Next Article in Journal
Avenanthramide Improves Colonic Damage Induced by Food Allergies in Mice through Altering Gut Microbiota and Regulating Hsp70-NF-κB Signaling
Next Article in Special Issue
Nutritional Effects of Removing a Serving of Meat or Poultry from Healthy Dietary Patterns—A Dietary Modeling Study
Previous Article in Journal
Açaí (Euterpe oleracea Mart.) in Health and Disease: A Critical Review
Previous Article in Special Issue
Nutrients and Dementia: Prospective Study
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Nutrients
Volume 15
Issue 4
10.3390/nu15040993
nutrients-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

The Consumption of Animal and Plant Foods in Areas of High Prevalence of Stroke and Colorectal Cancer

by
Kellie E. Mayfield
1,*,
Julie Plasencia
2,
Morgan Ellithorpe
3,
Raeda K. Anderson
4 and
Nicole C. Wright
5
1
Department of Nutrition, Georgia State University, Atlanta, GA 30302, USA
2
Department of Dietetics and Human Nutrition, University of Kentucky, Lexington, KY 40506, USA
3
Department of Communication, University of Delaware, Newark, DE 19716, USA
4
Virginia C. Crawford Research Institute, Shepherd Center and Department of Sociology, Georgia State University, Atlanta, GA 30302, USA
5
Department of Epidemiology, School of Public Health, University of Alabama at Birmingham, Birmingham, AL 35294, USA
*
Author to whom correspondence should be addressed.
Nutrients 2023, 15(4), 993; https://doi.org/10.3390/nu15040993
Submission received: 22 December 2022 / Revised: 8 February 2023 / Accepted: 10 February 2023 / Published: 16 February 2023
(This article belongs to the Special Issue Nutritional Value of Meat and Meat Products and Their Role in Human Health)
Versions Notes

Abstract

:
Diets of red and processed meat have been reported as important risk factors for developing colorectal cancer. Given the racial and ethnic differences in the incidence of colorectal cancer, patterns of food consumption, and areas of residence, particularly in the South, more data is needed on the relationship between residing in a high stroke area, colorectal cancer incidence levels, and red meat and processed meat consumption. We created online surveys to ascertain meat, red meat, and healthy food consumption levels. We used OLS regression to evaluate the association between residence in Stroke Belt states and colorectal cancer incidence quartiles with food consumption. We further used path analysis using structural equation modeling to evaluate if age, sex, race/ethnicity, income, and comorbidity index mediated the association between residence in the eight-state Stroke Belt, colorectal cancer incidence groups, and meat consumption. Our sample included 923 participants, with 167 (18.1%) residing in the Stroke Belt and 13.9% being in the highest colorectal cancer incidence group. The findings show that residing in a Stroke Belt state is predictive of the consumption of overall meat 0.93 more days per week or red meat 0.55 more days per week compared to those not residing in a Stroke Belt state. These data can be used to develop future diet interventions in these high-risk areas to reduce rates of colorectal cancer and other negative health outcomes.

1. Introduction

Chronic diseases associated with meat consumption are costly and unequally distributed. Overconsumption of red and processed meat is a well-established risk factor for multiple types of cancers, particularly colorectal cancer [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15], obesity [16], type 2 diabetes, [17] and cardiovascular disease [14]. In the US, colorectal cancer is the second costliest cancer, accounting for 12.6% of all cancer treatment costs [18]. Red and processed meat (e.g., beef, pork, sausages, and hot dogs) are strongly associated with risk of multiple cancers and mortality [8,9,11,12,14]. Studies show that more than 100–120 g of red meat per day increases the risk of colorectal cancer by up to 24%, and more than 25–30 g of processed meat daily increases colorectal cancer risk by up to 49% [1,19,20].
On average, Americans consumed 186 g of processed meat per week and 284 g of unprocessed red meat per week in 2016 [21]. Consumption volumes differ due to numerous factors, particularly race and ethnicity. Studies show that Black and Hispanic Americans consume more meat, especially red and processed meat, than other groups [22,23]. Non-Hispanic Black Americans consumed the lowest level of unprocessed red meat, whereas Hispanics consumed the highest level of unprocessed red meat [21]. Similarly, cancer incidence and mortality rates differ by type of cancer, ethnicity, and race. Hispanic Americans have a greater risk for liver and stomach cancers compared to White Americans [24], and they have equal or lower risk for colorectal [25], breast [26], and ovarian cancers [27]. Hispanic Americans have a higher risk than non-Hispanic White Americans for obesity [28,29,30,31] and diabetes [28,32,33]. However, Black Americans and Hispanic Americans have greater risk than non-Hispanic White Americans for obesity [28,29,30,34,35], diabetes [33,35,36,37], cardiovascular disease [28,35,38,39,40], and both cancer incidence and mortality [41,42].
Certain U.S. regions have higher rates of diet-related diseases. Evidence regarding the concomitant incidence of colorectal cancer and heart disease by region is limited. Stroke is a leading cause of mortality and is of particular concern in what is now labeled the Stroke Belt, which is composed of southern U.S. states. Treatments for stroke is expensive, costing over 50 billion between 2017 and 2018, and it is a contributing factor to long-term disability [43]. The highest concentrations of stroke deaths are mostly in the southeast region, with some of the heaviest concentration in the Deep South, a region of eight states that includes Alabama, Arkansas, Georgia, Louisiana, Mississippi, North Carolina, South Carolina, and Tennessee [44]. Geographic location matters because the south and southeast regions of the U.S. are also where the majority of its Black and Hispanic populations reside [45,46]. Evidence also shows that red meat consumption is a leading risk factor for type 2 diabetes in the U.S. and other parts of the world, such as Australasia [47].
The consumption of healthy foods, such as leafy greens and starches, is associated with reduced stroke risk [48], and evidence regarding the contribution of meat consumption, especially red and processed meat, to risk of stroke is weak [49]. Furthermore, adoption of alternative and plant-based diets such as pescatarian (no meats, except seafood), vegetarian (no meat, but eggs and dairy are included), and vegan (no animal or foods that are sourced from animals) are associated with lower morbidity and mortality from cancer, diabetes, and cardiovascular disease, as well as with reduced diet-related risk factors such as body mass index (BMI), cholesterol, and blood pressure [50,51,52].
Studies show that healthier diet patterns consisting of plant foods can reduce the risk and prevalence of chronic diseases such as cardiovascular disease, hypertension, and some cancers. However, the relationship between living in the Stroke Belt, CRC rates, and diet-related diseases to race/ethnicity is less clear. Therefore, using a nationally representative sample of Hispanics, Blacks, and non-Hispanic Whites, this cross-sectional study examined the association between Stroke Belt residence and colorectal cancer incidence and diet, and it evaluated if age, race/ethnicity, income, and number of health conditions mediates the association between Stroke Belt residence and colorectal cancer incidence with food consumption.

