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Cancer Incidence Rates in the US in 2016–2020 with Respect to Solar UVB Doses, Diabetes and Obesity Prevalence, Lung Cancer Incidence Rates, and Alcohol Consumption: An Ecological Study

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06 May 2024

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07 May 2024

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Abstract
Abstract: This article reports the results of an ecological study of cancer incidence rates by state in the US for the period 2016-2020. The goals of this study were to determine the extent to which solar UVB doses still reduced cancer risk compared to findings reported in 2006 for cancer mortality rates for the periods 1950–1969 and 1970-1794 as well as cancer incidence rates for the period 1998–2002 and to determine which factors were recently associated with cancer risk. The cancer data for non-Hispanic white (European American) men and women were obtained from the Centers for Disease Control and Prevention. Indices were obtained for solar UVB at the surface for July 1992, and alcohol consumption, and diabetes and obesity prevalence near the 2016–2020 period. Lung cancer incidence rates were also used in the analyses as a surrogate for smoking, diet, and air pollution. The cancers for which solar UVB is significantly associated with reduced incidence are bladder, brain (males), breast, corpus uteri, esophageal, gastric, non-Hodgkin’s lymphoma, pancreatic, and renal cancer. Lung cancer was significantly associated with colorectal, laryngeal, and renal cancer. Diabetes was also significantly associated with breast, liver, and lung cancer. Obesity prevalence was significantly associated with breast, colorectal and renal cancer. Alcohol consumption was associated with bladder and esophageal cancer. Thus diet has become a very important driver of cancer incidence rates. The role of solar UVB in reducing risk of cancer has been reduced due to people spending less time outdoors, wearing sunscreen that blocks UVB but not UVA radiation, and population increases in overweight and obesity, which lowers 25-hydroxyvitamin D concentrations and generates systemic inflammation, a risk factor for cancer. The dietary approach that would reduce the risk of diabetes, obesity, lung cancer, and, therefore cancer, would be one based mostly on whole-plants and restrictions on red and processed meats and ultraprocessed foods. Solar UVB exposure for a few minutes before applying sunscreen and taking vitamin D supplements would also help reduce risk of cancer.Keywords: alcohol consumption; cancer incidence;
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1. Introduction

Cancer is the second-leading cause of death in the US. In 2021, cancer was responsible for 146.6 deaths per 100,000 inhabitants, whereas heart disease caused 173.8 deaths [1]. In 2024, the American Cancer Society projected 2,001,000 new cancer cases and 612,000 cancer deaths [2]. The probability of developing invasive cancer from birth to death in the 2017–2019 period was estimated at 41.6% for males and 39.6% for females [2]. Cancer is therefore a major health issue in the US.
The major cancer risk factors are reasonably well known. A 2021 American Cancer Society review listed smoking, excess body weight, lack of adequate physical activity, poor diet, alcohol consumption, and infections as major risk factors [3]. A 2019 review by the International Agency for Research on Cancer offered a similar list for the Eastern Mediterranean region. However, for diet that report listed salt intake, red and processed meat, and insufficient fruit and vegetable intake for cancer at various body sites; suboptimal breast feeding for breast cancer; and air pollution for lung cancer [4].
Ecological studies have been used to investigate the role of solar ultraviolet-B (UVB) and vitamin D in reducing risk of cancer in the US [5,6,7,8] and elsewhere [9,10,11], as discussed in a 2022 review [12]. The brothers Cedric and Frank Garland proposed that vitamin D reduced the risk of colon cancer after seeing data for colon cancer mortality rates in the US in 1974 [5]. A 2002 ecological study [6] used data for the dose of solar UVB at the earth’s surface in July 1992 obtained from a map in [13] in comparison with cancer mortality rates for white Americans for more than 500 state economic areas as reported in the Atlas of Cancer Mortality in the United States, 1950–94 [14]. Significant inverse correlations between solar UVB doses and cancer mortality rates were found for 13 anatomical sites. That work was extended in 2006 by adding several cancer risk–modifying factors averaged at the state level: alcohol consumption, Hispanic heritage, lung cancer (an index for smoking and diet), poverty, and urban/rural residence. The findings regarding solar UVB were essentially unchanged from the previous study. Another ecological study for non-Hispanic white people for 1993–2002 reported strong inverse correlations between solar UVB doses and cancer incidence and mortality rates for 10 cancers, with weaker evidence for six cancers, and inverse relationships that varied by sex for three cancers [8]. That study made some adjustments for smoking, outdoor occupation, and atmospheric particulate matter. Thus, in the 1950–2002 period, ecological studies in the US showed significant inverse correlations between solar UVB doses and cancers at many anatomical sites.
A chance perusal of cancer incidence data for 2016–2020 in the US posted by the Centers for Disease Control and Prevention (CDC) [15] showed that the distribution of cancer incidence had changed in several important ways since 2002. The main difference was that cancer incidence rates in the southeastern states were much higher than before. In addition, the strong inverse correlations between solar UVB doses and cancer rates were either not as strong or absent for several cancer sites. Thus, this new ecological study was initiated. The goals were to determine the extent to which solar UVB exposure reduced cancer risk in the recent past and which factors seem to be important cancer risk factors at present.

2. Materials and Methods

Cancer incidence data (cases/100,000/year) were obtained from the CDC’s Cancer Statistics At a Glance website [15]. The data are age-adjusted to the 2000 U.S. standard population [16]. Those statistics include cancer registry data from the CDC’s National Program of Cancer Registries [17] and the National Cancer Institute’s Surveillance, Epidemiology, and End Results (SEER) Program [18 and End Results (SEER) Program, 2024]. The SEER Program is an authoritative information source on cancer incidence and survival in the United States. SEER collects and publishes cancer incidence and survival data from population-based cancer registries covering about 48.0% of the US population. SEER coverage includes 42.0% of white people, 44.7% of African Americans, 66.3% of Hispanic people, 59.9% of American Indians and Alaska Natives, 70.7% of Asian people, and 70.3% of Hawaiian/Pacific Islanders. Data were available for 2016–2020 with data available by race/ethnicity, sex, and anatomical site. No data were available for Indiana, Nevada, and North Dakota. The data values appear to have low 95% confidence intervals (95% CI). For example, for pancreatic cancer for males, for Nebraska, the rate is 15.3 (95% CI, 14.2–16.4) cases/100,000/year. For CRC for males, for Nebraska, the rate is 44.7 (95% CI, 42.9–46.6) cases/100,000/year. Only data for non-Hispanic white people (NHW) were used in this study. While data are available for other races/ethnicities, solar UVB has less impact for people with darker skin, making it more difficult to do an ecological study as shown in a 2006 ecological study of cancer mortality rates for African Americans with respect to solar UVB doses [19]. In addition, race/ethnicity-specific data for other cancer risk-modifying factors would not be easy to obtain.
Table 1. National average cancer incidence rates, 2016–2020 [15], for cancers with incidence and/or mortality rates inversely correlated with solar UVB doses [20] in ecological studies reported in 2006.
Table 1. National average cancer incidence rates, 2016–2020 [15], for cancers with incidence and/or mortality rates inversely correlated with solar UVB doses [20] in ecological studies reported in 2006.
Cancer Mean rate (cases/100,000/yr)
Males Females
Bladder, urinary 37 9
Brain 9 6
Breast 133
Colorectal 42 32
Corpus uteri 28
Esophageal 9 2
Gastric 7 3
Hodgkin’s lymphoma 3 3
Laryngeal 5 1
Leukemia 19 11
Liver 11 4
Lung 64 34
Myeloma 8 5
Non-Hodgkin’s lymphoma 24 16
Oral cavity 20 7
Ovarian 10
Pancreatic 15 11
prostate 105
Renal 24 12
The risk-modifying factors included in this ecological study were based on what was used in a previous ecological study of cancer mortality rates in the US [7] was indicated as being correlated with cancer incidence rates after looking at a number of recent maps at the CDCs website. These factors include data averaged at the state level for solar UVB dose, lung cancer incidence rates, diabetes mellitus (DM) rates, obesity rates, and ethanol consumption amounts. Several factors included in the 2006 study are not included in the present study since they no longer seem to be relevant: socioeconomic status, urban/rural residence, and Hispanic prevalence.
Solar UVB dose is used as the index of serum 25-hydroxyvitamin D [25(OH)D] concentration. Solar UVB dose data were obtained from a map in [13] based on data obtained with NASA’s Total Ozone Mapping Spectrometer satellite instrument [20]. Data in this map were carefully digitized for about 500 state economic areas corresponding to maps in the Atlas of Cancer Mortality Rate [14], then averaged by state, weighted by population densities.
Table 2 gives digital values determined from a map [13]. Data for Alaska and Hawaii were omitted because those two states are at the extreme latitudes for the US and, as a result, are not representative of solar UVB’s effect on cancer incidence due to either vitamin D supplementation or very high UVB doses. Wintertime serum 25(OH)D concentrations are about 60%–70% of summertime values [21,22]. An important reason is that 25(OH)D stored in muscles is released into the blood in a manner that keeps serum 25(OH)D concentrations reasonably high in the absence of vitamin D production or oral intake [23,24].
Lung cancer incidence rates were included as one determinant of cancer incidence. Though lung cancer is normally thought of as being from smoking, air pollution also plays a role [25,26], as does diet [27]. Thus, this study uses lung cancer incidence in 2016–2020 for males and females as an index of air pollution, diet, and smoking. Although indices for the three factors might be available, using lung cancer incidence is simpler.
Because rates of DM and obesity have increased considerably in the US and are highest in the southeast, this study includes data for the prevalence of both conditions by state. Data for DM came from the CDC. The data were for the prevalence of DM for non-Hispanic white people (NHWs) older than 18 years averaged over 2016–2020 [28]. The data were obtained by the Behavioral Risk Factor Surveillance System (BRFSS) [29], the nation’s premier system of health-related telephone surveys that collect state data about US residents regarding their health-related risk behaviors, chronic health conditions, and use of preventive services. The BRFSS completes more than 400,000 adult interviews each year. An estimate of the uncertainty of the data can be made using data from Nebraska. For 2019, the prevalence was 8.4 (95% CI, 7.8–9.0)%. Data for five years was used in the analysis, thereby reducing the 95% CI to about 0.6 x 0.6 = 0.4%.
Data for obesity came from the CDC [30], obtained by the BRFSS [29]. Data for NHWs for males and females combined were averaged for 2017–2019. Data were available in three-year averages. It was decided to use a value from the middle of the range of cancer data rather than combine data from two three-year periods. Obesity prevalence for 2015–2017 was compared to that for 2017–2019. The equation for the regression fit to the data was obesity(2017–2019) = 1.07 + 1.01 × obesity(2015–2017), r = 0.98. Thus, it appears that little is lost by using the single three-year averaged data.
Data for the percentage of the population in urban and rural regions by state were obtained from the US Census Bureau [31]. Because no cancer rates were significantly associated with urban/rural residence, those results are not presented.
Data for alcohol consumption is per capita gallons of ethanol consumed by state for 2016 [32] and 2022 [32]. The data from the Vinepair website were based on data from the National Institute on Alcohol Abuse and Alcoholism. The data were more readily accessible from Vinepair than from the US government site. A comparison of the data for the two years found that consumption rates in 2016 were much higher than those for 2022 for three states: Idaho, South Dakota, and Washington. When the 2016 consumption rates for those three states were omitted, the equation for the regression fit to the data was consumption(2022) = -0.032 + 1.05 × consumption(2016), r = 0.98. Thus, the consumption values for 2022 were used for those three states. Since there was very little change between 2016 and 2022, it seems that the 2016 consumption rates are appropriate for use, in part since alcohol consumption prior to cancer incidence is likely more important than near the time of cancer diagnosis.
Data were analyzed using SigmaStat 4.0 (Grafiti, Palo Alto, CA). Data plots were made using KaleidaGraph (Synergy Software, Reading, PA).

