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Could age increase the strength of inverse association between ultraviolet B exposure and colorectal cancer?

Abstract

Background

Vitamin D has been identified as a potential protective factor in the development of colorectal cancer (CRC). We expect to see a stronger association of ultraviolet B (UVB) exposure and CRC crude rates with increasing age since chronic vitamin D deficiency leads to sustained molecular changes that increase cancer risk. The DINOMIT (disjunction, initiation, natural selection, overgrowth, metastasis, involution, and transition) model postulates various stages of cancer development due to vitamin D deficiency and the associated latency period. The purpose of this study is to examine this age-dependent inverse relationship globally.

Methods

In this ecological study, a series of linear and polynomial regression tests were performed between country-specific UVB estimates adjusted for cloud cover and crude incidence rates of CRC for different age groups. Multiple linear regression was used to investigate the association between crude incidence rates of colorectal cancer and UVB estimate adjusting for urbanization, skin pigmentation, smoking, animal consumption, per capita GDP, and life expectancy. Statistical analysis was followed by geospatial visualization by producing choropleth maps.

Results

The inverse relationship between UVB exposure and CRC crude rates was stronger in older age groups at the country level. Quadratic curve fitting was preferred, and these models were statistically significant for all age groups. The inverse association between crude incidence rates of CRC and UVB exposure was statistically significant for age groups above 45 years, after controlling for covariates.

Conclusion

The age-dependent inverse association between UVB exposure and incidence of colorectal cancer exhibits a greater effect size among older age groups in global analyses. Studying the effect of chronic vitamin D deficiency on colorectal cancer etiology will help in understanding the necessity for population-wide screening programs for vitamin D deficiency, especially in regions with inadequate UVB exposure. Further studies are required to assess the need for adequate public health programs such as selective supplementation and food fortification.

Peer Review reports

Background

Colorectal cancer (CRC) is the third most common cancer globally with over 4 million prevalent cases. Nearly 2 million new cases of colorectal cancer were reported worldwide in 2018 [1]. It is the third most common cause of cancer in the United States with an estimated 460,714 cases in 2018 [1]. It is the second most common cause of death due to cancer worldwide and within the United States (880,000 deaths worldwide and fifty thousand deaths in the United States in 2018) [1]. The global burden of CRC is expected to increase by 60% to more than 2.2 million new cases and 1.1 million cancer deaths by 2030 [2]. The need for advanced prevention and treatment strategies has increased due to the need to reduce cancer morbidity and mortality for colorectal cancer.

Some of the risk factors linked to increased risk of developing CRC include obesity, sedentary lifestyle, consumption of high-fat, high-meat diets and calorie rich and fiber deficient food [3]. Apart from these known risk factors, inadequate vitamin D status as assessed by serum 25-hydroxyvitamin D (25(OH)D) concentration has also been identified as a potential risk factor in the pathogenesis of CRC. Vitamin D has been identified as a potential protective factor in the risk of developing CRC. Intake of 1000 IU/day of vitamin D is shown to be associated with 50% lower risk [4]. A meta-analysis using random effects model showed that the hazard ratio for mortality was lower with higher serum vitamin D status [5]. The results of this study suggested regular testing and restoration of vitamin D status to adequacy for lowering the mortality in colorectal cancer [5].

Vitamin D is a fat-soluble vitamin which has limited dietary sources and is predominantly obtained when exposed to ultraviolet B (UVB) radiation in sunlight [6]. Previtamin D3 is an intermediate product in the production of cholecalciferol. It is formed when UVB light of wavelengths between 280 and 315 nm from sunlight acts on 7-dehydrocholesterol present in the epidermal layers of the skin. It then converts by spontaneous isomerization into cholecalciferol which is converted into the active form of vitamin D through two-step hydroxylation [6]. Availability and exposure to UVB in sunlight is strongly correlated to the concentration of calcidiol and calcitriol levels in blood. UVB exposure and supplemental vitamin D both increase calcitriol in a dose-dependent fashion, [7] and increases in calcitriol have been shown to depend on baseline vitamin D status [8, 9]. In addition, a number of molecular factors may influence levels of serum 25(OH) D, including expression of the APOEε4 allele [10].

A recent study showed that most patients with a new diagnosis of CRC had deficient levels of serum 25(OH)D [11]. Better survival rates have been observed in patients with higher serum 25(OH) D concentrations compared to those with lower concentrations [12]. Reviews of ecological studies have shown evidence for the association between UVB–vitamin D–cancer to be convincing for several different types of cancer [13, 14]. However, not all studies have shown an increased cancer risk associated with inadequate circulating vitamin D levels. A Mendelian randomization study provided little evidence for the association of vitamin D and risk of several types of cancer [15].

