraw1 Author Orcid Image ;   Andrew Nicklawsky2 Author Orcid Image ;   Takeshi Yamauchi1 Author Orcid Image ;   Zili Zhai1 Author Orcid Image ;   Dinoop Ravindran Menon1 Author Orcid Image ;   Dexiang Gao2 Author Orcid Image ;   Robert P Dellavalle1, 3, 4 Author Orcid Image ;   Mayumi Fujita1, 3, 5 Author Orcid Image
Article Authors Cited by (3) Tweetations (6) Metrics

    Research Letter

    1Department of Dermatology, University of Colorado Anschutz Medical Campus, Aurora, CO, United States

    2Department of Biostatistics and Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO, United States

    3Rocky Mountain Regional Veterans Affairs Medical Center, Aurora, CO, United States

    4Department of Epidemiology, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, CO, United States

    5Department of Immunology and Microbiology, University of Colorado Anschutz Medical Campus, Aurora, CO, United States

    *these authors contributed equally

    Corresponding Author:

    Mayumi Fujita, MD, PhD

    Department of Dermatology

    University of Colorado Anschutz Medical Campus

    12801 E. 17th Avenue, RC-1 South, Rm 4124

    Aurora, CO, 80045

    United States

    Phone: 1 303 724 4045

    Email: mayumi.fujita@cuanschutz.edu



    The incidence of melanoma has continued to rise over the last few decades [1]. Although many explanations have been posited, such as increased screening, detection, and UV exposure [2], it is essential to examine non-UV–related risk factors contributing to its continued rise.

    We reviewed published data on obesity, smoking, and alcohol consumption trends worldwide to understand human behaviors and their relationship to melanoma. We collected data for the three risk factors from the Global Health Observatory (GHO), World Health Organization (WHO), published in 2010, as these were the most comprehensive available data with minimal changes in trends noted in the following years. We also collected data for melanoma incidence and mortality from the Global Cancer Observatory (GCO), WHO, published in 2018, as they were the most currently available data. Compiled data were displayed using choropleth maps with color gradients to visualize variations across geographic areas (Figure 1A). Subsequently, each country’s data were plotted, and Spearman rank correlation coefficient (R) was calculated for melanoma incidence and mortality with each risk factor. The choropleth map of each risk factor showed similar patterns to melanoma incidence and melanoma mortality. The statistical analysis depicted a positive correlation (with a positive R) between melanoma incidence/mortality and all risk factors (obesity, smoking, and alcohol consumption). Among them, alcohol consumption showed the strongest positive correlation with both melanoma incidence (R=0.72; P<.001) and mortality (R=0.59; P<.001). Because individuals with lighter skin color (eg, Caucasians) have a higher melanoma incidence, this correlation data might implicate that alcohol consumption is high in countries with lighter skin color, such as European ancestry. To address whether the correlation between alcohol consumption and melanoma incidence is skin color dependent or independent, we reanalyzed the data by continent (Figure 1B). A positive correlation still existed between alcohol consumption and melanoma incidence in Europe, Asia, and Africa (Multimedia Appendix 1, Supplementary Table S1). In particular, the strongest correlation (R=0.68; P<.001) was observed in European countries with exclusively lighter skin color (1-12 or 12-14 of skin color numbers, per a human skin color distribution map in the second figure by Barsh [3]), suggesting that the correlation between alcohol consumption and melanoma incidence is likely to be skin color independent. A positive correlation was also observed between alcohol consumption and melanoma mortality in all continents.

    To understand how genetic risk factors have a role in the observed correlation between alcohol consumption and melanoma incidence/mortality, we examined the correlation of aldehyde dehydrogenase 2 (ALDH2) rs671 polymorphism with both alcohol consumption and melanoma outcomes. ALDH2 is an alcohol-metabolizing enzyme, and its allelic variation affects alcohol detoxification [4]. The correlation analysis showed that it was the wild-type ALDH2 allele that was strongly positively correlated with melanoma incidence (R=0.70; P<.001) and mortality (R=0.74; P<.001; Table 1). On the other hand, the allelic variants had a modest to strong negative correlation with melanoma incidence (R=–0.70 to –0.51; P<.001) and mortality (R=–0.73 to –0.57; P<.001). Possible explanations for the opposing effect of ALDH2 polymorphism include that individuals with risk alleles consume less alcohol than wild-type individuals, which is consistent with our data showing a positive and a negative correlation to alcohol consumption in wild-type individuals and allelic variants, respectively.