2. Materials and Methods

The study was conducted in accordance with the Declaration of Helsinki, and it was determined exempt (category 2) by the Institutional Review Board of Michigan State University. A priori, our intention was to recruit a stratified sample of approximately equal numbers of Black, Hispanic, and non-Hispanic White participants. We first used Amazon’s Mechanical Turk (MTurk) [53], but data has found that the MTurk participant pool tends to overrepresent Whites compared to the general U.S. population [54]; thus, we also employed a second survey tool from Qualtrics Research Suite (Qualtrics, 2009, Provo, UT, USA. Version 12.018). Qualtrics maintains a non-probability, nationally representative panel based on airline lists, online shopping centers, and targeted customer profiles. Participants of this sample are compensated based on their preferred method of compensation (e.g., points redeemable for flyer miles, shopping points, and cash rewards). An e-mail was sent to their panelists who identified themselves as non-Hispanic White, non-Hispanic Black, and Hispanics living in the U.S. For both MTurk and Qualtrics, eligible and interested participants were provided with the link to the informed consent form for participation followed by the web-based survey that included sociodemographic questions. The combined datasets yielded a total of n = 1069. After combining samples, we excluded participants with missing demographic, state, or diet data. The final sample size was 923 total participants; 661 (71.6%) identified as female, the median age was 38.3 years (SD = 15.0, range 18 to 86), 261 (28.3%) were non-Hispanic Black or African American, 278 (30.1%) were Hispanic, and 384 (41.6%) were non-Hispanic White. For additional participant statistics, please see Table 1.
Colorectal cancer Incidence. Colorectal cancer incidence data was based on colorectal cancer rates per 100,000 people from 2015 data. We grouped states into quartiles based on colorectal cancer incidence, which included the following: Q4, the highest colorectal cancer incidence (≥42.3), included Alaska, Alabama, Mississippi, Louisiana, Arkansas, West Virginia, Ohio, Kentucky, Illinois, Iowa, North Dakota, and Nebraska; Q3, the second-highest colorectal cancer incidence (41.9–38.7), included Montana, South Dakota, Oklahoma, Missouri, Indiana, Tennessee, Georgia, South Carolina, Pennsylvania, Delaware, New Jersey, and New York; Q2, the second-lowest colorectal cancer incidence (38.4–35.0), included California, Idaho, Texas, Kansas, Minnesota, Wisconsin, Michigan, North Carolina, Maryland, Connecticut, Massachusetts, New Hampshire, and Maine; Q1, the lowest colorectal cancer incidence (<34.9), included Florida, New Mexico, Arizona, Nevada, Oregon, Washington, Wyoming, Utah, Colorado, Virginia, Vermont, and Rhode Island.
Stroke belt states. Participants provided their state of residence, and this information was used to delineate whether a participant was living in a Stroke Belt state, which was defined as residing in one of the following eight states: Alabama, Arkansas, Georgia, Louisiana, Mississippi, North Carolina, South Carolina, or Tennessee; n = 167 (18.1%) of the sample resided in a Stroke Belt state.
Food consumption. Participants were provided with a list of food categories and were asked to indicate in how many days in the past week they had consumed each food. We grouped foods into the following categories: (1) meat (beef, pork, poultry, fish, and venison), (2) red meat (beef, pork, and venison only), and (3) healthy foods (fruits, leafy green vegetables, starchy vegetables, grains, nuts and seeds, tofu, seitan, and tempeh). We calculated the sum of the days each food group was consumed in the past week. This means that the more foods in each category a person reported consuming over more days in the past week, the higher their score was on the measure. Eggs and dairy were also measured but were not included in these measures due to their controversiality in being included in measures of healthy foods, and they do not belong in either meat category.
Health condition diagnoses. Participants were asked to indicate whether they had ever been diagnosed with any of the following health conditions: heart disease, diabetes, high blood pressure, high cholesterol, liver disease, or kidney disease. These conditions were selected as being some that are well-known to be associated with meat consumption. The number of “yes” responses was summed to create an overall measure of health conditions. Those who responded yes to more than three were coded together with those who had three, as only one or two people had scores of four, five, or six; therefore, the final measure was scaled from zero to three as a count of the number of health conditions previously demonstrated to be associated with meat consumption (M = 0.59, SD = 0.87).
Covariates. Other variables of interest included age, gender, race/ethnicity, annual household income, and history of any of the following health conditions: heart disease, diabetes, high blood pressure, high cholesterol, liver disease, or kidney disease, which were self-reported by the participants. We counted the number of conditions reported and summed them together to create an overall measure of health conditions ranging from 0 to 3. Sample source (Qualtrics, n = 661, 71.6%, and MTurk, n = 262, 28.4%) was also included as a covariate in the analyses.
Statistical Analysis. The characteristics of the sample were compared, and the means and standard deviations (SD) for continuous variables and the frequency and proportion for categorical variables are reported. The sum of the days in which the participants ate each food group in the past week (meat, red meat, and healthy foods) overall, by Stroke Belt residence, and by CRC incidence quartile were estimated, and a t-test or ANOVA was used to compare the means within each variable. OLS regressions were used to evaluate the association between Stroke Belt residence and CRC incidence with food consumption. Models for each food group were ran separately. Models were adjusted for age, sex, race/ethnicity, income, and comorbidity score. Subsequently, a path analysis was conducted using structural equation modeling to test mediation effects. Models for overall meat and red meat were ran separately due to the nested nature of the measures. Indirect effects were assessed using 5000 bias-corrected bootstrap samples. We used Stata v.17 (College Station, TX, USA) for all analyses, with the level of significance set to 0.05. The full statistical results can be found in Table 2.