The DINOMIT model (disjunction, initiation, natural selection, overgrowth, metastasis, involution, and transition) postulates that the anti-cancer effects of vitamin D can occur across these various phases of cancer etiology [16]. Vitamin D plays a protective role in all phases by protecting intercellular gap junctions through regulation of cadherins. Tight junctions prevent cells from separating and reduce the rate of cancer progression and metastasis. The DINOMIT model also postulates the involution of cancer through replenishment of vitamin D. Vitamin D deficiency’s effect on carcinogenesis is modeled as a function of time. With increasing age, the consequence of vitamin D deficiency accumulates longitudinally. Hence, the inverse epidemiological association between vitamin D status and incidence of colorectal cancer is expected to increase with age due to the chronicity of vitamin D deficiency. Since UVB exposure is strongly correlated to serum concentrations of 25(OH) D in the body, the strength of the inverse association between UVB estimate of a geographical area and the crude incidence rate of colorectal cancer can be studied to assess the longitudinal accumulation of carcinogenesis from vitamin D deficiency.

UVB exposure in a geographic area is affected by cloud cover, stratospheric ozone, altitude over sea level, skin pigmentation, number of hours spent indoors and type of clothing. Vitamin D production from UVB exposure may also be affected by air pollution and environmental chemicals [17]. Influential covariates that can affect CRC incidence include stratospheric ozone, diet, smoking prevalence, life expectancy, and wealth. The primary objective of this study to explore the effect of age on the inverse association between UVB exposure and CRC incidence, while adjusting for influential covariates. We hypothesize that the strength of the inverse association between UVB exposure and CRC incidence increases with age.

Methods

Study design

We conducted an ecological study assessing the age-dependent strength of inverse relationships between cloud cover-adjusted UVB exposure and incidence of CRC globally.

Data sources

Primary outcome

The most recent age-stratified, country-specific crude rates of CRC worldwide were obtained from the Global Cancer (GLOBOCAN) database, using Cancer Today [1]. Cancer Today is a data visualization tool developed by the International Agency for Research on Cancer (IARC), a specialized cancer agency of World Health Organization (WHO). It provides estimates of the incidence, mortality, and prevalence of various cancers worldwide. Age-stratified crude incidence rates of CRC were available for 186 countries. Crude incidence rates were collected for the year 2018.

Primary predictor

Estimates for UVB (280–315 nm), adjusted for cloud cover and aerosols, were obtained from a visualization of April 2017 data from the National Aeronautics and Space Administration (NASA) EOS Aura spacecraft, available from a prior publication [18]. April was chosen as it was the closest month visualized to the spring equinox in 2017 (which occurred on March 20th). This image was processed using ArcGIS geospatial software to provide a mean UVB estimate for each country. Specifically, efforts were made to remove country borders, after which geospatial processing algorithms leveraged raster transformation and zonal statistics functions. The output of geoprocessing was a unitless measure of UVB between the lowest and highest possible values of 0 and 255, respectively.

Covariates

The objective of this study is to use UVB estimates to better understand whether low levels of vitamin D may be among the risk factors for development of CRC. As a number of factors influence the development of CRC, a wide variety of covariates were included in multiple regression analyses. Stratospheric ozone data was obtained from the NASA satellite instrument packages [19]. Data on life expectancy and GDP per capita (at purchasing power parity [PPP]) by country for those born in 2010 were provided by the World Bank [20]. GDP at PPP is nominal gross domestic product converted to international dollars using purchasing power parity rates [20]. Data on pigmentation by country was available from published literature [21]. Data on urbanization percent (urban population fraction) by country were available from a previous publication [22]. Data on smoking prevalence was collected from Global Health Data Exchange (GHDx) from the Institute for Health Metrics and Evaluation (IHME) [23]. Daily smoking prevalence was the percentage of the national population that smokes daily. Data on animal meat consumption (kilogram/capita/year) were available from the Food and Agricultural Organization of the United Nations (FAO) [24]. Data for all covariates were collected for the year 2010 (8 years prior to the incidence data).

Data for country-specific modeled serum 25(OH) D were available from a previous publication [25]. Modeling of the estimated serum 25(OH) D included measured levels of serum 25(OH) D during winter as a dependent variable, UVB irradiance was included as an independent variable and skin pigmentation as a covariate from 28 publications [25]. For countries where actual measured levels of serum 25(OH) D were not available, a prediction equation was obtained using the regression coefficients of the models for the countries with measured 25(OH) D levels [25].