3. Results

Figure 1, Figure 2 and Figure 3 are scatter plots of cancer incidence rates with respect to three of the factors used in this study. Figure 1 shows the correlation for CRC for females and males with respect to obesity rates for NHW people in 2017-2019 [30]. Figure 2 shows lung cancer incidence rates for females and males with respect for DM rates from 2016-2020 [28]. Figure 3 shows pancreatic cancer incidence rates for females and males with respect to solar UVB doses for 1992 [20]. These plots indicate that the various factors used in this study have high correlations with various types of cancer.
Table 3 gives the cross-correlation coefficients for the factors used in this ecological study. Factors that are significantly correlated should not be used in the same analysis. Instead, such factors can be used sequentially to see which results in the higher correlation with cancer incidence.
Table 4 and Table 5 give the important statistical analyses from this ecological study. Solar UVB is significantly associated with reduced incidence of bladder, brain (males), breast, corpus uteri, esophageal, gastric, non-Hodgkin’s lymphoma, pancreatic, and renal cancers. Lung cancer was the only risk factor found for laryngeal cancer. However, lung cancer also was significantly associated with colorectal and renal cancers. Diabetes also was significantly associated with breast, liver, and lung cancers. Obesity prevalence was significantly associated with breast, colorectal, and renal cancers. Alcohol (ethanol) consumption was associated with bladder and esophageal cancers. The associations of diabetes and obesity prevalence with incidence rates for various cancers can be due both to direct effects of diabetes and obesity as well as the effects of underlying causes such as lifestyle including diet. See the discussion section for more details.
Table 6 compares the results of this ecological study with the cancer incidence rate ecological study based on data from 1998 to 2002 by Boscoe and Schymura [8] and the ecological study based on cancer mortality rate data for 1950–1969 and 1970–1994 by Grant and Garland [7]. All three studies reported inverse correlations between solar UVB and cancer incidence and mortality rates for bladder, corpus uteri, esophageal, gastric, pancreatic cancer, and non-Hodgkin’s lymphoma (NHL). Brain cancer also was inversely correlated with solar UVB doses in the Boscoe and Schymura study [8]. Cancer sites inversely correlated with solar UVB in one or both of the earlier studies but no longer so associated are colorectal, laryngeal, ovarian, renal cancer, Hodgkin’s lymphoma, and myeloma. The discussion section describes the implications for understanding vitamin D’s role in reducing risk of cancer incidence and mortality rates.

4. Discussion

An analysis of the state of US health from 1990 to 2016 showed that the major risk factors for disability-adjusted life-years (DALYs) by state included, in descending order, tobacco use, high body mass index (BMI), dietary risks, alcohol and drug use, high fasting plasma glucose, high systolic blood pressure, high total cholesterol, impaired kidney function, occupational risks, air pollution, and low physical activity. [33]. The findings in this ecological study are generally consistent with the order of those factors, especially when considering that several are related to diet.

4.1. Diet

A large body of peer-reviewed journal literature reports that diet is a major risk-modifying factor for lung cancer. A case–control study in Texas involving 2139 non–small-cell lung cancer (NSCLC) cases who completed food frequency questionnaires for the year before cancer diagnosis were compared with 2163 matched controls [34]. Participants were from many races/ethnicities, which the analysis did not consider. Three dietary patterns were evaluated: fruits and vegetables, American/Western, and Tex-Mex. The multivariable adjusted odds ratio (aOR) for NSCLC for quantile 5 versus quantile 1 of fruits and vegetables was 0.68 (95% CI, 055–0.85); for American/Western, 1.45 (95% CI, 1.18–1.78); and Tex-Mex, 0.45 (95% CI, 0.37–0.56). For never smokers, the aOR for fruits and vegetables was 0.99 (95% CI, 062–1.58); for American/Western, 2.01 (95% CI, 1.25–3.24); and Tex-Mex, 0.50 (95% CI, 0.32–0.78). The aORs for former smokers and current smokers were similar to the results for all participants.
In this ecological study, the association with lung cancer for diabetes was stronger than for obesity. Obesity is not considered as strong a risk factor for lung cancer as is waist circumference [35]. The same holds true for diabetes [36,37].
Obesity has been identified as a risk factor for several cancers. A 2013 review listed six cancers caused by obesity: breast, colorectal, endometrial, pancreatic, prostate, and renal cell carcinoma [38]. The mechanisms for the three cancers which this study supports are, for breast cancer, decrease in sex hormone–binding globulin and hormonal factors; for colorectal cancer, steroid hormones and chronic inflammation; and for renal cell carcinoma, increased level of estrogen. A 2016 review also listed high BMI as a modifiable risk factor for breast cancer among white women in the US [39]. A 2019 review listed obesity, insulin resistance and adipokine aberrations as being jointly linked to cancer risk [40]. Adipose tissue increases in obesity and results in production of adipokines, which trigger low-grade inflammation and insulin resistance [41]. Also, the altered gut microbiome contributes to inflammation and carcinogenic products [40].
Obesity rates have risen in the US recently. Obesity rates for NHW adult men aged 20 years or older rose from a mean of 26.6% in 1999–2000 to 38.0% in 2015–2016 according to National Health and Nutrition Examination Survey (NHANES) data from 1999–2016 [42]. For NHW adult women, the corresponding values were 33.5% and 41.5%.
A recent article [43] suggested following the Mediterranean diet [44] to manage obesity. The main guidelines are low intake of red and processed meat and refined sugar; moderate intake of low-fat dairy products, poultry, fish, and red wine; and high intake of virgin olive oil, nuts, fruit and vegetables, legumes, and unrefined whole grains. Those recommendations are in general agreement with finding in a 2023 Harvard cohort study [45]
Good evidence exists that diet affects risk of colorectal cancer (CRC). A 2015 article from the Adventist Health Study 2 reported that in a prospective observational study of vegetarians and nonvegetarians, the adjusted hazard ratio for CRC was 0.78 (95% CI, 0.64–0.95) [46]. In an analysis of food intake based on data from NHANES, 2007–2010, and the USDA Food Patterns Equivalents Database, 2007–2010, vegetarians consumed 1862 kcal, whereas nonvegetarians consumed 2058 kcal [47]. A 2019 review listed the driving forces behind the increase in CRC as obesity, sedentary lifestyle, red meat consumption, alcohol, and tobacco
Studies of changes in cancer rates in countries that experienced the nutrition transition to the Western dietary pattern in the past half-century offer more support for diet’s role in cancer risk. For example, an analysis of data from China, Hong Kong, Japan, Korea, and Singapore showed remarkable increases in mortality rates of breast, colon, and prostate cancers and precipitous decreases in mortality of esophageal and gastric cancers [48]. Those results are consistent with findings in the present ecological study for breast and colorectal cancer (with obesity as a risk factor). They also are probably consistent for the findings for esophageal and gastric cancers in that neither diabetes nor obesity was found to be a risk factor. In an ecological study involving eight countries—Brazil, China, Cuba, Egypt, India, Nigeria, Republic of Korea, and Sri Lanka—20-year increases of dietary supply of energy and animal fat were significantly associated with increases in Alzheimer’s disease and dementia rate [49].
Diet is an important risk factor for type 2 diabetes mellitus (T2DM). A 2023 article reported findings from a cohort study involving 205,852 health professionals monitored for up to 32 years [45]. The participants completed food frequency questionnaires every 4 years and described changes in health status. The study included 37 food groups. The data were then correlated with various dietary patterns such as DASH and an American version of the Mediterranean diet. In addition, two empirical dietary patterns were developed: the reversed empirical dietary index for hyperinsulinemia (rEDIH) and reversed empirical dietary inflammatory pattern (rEDIP). Both insulin resistance and systemic inflammation, often associated with obesity, are significant risk factors for many diseases, including T2DM [50,51] and cancer [52]. The rEDIH and rEDIP dietary patterns had the strongest inverse correlations with T2DM. For the highest decile compared with the lowest decile, the multivariate adjusted risk for T2DM was 0.36 (95% CI, 0.35–0.37) for rEDIH and 0.38 (95% CI, 0.37–0.40) for rEDIP. When BMI was added, the values changed to 0.57 (95% CI, 0.54–0.59) and 0.57 (95% CI, 0.55–0.59), respectively. The food groups most strongly associated with high risk of disease were red meats, processed meats, energy drinks, french fries, and refined grains, whereas the food groups most strongly associated with reduced risk included coffee, leafy green vegetables, whole grains, fruit, dark-yellow vegetables, and salad dressing.
Further evidence shows that red meat and processed meat are important risk factors for cancer. A case–control study in Uruguay reported that both types of meat significantly correlated with incidence of NHL [53]. A 2015 review showed that nine of 10 meta-analyses reported red and/or processed meat to be significantly correlated with risk of CRC [54]. A 2021 meta-analysis of prospective studies showed red and/or processed meat to be significantly directly correlated with incidence of breast, colon, colorectal, lung, rectal and renal cancers [55]. It has been proposed that intestinal microbiota helps mediate the link between red/processed meat consumption and risk of colon cancer [56].
A study conducted from 2003 to 2007 reported that participants consuming the highest quartile of the Southern dietary pattern (characterized by added fats, fried food, eggs, organ and processed meats, and sugar-sweetened beverages) experienced an adjusted 37 (95% CI, 1–85)% higher risk of coronary heart disease than those in the lowest quartile [57].
T2DM was treated with a high-fiber, low-fat, plant-predominant diet in Virginia, USA [58], consisting of 40% vegetables, 20% beans, 15% whole grains, 10% fruits, 10% seeds/nuts, and 5% egg whites and nonfat milk. Mean BMI immediately before the lifestyle change was 33 (SD = 6), dropping to 30 (SD = 6) after 6 months. Fasting glucose decreased from 140 mg/dL (SD = 40 mg/dL) to 110 mg/dL (SD = 20 mg/dL). Twenty-two of 59 patients achieved T2DM remission.
An important but relatively little-known fact about the US food supply is that concentrations of essential minerals have been decreasing. A 2002 review outlined the evidence that mineral deficiencies are a major cancer risk [59]. A 2007 article reported the weighted average depletions of essential minerals in the US food supply [60]. It was based on data for cheeses, dairy, and meat from 1940 to 2002 and on fruits and vegetables from 1940 to 1991. Depletions were 29% for calcium, 62% for copper, 37% for iron, 19% for magnesium, 15% for potassium, and 34% for sodium. The reasons for the decreases include acid deposition [61], extraction by harvested agriculture products, and widespread use of glyphosate fertilizer. Glyphosate fertilizer reduces seed and leaf concentrations of important minerals [62]. It decreases mycorrhizal colonization and adversely affects plant–soil feedback [63]. The fertilizer adversely affected soil bacteria, soil chemistry, and mycorrhizal fungi during restoration of a Colorado grassland [64].
A quick search of publications regarding mineral intake and risk of cancer found that higher iron and zinc intake was associated with reduced risk of lung cancer in a 22-year study [65]. Higher combined mineral intakes of 11 minerals were inversely correlated with risk of CRC in postmenopausal women in a prospective study conducted in Iowa [66]. A 2022 review provides a recent overview of the importance of minerals in cancer risk [67].
Minerals are also important for reducing risk of T2DM. A 2020 review outlines the role of minerals and trace elements in reducing risk of insulin resistance and T2DM [68]. Studies in China found that copper and zinc concentrations were inversely correlated with T2DM [69], and that while iron was directly correlated with T2DM but that this association was reduced to a non-significant correlation with higher concentrations of antioxidant minerals including chromium, copper, magnesium, selenium, and zinc [70]
A 2022 review of spatial-temporal patterns of incidence, mortality, and attributable risk factors for T2DM from 1990 to 2019 among 21 world regions showed high BMI (52%), ambient particulate matter (14%), smoking (10%), and secondhand smoke (9%) to be the major contributing factors to T2DM disability-adjusted life-years [71].