3. Results

After excluding 397 participants with missing data, our final sample included 923 participants, of which the mean age was 38.3 years (SD = 15.0, range 18 to 86), 661 (71.6%) were female, 262 (28.4%) were male, 261 (28.3%) were Black, 278 (30.1%) were Hispanic, 384 (41.6%) were non-Hispanic White, 167 (18.1%) resided in the Stroke Belt, 25.2% were in the lowest colorectal cancer incidence group, and 13.9% were in the highest colorectal cancer incidence group (Table 1).

3.1. Dietary Habits

Overall, participants consumed 6.95 servings of meat on average in the previous week, with red meat accounting for 3.42 of those servings. About 15.4 servings of healthy foods were consumed in the previous week. As shown in Table 2, Stroke Belt state residence was significantly associated with increased overall meat consumption and red meat consumption but not with healthy food consumption. Colorectal cancer state residence was not significantly associated with meat or red meat consumption, but residence in the second quartile colorectal cancer states was associated with the highest healthy food consumption.

3.2. Relationship between Stroke Belt Residence, Colorectal Cancer Incidence, and Food Consumption

Residing in a Stroke Belt state was significantly associated with consuming more meat overall, as well as more red meat, in the past week. There was no significant relationship between Stroke Belt states and healthy food consumption. Colorectal state quartile was not significantly associated with any food consumption outcome, except for living in a second quartile state was associated with higher healthy food consumption, but once covariates were accounted for this relationship was no longer significant. Overall meat consumption and red meat consumption were both significantly associated with increased health conditions reported. The indirect effect of residing in a Stroke Belt state on health conditions was significant when mediated by overall meat consumption (b = 0.02, 95% CI (0.002, 0.04)) and by red meat consumption (b = 0.01, 95% CI (0.0002, 0.04)) but not by healthy food consumption (b = −0.003, 95% CI (−0.02, 0.002)). This suggests that the relationship between residing in a Stroke Belt state and health outcomes may be at least partially mediated by meat consumption. There was not, however, a relationship between CRC quartile and health outcomes.
In the crude analyses, Stroke Belt state residence was associated with more overall meat and red meat consumption. Residing in a Stroke Belt state was significantly associated with consuming more meat overall (b = 0.76 (0.38)) and more red meat (b = 0.48 (0.25)) in the past week (Table 3). After adjusting for age, sex, income, health conditions, and sample source, we found similar results.
Sample source was included as a covariate after independent-samples t-tests found a significant difference between sample sources in the frequency of healthy food consumption (Qualtrics M = 14.44, SD = 7.95; MTurk M = 17.70, SD = 7.03, t (921) = −5.80, p < 0.001). There was not a significant difference between sample sources for amounts of meat consumed (Qualtrics M = 6.88, SD = 4.82; MTurk M = 7.11, SD = 4.34, t (921) = −0.65, p = 0.51,) or for amount of red meat consumed (Qualtrics M = 3.36, SD = 3.07; MTurk M = 3.56, SD = 2.92, t (921) = −0.91, p = 0.36). Of the other covariates, age, gender, race/ethnicity, and income were all significant covariates predicting meat consumption and red meat consumption, but only income was a significant covariate for healthy food consumption. The measures of these covariates are as follows: age on meat, b = −0.04, 95% confidence interval (CI) (−0.06, −0.01); age on red meat, b = −0.03, 95% CI (−0.05, −0.02); age on healthy food, b = 0.01, 95% CI (−0.02, 0.05); gender on meat, b = −1.57, 95% CI (−2.25, −0.88); gender on red meat, b = −1.14, 95% CI (−1.58 −0.70); gender on healthy food, b = −0.01, 95% CI (−1.15, 1.13); Black compared to White on meat, b = 1.54, 95% CI (0.78, 2.30); Black compared to White on red meat, b = 0.55, 95% CI (0.06, 1.04); Black compared to White on healthy food, b = −0.34, 95% CI (−1.62, 0.94); Hispanic compared to White on meat, b = 1.09, 95% CI (0.31, 1.87); Hispanic compared to White on red meat, b = 0.46, 95% CI (−0.05, 0.96); Hispanic compared to White on healthy food, b = 0.45, 95% CI (−0.85, 1.76); income on meat, b = 0.34, 95% CI (0.16, 0.53); income on red meat, b = 0.15, 95% CI (0.03, 0.27); income on healthy food, b = 0.75, 95% CI (0.44, 1.06).