    Overall, our findings highlight an association between alcohol and melanoma outcomes globally. The association was observed not only with melanoma incidence but also with its mortality. We also found a potential involvement of the alcohol-related gene ALDH2. Limitations of our analyses include unavailability of the population statistics for some risk factors by some countries, binary questionnaire of alcohol use without reflecting the quantity of alcohol consumption, and country-based analysis rather than individualized data. To determine whether our cross-country data support individual-level conclusions at individual levels, individual-level studies, such as a recent one [5], need to be conducted. Furthermore, our findings do not necessarily indicate causation from alcohol, and other factors might be involved, including skin/hair color, ethnicity, geography, economy, and lifestyle. Further investigation is warranted to verify these associations at individual levels and elucidate alcohol’s effects on melanoma outcomes by eliminating potential confounding factors such as skin/hair color genotypes.

    ‎
    Figure 1. Global maps and scatter plots of melanoma burden and potential lifestyle factors. A: Correlation of melanoma outcomes and 3 lifestyle factors. Melanoma incidence (2018) is the number of new melanoma cases in 2018, including both sexes and all ages, and expressed as a rate per 100,000 persons per year. Melanoma mortality (2018) is the number of deaths due to melanoma in 2018, including both sexes and all ages, and expressed as a rate per 100,000 persons per year. Obesity prevalence (2010) refers to the percentage of obesity among adults (20+ years, both sexes) with a BMI of 30 kg/m2 or higher in 2010. Smoking prevalence (2010) refers to the percentage of men and women ages 15 and older who smoked any tobacco product daily or nondaily in 2010. Smokeless tobacco use is excluded and not available from the original data source. Alcohol consumption (2010) refers to the proportion of adults (15+ years, both sexes) who consumed any alcohol in 2010. Sources: Global Cancer Observatory (melanoma incidence and mortality) and Global Health Observatory (obesity prevalence, smoking prevalence, and alcohol consumption) from World Health Organization. The choropleth maps were created with MapChart. Scatter plots of each country’s metrics were created to visualize the distributions of lifestyle factors and outcomes. The left panel of scatter plots: correlation of melanoma incidence with obesity prevalence (n=174), smoking prevalence (n=140), and alcohol consumption (n=175). The right panel of scatter plots: correlation of melanoma mortality with obesity prevalence (n=174), smoking prevalence (n=140), and alcohol consumption (n=175). The original data set included the following number of countries: melanoma incidence and mortality (n=185), obesity prevalence (n=174), smoking prevalence (n=140), and alcohol consumption (n=190). B: Correlation of alcohol consumption and melanoma outcome by continent. Figure 1A data were reanalyzed by grouping countries into continents: Africa (n=55), Americas (n=39), Asia (n=48), Europe (n=40), and Oceania (n=17; Multimedia Appendix 1, Supplementary Table S1). Each symbol represents an individual country. Hashes on each axis are included to assist in visualizing the distribution of each variable. Spearman rank coefficient was used to assess correlation due to skewed data and the influence of outliers. P values were reported based on a null hypothesis of no monotonic association against a two-sided alternative at the .05 level. The statistical analysis was conducted using R version 4.0.2.
    View this figure
    Table 1. Correlations between melanoma outcomes and ALDH2 alleles in 23 countries.a
    VariableCorrelation with

    		Melanoma incidenceP valueMelanoma mortalityP valueAlcohol consumptionP value
    Genetic alleles