4.2. Cigarette Smoking

Cigarette smoking is, of course, an important risk factor for lung cancer as well as several others. A 2002 review listed cancers for which tobacco smoking was considered a risk factor for mortality: cervical, esophageal, laryngeal, lung trachea and bronchus, oral cavity; pancreatic, renal, and urinary bladder [72]. A 2001 review of observational studies of cigarette smoking and risk of colorectal adenoma and CRC showed strong support for causality [73]. Smoking can take 3–4 years to result in CRC. That study suggested that smoking could account for 20% of CRC deaths in the US. The present study shows that only four of those cancers were related to lung cancer: CRC, esophageal, laryngeal, and renal. However, lung cancer was significantly correlated with all less lung cancer for both males and females.
A 2014 article presented maps of cigarette smoking for 1996 and 2012 for males and females in US counties [74]. Smoking rates decreased considerably between the two periods. Rates were higher for males than for females. States in the continental US with the highest smoking rates were in the southeast, from Mississippi to West Virginia.

4.3. Particulate Air Pollution

Particulate air pollution (PM2.5) concentrations in the US are mostly higher in the eastern US but also in California and in and near Idaho [75,76]. A 2009 study based on MODIS satellite data of aerosol optical depth in 2003 and 2004 reported a high correlation of the aerosol optical depth with age- and race-standardized mortality rates of chronic coronary heart disease (βPM2.5 = 0.80; posterior 95% Bayesian credible interval, 0.39–1.23) [75]. For a cohort of 44,610 individuals in the southeast, a 2021 article based on correlations between satellite data and incident cardiovascular disease reported a 13.4% increase in risk with exposure to unhealthy levels of PM2.5 at time of enrollment [77].

4.4. Solar UVB and Vitamin D

The role of solar UVB and vitamin D in reducing risk of cancer incidence and mortality rates was reviewed in 2022 [12]. Supporting evidence comes from various studies stretching back to 1936, when researchers recognized that sun exposure can cause skin cancer but reduce risk of internal cancers [78]. As discussed, ecological studies in the US have yielded good evidence that solar UVB reduces risk of incidence and mortality rates for many cancers [7,8]. Similar results have been reported from China [9], Russia [10], and Nordic countries [11]. No factor other than vitamin D production has been proposed to explain the inverse correlation of solar UVB doses with cancer risk.
Solar UVB doses might have had lower correlations with cancer incidence rates in the 2016–2020 period than in earlier periods in the US for several reasons:
Reduced time spent in the sun when vitamin D can be produced. Because solar UVB reaching Earth’s surface increases as the solar elevation angle increases [79], it is generally recognized that vitamin D can be made effectively when the angle is greater than about 45°.
Wearing sunscreen or sunblock. Many cosmetics now contain sunscreen [80].
Increased prevalence of overweight and obesity. An inverse correlation generally exists between serum 25(OH)D concentration and weight or BMI. A meta-analysis reported: “The prevalence of vitamin D deficiency was 35% higher in obese subjects and 24% higher than in the overweight group [81]. Also, obesity is associated with increased systemic inflammation, thereby increasing risk of cancer [52]. This mechanism could possibly explain why solar UVB and vitamin D are less effective in reducing risk of cancer as found in the VITAL trial [82]. In that trial, participants in three BMI categories, <25 kg/m2, 25 to <30 kg/m2, and >30 kg/m2 had the same increased in 25(OH)D concentration, ~12 ng/mL, but only those with BMI <25 kg/m2 had a significantly reduced risk of cancer incidence.
Prospective cohort studies of cancer incidence with respect to serum 25(OH)D at time of enrollment have shown inverse correlations for bladder, breast, colorectal, liver, lung, and renal cancers [Table 5 in [12]]. An important problem in conducting meta-analyses of such studies is to properly account for changes in serum 25(OH)D since enrollment [83]. As shown in Figure 1 in [12], a nearly linear change occurs in the odds ratio with follow-up time for CRC. When properly accounted for, the relative risk (RR) drops to 0.74 for men and 0.77 for women. That finding differs from what was reported in the 2019 article by McCullough and colleagues in which it was reported that men had considerably lower reduction of CRC than did women [84].
Randomized controlled trials (RCTs) offer less support for vitamin D’s role in reducing risk of cancer incidence and death. The main reason is that most RCTs are based on guidelines for pharmaceutical drugs, not for nutrients. In drug trials, the only source of the drug is the trial itself, participants in the control arm are given a placebo, and results are analyzed on an intention-to-treat basis. That approach is not appropriate for vitamin D because vitamin D is available from other sources besides the trial, and cancer outcomes are related to serum 25(OH)D concentrations, not vitamin D dose. Heaney outlined guidelines for nutrients in 2014 [85]. The important guidelines include that serum 25(OH)D concentrations should be measured before enrollment and that people with low values should be included in the trial; that the vitamin D dose should be large enough to raise serum 25(OH)D concentrations enough to significantly reduce the risk of the health outcome of interest; and that achieved serum 25(OH)D concentration should be measured and used in analyzing the results. A 2022 review further discusses the topic [86].
An example of the importance of following such guidelines is found in the prediabetes-to-diabetes trial conducted by Tufts University gave people in the treatment group 4000 IU/d of vitamin D3 [87]. When results were analyzed by intention to treat, no significant difference in progression to diabetes was apparent between the treatment and placebo arms. However, when results were analyzed by achieved 25(OH)D concentration in the treatment group, researchers found that participants in the vitamin D treatment arm who had 25(OH)D concentrations above 50 ng/mL during the trial had a hazard ratio for progression to diabetes of 0.29 [95% CI, 0.17–0.50]compared with those who maintained a level of 20–30 ng/mL [88].
The largest vitamin D–cancer RCT conducted was Harvard Medical School’s VITamin D and OmegA-3 TriaL (VITAL) [82]. More than 25,000 participants were enrolled, including more than 5000 African Americans. Participants in the treatment arm were given 2000 IU/d of vitamin D3, but participants in both the treatment and placebo arm were permitted to take up to 600 or, if older than 70 years, 800 IU/d of vitamin D3. Nearly 17,000 participants submitted serum 25(OH)D concentrations near time of enrollment. The mean 25(OH)D concentration of those in the treatment arm was near 31 ng/mL. The median follow-up time was 5.3 years. The abstract reported that vitamin D did not significantly reduce risk of cancer incidence but seemed to modestly reduce risk of cancer mortality rates. However, the article reported that the HR for cancer incidence for those with BMI <25 kg/m2 was 0.76 (95% CI, 0.63–0.90). In addition, the HR for African Americans was 0.77 (95% CI, 0.59–101), which barely failed the p = 0.05 test of significance. Those results were not discussed in press conferences regarding the findings, and so busy physicians that read only the abstract were unaware of those results.
The mechanisms whereby vitamin D reduces risk of cancer incidence and mortality rates are well known [12]. Vitamin D reduces cancer risk by surveilling cells and regulating apoptosis, differentiation, and progression. Vitamin D reduces progression by reducing angiogenesis around tumors and reduces metastasis by regulating concentrations of MMP-9. Matrix metalloproteinases (MMPs) are zinc-dependent proteolytic metalloenzymes, of which MMP-9 is one of the most complex. MMP-9 can degrade the components of the extracellular matrix [89]. Many more mechanisms also exist.
Researchers recently determined that patients with digestive tract cancers who are p53-immunoreactive have a much better survival rate with vitamin D supplementation [90]. Holick wrote the accompanying editorial pointing out its importance in treating cancer [91]. That finding seems likely to apply to all types of cancer.
Several reviews make recommendations regarding vitamin D supplementation. A 2024 review outlined the rationale for supplementing with 2000 IU/d (50 μg/d) of vitamin D3 for most adults [92].