4. Discussion

In this cross-sectional study of a representative U.S. sample of non-Hispanic Black Americans, non-Hispanic White Americans, and Hispanic Americans, we found that living in the eight-state Stroke Belt region was associated with eating more meat, especially red meat. The same associations were not seen for colorectal cancer quartiles and meat consumption, nor were there associations between the Stroke Belt region and healthy food consumption. The key findings in this study were that Stroke Belt residency was associated with meat and red meat consumption but not with healthy food consumption, and colorectal cancer state residency was not associated with the diet patterns assessed in this study.
In addition to the findings regarding residence in a Stroke Belt or colorectal cancer state and meat consumption, our results also showed that the health conditions of heart disease, diabetes, high blood pressure, high cholesterol, liver disease, and kidney disease were associated with Stroke Belt residence as mediated by overall meat consumption. These findings are consistent with findings from other, larger studies where a review of epidemiological studies found an association between cardiovascular disease and type 2 diabetes as well as long-term consumption of red and processed meat [55]. Although studies show associations, there is a lack of evidence for the specific components of meat products that increase risks for cardiovascular disease and type 2 diabetes. There is an opening for future research to investigate this finding in more detail in order to better pinpoint the mechanisms and boundary conditions at play in this context.
Compared to White participants, Black participants did report significantly more overall meat and red meat consumption. The results were greater for Black participants compared to the Hispanic participants for both types of meat consumption, though the difference in overall meat consumption was not significant for Hispanics (see Table 2). Other studies showed that when compared to White women, Black women were significantly more likely to eat more total meat [56]. Additionally, compared to White Americans, Black Americans consumed more pork, and Mexican Americans consumed more beef compared to White Americans [23]. These findings were among a youth sample of over 14,000, ages 2 to 18 years, which is much younger than our sample, which had a median age of 38 years [23]. In this study, Hispanic youth did not report significantly more red meat consumption compared to White participants [23]. This is counter to literature reporting that Hispanics eat slightly more red meat than Whites or Blacks [57]. There are many complex factors at play in disparities in meat consumption by racial and ethnic identity. This includes the multifaceted relationships between identity and socioeconomic status, health care access, likelihood of residing in a food- and nutrition-insecure area, lifetime experience of allostatic stress and discrimination, and cultural differences in beliefs and practices surrounding food and nutrition.
A study examining household purchasing patterns, including the effect of spending on red meat on greenhouse gas emissions and diet quality [58] found that greenhouse gas emissions were lower and diet quality was higher in households that spent the least on red meat [58]. Although this study found no differences across race and ethnicity, lower education and study participants enrolled in SNAP were spending a larger percentage of their food income on red meat [58]. Similar to our findings, as income increases, healthier foods are preferred, and thus, a higher diet quality is attained.
When examining cultural factors, traditional or heritage foods may be influential in dietary practices. Within Hispanic ethnic groups, differences in meat consumption exist, and the risk of diet-related diseases differs. For example, in a study conducted in Costa Rica, there was a positive association between unprocessed and processed red meat and abdominal obesity, but this relationship only existed for overall meat consumption and metabolic syndrome [59]. Although this was not a U.S.-based study, immigrants and other ethnic groups build dietary practices around their heritage or traditional foods and are largely dependent on access and affordability. The percent of income spent on food in 2021 in the U.S. was 6.7% versus 31.6% in Costa Rica [60]. These differences may also be seen based on the type of retailers where Americans shop for food. Structural inequities may be more impactful risk factors on diet quality and health due to their impact on the food environment.
Some limitations should be considered. First, the retrospective, self-reported measure of meat consumption has limitations. Research participants often underreport diet information due to reliance on memory of prior intake [61]. The format of the measure was also not ideal, as averaging the number of days that each type of meat was consumed could be interpreted as the variety of meat consumption in addition to the amount or frequency. Although the measure is not ideal as a measure of absolute meat consumption, prior work examining the diet compositions of U.S. ethnic groups supports the self-reported food intake in the current study [23]. Second, we did not specifically ask about processed meat such as hot dogs, sausages, or bacon, nor did we collect information on preparation methods. According to the International Agency for Research on Cancer, red meat consumption and processed meat consumption are probably carcinogenic and carcinogenic to humans, respectively [62]. Although processed meat could not be analyzed in concert with red meat, evidence exists showing that the consumption of red meat is associated with cardiovascular disease mortality [63]. Additionally, a recent study showed that a higher intake of red and processed meat was associated with higher risk of cardiovascular events, but not poultry [63]. Third, dairy foods were not included as part of the analysis of the Stroke Belt and colorectal cancer. Dairy consumption is inversely associated with colorectal cancer and stroke [64,65], and data was not accessed nor was dairy included in our list of healthy foods. Fourth, we did not ask about disability status. A stroke-associated disability may have yielded higher gender differences in the southern Stroke Belt region, which is telling because the percentage of people with disabilities is highest in the southern U.S. [66]. Future studies are needed to address these important issues.
Colorectal cancer and Stroke Belt states are also some of the states with poor access to healthcare, making prevention through not only diet but also regular medical screenings more challenging [67]. Future studies should examine how access to preventative medical care and healthcare professionals confounds poor dietary choices associated with chronic diseases.

5. Conclusions

Health outcomes such as cancer and heart disease have a multitude of public health factors that predict their occurrence. It is known from previous research that meat consumption, especially red and processed meat, is one such factor [55,62]. Geographic location, such as state of residence in the U.S., can also be implicated as a risk factor in many related health outcomes, such as stroke and colorectal cancer incidence [44]. The present study suggests that overall and red meat consumption is associated with geographic location. Public health interventions aimed at reducing diet-related health disparities should consider the confluence of location and meat consumption in the development of lifestyle behavior change strategies and targeting practices. Dietary habits are inextricably linked to systemic and structural influences, which highlights the importance of not only continuing to examine the association of diet choices linked to diet-related health issues, but also to identify protective factors that can be incorporated in public health interventions.

Author Contributions

Conceptualization, K.E.M., M.E. and J.P.; data curation, M.E. and J.P.; Formal analysis, R.K.A. and N.C.W.; funding acquisition, M.E.; investigation, M.E. and J.P.; methodology, M.E. and R.K.A.; project administration, M.E. and J.P.; resources, M.E., J.P. and R.K.A.; supervision, K.E.M., M.E. and J.P.; validation, M.E. and R.K.A.; visualization, M.E., J.P. and K.E.M.; writing—original draft, K.E.M., J.P., M.E., R.K.A. and N.C.W.; writing—review & editing, K.E.M., J.P., M.E. and N.C.W. All authors have read and agreed to the published version of the manuscript.

Funding

This manuscript was funded in part by the Health and Risk Communication Center at Michigan State University.

Institutional Review Board Statement

The study was conducted in accordance with the Declaration of Helsinki and was determined exempt (category 2) by the Institutional Review Board of Michigan State University.