Statistical analysis

Age-stratified crude incidence rates of CRC were available for 185 countries. However, data for adjusted UVB estimates were available for only 166 countries (a list of excluded countries is provided in Appendix A). Data for all covariates were available for 148 countries. Primary statistical analyses were conducted for 166 countries and multiple linear regression was employed for 148 countries (a list of countries excluded from the multiple linear regression model is provided in Appendix B). Spearman’s correlation test was used to analyze the association between adjusted UVB estimates and country-specific crude incidence rates of CRC for every age group (0–14, 15–29, 30–44, 45–59, 60–74, >/= 75 years of age). A series of simple linear regression tests were performed followed by polynomial regressions between country-specific UVB estimates adjusted for cloud cover and crude incidence rates of CRC for different age groups. A better curve fit was obtained using a quadratic term. Labelled scatter plots with polynomial trend lines were plotted for each quadratic model (a list of the label codes for the names of countries (ISO 3166 standard) is provided in Appendix C). Unadjusted quadratic models for southern hemisphere countries and northern hemisphere countries were analyzed for over 75 years of age. In addition, a subset of countries had estimates available from high-quality registry data in the IARC’s CI5plus database, so a separate linear model was computed only using estimates from these countries. As sample size was relatively limited for this sub-analysis, a univariate model of the relationship between UVB and crude CRC rates above age 75 was assembled.

Multiple linear regression was used to investigate the relationship between crude incidence rates of colorectal cancer and cloud cover-adjusted UVB controlling for stratospheric ozone, urbanization, skin pigmentation, smoking, animal consumption, per capita GDP, and life expectancy. A smoking covariate was also included in the age group of 0–14 years to capture the impact of second-hand smoke on adolescents. Adjusted models for southern hemisphere countries and northern hemisphere countries were analyzed for over 75 years of age. A series of multiple linear regression models were employed to study the association between crude incidence rates of colorectal cancer and modeled 25(OH) D, while controlling for urbanization, smoking, animal consumption, per capita GDP and life expectancy. UVB irradiance and skin pigmentation were taken into account in the modeled 25(OH) D values. A p-value of < 0.05 was considered statistically significant for all analyses. Statistical analyses were performed using R version 3.6.0. Choropleth maps of country-specific CRC incidence for every age group were produced in which countries were color coded according to incidence rates. All choropleth maps were produced using QGIS software.

Results

Main results

Upon generating scatter plots for polynomial regression of adjusted UVB with crude incidence of CRC for each age group, an increasing trend was seen in the strength of the inverse relationship between incidence of CRC and adjusted UVB with increasing age (Figs. 1, 2, 3, 4, 5 and 6). Polynomial trend lines provided a better fit compared to linear fit trend lines. Spearman’s rank correlation tests between incidence of CRC and UVB for each age group showed negative correlations of increasing strength with increasing age (Table 1).

Fig. 1
figure1

Estimated crude incidence rates of CRC in 0–14 years of age by UVB estimates, 2018

Fig. 2
figure2

Estimated crude incidence rates of CRC in 15–29 years of age by UVB estimates, 2018

Fig. 3
figure3

Estimated crude incidence rates of CRC in 30–44 years of age by UVB estimates, 2018

Fig. 4
figure4

Estimated crude incidence rates of CRC in 45–59 years of age by UVB estimates, 2018

Fig. 5
figure5

Estimated crude incidence rates of CRC in 60–74 years of age by UVB estimates, 2018

Fig. 6
figure6

Estimated crude incidence rates of CRC in >/=75 years of age by UVB estimates, 2018

Table 1 Correlation between colorectal cancer crude incidence rate and ultraviolet B estimate for every age group

Table 2 illustrates the results of linear regression tests between cloud cover adjusted UVB and CRC crude incidence rate for every age group.

Table 2 UVB estimate in association with crude incidence rate of CRC using linear regression

Polynomial regression models for each age group between adjusted UVB estimates and CRC crude incidence rate showed a stronger inverse association for older age groups compared to younger age groups (Table 3). The overall p-value of the polynomial model was statistically significant for every age group. Also, the overall R2 of the polynomial model increased with age and the highest R2 (0.62) was obtained for 64–75 years of age. The overall R2 value of the polynomial model for countries in northern hemisphere for over 75 years of age was higher than that for countries in southern hemisphere before adjusting for covariates (Appendix Tables 5 and 6).

Table 3 UVB estimate in association with crude incidence rate of CRC using polynomial regression

Other analyses

In the multiple linear regression model, UVB was inversely associated with crude incidence rates of colorectal cancer for all age groups above 45 years, after controlling for covariates (p-value < 0.05) (Table 4; Appendix Tables 7, 8, 9, 10 and 11). The highest adjusted model R2 (0.71) was obtained for 64–75 years of age (Appendix Table 11) and over 75 years of age (Table 4). The tables for the other age groups are provided in the appendix (Appendix Tables 7, 8, 9, 10 and 11). After adjusting for covariates, the model R2 value of the polynomial model for countries in southern hemisphere for over 75 years of age was higher than that for countries in northern hemisphere (Appendix Tables 12 and 13). Also, univariate regression analysis for CRC rates above age 75, only among the 29 countries with high-quality cancer registries available from the CI5plus database, exhibited a statistically significant association with UVB (p <  0.001; Appendix Table 14). The overall models were statistically significant with a p-value < 0.001 for age groups above 45 years. The association between UVB estimates and crude incidence rates of CRC was not statistically significant in age groups below 45 years, after controlling for covariates.