    		ALDH2b *1/*10.70<.0010.74<.0010.39.07

    		ALDH2 *1/*2–0.70<.001–0.73<.001–0.38.07

    		ALDH2 *2/*2–0.51.01–0.57.005–0.25.26
    Alcohol consumption0.79<.0010.71<.001N/AcN/A

    aThe source of melanoma incidence (2018), melanoma mortality (2018), and alcohol consumption (2010) is explained in the Figure 1 legend. ALDH2 allele frequency was obtained by searching research papers (Multimedia Appendix 1, Supplementary Table S2). The original data set included the following number of countries: melanoma incidence (n=185), melanoma mortality (n=185), alcohol consumption (n=175), and ALDH2 alleles (n=23). Only 23 countries had all 4 factors available for the correlation analysis. Spearman rank coefficient was used to assess correlation due to skewed data and the influence of outliers. The data represent correlation coefficients (R) with P values. Alcohol consumption was reassessed to determine the correlation coefficient with an associated P value for the subset of countries that were considered. The statistical analysis was conducted using R version 4.0.2 (R Foundation for Statistical Computing).

    bALDH2: aldehyde dehydrogenase 2.

    cN/A: not applicable.

    Acknowledgments

    Supported by a grant from National Institutes of Health (NIH)/National Cancer Institute (NCI) R01CA197919 (to MF), NIH/National Institute of Allergy and Infectious Diseases (NIAID) R01AI156534-01A1 (to MF), Veterans Affairs Merit Review Award 5I01BX001228 (to MF), NIH/National Institute on Alcohol Abuse and Alcoholism (NIAAA) R21AA028904 (to MF), Cancer League of Colorado (to MF and TY), Dermatology Foundation (to TY) and University of Colorado Cancer Center-Nutrition Obesity Research Center (UCCC-NORC) Metabolism and Cancer Pilot Grant (to MF).

    Conflicts of Interest

    RPD serves as an Institute for Health Metrics and Evaluation (IHME) Global Burden of Disease collaborator, editor-in-chief of JMIR Dermatology, joint coordinating editor of Cochrane Skin, and a dermatology section editor of UpToDate. Other authors have no conflicts of interest.

    ‎
    Multimedia Appendix 1

    Supplemental tables.

    PPTX File , 48 KB
    1. Siegel RL, Miller KD, Fuchs HE, Jemal A. Cancer statistics, 2021. CA Cancer J Clin 2021 Jan;71(1):7-33. [CrossRef] [Medline]
    2. Brunssen A, Waldmann A, Eisemann N, Katalinic A. Impact of skin cancer screening and secondary prevention campaigns on skin cancer incidence and mortality: a systematic review. J Am Acad Dermatol 2017 Jan;76(1):129-139.e10. [CrossRef] [Medline]
    3. Barsh GS. What controls variation in human skin color? PLoS Biol 2003 Oct;1(1):E27 [FREE Full text] [CrossRef] [Medline]
    4. Chen CH, Ferreira JCB, Gross ER, Mochly-Rosen D. Targeting aldehyde dehydrogenase 2: new therapeutic opportunities. Physiol Rev 2014 Jan;94(1):1-34 [FREE Full text] [CrossRef] [Medline]
    5. Rota M, Pasquali E, Bellocco R, Bagnardi V, Scotti L, Islami F, et al. Alcohol drinking and cutaneous melanoma risk: a systematic review and dose-risk meta-analysis. Br J Dermatol 2014 May;170(5):1021-1028. [CrossRef] [Medline]

    ‎
    ALDH2: aldehyde dehydrogenase 2
    WHO: World Health Organization

    Edited by G Eysenbach; submitted 23.06.21; peer-reviewed by T Koritala, U Pfeffer; comments to author 10.08.21; revised version received 05.10.21; accepted 21.11.21; published 13.12.21

    Copyright

    ©Nisha Batta, Sarah Shangraw, Andrew Nicklawsky, Takeshi Yamauchi, Zili Zhai, Dinoop Ravindran Menon, Dexiang Gao, Robert P Dellavalle, Mayumi Fujita. Originally published in JMIR Dermatology (http://derma.jmir.org), 13.12.2021.

    This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Dermatology Research, is properly cited. The complete bibliographic information, a link to the original publication on http://derma.jmir.org, as well as this copyright and license information must be included.