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

Data described in the manuscript will be made available upon request from the corresponding author.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Norat, T.; Lukanova, A.; Ferrari, P.; Riboli, E. Meat consumption and colorectal cancer risk: Dose-response meta-analysis of epidemiological studies. Int. J. Cancer 2002, 98, 241–256. [Google Scholar] [CrossRef]
  2. Norat, T.; Bingham, S.; Ferrari, P.; Slimani, N.; Jenab, M.; Mazuir, M.; Overvad, K.; Olsen, A.; Tjønneland, A.; Clavel, F.; et al. Meat, fish, and colorectal cancer risk: The European Prospective Investigation into cancer and nutrition. J. Natl. Cancer Inst. 2005, 97, 906–916. [Google Scholar] [CrossRef] [PubMed]
  3. Larsson, S.C.; Wolk, A. Meat consumption and risk of colorectal cancer: A meta-analysis of prospective studies. Int. J. Cancer 2006, 119, 2657–2664. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  4. Zhao, Z.; Feng, Q.; Yin, Z.; Shuang, J.; Bai, B.; Yu, P.; Guo, M.; Zhao, Q. Red and processed meat consumption and colorectal cancer risk: A systematic review and meta-analysis. Oncotarget 2017, 8, 83306. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  5. World Cancer Research Fund/American Institute for Cancer Research. Diet, Nutrition, Physical Activity and Colorectal Cancer. 2018. In Continuous Update Project Expert Report 2018; World Cancer Research Fund/American Institute for Cancer: Washington, DC, USA, 2018. [Google Scholar]
  6. González, C.A.; Jakszyn, P.; Pera, G.; Agudo, A.; Bingham, S.; Palli, D.; Ferrari, P.; Boeing, H.; Del Giudice, G.; Plebani, M.; et al. Meat intake and risk of stomach and esophageal adenocarcinoma within the European Prospective Investigation Into Cancer and Nutrition (EPIC). J. Natl. Cancer Inst. 2006, 98, 345–354. [Google Scholar] [CrossRef] [PubMed]
  7. Salehi, M.; Moradi-Lakeh, M.; Salehi, M.H.; Nojomi, M.; Kolahdooz, F. Meat, fish, and esophageal cancer risk: A systematic review and dose-response meta-analysis. Nutr. Rev. 2013, 71, 257–267. [Google Scholar] [CrossRef]
  8. Choi, Y.; Song, S.; Song, Y.; Lee, J.E. Consumption of red and processed meat and esophageal cancer risk: Meta-analysis. World J. Gastroenterol. 2013, 19, 1020. [Google Scholar] [CrossRef] [PubMed]
  9. Lam, T.K.; Cross, A.J.; Consonni, D.; Randi, G.; Bagnardi, V.; Bertazzi, P.A.; Caporaso, N.E.; Sinha, R.; Subar, A.F.; Landi, M.T. Intakes of red meat, processed meat, and meat mutagens increase lung cancer risk. Cancer Res. 2009, 69, 932–939. [Google Scholar] [CrossRef] [Green Version]
  10. Tasevska, N.; Sinha, R.; Kipnis, V.; Subar, A.F.; Leitzmann, M.F.; Hollenbeck, A.R.; Caporaso, N.E.; Schatzkin, A.; Cross, A.J. A prospective study of meat, cooking methods, meat mutagens, heme iron, and lung cancer risks. Am. J. Clin. Nutrit. 2009, 89, 1884–1894. [Google Scholar] [CrossRef] [Green Version]
  11. Larsson, S.; Wolk, A. Red and processed meat consumption and risk of pancreatic cancer: Meta-analysis of prospective studies. Br. J. Cancer 2012, 106, 603. [Google Scholar] [CrossRef]
  12. Larsson, S.C.; Orsini, N.; Wolk, A. Processed meat consumption and stomach cancer risk: A meta-analysis. J. Natl. Cancer Inst. 2006, 98, 1078–1087. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  13. Cross, A.J.; Peters, U.; Kirsh, V.A.; Andriole, G.L.; Reding, D.; Hayes, R.B.; Sinha, R. A prospective study of meat and meat mutagens and prostate cancer risk. Cancer Res. 2005, 65, 11779–11784. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  14. Pan, A.; Sun, Q.; Bernstein, A.M.; Schulze, M.B.; Manson, J.E.; Stampfer, M.J.; Willett, W.C.; Hu, F.B. Red meat consumption and mortality: Results from 2 prospective cohort studies. Arch. Intern. Med. 2012, 172, 555–563. [Google Scholar] [PubMed] [Green Version]
  15. Marmot, M.; Atinmo, T.; Byers, T. Food, Nutrition, Physical Activity, and the Prevention of Cancer: A Global Perspective; World Cancer Research Fund/American Institute for Cancer Research: Washington, DC, USA, 2007. [Google Scholar]
  16. Wang, Y.; Beydoun, M.A. Meat consumption is associated with obesity and central obesity among US adults. Int. J. Obes. 2009, 33, 621. [Google Scholar] [CrossRef] [Green Version]
  17. Aune, D.; Ursin, G.; Veierød, M. Meat consumption and the risk of type 2 diabetes: A systematic review and meta-analysis of cohort studies. Diabetologia 2009, 52, 2277. [Google Scholar] [CrossRef] [Green Version]
  18. Health and Economic Benefits of Colorectal Cancer Interventions. 21 December 2022. Available online: https://www.cdc.gov/chronicdisease/programs-impact/pop/colorectal-cancer.htm (accessed on 26 January 2023).
  19. Gonzalez, C.A. Nutrition and cancer: The current epidemiological evidence. Br. J. Nutr. 2006, 96, S42–S45. [Google Scholar] [CrossRef] [Green Version]