Table 4 UVB in association with crude incidence of CRC: over 75 years of age, controlling for covariates

According to the multiple linear regression models, the inverse association between modeled 25(OH) D and crude incidence rates of CRC was statistically significant in age groups 60–74 years, 75 years and above (Appendix Tables 15, 16, 17, 18, 19 and 20). The association was marginally significant in the age groups of 45–59 years. In age groups below 45 years, the association was not statistically significant.

Choropleth maps produced using QGIS visualized the distribution of colorectal cancer worldwide. Supplementary file Figures S1, S2, S3, S4 and S5 illustrate the distribution of CRC in different countries for every age group.

Discussion

Key results

This study aims at assessing the strength of the inverse relationship between UVB exposure and CRC incidence with increasing age. The DINOMIT model [16] proposes an explanation of how vitamin D deficiency increases the risk of developing colorectal cancer. However, it is expected to take years for these phases to occur, and hence we expect increasing age to have a major role in explaining the inverse relationship between UVB estimates and incidence of colorectal cancer. Thus, older age groups can be expected to have a stronger inverse association between vitamin D status and crude incidence rate of colorectal cancer. Though there is mixed evidence for this inverse association, our study aims at taking into consideration the effect of age on this association. In this country-specific analysis, we have shown an increasing trend in the strength of the inverse association between adjusted UVB estimates and crude incidence rate of colorectal cancer as age increases. The proportion of variability in the outcome (crude incidence rate of CRC) explained by the adjusted UVB estimate also increased with age. This study assesses the age-dependent inverse association between vitamin D status and incidence of colorectal cancer globally. This is the first study to the authors’ knowledge to have explored the age-related effect in this inverse association. UVB estimates decrease with increasing latitude, and higher incidence of colorectal cancer has been reported at higher latitudes [26]. Another recent study mentions low vitamin D status as a possible explanation to higher incidence rates of colon cancer in cold countries (higher latitudes) [27]. This study demonstrates a significant inverse association between UVB exposure and CRC incidence in all age groups. Age-related differences in vitamin D status have been observed in the regions of Asia/Pacific and Middle East/Africa [28] and reduced vitamin D status with increasing age has been reported in previous studies [29]. Also, vitamin D deficiency has been observed across all age groups globally including countries with low latitude [30]. UVB exposure is strongly correlated with serum 25(OH) D levels and previous studies have shown significant associations between 25(OH) D levels and overall CRC incidence [31]. Photosynthesized vitamin D released from erythemal solar radiation to the skin has been found to have a greater effect on serum 25(OH) D levels than dietary vitamin D ingestion [32]. The tissue stores of cholecalciferol which are obtained through exposure to UVB radiation help in sustaining serum 25(OH) D levels [32].

Interpretation

Previous ecological studies have reported an inverse association between UVB exposure and incidence as well as mortality of various cancers, including colon cancer [33, 34]. In one of the prior ecological studies, a significant inverse association was observed for colon cancer (among ten other cancers), and the relative risk of colon cancer incidence related to solar UVB exposure was found to be 1.11 in males and 1.14 in females [33]. In another ecological study, inverse associations with UVB were found for 15 different cancers, including colon cancer [34]. The standardized regression coefficient for age-adjusted mortality rates of colon cancer versus UVB irradiance was found to be − 0.71 (p <  0.001) for males and − 0.76 (p <  0.001) for females. However, not all ecological studies have been able to demonstrate a significant inverse association between UV exposure and colon cancer [35]. The association between UVB exposure and global incidence of colorectal cancer was first analyzed in an ecological study [26] where simple linear regression and multiple linear regression methods were used to study the inverse association between UVB exposure and incidence of CRC. In this study, the age-adjusted crude incidence rates of colorectal cancer were higher at latitudes distant from the equator (R2 = 0.50, p <  0.001) [26]. In the adjusted model of that study, UVB exposure (adjusted for cloudiness) was inversely associated with age adjusted CRC crude incidence rates (p = 0.01), after controlling for covariates [26]. However, age-dependent strength of the inverse association between UVB exposure and colorectal cancer was not explored in that study [26].

Various studies have demonstrated the effect of diet on risk of colorectal cancer. Increased consumption of red meat and total meat were associated with higher risk of developing colorectal cancer in a study which analyzed data from a Japanese cohort [36]. Also, intake of fruits and vegetables have shown to have a protective effect against cancer [37]. Results from other studies suggest that changes in dietary pattern, specifically with increased meat consumption, can increase the risk of developing CRC [38]. A recent study observed highest number and proportion of diet-related cases for colorectal cancer [39]. Smoking is known to increase the risk of both colon and rectal cancer, with a stronger association for rectal cancer [40, 41]. The duration of smoking had a significant association with the risk of colorectal cancer [42].