4.5. Changes in Cancer Incidence Rates

At this point, it is interesting to examine what changes have been found regarding cancer incidence rates in the US. The source for such data is Cancer statistics, 2024 [2]. The calculations were made using the midpoints for the 1970—1994 and 2016—2020 incidence rate figures. The results are given in Table 7. Interestingly, rates tended to fall for men but to increase for women except for CRC. Lung and bronchus cancer incidence rates for men peaked ca. 1985 for men and 2005 for women [2]. A 2014 article reported that total cigarette smoking among men fell from 1996 to 2012 by 0.9% per year for men and 0.6% for women [74]. A meta-analysis of 77 articles found that active smoking increased the risk of breast cancer (OR -= 1.15 [95% CI, 1.11-1.20]) [93].
The fact that melanoma incidence rates have risen is very interesting. Risk of melanoma is related more to solar UVA (315—400 nm) than solar UVB (290—315 nm). This was demonstrated in an ecological study of melanoma mortality rates for men in 45 countries [94]. It was proposed in 1993 that increased use of sunscreens explained the increasing trends of melanoma incidence from 1935 to 1985 [95]. It was noted that a common ingredient of sunscreens, para-aminobenzoic acid, had peak blockage of UVB at 290 nm, falling to zero blockage at 330 nm, thereby blocking very little solar UVA radiation. A meta-analysis of sunscreen use found a significant increase of melanoma for sunscreen use in countries north of 40° N latitude (OR = 1.6 [95% CI, 1.3--1.9]) but not south of 40° N latitude (OR = 0.7 [95% CI, 0.4--1.0]) [96]. The evidence that vitamin D reduces risk of melanoma is very strong [97]. Thus, it is recommended that for people using sunscreen, they spend a few minutes in the sun before applying sunscreen [98]. This analysis provides strong support for the hypothesis that an important reason why the effect of solar UVB dose was diminished in the 2016—2020 period compared to the 1950—1969 and 1970—1994 periods.

4.6. Public Health Implications

The findings in this ecological study could play an important role in public health policy and cancer prevention. The results indicate that dietary risk factors for lung cancer, obesity, and T2DM and solar UVB exposure/vitamin D are imporant risk-modifying factors for cancer incidence. Government agencies and disease organizations are important determants of public health policies for diet and vitamin D. This section will compare public health policies from those sources with current scientific evidence.
Solar UV and vitamin D policies are considered first. The National Institutes of Health, Office of Dietary Supplements, states the following in their report on vitamin D for health professionals: “Although 25(OH)D functions as a biomarker of exposure, the extent to which 25(OH)D levels also serve as a biomarker of effect on the body (i.e., relating to health status or outcomes) is not clear. Researchers have not definitively identified serum concentrations of 25(OH)D associated with deficiency (e.g., rickets), adequacy for bone health, and overall health.” That statement is based on a report from 2011 [99], prepared before 14 years of additional research. It is in need of an update. While the American Cancer society has good information regarding several ways to reduce risk of cancer, it recommends staying safe in the sun through use of sunscreen, etc., without mentioning that UVB, through production of vitamin D, can reduce risk of cancer so that regular sunscreen use should be accomapied by regular vitamin D supplementation [100]. The US Preventative Services Task Force recommended in 2021 that adults should not be screened for vitamin D deficiency [101]. It concluded “Among asymptomatic, community-dwelling populations with low vitamin D levels, the evidence suggests that treatment with vitamin D has no effect on mortality or the incidence of fractures, falls, depression, diabetes, cardiovascular disease, cancer, or adverse events.” - a statement that clearly needs to be updated.
Red meat and processed meat are among the most important risk factors for T2DM [45,102,103]. There is also good evidence that T2DM can be reversed through a plant-based diet [104]. A 2020 consensus report of the American Diabetes Association and several other organizations recommended that patients work with dieticians but did not make any dietary recommendations [105]. Disease organizations, such as the American Diabetes Association, could do more to inform people how to prevent and reverse DM.
Meat and ultraprocessed food (UPF) are important risk factors for obesity. A 2009 study found that people in the upper quintile of meat consumption consumed 700 more kCal/day than those in the lowest quintile [106]. A 2022 paper opined that the major cause of the obesity epidemic in the US is UPF [107]. The author noted that healthy foods cost more than unhealthy foods, due perhaps to increased subsidies to farmers beginning in the 1970s, leading to more production of foods that could be made into UPF and sold cheaply. A 2023 meta-analysis found that a 10% increase in UPF consumption was associated with a 6% increased risk of obesity [108]. UPF consumption is also an important risk factor for T2DM [109].
The Dietary Guidelines for Americans, 2020—2025 recommended keeping added sugars to less than 10% of total calories/day, saturated fat to less than 10% of calories/day, and sodium to less than 2300 mg/day [110]. There was a recommendation to minimize foods with added sugars, UPF, processed meats, foods high in salt, alcoholic beverages, and topic oils. However, there was no recommendation to limit red meat consumption. A 2022 article noted that the vast majority of dieary guideline committee members had at least several conflicts of interest directly relevant to their work on the scientific report such as working for, or having research funded by, food and/or pharmaceutical companies [111].
Thus, at present, the effort to inform the general public on how to improve their health though dietary choices, vitamin D supplements, and careful sun exposure seems to fall mainly on non-profit organizations that recommend support such measures.

4.7. Strengths and Limitations

The strengths of this study include that it provides information regarding risk-modifying factors for cancer in the US in the period 2016–2020. It includes data for three factors related to diet, DM and obesity prevalence and lung cancer incidence rates in addition so solar UVB doses and alcohol consumption rates. It shows that dietary factors have become comparable if not stronger risk-modifying factors than solar UVB exposure. The ecological study approach is similar to satellite measurement of air quality, which has provided much useful information for health studies [112]. Among other things, it shows the regions of greatest and least risk, and provides data that would be very time consuming to obtain from observational studies.
The limitations include that further risk-modifying factors were not included such as food group consumption patterns, cigarette smoking rates, particulate matter pollution concentrations, and serum 25(OH)D concentrations. In addition, the data sets are approximations based on data for only a fraction of the population for cancer incidence rates and the other factors used. Also, the data for the various factors are for the population as a whole, not for those who developed cancer. Also, the UVB data are from one month in July 1992 and do not adjust for aerosols and clouds, changes in stratospheric ozone, or for and personal time in the sun at times when vitamin D can be produced. They have not been compared with serum 25(OH)D concentrations for older adults living in various states. However, the results of this study should pave the way to additional studies incorporating such data from individuals.

5. Conclusions

This ecological study shows that the contribution of various risk factors for cancer in the US over 36 years has changed from where solar UVB doses were strongly and significantly inversely correlated with many cancers to where only about 10 cancers are inversely correlated, and to a lesser extent. Most notable among those cancers for which solar UVB is no longer identifiable as a risk reduction factor are colorectal and renal cancers, myeloma, and NHL. Dietary factors linked to diabetes and obesity, which previous ecological studies in the US did not consider, now look very important. Additional research is indicated to determine how the different cancer risk-modifying factors interact and how best to change public health policies that affect cancer risk directly or indirectly.

Author Contributions

Conceptualization, William Grant; Formal analysis, William Grant; Methodology, William Grant; Writing – original draft, William Grant.

Funding

This research received no external funding.

Conflicts of Interest

I have had funding from Bio-Tech Pharmacal, Inc. (Fayetteville, AR, USA). The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

References

  1. Xu, J.; Murphy, S.L.; Kochanek, K.D.; Arias, E. Mortality in the United States, 2021. NCHS Data Brief 2022, 1–8. [Google Scholar]
  2. Siegel, R.L.; Giaquinto, A.N.; Jemal, A. Cancer statistics, 2024. CA Cancer J Clin 2024, 74, 12–49. [Google Scholar] [CrossRef] [PubMed]
  3. Gnagnarella, P.; Muzio, V.; Caini, S.; Raimondi, S.; Martinoli, C.; Chiocca, S.; Miccolo, C.; Bossi, P.; Cortinovis, D.; Chiaradonna, F.; et al. Vitamin D Supplementation and Cancer Mortality: Narrative Review of Observational Studies and Clinical Trials. Nutrients 2021, 13, 3285. [Google Scholar] [CrossRef] [PubMed]
  4. Kulhanova, I.; Znaor, A.; Shield, K.D.; Arnold, M.; Vignat, J.; Charafeddine, M.; Fadhil, I.; Fouad, H.; Al-Omari, A.; Al-Zahrani, A.S.; et al. Proportion of cancers attributable to major lifestyle and environmental risk factors in the Eastern Mediterranean region. Int J Cancer 2020, 146, 646–656. [Google Scholar] [CrossRef] [PubMed]
  5. Garland, C.F.; Garland, F.C. Do sunlight and vitamin D reduce the likelihood of colon cancer? Int J Epidemiol 1980, 9, 227–231. [Google Scholar] [CrossRef]
  6. Grant, W.B. An estimate of premature cancer mortality in the U.S. due to inadequate doses of solar ultraviolet-B radiation. Cancer 2002, 94, 1867–1875. [Google Scholar] [CrossRef]
  7. Grant, W.B.; Garland, C.F. The association of solar ultraviolet B (UVB) with reducing risk of cancer: multifactorial ecologic analysis of geographic variation in age-adjusted cancer mortality rates. Anticancer Res 2006, 26, 2687–2699. [Google Scholar]
  8. Boscoe, F.P.; Schymura, M.J. Solar ultraviolet-B exposure and cancer incidence and mortality in the United States, 1993-2002. BMC Cancer 2006, 6, 264. [Google Scholar] [CrossRef]
  9. Chen, W.; Clements, M.; Rahman, B.; Zhang, S.; Qiao, Y.; Armstrong, B.K. Relationship between cancer mortality/incidence and ambient ultraviolet B irradiance in China. Cancer Causes Control 2010, 21, 1701–1709. [Google Scholar] [CrossRef]
  10. Borisenkov, M.F. Latitude of residence and position in time zone are predictors of cancer incidence, cancer mortality, and life expectancy at birth. Chronobiol Int 2011, 28, 155–162. [Google Scholar] [CrossRef]
  11. Grant, W.B. Role of solar UVB irradiance and smoking in cancer as inferred from cancer incidence rates by occupation in Nordic countries. Dermatoendocrinol 2012, 4, 203–211. [Google Scholar] [CrossRef] [PubMed]
  12. Muñoz, A.; Grant, W.B. Vitamin D and Cancer: An Historical Overview of the Epidemiology and Mechanisms. Nutrients 2022, 14, 1448. [Google Scholar] [CrossRef]
  13. Leffell, D.J.; Brash, D.E. Sunlight and skin cancer. Sci Am 1996, 275, 52-53, 56-59. [Google Scholar] [CrossRef]
  14. Devesa, S.S.; Grauman, D.J.; Blot, W.J.; Pennello, G.A.; Hoover, R.N.; Fraumeni, J.F., Jr. Atlas of Cancer Mortality in the United States, 1950-94; National Institutes of Health; National Cancer Institue, Septgember 1999. [Google Scholar]
  15. Group, U.S.C.S.W. U.S. Cancer Statistics Data Visualizations Tool, based on 2022 submission data (1999-2020). Availabe online: https://www.cdc.gov/cancer/dataviz (accessed on March 15, 2024).
  16. Data Visualizations Tool Technical Notes. Availabe online: https://www.cdc.gov/cancer/uscs/technical_notes/stat_methods/rates.htm (accessed on May 3, 2024).
  17. CDC. National Program of Cancer Registries. Availabe online: https://www.cdc.gov/cancer/npcr/index.htm (accessed on.
  18. National Cancer Institute, S., Epidemiology, and End Results (SEER) Program. Overview of the SEER Program. Availabe online: https://seer.cancer.gov/about/overview.html (accessed on April 12, 2024).
  19. Grant, W.B. Lower vitamin-D production from solar ultraviolet-B irradiance may explain some differences in cancer survival rates. J Natl Med Assoc 2006, 98, 357–364. [Google Scholar]
  20. Herman, J.R.; Krotkov, N.; Celarier, E.; Larko, D.; Lebow, G. Distribution of UV radiation at the Earth’s surface from TOMSmeasured UV-backscattered radiances. Journal of Geophysical Research 1999, 104, 12,059–012,076. [Google Scholar] [CrossRef]
  21. Hypponen, E.; Power, C. Hypovitaminosis D in British adults at age 45 y: nationwide cohort study of dietary and lifestyle predictors. Am J Clin Nutr 2007, 85, 860–868. [Google Scholar] [CrossRef] [PubMed]
  22. Kroll, M.H.; Bi, C.; Garber, C.C.; Kaufman, H.W.; Liu, D.; Caston-Balderrama, A.; Zhang, K.; Clarke, N.; Xie, M.; Reitz, R.E.; et al. Temporal relationship between vitamin D status and parathyroid hormone in the United States. PLoS One 2015, 10, e0118108. [Google Scholar] [CrossRef]
  23. Mason, R.S.; Rybchyn, M.S.; Abboud, M.; Brennan-Speranza, T.C.; Fraser, D.R. The Role of Skeletal Muscle in Maintaining Vitamin D Status in Winter. Curr Dev Nutr 2019, 3, nzz087. [Google Scholar] [CrossRef] [PubMed]
  24. Rybchyn, M.S.; Abboud, M.; Puglisi, D.A.; Gordon-Thomson, C.; Brennan-Speranza, T.C.; Mason, R.S.; Fraser, D.R. Skeletal Muscle and the Maintenance of Vitamin D Status. Nutrients 2020, 12, 3270. [Google Scholar] [CrossRef]
  25. Grant, W.B. Air pollution in relation to U.S. cancer mortality rates: an ecological study; likely role of carbonaceous aerosols and polycyclic aromatic hydrocarbons. Anticancer Res 2009, 29, 3537–3545. [Google Scholar]
  26. Moore, J.X.; Akinyemiju, T.; Wang, H.E. Pollution and regional variations of lung cancer mortality in the United States. Cancer Epidemiol 2017, 49, 118–127. [Google Scholar] [CrossRef] [PubMed]
  27. Sun, Y.; Li, Z.; Li, J.; Li, Z.; Han, J. A Healthy Dietary Pattern Reduces Lung Cancer Risk: A Systematic Review and Meta-Analysis. Nutrients 2016, 8, 134. [Google Scholar] [CrossRef] [PubMed]
  28. CDC. Diagnosed Diabetes - Non-Hispanic White (Race-Ethnicity), Adults Aged 18+ Years, Age-Adjusted Percentage, Natural Breaks, All States. Availabe online: https://gis.cdc.gov/grasp/diabetes/diabetesatlas-surveillance.html# (accessed on April 12, 2024).
  29. CDC. Behavioral Risk Factor Surveillance System. Availabe online: https://www.cdc.gov/brfss/index.html (accessed on April 12, 2024).
  30. CDC. Adult Obesity Prevalence Maps. Availabe online: https://www.cdc.gov/obesity/data/maps/2022/downloads/obesity-prevalence-map-by-race-ethnicity-2011-2021-508.pptx (accessed on April 12, 2024).
  31. USCensusBureau. State-level Urban and Rural Information for the 2020 Census and 2010 Census. Availabe online: https://www2.census.gov/geo/docs/reference/ua/ (accessed on April 12, 2024).
  32. The States That Drink the Most Alcohol in America, Mapped and Ranked, 2016. Availabe online: https://vividmaps.com/us-alcohol-consumption/ (accessed on April 1, 2024).
  33. Collaborators, U.S.B.o.D.; Mokdad, A.H.; Ballestros, K.; Echko, M.; Glenn, S.; Olsen, H.E.; Mullany, E.; Lee, A.; Khan, A.R.; Ahmadi, A.; et al. The State of US Health, 1990-2016: Burden of Diseases, Injuries, and Risk Factors Among US States. JAMA 2018, 319, 1444–1472. [Google Scholar] [CrossRef]
  34. Tu, H.; Heymach, J.V.; Wen, C.P.; Ye, Y.; Pierzynski, J.A.; Roth, J.A.; Wu, X. Different dietary patterns and reduction of lung cancer risk: A large case-control study in the U.S. Sci Rep 2016, 6, 26760. [Google Scholar] [CrossRef]
  35. Yu, D.; Zheng, W.; Johansson, M.; Lan, Q.; Park, Y.; White, E.; Matthews, C.E.; Sawada, N.; Gao, Y.T.; Robien, K.; et al. Overall and Central Obesity and Risk of Lung Cancer: A Pooled Analysis. J Natl Cancer Inst 2018, 110, 831–842. [Google Scholar] [CrossRef] [PubMed]
  36. Feller, S.; Boeing, H.; Pischon, T. Body mass index, waist circumference, and the risk of type 2 diabetes mellitus: implications for routine clinical practice. Dtsch Arztebl Int 2010, 107, 470–476. [Google Scholar] [CrossRef] [PubMed]
  37. Dhokte, S.; Czaja, K. Visceral Adipose Tissue: The Hidden Culprit for Type 2 Diabetes. Nutrients 2024, 16, 1015. [Google Scholar] [CrossRef]
  38. Davoodi, S.H.; Malek-Shahabi, T.; Malekshahi-Moghadam, A.; Shahbazi, R.; Esmaeili, S. Obesity as an important risk factor for certain types of cancer. Iran J Cancer Prev 2013, 6, 186–194. [Google Scholar]
  39. Maas, P.; Barrdahl, M.; Joshi, A.D.; Auer, P.L.; Gaudet, M.M.; Milne, R.L.; Schumacher, F.R.; Anderson, W.F.; Check, D.; Chattopadhyay, S.; et al. Breast Cancer Risk From Modifiable and Nonmodifiable Risk Factors Among White Women in the United States. JAMA Oncol 2016, 2, 1295–1302. [Google Scholar] [CrossRef]
  40. Avgerinos, K.I.; Spyrou, N.; Mantzoros, C.S.; Dalamaga, M. Obesity and cancer risk: Emerging biological mechanisms and perspectives. Metabolism 2019, 92, 121–135. [Google Scholar] [CrossRef]
  41. Jung, U.J.; Choi, M.S. Obesity and Its Metabolic Complications: The Role of Adipokines and the Relationship between Obesity, Inflammation, Insulin Resistance, Dyslipidemia and Nonalcoholic Fatty Liver Disease. Int J Mol Sci 2014, 15, 6184–6223. [Google Scholar] [CrossRef] [PubMed]
  42. Wang, Y.; Beydoun, M.A.; Min, J.; Xue, H.; Kaminsky, L.A.; Cheskin, L.J. Has the prevalence of overweight, obesity and central obesity levelled off in the United States? Trends, patterns, disparities, and future projections for the obesity epidemic. Int J Epidemiol 2020, 49, 810–823. [Google Scholar] [CrossRef] [PubMed]
  43. Lee, V. Introduction to the dietary management of obesity in adults. Clin Med (Lond) 2023, 23, 304–310. [Google Scholar] [CrossRef] [PubMed]
  44. Sofi, F.; Macchi, C.; Abbate, R.; Gensini, G.F.; Casini, A. Mediterranean diet and health status: an updated meta-analysis and a proposal for a literature-based adherence score. Public Health Nutr 2014, 17, 2769–2782. [Google Scholar] [CrossRef] [PubMed]
  45. Wang, P.; Song, M.; Eliassen, A.H.; Wang, M.; Fung, T.T.; Clinton, S.K.; Rimm, E.B.; Hu, F.B.; Willett, W.C.; Tabung, F.K.; et al. Optimal dietary patterns for prevention of chronic disease. Nat Med 2023, 29, 719–728. [Google Scholar] [CrossRef] [PubMed]
  46. Orlich, M.J.; Singh, P.N.; Sabate, J.; Fan, J.; Sveen, L.; Bennett, H.; Knutsen, S.F.; Beeson, W.L.; Jaceldo-Siegl, K.; Butler, T.L.; et al. Vegetarian dietary patterns and the risk of colorectal cancers. JAMA Intern Med 2015, 175, 767–776. [Google Scholar] [CrossRef] [PubMed]
  47. Juan, W.J.; Yamini, S.; Britten, P. Food Intake Patterns of Self-identified Vegetarians among the U.S. Population, 2007-2010. Procedia Food Science 2015, 4, 86–93. [Google Scholar] [CrossRef]
  48. Zhang, J.; Dhakal, I.B.; Zhao, Z.; Li, L. Trends in mortality from cancers of the breast, colon, prostate, esophagus, and stomach in East Asia: role of nutrition transition. Eur J Cancer Prev 2012, 21, 480–489. [Google Scholar] [CrossRef] [PubMed]
  49. Grant, W.B. Trends in diet and Alzheimer's disease during the nutrition transition in Japan and developing countries. J Alzheimers Dis 2014, 38, 611–620. [Google Scholar] [CrossRef]
  50. Wu, H.; Ballantyne, C.M. Metabolic Inflammation and Insulin Resistance in Obesity. Circ Res 2020, 126, 1549–1564. [Google Scholar] [CrossRef]
  51. Zatterale, F.; Longo, M.; Naderi, J.; Raciti, G.A.; Desiderio, A.; Miele, C.; Beguinot, F. Chronic Adipose Tissue Inflammation Linking Obesity to Insulin Resistance and Type 2 Diabetes. Front Physiol 2019, 10, 1607. [Google Scholar] [CrossRef]
  52. Deng, T.; Lyon, C.J.; Bergin, S.; Caligiuri, M.A.; Hsueh, W.A. Obesity, Inflammation, and Cancer. Annu Rev Pathol 2016, 11, 421–449. [Google Scholar] [CrossRef] [PubMed]
  53. De Stefani, E.; Fierro, L.; Barrios, E.; Ronco, A. Tobacco, alcohol, diet and risk of non-Hodgkin's lymphoma: a case-control study in Uruguay. Leuk Res 1998, 22, 445–452. [Google Scholar] [CrossRef] [PubMed]
  54. Aykan, N.F. Red Meat and Colorectal Cancer. Oncol Rev 2015, 9, 288. [Google Scholar] [CrossRef]
  55. Farvid, M.S.; Sidahmed, E.; Spence, N.D.; Mante Angua, K.; Rosner, B.A.; Barnett, J.B. Consumption of red meat and processed meat and cancer incidence: a systematic review and meta-analysis of prospective studies. Eur J Epidemiol 2021, 36, 937–951. [Google Scholar] [CrossRef] [PubMed]
  56. Abu-Ghazaleh, N.; Chua, W.J.; Gopalan, V. Intestinal microbiota and its association with colon cancer and red/processed meat consumption. J Gastroenterol Hepatol 2021, 36, 75–88. [Google Scholar] [CrossRef]
  57. Shikany, J.M.; Safford, M.M.; Newby, P.K.; Durant, R.W.; Brown, T.M.; Judd, S.E. Southern Dietary Pattern is Associated With Hazard of Acute Coronary Heart Disease in the Reasons for Geographic and Racial Differences in Stroke (REGARDS) Study. Circulation 2015, 132, 804–814. [Google Scholar] [CrossRef]
  58. Panigrahi, G.; Goodwin, S.M.; Staffier, K.L.; Karlsen, M. Remission of Type 2 Diabetes After Treatment With a High-Fiber, Low-Fat, Plant-Predominant Diet Intervention: A Case Series. Am J Lifestyle Med 2023, 17, 839–846. [Google Scholar] [CrossRef]
  59. Ames, B.N.; Wakimoto, P. Are vitamin and mineral deficiencies a major cancer risk? Nat Rev Cancer 2002, 2, 694–704. [Google Scholar] [CrossRef]
  60. Thomas, D. The mineral depletion of foods available to us as a nation (1940-2002)--a review of the 6th Edition of McCance and Widdowson. Nutr Health 2007, 19, 21–55. [Google Scholar] [CrossRef]
  61. Driscoll, C.T.; Lawrence, G.B.; Bulger, A.J.; Butler, T.J.; Cronan, C.S.; Eagar, C.; Lambert, K.F.; Likens, G.E.; Stodard, J.L.; Weathers, K.C. Acidic Deposition in the Northeastern United States: Sources and Inputs, Ecosystem Effects, and Management Strategies. BioScience 2001, 51, 180–198. [Google Scholar] [CrossRef]
  62. Cakmak, I.; Yazici, A.; Tutus, Y.; Ozturk, L. Glyphosate reduced seed and leaf concentrations of calcium, manganese, magnesium, and iron in non-glyphosate resistant soybean. European Journal of Agronomy 2999, 31, 114–119. [Google Scholar] [CrossRef]
  63. Helander, M.; Saloniemi, I.; Omacini, M.; Druille, M.; Salminen, J.P.; Saikkonen, K. Glyphosate decreases mycorrhizal colonization and affects plant-soil feedback. Sci Total Environ 2018, 642, 285–291. [Google Scholar] [CrossRef] [PubMed]
  64. Bueno de Mesquita, C.P.; Solon, A.J.; Barfield, A.; al., e. Adverse impacts of Roundup on soil bacteria, soil chemistry and mycorrhizal fungi during restoration of a Colorado grassland. Applied Soil Ecology 2023, 185, 104778. [CrossRef]
  65. Muka, T.; Kraja, B.; Ruiter, R.; Lahousse, L.; de Keyser, C.E.; Hofman, A.; Franco, O.H.; Brusselle, G.; Stricker, B.H.; Kiefte-de Jong, J.C. Dietary mineral intake and lung cancer risk: the Rotterdam Study. Eur J Nutr 2017, 56, 1637–1646. [Google Scholar] [CrossRef] [PubMed]
  66. Swaminath, S.; Um, C.Y.; Prizment, A.E.; Lazovich, D.; Bostick, R.M. Combined Mineral Intakes and Risk of Colorectal Cancer in Postmenopausal Women. Cancer Epidemiol Biomarkers Prev 2019, 28, 392–399. [Google Scholar] [CrossRef] [PubMed]
  67. Venturelli, S.; Leischner, C.; Helling, T.; Renner, O.; Burkard, M.; Marongiu, L. Minerals and Cancer: Overview of the Possible Diagnostic Value. Cancers (Basel) 2022, 14. [Google Scholar] [CrossRef] [PubMed]
  68. Dubey, P.; Thakur, V.; Chattopadhyay, M. Role of Minerals and Trace Elements in Diabetes and Insulin Resistance. Nutrients 2020, 12, 1864. [Google Scholar] [CrossRef]
  69. Visvanathan, K.; Mondul, A.M.; Zeleniuch-Jacquotte, A.; Wang, M.; Gail, M.H.; Yaun, S.S.; Weinstein, S.J.; McCullough, M.L.; Eliassen, A.H.; Cook, N.R.; et al. Circulating vitamin D and breast cancer risk: an international pooling project of 17 cohorts. Eur J Epidemiol 2023. [Google Scholar] [CrossRef]
  70. Xu, T.; Wan, S.; Shi, J.; Xu, T.; Wang, L.; Guan, Y.; Luo, J.; Luo, Y.; Sun, M.; An, P.; et al. Antioxidant Minerals Modified the Association between Iron and Type 2 Diabetes in a Chinese Population. Nutrients 2024, 16, 335. [Google Scholar] [CrossRef]
  71. Nanda, M.; Sharma, R.; Mubarik, S.; Aashima, A.; Zhang, K. Type-2 Diabetes Mellitus (T2DM): Spatial-temporal Patterns of Incidence, Mortality and Attributable Risk Factors from 1990 to 2019 among 21 World Regions. Endocrine 2022, 77, 444–454. [Google Scholar] [CrossRef] [PubMed]
  72. Thun, M.J.; Henley, S.J.; Calle, E.E. Tobacco use and cancer: an epidemiologic perspective for geneticists. Oncogene 2002, 21, 7307–7325. [Google Scholar] [CrossRef] [PubMed]
  73. Giovannucci, E. An updated review of the epidemiological evidence that cigarette smoking increases risk of colorectal cancer. Cancer Epidemiol Biomarkers Prev 2001, 10, 725–731. [Google Scholar]
  74. Dwyer-Lindgren, L.; Mokdad, A.H.; Srebotnjak, T.; Flaxman, A.D.; Hansen, G.M.; Murray, C.J. Cigarette smoking prevalence in US counties: 1996-2012. Popul Health Metr 2014, 12, 5. [Google Scholar] [CrossRef]
  75. Hu, Z. Spatial analysis of MODIS aerosol optical depth, PM2.5, and chronic coronary heart disease. Int J Health Geogr 2009, 8, 27. [Google Scholar] [CrossRef] [PubMed]
  76. Bennett, J.E.; Tamura-Wicks, H.; Parks, R.M.; Burnett, R.T.; Pope, C.A., 3rd; Bechle, M.J.; Marshall, J.D.; Danaei, G.; Ezzati, M. Particulate matter air pollution and national and county life expectancy loss in the USA: A spatiotemporal analysis. PLoS Med 2019, 16, e1002856. [Google Scholar] [CrossRef]
  77. Prada, D.; Baccarelli, A.A.; Terry, M.B.; Valdez, L.; Cabrera, P.; Just, A.; Kloog, I.; Caro, H.; Garcia-Cuellar, C.; Sanchez-Perez, Y.; et al. Long-term PM(2.5) exposure before diagnosis is associated with worse outcome in breast cancer. Breast Cancer Res Treat 2021, 188, 525–533. [Google Scholar] [CrossRef]
  78. Peller, S. Carcinogenesis as a means of reducing cancer mortality. Lancet 1936, 228, 552–556. [Google Scholar] [CrossRef]
  79. Engelsen, O. The relationship between ultraviolet radiation exposure and vitamin D status. Nutrients 2010, 2, 482–495. [Google Scholar] [CrossRef]
  80. Shanbhag, S.; Nayak, A.; Narayan, R.; Nayak, U.Y. Anti-aging and Sunscreens: Paradigm Shift in Cosmetics. Adv Pharm Bull 2019, 9, 348–359. [Google Scholar] [CrossRef]
  81. Pereira-Santos, M.; Costa, P.R.; Assis, A.M.; Santos, C.A.; Santos, D.B. Obesity and vitamin D deficiency: a systematic review and meta-analysis. Obes Rev 2015, 16, 341–349. [Google Scholar] [CrossRef]
  82. Manson, J.E.; Cook, N.R.; Lee, I.M.; Christen, W.; Bassuk, S.S.; Mora, S.; Gibson, H.; Gordon, D.; Copeland, T.; D'Agostino, D.; et al. Vitamin D Supplements and Prevention of Cancer and Cardiovascular Disease. N Engl J Med 2019, 380, 33–44. [Google Scholar] [CrossRef]
  83. Grant, W.B. 25-hydroxyvitamin D and breast cancer, colorectal cancer, and colorectal adenomas: case-control versus nested case-control studies. Anticancer Res 2015, 35, 1153–1160. [Google Scholar] [PubMed]
  84. McCullough, M.L.; Zoltick, E.S.; Weinstein, S.J.; Fedirko, V.; Wang, M.; Cook, N.R.; Eliassen, A.H.; Zeleniuch-Jacquotte, A.; Agnoli, C.; Albanes, D.; et al. Circulating Vitamin D and Colorectal Cancer Risk: An International Pooling Project of 17 Cohorts. J Natl Cancer Inst 2019, 111, 158–169. [Google Scholar] [CrossRef] [PubMed]
  85. Heaney, R.P. Guidelines for optimizing design and analysis of clinical studies of nutrient effects. Nutr Rev 2014, 72, 48–54. [Google Scholar] [CrossRef] [PubMed]
  86. Grant, W.B.; Boucher, B.J.; Al Anouti, F.; Pilz, S. Comparing the Evidence from Observational Studies and Randomized Controlled Trials for Nonskeletal Health Effects of Vitamin D. Nutrients 2022, 14, 3811. [Google Scholar] [CrossRef] [PubMed]
  87. Pittas, A.G.; Dawson-Hughes, B.; Sheehan, P.; Ware, J.H.; Knowler, W.C.; Aroda, V.R.; Brodsky, I.; Ceglia, L.; Chadha, C.; Chatterjee, R.; et al. Vitamin D Supplementation and Prevention of Type 2 Diabetes. N Engl J Med 2019, 381, 520–530. [Google Scholar] [CrossRef]
  88. Dawson-Hughes, B.; Staten, M.A.; Knowler, W.C.; Nelson, J.; Vickery, E.M.; LeBlanc, E.S.; Neff, L.M.; Park, J.; Pittas, A.G.; Group, D.d.R. Intratrial Exposure to Vitamin D and New-Onset Diabetes Among Adults With Prediabetes: A Secondary Analysis From the Vitamin D and Type 2 Diabetes (D2d) Study. Diabetes Care 2020, 43, 2916–2922. [Google Scholar] [CrossRef]
  89. Mondal, S.; Adhikari, N.; Banerjee, S.; Amin, S.A.; Jha, T. Matrix metalloproteinase-9 (MMP-9) and its inhibitors in cancer: A minireview. Eur J Med Chem 2020, 194, 112260. [Google Scholar] [CrossRef]
  90. Kanno, K.; Akutsu, T.; Ohdaira, H.; Suzuki, Y.; Urashima, M. Effect of Vitamin D Supplements on Relapse or Death in a p53-Immunoreactive Subgroup With Digestive Tract Cancer: Post Hoc Analysis of the AMATERASU Randomized Clinical Trial. JAMA Netw Open 2023, 6, e2328886. [Google Scholar] [CrossRef]
  91. Holick, M.F. The Death D-Fying Vitamin D3 for Digestive Tract Cancers-The p53 Antibody Connection. JAMA Netw Open 2023, 6, e2328883. [Google Scholar] [CrossRef] [PubMed]
  92. Pludowski, P.; Grant, W.B.; Karras, S.N.; Zittermann, A.; Pilz, S. Vitamin D Supplementation: A Review of the Evidence Arguing for a Daily Dose of 2000 International Units (50 microg) of Vitamin D for Adults in the General Population. Nutrients 2024, 16, 391. [Google Scholar] [CrossRef] [PubMed]
  93. He, Y.; Si, Y.; Li, X.; Hong, J.; Yu, C.; He, N. The relationship between tobacco and breast cancer incidence: A systematic review and meta-analysis of observational studies. Front Oncol 2022, 12, 961970. [Google Scholar] [CrossRef] [PubMed]
  94. Garland, C.F.; Garland, F.C.; Gorham, E.D. Epidemiologic evidence for different roles of ultraviolet A and B radiation in melanoma mortality rates. Ann Epidemiol 2003, 13, 395–404. [Google Scholar] [CrossRef] [PubMed]
  95. Garland, C.F.; Garland, F.C.; Gorham, E.D. Rising trends in melanoma. An hypothesis concerning sunscreen effectiveness. Ann Epidemiol 1993, 3, 103–110. [Google Scholar] [CrossRef]
  96. Gorham, E.D.; Mohr, S.B.; Garland, C.F.; Chaplin, G.; Garland, F.C. Do sunscreens increase risk of melanoma in populations residing at higher latitudes? Ann Epidemiol 2007, 17, 956–963. [Google Scholar] [CrossRef] [PubMed]
  97. Brozyna, A.A.; Hoffman, R.M.; Slominski, A.T. Relevance of Vitamin D in Melanoma Development, Progression and Therapy. Anticancer Res 2020, 40, 473–489. [Google Scholar] [CrossRef] [PubMed]
  98. Pludowski, P.; Kos-Kudla, B.; Walczak, M.; Fal, A.; Zozulinska-Ziolkiewicz, D.; Sieroszewski, P.; Peregud-Pogorzelski, J.; Lauterbach, R.; Targowski, T.; Lewinski, A.; et al. Guidelines for Preventing and Treating Vitamin D Deficiency: A 2023 Update in Poland. Nutrients 2023, 15. [Google Scholar] [CrossRef] [PubMed]
  99. Ross, A.C.; Manson, J.E.; Abrams, S.A.; Aloia, J.F.; Brannon, P.M.; Clinton, S.K.; Durazo-Arvizu, R.A.; Gallagher, J.C.; Gallo, R.L.; Jones, G.; et al. The 2011 report on dietary reference intakes for calcium and vitamin D from the Institute of Medicine: what clinicians need to know. J Clin Endocrinol Metab 2011, 96, 53–58. [Google Scholar] [CrossRef]
  100. Cancer Risk and Prevention. Availabe online: https://www.cancer.org/cancer/risk-prevention.html (accessed on May 3, 2024).
  101. Kahwati, L.C.; LeBlanc, E.; Weber, R.P.; Giger, K.; Clark, R.; Suvada, K.; Guisinger, A.; Viswanathan, M. Screening for Vitamin D Deficiency in Adults: Updated Evidence Report and Systematic Review for the US Preventive Services Task Force. JAMA 2021, 325, 1443–1463. [Google Scholar] [CrossRef]
  102. Grant, W.B.; Blake, S.M. Diet's Role in Modifying Risk of Alzheimer's Disease: History and Present Understanding. J Alzheimers Dis 2023, 96, 1353–1382. [Google Scholar] [CrossRef] [PubMed]
  103. Toi, P.L.; Anothaisintawee, T.; Chaikledkaew, U.; Briones, J.R.; Reutrakul, S.; Thakkinstian, A. Preventive Role of Diet Interventions and Dietary Factors in Type 2 Diabetes Mellitus: An Umbrella Review. Nutrients 2020, 12. [Google Scholar] [CrossRef] [PubMed]
  104. McMacken, M.; Shah, S. A plant-based diet for the prevention and treatment of type 2 diabetes. J Geriatr Cardiol 2017, 14, 342–354. [Google Scholar] [CrossRef] [PubMed]
  105. Powers, M.A.; Bardsley, J.K.; Cypress, M.; Funnell, M.M.; Harms, D.; Hess-Fischl, A.; Hooks, B.; Isaacs, D.; Mandel, E.D.; Maryniuk, M.D.; et al. Diabetes Self-management Education and Support in Adults With Type 2 Diabetes: A Consensus Report of the American Diabetes Association, the Association of Diabetes Care & Education Specialists, the Academy of Nutrition and Dietetics, the American Academy of Family Physicians, the American Academy of PAs, the American Association of Nurse Practitioners, and the American Pharmacists Association. Diabetes Care 2020, 43, 1636–1649. [Google Scholar] [CrossRef] [PubMed]
  106. Wang, Y.; Beydoun, M.A. Meat consumption is associated with obesity and central obesity among US adults. Int J Obes (Lond) 2009, 33, 621–628. [Google Scholar] [CrossRef]
  107. Temple, N.J. The Origins of the Obesity Epidemic in the USA-Lessons for Today. Nutrients 2022, 14. [Google Scholar] [CrossRef] [PubMed]
  108. Moradi, S.; Entezari, M.H.; Mohammadi, H.; Jayedi, A.; Lazaridi, A.V.; Kermani, M.A.H.; Miraghajani, M. Ultra-processed food consumption and adult obesity risk: a systematic review and dose-response meta-analysis. Crit Rev Food Sci Nutr 2023, 63, 249–260. [Google Scholar] [CrossRef]
  109. Moradi, S.; Hojjati Kermani, M.A.; Bagheri, R.; Mohammadi, H.; Jayedi, A.; Lane, M.M.; Asbaghi, O.; Mehrabani, S.; Suzuki, K. Ultra-Processed Food Consumption and Adult Diabetes Risk: A Systematic Review and Dose-Response Meta-Analysis. Nutrients 2021, 13. [Google Scholar] [CrossRef]
  110. Snetselaar, L.G.; de Jesus, J.M.; DeSilva, D.M.; Stoody, E.E. Dietary Guidelines for Americans, 2020-2025: Understanding the Scientific Process, Guidelines, and Key Recommendations. Nutr Today 2021, 56, 287–295. [Google Scholar] [CrossRef]
  111. Mialon, M.; Serodio, P.M.; Crosbie, E.; Teicholz, N.; Naik, A.; Carriedo, A. Conflicts of interest for members of the US 2020 dietary guidelines advisory committee. Public Health Nutr 2022, 27, e69. [Google Scholar] [CrossRef]
  112. Holloway, T.; Miller, D.; Anenberg, S.; Diao, M.; Duncan, B.; Fiore, A.M.; Henze, D.K.; Hess, J.; Kinney, P.L.; Liu, Y.; et al. Satellite Monitoring for Air Quality and Health. Annu Rev Biomed Data Sci 2021, 4, 417–447. [Google Scholar] [CrossRef] [PubMed]
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Figure 1. Scatter plot of CRC incidence for NHW males and females [15] vs. obesity prevalence (%) for NHW men and women in the period 2017-2019 [30].
Figure 1. Scatter plot of CRC incidence for NHW males and females [15] vs. obesity prevalence (%) for NHW men and women in the period 2017-2019 [30].
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Figure 2. Scatter plot for lung cancer incidence rates by state for NHW males and females [15] vs. diabetes mellitus prevalence (%) for 2016-2020 [28].
Figure 2. Scatter plot for lung cancer incidence rates by state for NHW males and females [15] vs. diabetes mellitus prevalence (%) for 2016-2020 [28].
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Figure 3. Scatter plot of pancreatic cancer incidence rates by state for the period 2016-2020 [15] vs. solar UVB doses for July 1992 [20].
Figure 3. Scatter plot of pancreatic cancer incidence rates by state for the period 2016-2020 [15] vs. solar UVB doses for July 1992 [20].
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Table 2. DNA-weighted UVB dose at Earth’s surface, by US state, July 1992. Adapted from a map from [13].
Table 2. DNA-weighted UVB dose at Earth’s surface, by US state, July 1992. Adapted from a map from [13].
State UVB Dose (kJ/m2)
Alabama 6.0
Alaska
Arkansas 5.7
Arizona 9.0
California 7.5
Colorado 8.2
Connecticut 4.7
Delaware 4.7
District of Columbia 4.7
Florida 8.0
Georgia 7.2
Hawaii
Idaho 6.0
Illinois 4.5
Iowa 4.7
Indiana 4.7
Kansas 6.3
Kentucky 5.8
Louisiana 7.5
Massachusetts 4.6
Maine 4.1
Maryland 4.7
Michigan 4.2
Minnesota 4.1
Missouri 6.5
Mississippi 7.0
Montana 4.7
North Carolina 6.6
North Dakota 6.2
Nebraska 5.1
New Hampshire 4.1
New Jersey 5.2
New Mexico 9.5
Nevada 8.5
New York 4.7
Ohio 4.7
Oklahoma 7.5
Oregon 5.2
Pennsylvania 4.5
Rhode Island 4.7
South Carolina 7.2
South Dakota 4.5
Tennessee 6.3
Texas 7.8
Utah 8.0
Virginia 6.0
Vermont 4.2
Washington 4.5
Wisconsin 4.5
West Virginia 5.2
Wyoming 6.0
UVB, ultraviolet-B radiation.
Table 3. Cross-correlation analysis, r, adjusted r2, p value.
Table 3. Cross-correlation analysis, r, adjusted r2, p value.
Factor DM LCF LCM Obs UVB
Alcohol 0.40, 0.14, 0.007 0.03 0.00, xx 0.24, 0.03, 0.12 0.25, 0.04, 0.11 0.35, 0.10, 0.02
Diabetes 0.59, 0.33, * 0.84, 0.69, * 0.84, 0.69, * 0.13, 0.00, --
Lung cancer, F 0.88, 0.77, * 0.54, 0.28, * 0.14, 0.00, --
Lung cancer, M 0.75, 0.55, * 0.39, 0.13, 0.008
Obesity 0.11, 0.00, --
(*) <0.001; Alc, alcohol consumption, 2016; DM, diabetes rates for non-Hispanic white (NHW) males and females, 2016; LCM, lung cancer incidence rate, NHW, 2016–2020 (M, males); Obs, obesity rates for NHW males and females, 2017–2019; UVB, solar ultraviolet-B at Earth’s surface in July 1992, adapted from [20].
Table 4. Regression results for cancer incidence rates for males, by US state, 2016–2020.
Table 4. Regression results for cancer incidence rates for males, by US state, 2016–2020.
Cancer Equation r, adjusted r2, p (p)
All 410 + (4.2 × Obs) − (7.3 × UVB) 0.58, 0.30, 0.002, 0.02
350 + (4.8 × Obs) 0.49, 0.23, <0.001
549 − (9.5 × UVB) 0.39, 0.13, 0.008
All less lung 420 - (7.34 × UVB) + (0.77 * LCM) 0.54, 0.26, 0.006, 0.01
480 - (8.3 × UVB) 0.43, 0.16, 0.004
370 + (0.89 × LCM) 0.39, 0.13, 0.008
Bladder 43 − (1.7 × UVB) + (1.5 × Alc) 0.72, 0.50, <0.001, 0.09
48 − (1.9 × UVB) 0.70, 0.47, <0.001
29 + (3.1 × Alc) 0.42, 0.16, 0.004
Brain 8.5 − (0.14 × UVB) 0.31, 0.08, 0.03
Colorectal 12 + (0.13 × LCM) + (0.70 × Obs) 0.82, 0.65, <0.001
10 + (1.0 × Obs) 0.78, 0.61, <0.001
24 + (0.28 × LCM) 0.73, 0.52, <0.001
Esophageal 5.7+ (0.048 × LCM) − (0.29 × UVB) + (0.69 × Alc) 0.77, 0.56, <0.001, 0.001, 0.006
8.5 − (0.39 × UVB) + (0.039 × LCM) 0.71, 0.48, <0.001, <0.001
11 − (0.44 × UVB) 0.57, 0.31, <0.001
7.1 + (0.69 × Alc) 0.33, = 0.09, 0.03
Gastric 9.0 − (0.36 × UVB) 0.55, 0.29, <0.001
Larynx 1.3 + (0.058 × LCM) 0.57, 0.31, <0.001
Liver 4.5 + (0.66 × DM) 0.37, 0.12, 0.01
Lung 11 + (6.4 × DM) 0.84, 0.69, <0.001
72 − (1.2 × UVB) 0.14, 0.000
Non-Hodgkin’s lymphoma 28 − (0.84 × UVB) 0.56, 0.29, <0.001
Pancreatic 18 − (0.40 × UVB) 0.55, 0.29, <0.001
Prostate 130 − (3.4 × UVB) 0.14, 0.15, 0.005
Renal 11 + (0.19 × LCM) 0.75, 0.55, <0.001
3.6 + (0.66 × Obs) 0.74, 0.53, <0.001
Alc, alcohol consumption, 2016; DM, diabetes rates for non-Hispanic white (NHW) males and females, 2016; LCM, lung cancer incidence rate, NHW, 2016–2020, males; Obs, obesity rates for NHW males and females, 2017–2019; UVB, solar ultraviolet-B at Earth’s surface in July 1992, adapted from [20].
Table 5. Regression results for cancer incidence rates for females, by US state, 2016–2020.
Table 5. Regression results for cancer incidence rates for females, by US state, 2016–2020.
Type Equation r, adjusted r2, p (p)
All 440 − (9.8 × UVB)+ (1.7 × Obs) 0.63, 0.37, <0.001, 0.06
430 − (7.7 × UVB) 0.53, 0.26, <0.001
360 + (2.5 × Obs) 0.34, 0.09, 0.03
All less lung 36 − (5.6 × UVB) + (0.94 × LCF) 0.63, 0.36, 0.006, 0.009
430 − (7.7 × UVB) 0.53, 0.26, <0.001
310 + (1.3 × LCF) 0.52, 0.25, <0.001
Bladder 9.5 − (0.50 × UVB) + (1.0 × Alc) 0.76, 0.56, <0.001, <0.001
13 − (0.64 × UVB) 0.65, 0.41, <0.001
5.5 + (1.5 × Alc) 0.59, 0.33, <0.001
5.2 + (0.074 × LCF) 0.45, 0.18, 0.002
Breast 160 − (2.3 × UVB) − (2.2 × DM) 0.66, 0.41, <0.001, <0.001
180 − (2.8 × UVB) − (0.91 × Obs) 0.65, 0.39, <0.001, <0.001
150 − (2.4 × DM) 0.51, 0.24, <0.001
150 − (2.5 × UVB) 0.47, 0.20, 0.001
150 – (0.70 × Obs) 0.34, 0.10, 0.02
Colorectal 10 + (0.73 × Obs) 0.80, 0.63, <0.001
21 + (0.21 × LCF) 0.53, 0.27, <0.001
Corpus uteri 38 − (1.9 × UVB) 0.72, 0.50, <0.001
Esophageal 2.2 − (0.13 × UVB)+ (0.16 × Alc) 0.72. 0.49, <0.001, 0.02
2.7 − (0.15 × UVB) 0.67, 0.44, <0.001
1.1 + (0.29 × Alc) 0.47, 0.21, 0.001
1.0 + (0.015 × LVF) 0.41, 0.15, 0.006
Gastric 4.1 − (0.14 × UVB) 0.38, 0.12, 0.01
Laryngeal 0.19 + (0.021 × LCF) 0.39, 0.13, 0.01
Liver 2.8 + (0.13 × DM) 0.36, 0.11, 0.02
Lung 43 + (3.2 × DM) − (2.7 × UVB) 0.75, 0.54, <0.001, <0.001
30 + (1.1 × Obs) − (1.8 × UVB) 0.63, 0.36, <0.001, 0.02
30 + (2.9 × DM) 0.59, 0.33, <0.001
67 − (2.2 × UVB) 0.39, 0.13, 0.008
Non-Hodgkin’s lymphoma 19 − (0.60 × UVB) 0.51, 0.25, <0.001
Pancreatic 14 − (0.44 × UVB) 0.67, 0.44, <0.001
Renal −2.0 + (0.46 × Obs) 0.84, 0.69, <0.001
4.9 + (0.13 × LCF) 0.55, 0.29, <0.001
Alc, alcohol consumption, 2016; DM, diabetes rates for non-Hispanic white (NHW) males and females, 2016; LCF, lung cancer incidence rate, NHW, 2016–2020, females; Obs, obesity rates for NHW males and females, 2017–2019; UVB, solar UVB at Earth’s surface in July 1992, adapted from [20].
Table 6. Comparison of findings regarding solar UVB dose and cancer incidence between the present study and two ecological studies in 2006 [7,8].
Table 6. Comparison of findings regarding solar UVB dose and cancer incidence between the present study and two ecological studies in 2006 [7,8].
Cancer UVB (2016–2020) UVB, males
[8]*		 UVB, females
[8]*		 UVB (2006), males
[7]		 UVB (2006), females
[7]
Bladder y 1.13 1.15 y y
Brain M only 1.08 1.07
Breast y 1.06 y y
Cervical 0.84 n
Colon 1.11 1.14 y y
Colorectal n
Corpus uteri y 1.49 y
Esophageal y 1.27 1.07 y y
Gastric y 1.42 1.27 y y
Hodgkin’s lymphoma n 1.16 1.19 y y
Laryngeal n 0.87 0.80 y y
Leukemia n 1.09 1.15 n n
Liver n 1.01 1.05 n n
Lung F only n n
Myeloma n 1.19 1.22 n n
Non-Hodgkin’s lymphoma y 1.08 1.09 y y
Oral cavity n 0.77 0.83 n n
Ovarian n 1.03 y
Pancreatic y 1.09 1.17 y n
Prostate y 1.20 ?
Rectal 1.27 1.14 y y
Renal n 1.09 1.17 y y
*A value greater than 1.00 indicates higher cancer rates at higher latitudes (lower solar UVB doses). F, females; M, males; UVB, ultraviolet-B radiation.
Table 7. Incidence rates for various cancer sites for 1982 and 2018 [2].
Table 7. Incidence rates for various cancer sites for 1982 and 2018 [2].
Cancer site M, Inc. rate*1982 M, Inc. rate* 2018 M Inc. rate*, ratio 2018 to 1982 F, Inc. rate*, 1982 F, Inc. rate* 2018 F, Inc. rate* ratio 2018 to 1982
All 501 483 0.96 376 421 1.12
Breast 107 134 1.25
Colorectum 68 40 0.59 55 33 0.60
Corpus uteri 28 28 1.00
Liver 5 12 2.4 3 4 1.3
Lung & bronchus 94 51 0.54 36 43 1.2
Melanoma 12 34 2.8 11 20 1.8
Prostate 105 114 1.09
Urinary bladder 37 31 0.84
F, female; Inc, incidence; M, male; *, cases/100/000/year.
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