  20. Sandhu, M.S.; White, I.R.; McPherson, K. Systematic review of the prospective cohort studies on meat consumption and colorectal cancer risk: A meta-analytical approach. Cancer Epidemiol. Biomark. Prev. 2001, 10, 439–446. [Google Scholar]
  21. Zeng, L.; Ruan, M.; Liu, J.; Wilde, P.; Naumova, E.N.; Mozaffarian, D.; Zhang, F.F. Trends in processed meat, unprocessed red meat, poultry, and fish consumption in the United States, 1999–2016. J. Acad. Nutr. Diet. 2019, 119, 1085–1098. [Google Scholar] [CrossRef]
  22. Gossard, M.H.; York, R. Social structural influences on meat consumption. Hum. Ecol. Rev. 2003, 10, 1–9. [Google Scholar]
  23. Guenther, P.M.; Jensen, H.H.; Batres-Marquez, S.P.; Chen, C.-F. Sociodemographic, knowledge, and attitudinal factors related to meat consumption in the United States. J. Am. Diet. Assoc. 2005, 105, 1266–1274. [Google Scholar] [CrossRef]
  24. Miller, K.D.; Goding Sauer, A.; Ortiz, A.P.; Fedewa, S.A.; Pinheiro, P.S.; Tortolero-Luna, G.; Martinez-Tyson, D.; Jemal, A.; Siegel, R.L. Cancer statistics for hispanics/latinos, 2018. CA Cancer J. Clin. 2018, 68, 425–445. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  25. Siegel, R.L.; Miller, K.D.; Goding Sauer, A.; Fedewa, S.A.; Butterly, L.F.; Anderson, J.C.; Cercek, A.; Smith, R.A.; Jemal, A. Colorectal cancer statistics, 2020. CA Cancer J. Clin. 2020, 70, 145–164. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  26. DeSantis, C.E.; Ma, J.; Gaudet, M.M.; Newman, L.A.; Miller, K.D.; Goding Sauer, A.; Jemal, A.; Siegel, R.L. Breast cancer statistics, 2019. CA Cancer J. Clin. 2019, 69, 438–451. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  27. Torre, L.A.; Trabert, B.; DeSantis, C.E.; Miller, K.D.; Samimi, G.; Runowicz, C.D.; Gaudet, M.M.; Jemal, A.; Siegel, R.L. Ovarian cancer statistics, 2018. CA Cancer J. Clin. 2018, 68, 284–296. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  28. Romero, C.X.; Romero, T.E.; Shlay, J.C.; Ogden, L.G.; Dabelea, D. Changing trends in the prevalence and disparities of obesity and other cardiovascular disease risk factors in three racial/ethnic groups of USA adults. Adv. Prev. Med. 2012, 2012, 172423. [Google Scholar] [CrossRef] [Green Version]
  29. Ogden, C.; Carroll, M.D.; Kit, B.K.; Flegal, K.M. Prevalence of childhood and adult obesity in the United States, 2011–2012. J. Am. Med. Assoc. 2014, 311, 806–814. [Google Scholar] [CrossRef] [Green Version]
  30. Skinner, A.C.; Skelton, J.A. Prevalence and trends in obesity and severe obesity among children in the United States, 1999–2012. JAMA Pediatr. 2014, 168, 561–566. [Google Scholar] [CrossRef] [Green Version]
  31. Hales, C.M.; Carroll, M.D.; Fryar, C.D.; Ogden, C.L. Prevalence of obesity among adults and youth: United States, 2015–2016. NCHS Data Brief. 2017, 288, 1–8. [Google Scholar]
  32. Diabetes Report Card 2019. 2020. Available online: https://stacks.cdc.gov/view/cdc/103877 (accessed on 24 January 2023).
  33. Golden, S.H.; Brown, A.; Cauley, J.A.; Chin, M.H.; Gary-Webb, T.L.; Kim, C.; Sosa, J.A.; Sumner, A.E.; Anton, B. Health disparities in endocrine disorders: Biological, clinical, and nonclinical factors—An Endocrine Society scientific statement. J. Clin. Endocrinol. Metab. 2012, 97, E1579–E1639. [Google Scholar] [CrossRef] [Green Version]
  34. Pan, L.; Galuska, D.A.; Sherry, B.; Hunter, A.S.; Rutledge, G.E.; Dietz, W.H.; Balluz, L.S. Differences in prevalence of obesity among black, white, and Hispanic adults-United States, 2006–2008. Morb. Mortal. Wkly. Rep. 2009, 58, 740–744. [Google Scholar]
  35. Chang, S.H.; Yu, Y.C.; Carlsson, N.P.; Liu, X.; Colditz, G.A. Racial disparity in life expectancies and life years lost associated with multiple obesity-related chronic conditions. Obesity 2017, 25, 950–957. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  36. Centers for Disease Control and Prevention. National Diabetes Fact Sheet. 2011. Available online: http://www.cdc.gov/diabetes/pubs/pdf/ndfs_2011.pdf (accessed on 5 April 2018).
  37. Kirk, J.K.; D’Agostino, R.B., Jr.; Bell, R.A.; Passmore, L.V.; Bonds, D.E.; Karter, A.J.; Narayan, K.V. Disparities in HbA1c levels between African-American and non-Hispanic white adults with diabetes: A meta-analysis. Diabetes Care 2006, 29, 2130–2136. [Google Scholar] [CrossRef] [Green Version]
  38. Hollar, D.; Agatston, A.S.; Hennekens, C.H. Hypertension: Trends, risks, drug therapies and clinical challenges in African Americans. Ethn. Dis. 2004, 14, S2–S23. [Google Scholar] [PubMed]
  39. Davidson, J.A.; Kannel, W.B.; Lopez-Candales, A.; Morales, L.; Moreno, P.R.; Ovalle, F.; Rodriguez, C.J.; Rodbard, H.W.; Rosenson, R.S.; Stern, M. Avoiding the looming Latino/Hispanic cardiovascular health crisis: A call to action. J. Cardiometab. Syndr. 2007, 2, 238–243. [Google Scholar] [CrossRef] [PubMed]