A nested case-control study from the Women’s Health Study (WHS) found a significant inverse association between pre-diagnostic 25(OH) D levels and risk of CRC [43]. This case-control study with 274 controls and 274 colorectal cases observed a significant inverse association between plasma vitamin D and odds of colorectal cancer in multivariable adjusted logistic regression models [43]. MEG3 (non-coding RNA maternally expressed gene) functions as a tumor suppressor in CRC by regulating the activity of clusterin, which is stimulated by the binding of vitamin D receptor to its promoter [44]. Meta-analysis of the relationship between serum 25(OH) D and mortality of patients with colorectal cancer has shown that higher serum 25(OH) D was associated with lower mortality of patients with colorectal cancer [5]. Mortality rates were also decreased in summertime, where UVB wavelengths of solar radiation are more available [45].

The main strength of this study is the novelty of assessing the age-dependent inverse relationship between UVB exposure and CRC incidence. The unadjusted analysis included 166 countries in comparison to 139 countries in a previous study [26]. The results of this analysis are in line with the previous study [26] in having obtained a significant inverse association between UVB exposure and incidence of colorectal cancer. As with prior analyses, this analysis employed multiple linear regression to account for other risk-modifying factors. UVB estimates were significantly associated with the risk of colorectal cancer in age groups over 45 years after adjusting for covariates. These findings are consistent with other studies which have found significantly different risk factors for individuals receiving a diagnosis of CRC prior to age 50, compared to those receiving a diagnosis after age 50 [46]. We suggest that several risk factors for later-age development of CRC may derive from chronic exposures, and we suggest that vitamin D deficiency is among these. The significant increase in the strength of this inverse association with age was observed in the analysis, as hypothesized. Also, the proportion of variation in the age-specific crude incidence rates due to UVB exposure (R2) increased consistently with age.

Limitations

Data for all variables that were included in the multiple linear regression were available for only 148 countries out of the 185 countries for which CRC crude incidence rate data were available. However, the excluded countries account for approximately 3% of global population. Also, the inherent limitations of the data used in this study include use of neighboring country’s CRC estimate in some cases of unavailability, as well as some uses of hospital-based data instead of population/registry-based data. Furthermore, countries which lacked data for UVB estimates were mostly countries with lower per capita income and limited access to healthcare, which were also closer to equator with high UVB exposure. Exclusion of these countries from the study could have reduced the strength of associations. Though laboratory research and studies on individuals have produced evidence validating the influence of UVB on serum 25(OH) D, we note that UVB is an imperfect proxy measure of 25(OH) D status. National mean 25(OH) D concentration depends on a large number of factors. These include UVB irradiation, cloud cover, skin pigmentation, and urbanization, which are factors which were included in this study’s multivariable modeling. However, additional factors, including vitamin D supplementation, clothing cover area, altitude over sea level, air pollution and environmental chemicals are also likely to be relevant, but were not included in this study due to limited availability of data or to preserve model parsimony.

Generalizability

The results of the study cannot be applied directly at the level of individuals due to ecological fallacy. However, the study findings do reveal a significant effect of age on the inverse association between UVB exposure and colorectal cancer incidence rates. The multivariate models with modeled 25(OH) D had lower R2 values compared to those with the UVB estimates adjusted for cloud cover, reducing the proportion of variability in the crude incidence of CRC explained by vitamin D levels. Also, adjusted UVB estimate was statistically significant in the age group of 45–60 years, whereas modeled 25(OH) D was only marginally significant although both the covariates retained statistical significance in age groups above 60 years. Though our study used the modeled 25(OH) D data calculated from 28 publications, there was a mean difference of 5.26 nmol/L between the values used in the study and the published annual values of 25(OH)D [28, 29].

Conclusion

Ecological studies help in generating novel, relevant hypothesis that may help in identifying causal relationships that can be further explored through studies on individuals. This study supports the need for adequate public health programs to avoid vitamin D inadequacy at national and global levels, whether through screening those at risk, through selective supplementation, or through population-based measures such as food fortification. Future studies can aim at identifying the cancer types which show significant improvement with vitamin D supplementation. Studying the association between chronic vitamin D deficiency and CRC incidence will help in understanding the necessity for population-wide screening programs for vitamin D deficiency, especially in regions with inadequate UVB exposure. These programs may help decrease risk of CRC, as well as other cancers whose risk is associated with vitamin D deficiency, for high-risk populations whose vitamin D deficiency has been especially chronic.

Availability of data and materials

The following data sources (open to public access) were used to collect data for this study:

1. Colorectal cancer incidence rates: Cancer Today, Ferlay J, Ervik M, Lam F, Colombet M, Mery L, Piñeros M, Znaor A, Soerjomataram I, Bray F (2018). Global Cancer Observatory: Cancer Today. Lyon, France: International Agency for Research on Cancer. Available from: https://gco.iarc.fr/today

2. Estimates for UVB (280–315 nm), adjusted for cloud cover and aerosols: Beckmann M, Václavík T, Manceur AM, Šprtová L, von Wehrden H, Welk E, et al. glUV: a global UV-B radiation data set for macroecological studies. Tatem A, editor. Methods Ecol Evol [Internet]. 2014 Apr 1 [cited 2020 Apr 1];5 (4):372–83. Available from: http://0-doi.wiley.com.brum.beds.ac.uk/10.1111/2041-210X.12168

3. Stratospheric ozone data: Earthdata [Internet]. Available from: https://earthdata.nasa.gov/

4. Data on life expectancy and GDP per capita (at purchasing power parity [PPP]): World Bank Open Data; https://data.worldbank.org/

5. Pigmentation data: Jablonski NG, Chaplin G. The evolution of human skin coloration. J Hum Evol 2000; 39:57–106; PMID:10896812; https://0-doi-org.brum.beds.ac.uk/10.1006/jhev.2000.0403.

6. Urbanization data: Christenson E, Elliott M, Banerjee O, Hamrick L, Bartram J. Climate-related hazards: a method for global assessment of urban and rural population exposure to cyclones, droughts, and floods. Int J Environ Res Public Health. 2014;11 (2):2169–2192.

7. Smoking prevalence: Institute for Health Metrics and Evaluation (IHME). Global Smoking Prevalence and Cigarette Consumption 1980–2012. Seattle, United States: Institute for Health Metrics and Evaluation (IHME), 2014.

8. Data on animal meat consumption: The Food and Agricultural Organization of the United Nations. http://www.fao.org/faostat/en/#data/FBS.

The datasets used and/or analyzed during the current study can be accessed on GitHub repository https://github.com/ghpi2021/vitD_age

Abbreviations

CRC:

Colorectal Cancer

UVB:

Ultraviolet B

DINOMIT:

Disjunction, Initiation, Natural selection, Overgrowth, Metastasis, Involution, Transition

25(OH)D:

25-hydroxyvitamin D

Vitamin D status:

as assessed by serum 25(OH) D concentration

VDR:

Vitamin D Receptor

BMI:

Body Mass Index

WHS:

Women’s Health Study

MEG3:

Maternally expressed gene

GLOBOCAN:

Global Cancer Database

NASA:

National Aeronautics and Space Administration

ISCCP:

International Satellite Cloud Climatology Project

GDP:

Gross Domestic Product

PPP:

Purchasing Power Parity

GHDx:

Global Health Data Exchange

IHME:

Institute for Health Metrics and Evaluation

FAO:

Food and Agricultural Organization of the United Nations

QGIS:

Quantum Geographic Information System

Choropleth map:

Areas in the map are color coded to a variable.

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Acknowledgements

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Funding

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

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Authors

Contributions

VP and RC jointly contributed to the formulation, drafting, completion, and approval of the final manuscript. CG and TM provided guidance for data analyses and interpretation of findings. All authors have read and approved the manuscript.

Corresponding author

Correspondence to Timothy K. Mackey.

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Not applicable. Data were not collected for individuals, but rather as aggregated statistics from publicly available data sources.

Competing interests

TM is a senior editorial board member of BMC Public Health. TM is an employee of the startup company S-3 Research LLC. S-3 Research is a startup funded and currently supported by the National Institutes of Health – National Institute on Drug Abuse through a Small Business Innovation and Research contract for opioid-related social media research and technology commercialization. Author reports no other conflict of interest associated with this manuscript.

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Supplementary Information

Additional file 1: Figure S1.

Colorectal cancer crude incidence rates, 15–29 years of age, all races, both sexes, 2018.

Additional file 2: Figure S2.

Colorectal cancer crude incidence rates, 30–44 years of age, all races, both sexes, 2018.

Additional file 3: Figure S3.

Colorectal cancer crude incidence rates, 45–59 years of age, all races, both sexes, 2018.

Additional file 4: Figure S4.

Colorectal cancer crude incidence rates, 60–74 years of age, all races, both sexes, 2018.

Additional file 5: Figure S5.

Colorectal cancer crude incidence rates, >/=75 years of age, all races, both sexes, 2018.

Appendices

Appendix

Table 1 UVB estimate in association with crude incidence rate of CRC using polynomial regression for countries in northern hemisphere
Table 2 UVB estimate in association with crude incidence rate of CRC using polynomial regression for countries in southern hemisphere
Table 3 UVB in association with crude incidence of CRC: 0–14 years of age, controlling for covariates
Table 4 UVB in association with crude incidence of CRC: 15–29 years of age, controlling for covariates
Table 5 UVB in association with crude incidence of CRC: 30–44 years of age, controlling for covariates
Table 6 UVB in association with crude incidence of CRC: 45–59 years of age, controlling for covariates
Table 7 UVB in association with crude incidence of CRC: 60–74 years of age, controlling for covariates
Table 8 UVB in association with crude incidence of CRC: over 75 years of age for countries in northern hemisphere after controlling for covariates
Table 9 UVB in association with crude incidence of CRC: over 75 years of age for countries in southern hemisphere after controlling for covariates
Table 10 UVB estimate in association with crude incidence rate of CRC using linear regression among countries with high-quality registry data in the CI5 plus database
Table 11 Modeled 25(OH) D in association with incidence of CRC in 0–14 years of age, controlling for covariates
Table 12 Modeled 25(OH) D in association with incidence of CRC in 15–29 years of age, controlling for covariates
Table 13 Modeled 25(OH) D in association with incidence of CRC in 30–44 years of age, controlling for covariates
Table 14 Modeled 25(OH) D in association with incidence of CRC in 45–59 years of age, controlling for covariates
Table 15 Modeled 25(OH) D in association with incidence of CRC in 60–74 years of age, controlling for covariates
Table 16 Modeled 25(OH) D in association with incidence of CRC >/=75 years of age, controlling for covariates

Appendix A

List of excluded countries for which UVB estimates were unavailable

  1. 1.

    Cabo Verde

  2. 2.

    Côte d’Ivoire

  3. 3.

    France, Guadeloupe

  4. 4.

    France, La Réunion

  5. 5.

    France, Martinique

  6. 6.

    France, New Caledonia

  7. 7.

    French Guyana

  8. 8.

    French Polynesia

  9. 9.

    Gaza Strip and West Bank

  10. 10.

    Korea, Democratic Republic of

  11. 11.

    Korea, Republic of

  12. 12.

    Maldives

  13. 13.

    Montenegro

  14. 14.

    Republic of Moldova

  15. 15.

    Saint Lucia

  16. 16.

    Sao Tome and Principe

  17. 17.

    South Sudan

  18. 18.

    The former Yugoslav Republic of Macedonia

  19. 19.

    Timor-Leste

Appendix B

List of countries excluded from the multiple linear regression model

  1. 1.

    Bahrain

  2. 2.

    Bhutan

  3. 3.

    Burundi

  4. 4.

    Comoros

  5. 5.

    Congo, Democratic Republic of

  6. 6.

    Cuba

  7. 7.

    Djibouti

  8. 8.

    Equatorial Guinea

  9. 9.

    Eritrea

  10. 10.

    Guam

  11. 11.

    Libya

  12. 12.

    Papua New Guinea

  13. 13.

    Puerto Rico

  14. 14.

    Qatar

  15. 15.

    Singapore

  16. 16.

    Somalia

  17. 17.

    Swaziland

  18. 18.

    Syrian Arab Republic

Appendix C

List of the codes for the representation of names of countries (ISO 3166 standard)

  1. 1.

    AFG Afghanistan

  2. 2.

    AGO Angola

  3. 3.

    ALB Albania

  4. 4.

    ARE United Arab Emirates

  5. 5.

    ARG Argentina

  6. 6.

    ARM Armenia

  7. 7.

    ATA Antarctica

  8. 8.

    AUS Australia

  9. 9.

    AUT Austria

  10. 10.

    AZE Azerbaijan

  11. 11.

    BDI Burundi

  12. 12.

    BEL Belgium

  13. 13.

    BEN Benin

  14. 14.

    BFA Burkina Faso

  15. 15.

    BGD Bangladesh

  16. 16.

    BGR Bulgaria

  17. 17.

    BHR Bahrain

  18. 18.

    BHS Bahamas

  19. 19.

    BIH Bosnia and Herzegovina

  20. 20.

    BLR Belarus

  21. 21.

    BLZ Belize

  22. 22.

    BOL Bolivia

  23. 23.

    BRA Brazil

  24. 24.

    BRB Barbados

  25. 25.

    BRN Brunei Darussalam

  26. 26.

    BTN Bhutan

  27. 27.

    BWA Botswana

  28. 28.

    CAF Central African Republic

  29. 29.

    CAN Canada

  30. 30.

    CHE Switzerland

  31. 31.

    CHL Chile

  32. 32.

    CHN China

  33. 33.

    CMR Cameroon

  34. 34.

    COD Congo, Democratic Republic of the

  35. 35.

    COG Congo

  36. 36.

    COL Colombia

  37. 37.

    COM Comoros

  38. 38.

    CRI Costa Rica

  39. 39.

    CUB Cuba

  40. 40.

    CYP Cyprus

  41. 41.

    CZE Czechia

  42. 42.

    DEU Germany

  43. 43.

    DJI Djibouti

  44. 44.

    DNK Denmark

  45. 45.

    DOM Dominican Republic

  46. 46.

    DZA Algeria

  47. 47.

    ECU Ecuador

  48. 48.

    EGY Egypt

  49. 49.

    ERI Eritrea

  50. 50.

    ESP Spain

  51. 51.

    EST Estonia

  52. 52.

    ETH Ethiopia

  53. 53.

    FIN Finland

  54. 54.

    FJI Fiji

  55. 55.

    FRA France

  56. 56.

    GAB Gabon

  57. 57.

    GBR United Kingdom

  58. 58.

    GEO Georgia

  59. 59.

    GHA Ghana

  60. 60.

    GIN Guinea

  61. 61.

    GMB Gambia

  62. 62.

    GNB Guinea-Bissau

  63. 63.

    GNQ Equatorial Guinea

  64. 64.

    GRC Greece

  65. 65.

    GTM Guatemala

  66. 66.

    GUM Guam

  67. 67.

    GUY Guyana

  68. 68.

    HND Honduras

  69. 69.

    HRV Croatia

  70. 70.

    HTI Haiti

  71. 71.

    HUN Hungary

  72. 72.

    IDN Indonesia

  73. 73.

    IND India

  74. 74.

    IRL Ireland

  75. 75.

    IRN Iran

  76. 76.

    IRQ Iraq

  77. 77.

    ISL Iceland

  78. 78.

    ISR Israel

  79. 79.

    ITA Italy

  80. 80.

    JAM Jamaica

  81. 81.

    JOR Jordan

  82. 82.

    JPN Japan

  83. 83.

    KAZ Kazakhstan

  84. 84.

    KEN Kenya

  85. 85.

    KGZ Kyrgyzstan

  86. 86.

    KHM Cambodia

  87. 87.

    KWT Kuwait

  88. 88.

    LAO Laos

  89. 89.

    LBN Lebanon

  90. 90.

    LBR Liberia

  91. 91.

    LBY Libya

  92. 92.

    LKA Sri Lanka

  93. 93.

    LSO Lesotho

  94. 94.

    LTU Lithuania

  95. 95.

    LUX Luxembourg

  96. 96.

    LVA Latvia

  97. 97.

    MAR Morocco

  98. 98.

    MDG Madagascar

  99. 99.

    MEX Mexico

  100. 100.

    MLI Mali

  101. 101.

    MLT Malta

  102. 102.

    MMR Myanmar

  103. 103.

    MNG Mongolia

  104. 104.

    MOZ Mozambique

  105. 105.

    MRT Mauritania

  106. 106.

    MUS Mauritius

  107. 107.

    MWI Malawi

  108. 108.

    MYS Malaysia

  109. 109.

    NAM Namibia

  110. 110.

    NER Niger

  111. 111.

    NGA Nigeria

  112. 112.

    NIC Nicaragua

  113. 113.

    NLD Netherlands

  114. 114.

    NOR Norway

  115. 115.

    NPL Nepal

  116. 116.

    NZL New Zealand

  117. 117.

    OMN Oman

  118. 118.

    PAK Pakistan

  119. 119.

    PAN Panama

  120. 120.

    PER Peru

  121. 121.

    PHL Philippines

  122. 122.

    PNG Papua New Guinea

  123. 123.

    POL Poland

  124. 124.

    PRI Puerto Rico

  125. 125.

    PRT Portugal

  126. 126.

    PRY Paraguay

  127. 127.

    QAT Qatar

  128. 128.

    ROU Romania

  129. 129.

    RUS Russian Federation

  130. 130.

    RWA Rwanda

  131. 131.

    SAU Saudi Arabia

  132. 132.

    SDN Sudan

  133. 133.

    SEN Senegal

  134. 134.

    SGP Singapore

  135. 135.

    SLB Solomon Islands

  136. 136.

    SLE Sierra Leone

  137. 137.

    SLV El Salvador

  138. 138.

    SOM Somalia

  139. 139.

    SRB Serbia

  140. 140.

    SUR Suriname

  141. 141.

    SVK Slovakia

  142. 142.

    SVN Slovenia

  143. 143.

    SWE Sweden

  144. 144.

    SYR Syrian Arab Republic

  145. 145.

    TCD Chad

  146. 146.

    TGO Togo

  147. 147.

    THA Thailand

  148. 148.

    TJK Tajikistan

  149. 149.

    TKM Turkmenistan

  150. 150.

    TTO Trinidad and Tobago

  151. 151.

    TUN Tunisia

  152. 152.

    TUR Turkey

  153. 153.

    TZA Tanzania

  154. 154.

    UGA Uganda

  155. 155.

    UKR Ukraine

  156. 156.

    URY Uruguay

  157. 157.

    USA United States of America

  158. 158.

    UZB Uzbekistan

  159. 159.

    VEN Venezuela

  160. 160.

    VNM Viet Nam

  161. 161.

    VUT Vanuatu

  162. 162.

    WSM Samoa

  163. 163.

    YEM Yemen

  164. 164.

    ZAF South Africa

  165. 165.

    ZMB Zambia

  166. 166.

    ZWE Zimbabwe

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Purushothaman, V.L., Cuomo, R.E., Garland, C.F. et al. Could age increase the strength of inverse association between ultraviolet B exposure and colorectal cancer?. BMC Public Health 21, 1238 (2021). https://0-doi-org.brum.beds.ac.uk/10.1186/s12889-021-11089-w

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Keywords

  • Colorectal cancer
  • Ultraviolet B rays
  • UVB
  • Vitamin D
  • Aging
  • Crude incidence rates