  40. Brown, A.F.; Liang, L.J.; Vassar, S.D.; Escarce, J.J.; Merkin, S.S.; Cheng, E.; Richards, A.; Seeman, T.; Longstreth, W.T., Jr. Trends in Racial/Ethnic and Nativity Disparities in Cardiovascular Health Among Adults Without Prevalent Cardiovascular Disease in the United States, 1988 to 2014. Ann. Intern. Med. 2018, 168, 541–549. [Google Scholar] [CrossRef]
  41. Siegel, R.L.; Miller, K.D.; Jemal, A. Cancer statistics, 2017. CA Cancer J. Clin. 2017, 67, 7–30. [Google Scholar] [CrossRef] [Green Version]
  42. Ward, E.; Jemal, A.; Cokkinides, V.; Singh, G.K.; Cardinez, C.; Ghafoor, A.; Thun, M. Cancer disparities by race/ethnicity and socioeconomic status. CA Cancer J. Clin. 2004, 54, 78–93. [Google Scholar] [CrossRef] [Green Version]
  43. Tsao, C.W.; Aday, A.W.; Almarzooq, Z.I.; Alonso, A.; Beaton, A.Z.; Bittencourt, M.S.; Boehme, A.K.; Buxton, A.E.; Carson, A.P. Heart Disease and Stroke Statistics-2022 Update: A Report From the American Heart Association. Circulation 2022, 145, e153–e639. [Google Scholar]
  44. Stroke Death Rates, Total Population 35+, by County. Available online: https://www.cdc.gov/dhdsp/maps/national_maps/stroke_all.htm (accessed on 15 December 2022).
  45. Pew Research Center. Hispanics Have Accounted for More Than Half of Total U.S. Population Growth Since 2010. Available online: https://www.pewresearch.org/fact-tank/2020/07/10/hispanics-have-accounted-for-more-than-half-of-total-u-s-population-growth-since-2010/ (accessed on 9 December 2022).
  46. Pew Research Center. Facts about the U.S. Black Population. Available online: https://www.pewresearch.org/social-trends/fact-sheet/facts-about-the-us-black-population/ (accessed on 9 December 2022).
  47. 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]
  48. Larsson, S.C. Dietary approaches for stroke prevention. Stroke 2017, 48, 2905–2911. [Google Scholar] [CrossRef]
  49. Lescinsky, H.; Afshin, A.; Ashbaugh, C.; Bisignano, C.; Brauer, M.; Ferrara, G.; Hay, S.I.; He, J.; Iannucci, V.; Marczak, L.B.; et al. Health effects associated with consumption of unprocessed red meat: A Burden of Proof study. Nat. Med. 2022, 28, 2075–2082. [Google Scholar] [CrossRef] [PubMed]
  50. Craig, W.J. Health effects of vegan diets. Am. J. Clin. Nutr. 2009, 89, 1627S–1633S. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  51. Key, T.J.; Appleby, P.N.; Rosell, M.S. Health effects of vegetarian and vegan diets. Proc. Nutr. Soc. 2006, 65, 35–41. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  52. Tilman, D.; Clark, M. Global diets link environmental sustainability and human health. Nature 2014, 515, 518. [Google Scholar] [CrossRef]
  53. Buhrmester, M.; Kwang, T.; Gosling, S.D. Amazon’s Mechanical Turk: A new source of inexpensive, yet high-quality data? Perspect. Psychol. Sci. 2011, 6, 3–5. [Google Scholar] [CrossRef]
  54. Burnham, M.J.; Le, Y.K.; Piedmont, R.L. Who is Mturk? Personal characteristics and sample consistency of these online workers. Ment. Health Relig. Cult. 2018, 21, 934–944. [Google Scholar] [CrossRef]
  55. Richi, E.B.; Baumer, B.; Conrad, B.; Darioli, R.; Schmid, A.; Keller, U. Health risks associated with meat consumption: A review of epidemiological studies. Int. J. Vitam. Nutr. Res. 2015, 85, 70–78. [Google Scholar] [CrossRef]
  56. Choi, S.E.; Lee, K.J. Ethnic differences in attitudes, beliefs, and patterns of meat consumption among American young women meat eaters. Nutr. Res. Pract. 2022, 16, e48. [Google Scholar] [CrossRef]
  57. Daniel, C.R.; Cross, A.J.; Koebnick, C.; Sinha, R. Trends in meat consumption in the USA. Public Health Nutr. 2011, 14, 575–583. [Google Scholar] [CrossRef] [Green Version]
  58. Boehm, R.; Ver Ploeg, M.; Wilde, P.E.; Cash, S.B. Greenhouse gas emissions, total food spending and diet quality by share of household food spending on red meat: Results from a nationally representative sample of US households. Public Health Nutr. 2019, 22, 1794–1806. [Google Scholar] [CrossRef] [Green Version]
  59. Luan, D.; Wang, D.; Campos, H.; Baylin, A. Red meat consumption and metabolic syndrome in the Costa Rica Heart Study. Eur. J. Nutr. 2020, 59, 185–193. [Google Scholar] [CrossRef] [PubMed]
  60. Share of Consumer Expenditure Spent on Food vs. Total Consumer Expenditure. 2021. Available online: https://ourworldindata.org/grapher/food-expenditure-share-gdp?country=~CRI (accessed on 2 February 2023).
  61. Subar, A.F.; Freedman, L.S.; Tooze, J.A.; Kirkpatrick, S.I.; Boushey, C.; Neuhouser, M.L.; Thompson, F.E.; Potischman, N.; Guenther, P.M.; Tarasuk, V.; et al. Addressing Current Criticism Regarding the Value of Self-Report Dietary Data. J. Nutr. 2015, 145, 2639–2645. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  62. IARC Working Group on the Evaluation of Carcinogenic Risks to Humans. Red Meat and Processed Meat; International Agency for Research on Cancer: Lyon, France, 2018. [Google Scholar]
  63. Zhong, V.W.; Van Horn, L.; Greenland, P.; Carnethon, M.R.; Ning, H.; Wilkins, J.T.; Lloyd-Jones, D.M.; Allen, N.B. Associations of processed meat, unprocessed red meat, poultry, or fish intake with incident cardiovascular disease and all-cause mortality. JAMA Intern. Med. 2020, 180, 503–512. [Google Scholar] [CrossRef] [PubMed]
  64. Barrubés, L.; Babio, N.; Becerra-Tomás, N.; Rosique-Esteban, N.; Salas-Salvadó, J. Association Between Dairy Product Consumption and Colorectal Cancer Risk in Adults: A Systematic Review and Meta-Analysis of Epidemiologic Studies. Adv. Nutr. 2019, 10 (Suppl. 2), S190–S211. [Google Scholar] [CrossRef] [PubMed]
  65. De Goede, J.; Soedamah-Muthu, S.S.; Pan, A.; Gijsbers, L.; Geleijnse, J.M. Dairy Consumption and Risk of Stroke: A Systematic Review and Updated Dose-Response Meta-Analysis of Prospective Cohort Studies. J. Am. Heart Assoc. 2016, 5, e002787. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  66. Disability Impacts Us All. 2022. Available online: https://www.cdc.gov/ncbddd/disabilityandhealth/infographic-disability-impacts-all.html (accessed on 13 October 2022).
  67. United Health Foundation (UHF) & American Public Health Association (APHA). A Call to Action for Individuals and Their Communities. Am. Health Rankings 2017. Available online: https://assets.americashealthrankings.org/app/uploads/ahrannual17_complete-121817.pdf (accessed on 23 January 2023).
Table
Table 1. Participant demographics and characteristics. Note: colorectal cancer is abbreviated in this and all other tables as “CRC”.
Table 1. Participant demographics and characteristics. Note: colorectal cancer is abbreviated in this and all other tables as “CRC”.
Total
n = 923
Sample Source, n (%)
MTurk262 (28.4)
Qualtrics661 (71.6)
Age, mean years (range)38.3 (18–86)
Female, n (%)661 (71.61)
Race and ethnicity, n (%)
Non-Hispanic White384 (41.6)
Black Americans261 (28.3)
Hispanic278 (30.1)
Income, n (%)
<$25,000243 (26.3)
$25,000–$50,000307 (33.3)
$50,001–$75,000191 (20.7)
$75,001-$100,00084 (9.1)
>$100,00098 (10.6)
Residence in Stroke Belt, n (%)167 (18.1)
Health conditions, mean (SD)0.49 (0.82)
CRC incidence quartile, n (%)
1233 (25.2)
2359 (38.9)
3203 (22.0)
4128 (13.9)
Table
Table 2. Dietary habits of participants.
Table 2. Dietary habits of participants.
Overall Meat
Mean Days (Range)		p-ValueRed Meat
Mean Days (Range)		p-ValueHealthy Food
Mean Days (Range)		p-Value
Overall6.95 (0–35) 3.42 (0–21) 15.4 (0–42)
Stroke Belt Residence, mean (SD) 0.047 0.050 0.643
Yes7.60 (5.55) 3.83 (3.61) 15.23 (8.09)
No6.84 (4.43) 3.35 (2.85) 15.53 (7.84)
CRC Incidence Quartile, mean (SD) 0.961 0.708 0.015
17.01 (4.24) 3.44 (2.79) 14.93 (8.42)
27.00 (4.67) 3.48 (2.99) 16.36 (7.66)
36.91 (4.99) 3.23 (3.17) 15.01 (7.34)
46.77 (5.12) 3.58 (5.18) 14.09 (8.19)
Table
Table 3. Relationship between Stroke Belt residence, colorectal cancer incidence group, and food consumption.
Table 3. Relationship between Stroke Belt residence, colorectal cancer incidence group, and food consumption.
Overall MeatRed MeatHealthy Food
Crude b
(95% CI)		Adjusted b
(95% CI)		Crude b
(95% CI)		Adjusted b
(95% CI)		Crude b
(95% CI)		Adjusted b
(95% CI)
Stroke Belt
Residence		0.76
(0.01, 1.52)		1.00
(0.14, 1.86)		0.48
(0.00, 0.97)		0.65
(0.09, 1.21)		−0.30
(−1.59, 0.98)		0.87
(−0.58, 2.31)
CRC Quartile
1Ref.Ref.Ref.Ref.Ref.Ref.
2−0.01
(−0.77, 0.75)		0.09
(−0.66, 0.85)		0.04
(−0.045, 0.52)		0.07
(−0.42, 0.56)		1.42
(0.15, 2.69)		1.14
(−0.13, 2.41)
3−0.10
(−0.97, 0.77)		−0.48
(−1.40, 0.45)		−0.22
(−0.78, 0.34)		−0.41
(−1.00, 0.19)		0.07
(−1.38, 1.52)		−0.12
(−1.67, 1.43)
4−0.24
(−1.25, 0.76)		−0.24
(−1.28, 0.80)		0.14
(−0.51, 0.78)		0.15
(−0.53, 0.82)		−0.84
(−2.53, 0.84)		−0.74
(−2.49, 1.00)
B: unstandardized coefficient; CI: confidence interval; CRC: colorectal cancer. Adjusted for age, sex, income, health conditions, and sample source.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Mayfield, K.E.; Plasencia, J.; Ellithorpe, M.; Anderson, R.K.; Wright, N.C. The Consumption of Animal and Plant Foods in Areas of High Prevalence of Stroke and Colorectal Cancer. Nutrients 2023, 15, 993. https://doi.org/10.3390/nu15040993

AMA Style

Mayfield KE, Plasencia J, Ellithorpe M, Anderson RK, Wright NC. The Consumption of Animal and Plant Foods in Areas of High Prevalence of Stroke and Colorectal Cancer. Nutrients. 2023; 15(4):993. https://doi.org/10.3390/nu15040993

Chicago/Turabian Style

Mayfield, Kellie E., Julie Plasencia, Morgan Ellithorpe, Raeda K. Anderson, and Nicole C. Wright. 2023. "The Consumption of Animal and Plant Foods in Areas of High Prevalence of Stroke and Colorectal Cancer" Nutrients 15, no. 4: 993. https://doi.org/10.3390/nu15040993

APA Style

Mayfield, K. E., Plasencia, J., Ellithorpe, M., Anderson, R. K., & Wright, N. C. (2023). The Consumption of Animal and Plant Foods in Areas of High Prevalence of Stroke and Colorectal Cancer. Nutrients, 15(4), 993. https://doi.org/10.3390/nu15040993

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop