Burden and Trends of Diet-Related Colorectal Cancer in Global, East Mediterranean, and China ：Systematic Analysis Based on Global Burden of Disease Study 1990-2021 with Projections to 2050

Burden and Trends of Diet-Related Colorectal Cancer in Global, East Mediterranean, and China ：Systematic Analysis Based on Global Burden of Disease Study 1990-2021 with Projections to 2050 | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Burden and Trends of Diet-Related Colorectal Cancer in Global, East Mediterranean, and China ：Systematic Analysis Based on Global Burden of Disease Study 1990-2021 with Projections to 2050 Yining Lai, Hongliang Diao, Xiaoyan Zhu, Min Zhu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7242118/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Colorectal cancer (CRC) is the third most common cancer and the second most frequent cause of cancer death worldwideThis study aimed to systematically analyze Burden and trends of the diet-related CRC in global,the Eastern Mediterranean Region (EMR), and China from 1990 to 2021. so as to provide a basis for region-specific prevention and control strategies. From 1990 to 2021, dietary factors remained the primary preventable cause of CRC globally. While the number of diet-related CRC deaths and DALYs increased significantly in global, the age-standardized rates declined. China showed a consistent trend with the global. In contrast,EMR exhibited an opposite trend to the global CRC burden: the number of deaths increased by 171.37% and DALYs by 169.53%, with rising age-standardized rates (EAPC = 0.46% for mortality and 0.30% for DALYs). Dietary factors remain the primary preventable contributor to the global burden of CRC, with an overall increasing trend accompanied by significant regional and population heterogeneities. Developing targeted strategies tailored to regional dietary patterns, gender, and age-specific characteristics is crucial for effectively alleviating the burden of colorectal cancer. Colorectal cancer Diet-related risk factors EMR Burden Trends Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Background CRC is the third most common cancer and the second most frequent cause of cancer death worldwide [ 1 ] . The incidence of CRC is increasing, and it has been estimated that there will be 2.5 million new cases by the year 2035. [ 2 ] As an important piece of information, 70–75 percent of CRC patients where it develops sporadically which is predominantly influenced by lifestyle and dietary habits. [ 3 ] In this GBD, The 11 common risk factors include: low dietary fiber intake, insufficient milk and calcium consumption, high red meat and processed meat intake, smoking, alcohol consumption, physical inactivity, as well as overweight/obesity and high fasting blood glucose levels. [ 4 , 5 ] In the context of CRC, the dietary factors is a key risk factor. Research shows that adherence to a Mediterranean diet characterized by olive oil, fish, plant-based foods, and moderate wine consumption offers protective CRC benefits [ 6 , 7 ] .There is quite a difference in region-specific adherence to this eating pattern. In EMR, traditional diets coexist with Westernized dietary patterns rich in refined carbohydrates and added sugars.EMR country case-control studies demonstrate this divergence: Moroccan research has shown that risk mitigation is consistent with adherence to the Mediterranean diet [ 8 ] , and Tunisian data revealing over two-fold increased CRC risk (95% CI,1.22–3.87) associated with high processed meat consumption (> 100g/day) [ 9 ] .Dietary patterns are shifting,Turkey and Greece retain traditional eating habits. Such habits may explain why their CRC mortality is lower than Western-diet nations [ 10 , 11 ] .This diversity makes the EMR indispensable for studying the link between diet and CRC. China is experiencing a swift change in food consumption, as its CRC burden has assumed the global top position [ 12 ] .The socio-economic advancements in China have triggered a movement from lower meat consumption and relying on plants and vegetables to high processed foods and red meat intake which might contribute to CRC rates [ 13 ] .Chinese dietary patterns fundamentally differ from Mediterranean diets in core protective components (fiber density, seafood/olive oil use) and culinary techniques (low-temperature cooking versus high-temperature processing) [ 14 ] .In view of the role of dietary transition in promoting the incidence of CRC and China's unique nutritional context (a large population base and a dietary transition speed far exceeding that of Western developed countries), studying the association between dietary risk factors and colorectal cancer burden can not only provide a reference for global public health regarding disease evolution during the dietary transition period, but also enable the formulation of precise prevention strategies based on the dietary characteristics of the Chinese population. Observational cohort studies and mechanistic research have clearly shown that diet plays a key role in the progression of CRC. Understanding trends in the EMR, China, and globally—where dietary patterns are changing significantly—is therefore of particular importance. Our study has three main objectives. First, we will outline the burden of Diet-related CRC in global, EMR, and China from 1990 to 2021. Second, we will analyze differences in disease burden across sex and age groups, and will also examine how dietary burdens vary across different SDI levels. Finally, we will forecast CRC deaths and DALYs attributable to dietary factors from 2022 to 2050. By meeting these objectives, we seek to shed light on the temporal epidemiological effects of dietary factors on chronic diseases such as CRC, while providing a solid foundation for multi-regional public health interventions. Methods 1. Data sources This study drew on data from GBD Study 2021, which offers comprehensive estimates of CRC mortality, DALYs, and related risk factors from 1990 to 2021. Specifically, we extracted the following information: CRC mortality and DALYs data categorized by age (in 5-year groups), sex, and region (global, EMR, and China); exposure data on diet-related risk factors, such as low in milk, fiber,calcium, and whole-grains, as well as high in red meat and processed meat; and population data along with the world standard population for age standardization. The WHO groups countries into six regional, including: (1)African Region,(2)Eastern Mediterranean Region,(3)European Region,(4)Region of the Americas,(5)South-East Asia Region,(6)Western Pacific Region;The Eastern Mediterranean Region comprises 22 countries: Afghanistan, Algeria, Bahrain, Djibouti, Egypt, Iran (Islamic Republic of), Iraq, Jordan, Kuwait, Lebanon, Libya, Morocco, Oman, Pakistan, Palestine, Qatar, Saudi Arabia, Somalia, Sudan, Syrian Arab Republic, Tunisia, United Arab Emirates, and Yemen. SDI is a composite indicator of total fertility rate under 25 years old, average years of schooling, and lag distributed income percapita [ 15 ] . We used the reference SDI quantile to classify EMR by their SDIs in 2021 into five groups including low, low-middle, middle, high-middle, and high [ 16 ] . 2.Statistical analysis Calculation of Age-Standardized Rates (ASR): Mortality rates and DALYs rates were age-standardized using the World Standard Population to eliminate the impact of differences in population age structures [ 17 ] . The Joinpoint 4.2.0.1 software was used to calculate the estimated annual percentage changes (EAPCs) and 95% confidence intervals (CIs) for age-standardized mortality rates (ASDRs) and DALYs rates, so as to assess the trend changes during 1990–2021. When the Joinpoint model detected significant trend turning points, piecewise linear regression was employed for fitting; otherwise, a single linear trend was fitted [ 3 ] . 3. Future projection models Log-linear Age-Period-Cohort Model: We used the FORECAST package in R to apply the log-linear age-period-cohort model for predicting sex-specific mortality rates from 2021 to 2050. This model works by removing exponential growth elements and limiting predictions to linear trends to fit recent data patterns, which makes it especially well-suited for forecasting cancer mortality. Autoregressive Integrated Moving Average (ARIMA) Model for Validation: For time-series data on certain risk factors (such as high red meat consumption and low whole grain intake), we used an ARIMA model to fit trends and produce short-term projections. The results were cross-validated against those from the age-period-cohort model to ensure the predictions are consistent and reliable. Results 1. Burden of CRC due to dietary risks factors: a comparative analysis of Global, EMR and China 1.1 Dietary factors are the primary determinants affecting CRC mortality According to GBD Study conducted between 1990–2021, there are eleven modifiable risk factors with metabolic disorders, diet imbalance, and Low Physical Activity which greatly impact the burden of CRC in terms of disease economics (Table 1 ). Epidemiological data demonstrate an unabated upward trend in CRC mortality and DALYs associated with these risk factors. Furthermore, dietary risks contributed to approximately 38.90% of global CRC deaths and 38.76% of CRC-related DALYs in the year 2021, signifying an imbalance in diet as the foremost preventable cause influencing mortality due to CRC. Table 1 Global CRC burden of associated with 11 risk factors (1990–2021) 1990 2021 Attributable fraction in 2021 Death number (95% UI) Age-standardised rate of deaths (95% UI) per 100,000 DALYs (95% UI) Age-standardised rate of DALYs (95% UI) per 100,000 Death number (95% UI) Age-standardised rate of deaths (95% UI) per 100,000 DALYs (95% UI) Age-standardised rate of DALYs (95% UI) per 100,000 Deaths DALYs Global Burden 570318(536544–597668) 15.56 (14.49–16.31) 14396657.72 (13568749.36-15166575.84) 357.33 (336.62-375.74) 1044072(950187 − 112016) 12.40 (11.24–13.31) 24401100.18 (22689368.55-26161517.73) 283.24 (263.11-303.33) - - Dietary factors 231758(83612–346321) 6.33(2.26–9.47) 5810277.38(2145982.22-8636186.62) 144.88(53.10-215.52) 406099(138065–628056) 4.82(1.64–7.46) 9458464.22(3251667.63-14521174.19) 109.71(37.68-168.52) 38.90% 38.76% Alcohol use 5569(3961–7269) 0.65(0.46–0.85) 181730.46(127132.86-236829.45) 18.89(13.29–24.64) 14936(10532–20494) 0.72(0.51–0.99) 421730.92(299867.94-578279.62) 20.34(14.46–27.96) 1.43% 1.73% Diet high in red meat 84263(-26-168170) 2.31(-0.00-4.61) 2094732.37(-690.76-4190753.33) 52.39(-0.02-104.82) 152985(-51-314249) 1.82(-0.00-3.74) 3552238.54(-1303.11-7213991.01) 41.19(-0.02-83.67) 14.65% 14.56% Diet low in whole grains 101812(42588–151170) 2.79(1.17–4.15) 2540867.41(1050794.36-3754415.59) 63.47(26.35–93.84) 186256(76126–284803) 2.21(0.91–3.38) 4327218.86(1754865.24-6578232.30) 50.19(20.37–76.30) 17.84% 17.73% Diet low in fibre 9689(4409–14808) 0.27(0.12–0.42) 247015.19(112606.13-380299.24) 6.17(2.80–9.47) 13144(5761–20265) 0.16(0.07–0.24) 305675.95(135088.79-469863.48) 3.58(1.58–5.50) 1.26% 1.25% Diet low in calcium 57363(42914–71257) 1.54(1.15–1.92) 1512762.13(1132026.04-1881666.67) 37.04(27.74–46.14) 89089(65018–112297) 1.06(0.77–1.33) 2128938.58(1565530.27-2672450.44) 24.70(18.17–31.02) 8.53% 8.72% Diet low in milk 81405(22743–133923) 2.22(0.62–3.65) 2074171.90(575525.07-3392723.24) 51.52(14.33–84.17) 157562(42973–262535) 1.87(0.51–3.12) 3706461.02(1010584.13-6138022.35) 42.99(11.73–71.23) 15.09% 15.19% Diet high in processed meat 37083(-9050-75308) 1.03(-0.25-2.09) 886133.12(-218150.41-1802022.13) 22.49(-5.52-45.73) 57148(-13411-117694) 0.68(-0.16-1.40) 1301644.06(-310251.07-2664056.37) 15.11(-3.60-30.93) 5.47% 5.33% High body-mass index [BMI] 41535(17665-67379.01) 1.14(0.48–1.86) 1015042.12(429787.23-1631973.77) 25.54(10.83–41.20) 99267(42956–157948) 1.17(0.51–1.87) 2364664.16(1021593.57-3752340.44) 27.33(11.80-43.37) 9.51% 9.69% High fasting plasma glucose 31906(16052–48058) 0.89(0.45–1.34) 715715.78(358248.80-1089711.73) 18.46(9.27–28.03) 82421(42426–125402) 0.98(0.51–1.49) 1750923.34(900573.43-2657994.92) 20.31(10.46–30.81) 7.89% 7.18% Low physical activity 5734.54(3368–8371) 0.93(0.55–1.36) 128352.60(74846.96-188831.95) 17.12(10.06–24.93) 16698(10065–24625) 0.87(0.53–1.29) 320464.35(192274.95-474069.92) 15.63(9.47–22.88) 1.60% 1.31% smoking 7774(4960–10887) 0.95(0.60–1.33) 232203.99(149570.35-325169.01) 25.32(16.29–35.44) 17276(10519–25641) 0.82(0.50–1.21) 459249.52(276317.14-684961.95) 21.44(12.95–31.98) 1.65% 1.88% 1.2 Trends in diet-related CRC burden Figure 1 and Table 2 illustrate the numbers of Diet-Related CRC deaths and DALYs, as well as the rates of Diet-Related CRC deaths and DALYs in China,EMR and global between 1990 and 2021.Globally, the following metrics demonstrated changes:a 75.23% increase in deaths and a 62.79% increase in DALYs.The estimated annual percentage change (EAPC) for age-standardized mortality rates was − 0.95%, and the EAPC for age-standardized DALYs rates was − 0.98%. reflecting consistent declines in relative burden despite absolute case growth.In China experienced a 108.08% increase in diet-related CRC deaths, with 37.33% of CRC deaths attributable to dietary risk factors. DALYs increase by 77.12%, and 37.31% of CRC DALYs were diet-related. Age-standardized mortality rates declined EAPC = − 0.86%, and DALYs rates fell (EAPC = − 0.94%), aligning with global trends. In EMR witnessed a striking 171.37% increase in diet-related CRC deaths, from 4,576 (95% UI: 2,020–6,784) to 12,420 (95% UI: 4,964–18,882), comprising 38.65% of all CRC mortality.DALYs followed suit, rising 169.53% from 131,436.78 (95% UI: 58,001.45–195,770.82) to 354,258.08 (95% UI: 138,866.28–541,535.80), comprising 38.31% of all CRC DALYs.In 2021, CRC diet-relateddeaths in EMR accounted for 3.06% of the global total and 12.09% of China's CRC deaths.Contrary to global trends, age-standardized mortality rates increased from 2.69 to 2.96 per 100,000 (EAPC = + 0.46%,), while DALYs rates rose from 67.07 to 70.85 per 100,000 (EAPC = + 0.30%). Table 2 CRC burden of associated with diet-related risk factors in Global,EMR,and China 1990 2021 EAPC Death number (95% UI) Age-standardised rate of deaths (95% UI) per 100,000 DALYs (95% UI) Age-standardised rate of DALYs (95% UI) per 100,000 Death number (95% UI) Age-standardised rate of deaths (95% UI) per 100,000 DALYs (95% UI) Age-standardised rate of DALYs (95% UI) per 100,000 Deaths(95% CI) DALYs(95% CI) Dietary factors(Global) 231758(83612–346321) 6.33(2.26–9.47) 5810277.38(2145982.22-8636186.62) 144.88(53.10-215.52) 406099(138065–628056) 4.82(1.64–7.46) 9458464.22(3251667.63-14521174.19) 109.71(37.68-168.52) -0.95 (-0.99–0.92) -0.98 (-1.02–0.95) Diet high in processed meat(Global) 37083(-9050-75308) 1.03(-0.25-2.09) 886133.12(-218150.41-1802022.13) 22.49(-5.52-45.73) 57148(-13411-117694) 0.68(-0.16-1.40) 1301644.06(-310251.07-2664056.37) 15.11(-3.60-30.93) -1.39 (-1.45–1.33) -1.33 (-1.39–1.27) Diet low in milk(Global) 81405(22743–133923) 2.22(0.62–3.65) 2074171.90(575525.07-3392723.24) 51.52(14.33–84.17) 157562(42973–262535) 1.87(0.51–3.12) 3706461.02(1010584.13-6138022.35) 42.99(11.73–71.23) -0.62 (-0.68–0.57) -0.66 (-0.73–0.60) Diet high in red meat(Global) 84263(-26-168170) 2.31(-0.00-4.61) 2094732.37(-690.76-4190753.33) 52.39(-0.02-104.82) 152985(-51-314249) 1.82(-0.00-3.74) 3552238.54(-1303.11-7213991.01) 41.19(-0.02-83.67) -0.84 (-0.87–0.81) -0.85 (-0.88–0.81) Diet low in fibre(Global) 9689(4409–14808) 0.27(0.12–0.42) 247015.19(112606.13-380299.24) 6.17(2.80–9.47) 13144(5761–20265) 0.16(0.07–0.24) 305675.95(135088.79-469863.48) 3.58(1.58–5.50) -1.86 (-1.90–1.82) -1.89 (-1.95–1.83) Diet low in whole grains (Global) 101812(42588–151170) 2.79(1.17–4.15) 2540867.41(1050794.36-3754415.59) 63.47(26.35–93.84) 186256(76126–284803) 2.21(0.91–3.38) 4327218.86(1754865.24-6578232.30) 50.19(20.37–76.30) -0.82 (-0.85–0.78) -0.84 (-0.87–0.81) Diet low in calcium(Global) 57363(42914–71257) 1.54(1.15–1.92) 1512762.13(1132026.04-1881666.67) 37.04(27.74–46.14) 89089(65018–112297) 1.06(0.77–1.33) 2128938.58(1565530.27-2672450.44) 24.70(18.17–31.02) -1.33 (-1.37–1.29) -1.45 (-1.50–1.40) Dietary factors(EMR) 4576(2020–6784) 2.69(1.20–3.96) 131436.78(58001.45-195770.82) 67.07(29.64–99.54) 12420(4964–18882) 2.96(1.21–4.47) 354258.08(138866.28-541535.80) 70.85(28.14-108.15) 0.46 (0.40–0.52) 0.30 (0.24–0.35) Diet high in processed meat(EMR) 308(-74-649) 0.18(-0.04-0.37) 9228.81(-2236.14-19439.06) 4.59(-1.11-9.65) 972(-223-2060) 0.22(-0.05-0.47) 29561.54(-6858.08-62271.55) 5.63(-1.30-11.94) 0.76 (0.68–0.85) 0.71 (0.62–0.80) Diet low in milk(EMR) 1995(528–3229) 1.17(0.32–1.89) 57656.11(15160.60-94132.17) 29.33(7.75–47.51) 5638(1518–9309) 1.35(0.36–2.23) 161039.48(43328.26-267574.28) 32.19(8.68–53.01) 0.61 (0.55–0.67) 0.42 (0.38–0.47) Diet high in red meat(EMR) 1520(-0-3097) 0.88(-0.00-1.80) 44353.55(-4.98-90404.71) 22.42(-0.00-45.55) 4401(-0-9018) 1.04(-0.00-2.13) 126970.79(-19.74-258279.13) 25.19(-0.00-51.46) 0.72 (0.64–0.80) 0.54 (0.47–0.61) Diet low in fibre(EMR) 143(64–222) 0.09(0.04–0.14) 4067.11(1842.60-6320.38) 2.09(0.94–3.23) 350(160–565) 0.08(0.04–0.13) 10345.60(4786.20-16412.50) 2.00(0.91–3.22) -0.31 (-0.52–0.10) -0.30 (-0.53–0.07) Diet low in whole grains (EMR) 2055(843–3098) 1.21(0.50–1.81) 59035.67(24286.61-89512.05) 30.13(12.36–45.49) 6001(2471–9044) 1.43(0.60–2.17) 170688.51(69845.64-259403.60) 34.23(14.09–51.51) 0.73 (0.65–0.81) 0.56 (0.48–0.63) Diet low in calcium(EMR) 1664(1254–2121) 1.00(0.75–1.27) 46827.77(34716.31-59974.09) 24.18(18.19–30.88) 3416(2500–4462) 0.84(0.61–1.08) 95292.20(69532.17-126036.00) 19.33(14.17–25.30) -0.51 (-0.56–0.47) -0.69 (-0.73–0.66) Dietary factors(China) 49354(22956–73117) 6.50(3.09–9.63) 1442747.31(666850.39-2133156.24) 160.95(74.99–237.30) 102694(36136–166536) 5.08(1.79–8.21) 2555444.51(880336.60-4134442.40) 122.93(42.56-198.49) -0.86 (-0.93–0.80) -0.94 (-1.03–0.85) Diet high in processed meat(China) 1764(-413-3790) 0.23(-0.05-0.48) 53167.50(-12352.58-115432.45) 5.83(-1.37-12.56) 6612(-1355-14767) 0.32(-0.07-0.71) 178186.62(-37002.76-405024.52) 8.57(-1.78-19.35) 1.46 (1.31–1.60) 1.64 (1.44–1.83) Diet low in milk(China) 22540(5848–37330) 2.98(0.78–4.97) 652029.04(167977.40-1085153.89) 73.14(18.96-121.15) 51030(13916–86781) 2.53(0.69–4.30) 1253643.02(337481.84-2128072.72) 60.25(16.22-102.21) -0.63 (-0.70–0.57) -0.73 (-0.82–0.65) Diet high in red meat(China) 17607(-3-36613) 2.29(-0.00-4.76) 518212.88(-104.50-1074174.40) 57.50(-0.01-119.28) 43579(-16-92082) 2.15(-0.00-4.55) 1091787.60(-508.62-2295778.88) 52.47(-0.02-110.35) -0.24 (-0.30–0.19) -0.33 (-0.41–0.25) Diet low in fibre(China) 2100(927–3451) 0.27(0.12–0.44) 68081.50(29789.69-111751.10) 7.32(3.23–11.87) 1738(693–3023) 0.09(0.04–0.15) 48099.59(18866.72-84760.77) 2.40(0.94–4.16) -3.74 (-3.85–3.62) -3.77 (-3.86–3.67) Diet low in whole grains (China) 21329(8463–32783) 2.79(1.11–4.29) 624948.36(248199.78-959563.32) 69.54(27.62-106.93) 49990(20099–79928) 2.47(0.99–3.94) 1241927.75(503164.76-1978508.33) 59.70(24.17–94.95) -0.46 (-0.52–0.41) -0.57 (-0.65–0.50) Diet low in calcium(China) 18902(13485–24551) 2.55(1.81–3.28) 546368.99(389713.79-718067.19) 61.59(43.88–80.49) 20719(14553–28271) 1.04(0.73–1.41) 500468.35(356218.76-682873.06) 24.20(17.14–33.05) -3.06 (-3.17–2.96) -3.18 (-3.28–3.08) 2 Genders and ages burden of diet-related CRC In 2021, the number of CRC deaths attributable to dietary risk factors varied by age group(Fig. 2 a),Male mortality peaks at 70–74 years, while females lag by a decade at 80–84 years.Males outnumber females in deaths below the age of 80, but surpasses the female population after 80 years.Males exhibit higher rates than females overall under the age of 95, with both sexes showing age-dependant increase,with both sexes showing age-dependant increase.In 2021, the peak number of DALYs due to CRC attributable to dietary risk factors occurred in both sexes aged 65 to 69((Fig. 2 d).In addition, the number of DALYs due to CRC attributable to dietary risk factors was higher in males than in females under the age of 80 and higher in females than in males over the age of 80.The DALY rate due to CRC attributable to dietary risk factors was higher in males than in females under the age of 80 ,after 80 years females exhibit higher rates and increased with age in both sexes. In EMR, the number of CRC deaths peak at 65–69 years for both sexes,with male deaths consistently exceeding those of females from the age of 55 onward.Additionally, the CRC mortality rate attributable to dietary risk factors showed minimal differences between males and females below 90 years, and increased with age in both sexes(Fig. 2 b).The peak number of DALYs due to CRC attributable to dietary risk factors occurred in both sexes aged 60 to 64(Fig. 2 e).The trend of DALY rate paralleled that of mortality rate. In China,The peak number of deaths due to CRC attributable to dietary risk factors occurred in both sexes aged 70 to 74, with males outnumbering females in deaths under the age of 90(Fig. 2 c).Male mortality rates were higher than those of females across all age groups, with the disparity being particularly pronounced in individuals aged after 80 years.The peak number of DALYs due to CRC attributable to dietary risk factors occurred in both sexes aged 65 to 69(Fig. 2 f).The DALY rate due to CRC attributable to dietary risk factors was higher in males than in females, DALY rate in males decreases after 90 years, while females continue to show upward trends. 3 CRC-related DALYs attributable to dietary risk factors Globally, the primary diet-related CRC risk factor for females was inadequate milk intake, contributing to 1.91 deaths per 100,000(95% UI:0.53–3.17) and 43.69 DALYs per 100,000(95% UI:11.87–72.18).the primary diet-related CRC risk factor for males was inadequate intake of whole-grain diets, contributing to 2.75 deaths per 100,000(95% UI: 1.11–4.20)and 62.39 DALYs per 100,000(95% UI:25.20–95.64).The trend was constant across different age groups.By contrast, diets low in fiber and high in possessed meat had the least share in CRC deaths and DALYs.Globally, in the context of the "diet low in milk" factor, Females had higher death and DALY rates than males at all ages, and the gap widened with age.in the case of inadequate whole-grain consumption, Males had higher death and DALY rates than females at all ages. In EMR, the primary dietary risk factor for females was inadequate milk intake, contributing to 1.59 deaths per 100,000(95% UI: 0.41–2.59)and 38.23 DALYs(95% UI: 9.88–62.04). For males, the primary dietary risk factor was inadequate whole-grain intake, associated with 1.50 deaths per 100,000(95% UI: 0.63–2.27)and 35.46 DALYs per 100,000(95% UI: 14.81–53.82).In EMR, the trends in mortality and DALYs associated with the "diet low in milk" factor are consistent with global patterns.Below the age of 95, in the context of inadequate whole-grain consumption, males exhibited slightly higher death and DALY rates than females. In China, the primary dietary risk factor for females was inadequate milk intake,(2.18 deaths [95% UI: 0.60–3.73], 50.84 DALYs [95% UI: 13.95–87.60]);For males, the primary dietary risk factor was whole-grain intake (3.44 deaths [95% UI: 1.36–5.59], 81.74 DALYs [95% UI: 32.46–133.71]).Across the six factors, the overall trends of mortality and disability rates among males were consistently greater than those among females across different age stages. Cross-Regional Insights from Figures Key risk factors: Inadequate milk intake、Inadequate whole-grain intake and excessive red meat intake were predominant.Excessive red meat intake:Male mortality/DALYs rates exceeded females’ across regions; rates increased with age, with steeper male increases after 80 years (most pronounced in China).Inadequate whole-grain intake:Male mortality/DALYs rates were higher than females’, consistent with the trends of excessive red meat intake.Inadequate milk intake:Females had higher rates than males globally/Eastern Mediterranean, while China showed reversed patterns. 4. Diet-related CRC DALYs across different SDI countries EMR countries were categorised into five groups based on SDI quintiles as presented in Table 3. There was a considerable variation in age-standardised DALYs rates of CRC across the five groups of the EMR (Table 3).Notably, the diet-related CRC DALYs rate were highest in the Middle-low SDI group (ranged from 69.80 to 156.70 per 100,000 persons).Middle-SDI countries show a relatively lower DALYs rate(ranged from 54.86 to 77.27 per 100,000 persons), indicating early successes in dietary health management and food quality regulation. When it comes to the burden of CRC caused by specific dietary factors, different characteristics emerge. In terms of insufficient whole-grain intake, the CRC DALYs rates in middle - high SDI countries are the highest(ranged from 35.33 to 65.60 per 100,000 persons).In the case of insufficient milk intake,the CRC DALYs rates were highest in middle-low SDI countries, with a rate (ranged from 32.73 to 64.65 per 100,000 persons).Regarding insufficient fiber intake, the CRC DALYs rates were highest in low SDI countries, with a rate ranging from 3.31 to 10.97 per 100,000 persons. Moreover, the DALYs values resulting from excessive red meat consumption are comparable across different SDI levels. This phenomenon implies that regardless of the level of economic development, meat-eating habits have universal health implications. Tabel 3 DALYs rate of diet-related factors for CRC across different SDI in EMR Location Dietary factors Diet low in whole grains Diet low in milk Diet high in red meat Diet low in calcium Diet high in processed meat Diet low in fibre High SDI Qatar 79.42301314648(21.32774683527,131.5561049587) 43.65166427012(17.48086307179,69.7933796147) 38.71126636463(9.64975483083,66.14011377026) 34.39692722042(-0.00721171035,70.63698894662) 7.29893236978(4.38430147412,10.9770018894) 5.82295261382(-1.18739176824,12.596782744159999) 0.1333743912(0.03768194247,0.26083633259) Kuwait 66.42981823510999(19.128050112,110.19533808962) 35.9625003945(14.46090216573,55.8913222857) 33.79405726224(8.86977321642,55.94402410387) 29.585903600109997(-0.00528906359999999,61.535143568769996) 6.32382567627(4.04750437784,8.88792317495) 4.42370996746(-0.9491437174,9.64181147152) 1.27836579888(0.53540807876,2.0605528675299998) United Arab Emirates 94.01226343076999(28.323281371870003,155.98221484016) 49.14615906563(19.602442085470003,81.96111851684) 47.37579154765(12.84789312457,81.09902958194) 38.50510495749(-0.00604913319,78.37600432162) 14.1573105252(8.98134422187,21.10321408295) 7.76706486831(-1.80175807568,16.35626179217) 1.03973404896(0.39209669599999997,1.83723152291) Middle-high SDI Libya 120.35750551192(44.33728275815,197.38395526533) 62.59821930428(26.20484839142,101.64733467446) 60.40554034723(16.45518584477,105.42669173435002) 46.7385798792(-0.01433979135,99.4097535628) 24.52419957458(15.65981830559,35.1256290203) 6.70973821549(-1.56147410224,14.96799050702) 2.24674210025(0.92692758078,3.78785810621) Turkey 101.22287591536(31.265273342209998,165.67396301436) 56.19709114359(24.0383863579,87.16195560045) 32.53014435482(9.23401761871,56.4819190515) 42.242381607889996(-0.01096803222,88.81219773291) 10.75750484763(6.87959823301,15.259884188789998) 6.965528708349999(-1.7441818288,15.11880803563) 0.34806233309(0.13175714523,0.66628499938) Bahrain 79.34863258349(24.05378761259,127.73773609969) 42.59637900685(17.02402106648,66.62224523382001) 38.738291602669996(10.34373243278,64.91702592502999) 32.68234498073(-0.01049923015,67.11707936503) 11.50929031636(7.22082990231,16.55014773471) 5.00685755751(-1.12739563685,10.94212556978) 0.45019960883(0.16628261085,0.80724392856) Saudi Arabia 65.99437247821(21.55248774999,107.12787060273) 35.327740634870004(14.29755254504,56.238842747780005) 32.045172597770005(8.6852620825,53.07458913778) 26.22912016709(-0.00855470371,54.505299414380005) 10.51951129063(6.79177701593,14.6024819193) 4.23845066317(-0.92154661583,9.209361692309999) 0.60159994324(0.23316760295,1.01562625186) Jordan 75.22276780444(26.249353037520002,122.16908656345998) 38.80227445069(15.740080627920001,62.74034200834) 36.07928172838(9.55158229794,61.94846018078) 28.99037530394(-0.00864038202,60.5554555107) 16.00924407192(9.86519638585,24.39858675291) 4.2750417587000005(-0.897067248059999,9.62666329113) 1.97352098862(0.81593129261,3.27173423802) Lebanon 82.42049161606(25.88926546786,132.76627065482) 44.60873392642(18.64635295472,68.87266574722) 42.894735644650005(11.65661220396,72.71248485990999) 34.91005741649(-0.00286057065,72.25461226986) 10.87770045582(7.05314826077,15.723729098789999) 4.70513543772(-1.16008289029,10.45542633503) 0.61920491542(0.24585017064000003,1.09059188961) Middle SDI Iraq 63.97720545505(31.94650929765,97.71502053539001) 28.98034714245(11.155779293610001,46.66300722706) 28.835831980499997(7.49927734735,48.75881974479) 15.08751645026(-0.00533782398,33.90683068145) 29.7118588703(20.22451872991,40.57565967262) 2.83542997848(-0.6221042655,6.19875170894) 1.3665828839999998(0.5810388859,2.28107791485) Islamic Republic of Iran 64.37057719749(25.7253712386,97.05923110768) 32.604782969030005(13.59573403334,48.882356157909996) 30.274581780069997(8.13446737549,49.474889644070004) 23.072284446610002(-0.00326862446,48.03804889344) 16.43690567662(11.83824466261,21.13349600176) 3.66802203622(-0.83525487118,7.834285184570001) 0.62804906275(0.26697665526000003,1.00864980434) Egypt 77.2702882687(25.892744691089998,123.80266740191) 38.14763152127(14.883870462039999,59.57100785787) 40.22477302707(10.55312507454,66.01097379408999) 30.601766710240003(-0.00876105164,63.88935454237) 15.82551907372(10.85564211518,21.75577396761) 6.268030982499999(-1.45414080621,13.165397161) 0.1768473515(0.06513642554,0.33395937526999997) Tunisia 60.89627920209001(17.754091024559997,102.53244383542) 32.729423903960004(12.008084226800001,52.74074332395) 23.23462935305(5.65069012827,42.082115465) 24.30840793075(-0.00811847393999999,51.625659347050004) 9.58442087387(6.04144422638,14.30520748702) 3.93064469934(-0.839520151379999,8.73350670188) 0.34686224211(0.13816665726,0.64884697415) Syrian Arab Republic 54.86326536622(20.260894730540002,93.39225571745) 28.251116757210003(11.52286924544,46.80585868742) 25.39948169592(6.5627845084,45.210352726079996) 21.196327887960003(-0.00707465135,45.66607661131) 11.778191739679999(7.57126869772,17.00189645634) 3.0289522288(-0.70093675401,6.87663437582) 1.27988843261(0.53947352553,2.22013164597) Middle-low SDI Djibouti 120.5180302(48.76704775,202.7889325) 53.9969666(21.12613369,93.10616129) 52.75789454(14.16008804,99.64942405) 42.30323366(-0.00932404,95.11765774) 40.549992(24.83106644,61.21396927) 9.477032733(-2.364577124,21.93238522) 6.432717997(2.68676764,11.38958248) Palestine 156.7029036(86.01251358,222.4554177) 64.16814941(26.94094208,98.1300546) 64.64759462(17.67176106,105.0655625) 41.29159633(-0.013084919,84.77451682) 81.01995803(58.04225988,104.6820841) 5.471038366(-1.319687683,11.83106778) 8.901592205(3.911870175,14.80810425) Morocco 75.75619058(29.89058692,121.7852637) 37.87540507(16.45389597,59.75801324) 36.12116949(10.03359598,62.40103435) 28.93398224(-0.009198074,61.7321822) 18.65942964(11.78502889,26.93477761) 3.953154824(-0.920081218,8.791341961) 0.092970025(0.030463997,0.190859919) Sudan 69.79536625(28.27906892,122.1323225) 32.63481717(11.95998076,57.44469094) 32.72579266(8.012918485,61.35161061) 23.31728291(-0.007111327,55.18519814) 24.54278929(15.26778569,38.529264) 3.180556142(-0.723040131,7.692953566) 2.225395771(0.846721198,4.048290408) Low SDI Yemen 82.22511758(42.85171535,131.530727) 32.00640923(13.13757634,55.14832477) 33.19752155(8.810890672,58.88942243) 21.20307218(-0.005746259,47.50027185) 45.8883243(28.09814722,69.26278345) 2.725443585(-0.628902941,6.338740968) 3.306001498(1.420352587,6.178403401) Pakistan 60.86098659(20.64748979,98.17081858) 28.84301299(11.58949054,45.99382385) 24.38688663(6.538440538,42.71877192) 21.16604685(-0.004247079,45.4833899) 14.45177861(9.99979734,19.88434579) 9.137234087(-2.188888063,20.2686007) 3.856962962(1.724541887,6.209863677) Somalia 129.3330956(67.55629347,206.9294694) 44.73812499(17.75630979,77.41221739) 48.09190063(13.66401944,86.75084512) 35.4889202(-0.006826,81.68307539) 77.34327838(46.35413305,116.6838711) 7.316963143(-1.705589725,17.33204029) 10.97073875(4.818097689,19.20149546) Afghanistan 142.4854738(50.61494892,268.7431011) 60.81900975(20.57220882,116.9155354) 62.81728936(12.39839868,125.1683047) 46.99544183(-0.013499085,116.9429898) 61.18492546(27.33895739,101.2073307) 5.395333668(-0.984808578,13.78366839) 8.013870695(2.936446155,14.87700899) 5. CRC burden attributable to diet death and DALYs rate projections till 2050 According to projections, the global age-standardized death rate and age-standardized DALYs for diet-related CRC will continue the downward trend observed during 1990–2021(3.44[95%UI,3.07–3.81]per 100,000,77.74[95%UI,68.69–86.80]per 100,000, respectively), with a more pronounced decline magnitude in females (Fig. 5 ).In 2050, The trend of China remains relatively stable or shows a slight decline for all factors (Fig. 7 ).In EMR(Fig. 6 ), the age-standardized mortality rate for diet-related CRC in males is projected to exhibit an upward trend(3.26[95%UI,2.93–3.58]per 100,000), while that in females tends to stabilize. The age-standardized DALYs for diet-related CRC show a slight decline in both sexes. Discussion The impact of diet-related CRC burden: Trends and the role of screening policies in Global, EMR, and China (1990–2021) This study analyzed diet-related CRC burden based on the data from GBD 2021. The research found that globally, 38.90% of CRC deaths and 38.76% of CRC DALYs could be attributed to dietary risk factors. From 1990 to 2021, the number of diet-related CRC deaths globally increased by 75.23%, and the DALYs grew by 62.79%, highlighting the significant challenge posed by dietary imbalance to global public health.Notably, the diet-related CRC burden in EMR has shown a particularly remarkable growth. The number of CRC deaths in EMR increased by 171.37%, and DALYs grew by 169.53%, with the growth rates far exceeding the global average. This phenomenon is closely related to the impact of the Western dietary pattern on the traditional Mediterranean dietary pattern (rich in plant-based foods, fish, and olive oil) in the region. Such significant changes in dietary structure have directly led to the sharp rise in CRC burden in this region.Epidemiological evidence shows that the Mediterranean dietary pattern can reduce the risk of CRC by 10–15% through mechanisms such as regulating the gut microbiota and reducing oxidative stress [ 18 ] . In contrast, the Western dietary pattern (high intake of processed foods and red meat) is positively correlated with the risk of CRC [ 7 ] . During the same period, diet-related CRC deaths and DALYs in China increased by 108.08% and 77.12% respectively.This upward trend is closely linked to dietary changes brought about by urbanization – specifically, a notable rise in the consumption of red meat and processed meat, alongside a steady drop in the intake of whole grains and dietary fiber [ 19 ] .This dietary shift drives CRC pathogenesis through dual pathological mechanisms: nitrates in processed meats work to worsen long-term intestinal inflammation [ 20 ] , while iron ions (Fe³⁺) in red meat impair the body’s ability to repair damaged DNA [ 21 ] , collectively advancing disease progression. Global EAPC has shown consistent improvement with an average annual decrease of 0.95%. China's EAPC decline (0.86% per year) aligns with this global trend. Conversely, EMR exhibits a concerning upward trajectory, with EAPC increasing at an average annual rate of 0.46%. This notable difference is closely tied to how dietary changes and cancer screening efforts interact.In China, public health interventions,such as the promotion of the Dietary Guidelines for Chinese Residents and alongside a nationwide CRC screening program initiated in the 1970s, have partially offset the negative impact of dietary Westernization. The screening program evolved into a comprehensive system covering both urban and rural populations: the rural component launched in 2005 achieved coverage across 234 counties in 31 provincial-level administrative divisions by 2016; the urban component, initiated in 2012, expanded to 42 cities within 20 provincial-level divisions by 2021 [ 22 ] .In contrast, EMR faces worsening disease burden trends. This stems from the replacement of traditional diets with high-fat dietary patterns during urbanization [ 23 ] , compounded by underdeveloped cancer screening infrastructures [ 24 ] . Disease burden characteristics and mechanistic interpretation from gender and age dimensions The findings showed that males CRC mortality risk was generally higher than females under the age of 95,The peak age for colorectal cancer deaths comes about 10 years earlier in men, at 70–74 years old, compared to 80–84 years old in women.A similar pattern of delayed peaks in DALYs were observed (male 65–69 years, female 70–74 years), closely linked to males consumed 13.3g/d more red/processed meat and less whole-grains/fiber than females [ 25 , 26 ] . Notably, females over 80 years exhibited higher disease burden than males, while Chinese females showed inverse trends,Chinese males over 80 had a significantly steeper CRC mortality increase,indicating the need to strengthen healthy diet education and optimize screening protocols for this subgroup to improve early diagnosis rates. Attributable contributions of key dietary risk factors and regional specificities This study confirms that inadequate whole-grain intake, insufficient milk consumption, and excessive red meat intake constitute the "core triad" of dietary risk factors for CRC burden, collectively accounting for 81.61% of global diet-related CRC deaths and 81.64% of DALYs. The whole-grains exert synergistic protective effects through dietary fiber (a 10% increase in daily intake reduces CRC risk by 10% [ 27 ] ), polyphenols, lignans, and other bioactive components, promoting intestinal peristalsis and exerting antioxidant-anti-inflammatory effects [ 28 – 30 ] . Calcium in milk (a 300mg daily increase reduces risk by 17% [ 31 ] ), along with lactoferrin and conjugated linoleic acid, inhibits carcinogenesis by regulating intestinal cell proliferation and immune responses [ 32 ] . Excessive red meat intake (50g daily increase elevates risk by 18% [ 33 ] ).Drives carcinogenesis through multiple mechanisms: heme iron-induced formation of N-nitroso compounds, heterocyclic amines/polycyclic aromatic hydrocarbons generated by high-temperature cooking [ 34 ] , and telomerase activation via iron-Pirin complexes (OR = 2.41 [ 35 ] ). Regional analyses reveal distinct patterns: red meat contributes 32.7% to CRC burden in EMR, exceeding the global rate of 28.1%, attributed to high-temperature grilling traditions [ 36 ] . In China, inadequate whole grain intake accounts for 34.5% (vs. global 30.2%), driven by refined grain dominance (whole-grains < 1% of diet per *National Whole Grain Action Plan (2024–2035)* [ 37 ] ). Combined with rising red meat consumption and milk insufficiency, this exacerbates risk via synergistic induction of intestinal oxidative stress and DNA damage [ 37 – 39 ] . Notably, milk insufficiency contributes 29.1% and 27.8% to female CRC deaths globally and in EMR, respectively, whereas Chinese females show only 18.3% risk contribution—likely due to daily isoflavone intake > 30mg (vs. <1mg/d in Western populations). Isoflavones in soy products may partially offset milk deficiency, with a 20mg/d increase reducing colorectal tumor risk by 8% [ 40 ] . Research limitations and future research directions This study has three main limitations:GBD study relies on model-based estimations, introducing significant biases in regions with collapsed health systems (e.g., war-torn countries). Inadequate cancer registration systems in low-income nations necessitate estimations based on predictive covariates or neighboring countries' trends, potentially skewing results [ 41 ] .The research did not differentiate the differential responses of CRC pathological types (e.g., adenocarcinoma vs. squamous cell carcinoma) to dietary factors. A 2024 Cancers study found red meat intake more significantly impacted adenocarcinoma risk (HR = 1.21), whereas squamous cell carcinoma showed stronger associations with alcohol consumption (HR = 1.15) [ 42 ] . Future Directions: (1)Regional Biobank Studies(Leverage biobanks in the Middle East and China to investigate diet-microbiota-CRC associations) [ 43 ] .(2)Machine Learning Models(Develop personalized risk prediction models by integrating dietary patterns, genetic markers) [ 44 , 45 ] .(3)Digital Nutritional Interventions: Evaluate the feasibility of mobile APP-based dietary monitoring in resource-constrained regions [ 46 ] . Conclusions From 1990 to 2021, dietary risk factors have consistently ranked as the leading preventable cause of the global CRC burden. This burden has shown an overall increasing trend, accompanied by striking disparities across regions and population groupsIn China, despite a downward trend in CRC mortality, inadequate intake of whole grains and dairy products remains a critical issue requiring urgent attention. In the Eastern Mediterranean region, the CRC burden has continued to deteriorate, driven by the influence of Western dietary patterns.The core direction for future global CRC prevention and control should be building multi-dimensional strategies that take into account regional features, dietary patterns, gender differences, and age distributions. Through targeted, precise measures, we can reduce CRC burden effectively. Abbreviations CRC Colorectal cancer EMR Eastern Mediterranean Region ARIMA Autoregressive Integrated Moving Average UI uncertainty interval CI confidence interval DALYs disability-adjusted life years GBD Global Burden of Disease SDI socio-demographic index Declarations Ethics approval and consent to participate This research made use of publicly accessible, anonymized data sourced from the GBD database. Since it did not entail direct interaction with human subjects nor the gathering of personal health details, there was no need for ethical approval or informed consent. The study conformed to the guidelines set forth in the Declaration of Helsinki, guaranteeing ethical practice in the utilization of secondary data for public health-related research. Consent for publication Not applicable Funding This study was funded by the National Natural Science Foundation of China (Regional Science Foundation, Grant No. 82260322) and the Young Top Talents Program of the "Tianshan Talents" Cultivation Plan of the Xinjiang Uygur Autonomous Region (Grant No. 2023TSYCJC0058).It was also supported by the Xinjiang Uygur Autonomous Region Natural Science Foundation (Grant No. 2023D01F19) and the Science and Technology Project of Karamay Central Hospital (Grant No. YK2023-2). Author Contribution The overall conception and design of this study were developed by Zhu Min. Diao Hongliang analyzed the research data. Zhu Xiaoyan contributed to the interpretation of the data. Lai Yining drafted the manuscript and verified the disease burden data, with other authors conducting rigorous revisions. All authors read and approved the final manuscript.Availability of data and materialsThe data used in this study were obtained from the GBD 2021 database, which is publicly available through the Institute for Health Metrics and Evaluation (IHME) website. All data analyzed during this study are accessible at the GBD Results Tool (http://ghdx.healthdata.org/gbd-results-tool) and can be requested for research purposes in accordance with IHME’s data-sharing policies. No additional data were generated or analyzed in this study. References Wang J, He S, Cao M, Teng Y, Li Q, Tan N, et al. Global, regional, and national burden of colorectal cancer, 1990-2021: An analysis from global burden of disease study 2021. Chinese journal of cancer research = Chung-kuo yen cheng yen chiu. 2024;36(6):752-67. Yao J, Chen G. The global, regional, and national alcohol-related colorectal cancer burden and forecasted trends: results from the global burden of disease study 2021. Frontiers in nutrition. 2024;11:1520852. Zhu N, Zhang Y, Mi M, Ding Y, Weng S, Zheng J, et al. The death burden of colorectal cancer attributable to modifiable risk factors, trend analysis from 1990 to 2019 and future predictions. Cancer medicine. 2024;13(7):e7136. Global, regional, and national burden of colorectal cancer and its risk factors, 1990-2019: a systematic analysis for the Global Burden of Disease Study 2019. The lancet Gastroenterology & hepatology. 2022;7(7):627-47. Liang Y, Zhang N, Wang M, Liu Y, Ma L, Wang Q, et al. Distributions and Trends of the Global Burden of Colorectal Cancer Attributable to Dietary Risk Factors over the Past 30 Years. Nutrients. 2023;16(1). Mattioli AV, Farinetti A, Gelmini R. The beneficial effect of Mediterranean diet on colorectal cancer. International journal of cancer. 2019;145(1):306. Mahmod AI, Haif SK, Kamal A, Al-Ataby IA, Talib WH. Chemoprevention effect of the Mediterranean diet on colorectal cancer: Current studies and future prospects. Frontiers in nutrition. 2022;9:924192. Elbaylek H, Ammor S. Adherence to the Mediterranean Diet and Colorectal Cancer Risk Among Moroccan Population: Hospital-Based Case Control Study. Asian Pacific journal of cancer prevention : APJCP. 2024;25(8):2853-60. Gharbi I, Letaief F, Dadaa Z, Yahyaoui Y, Gabsi A, Maghrebi H, et al. The Consumption of Red and Processed Meat and The Risk of Colorectal Cancer: A Case-Control Study among the Tunisian Population. La Tunisie medicale. 2020;98(10):726-9. Garcia-Closas R, Berenguer A, González CA. Changes in food supply in Mediterranean countries from 1961 to 2001. Public health nutrition. 2006;9(1):53-60. Thomson CA, McCullough ML, Wertheim BC, Chlebowski RT, Martinez ME, Stefanick ML, et al. Nutrition and physical activity cancer prevention guidelines, cancer risk, and mortality in the women's health initiative. Cancer prevention research (Philadelphia, Pa). 2014;7(1):42-53. Liu A, Zhuang Z, Li J, Wang Q, Liu S, Fang H, et al. Burden and trend of dietary risk-related colorectal cancer in China and its provinces: findings from the Global Burden of Disease Study 2019. Public health. 2024;230:21-8. Liu Y, Zhu C, Song H, Che M, Xu B, An B. Temporal trends in disability adjusted life year and mortality for colorectal cancer attributable to a high red meat diet in China from 1990 to 2021: an analysis of the global burden of disease study 2021. BMC gastroenterology. 2024;24(1):476. Dou YFJHiS, Engineering, Technology. The Role of the Mediterranean Diet on Health and Its Inspiration for Chinese Diet. 2023. Haagsma JA, James SL, Castle CD, Dingels ZV, Fox JT, Hamilton EB, et al. Burden of injury along the development spectrum: associations between the Socio-demographic Index and disability-adjusted life year estimates from the Global Burden of Disease Study 2017. Injury prevention : journal of the International Society for Child and Adolescent Injury Prevention. 2020;26(Supp 1):i12-i26. Chaulagain P, Poudel A, Aryal S, Sainatham C, Lutfi FJB. Global Trends of Burden of Chronic Myeloid Leukemia Based on Socio-Demographic Index (SDI): A Comparative Epidemiological Study. 2024;144:7918-. Tong Z, Xie Y, Li K, Yuan R, Zhang L. The global burden and risk factors of cardiovascular diseases in adolescent and young adults, 1990-2019. BMC public health. 2024;24(1):1017. . !!! INVALID CITATION !!! [7, 18]. Wang X, Bodirsky BL, Müller C, Chen KZ, Yuan C. The triple benefits of slimming and greening the Chinese food system. Nature food. 2022;3(9):686-93. Ross FC, Patangia D, Grimaud G, Lavelle A, Dempsey EM, Ross RP, et al. The interplay between diet and the gut microbiome: implications for health and disease. Nature reviews Microbiology. 2024;22(11):671-86. Shanmugam R, Majee P, Shi W, Ozturk MB, Vaiyapuri TS, Idzham K, et al. Iron-(Fe3+)-Dependent Reactivation of Telomerase Drives Colorectal Cancers. Cancer discovery. 2024;14(10):1940-63. Chen H, Lu B, Dai M. Colorectal Cancer Screening in China: Status, Challenges, and Prospects - China, 2022. China CDC weekly. 2022;4(15):322-8. Musaiger AO, Al-Hazzaa HM, Takruri HR, Mokhatar N. Change in nutrition and lifestyle in the eastern mediterranean region: health impact. Journal of nutrition and metabolism. 2012;2012:436762. Hartley C, Chhachhi N, Khader Y, Farhat GN. Barriers to colorectal cancer screening in the Eastern Mediterranean Region: a scoping review using the theoretical domains framework. Journal of gastrointestinal oncology. 2023;14(3):1576-92. Wang Q, Liu S, Wang H, Su C, Liu A, Jiang L. Consumption of aquatic products and meats in Chinese residents: A nationwide survey. Frontiers in nutrition. 2022;9:927417. Abebe Z, Wassie MM, Reynolds AC, Melaku YA. Burden and Trends of Diet-Related Colorectal Cancer in OECD Countries: Systematic Analysis Based on Global Burden of Disease Study 1990-2021 with Projections to 2050. Nutrients. 2025;17(8). Aune D, Chan DS, Lau R, Vieira R, Greenwood DC, Kampman E, et al. Dietary fibre, whole grains, and risk of colorectal cancer: systematic review and dose-response meta-analysis of prospective studies. BMJ (Clinical research ed). 2011;343:d6617. Hullings AG, Sinha R, Liao LM, Freedman ND, Graubard BI, Loftfield E. Whole grain and dietary fiber intake and risk of colorectal cancer in the NIH-AARP Diet and Health Study cohort. The American journal of clinical nutrition. 2020;112(3):603-12. Al-Khayri JM, Sahana GR, Nagella P, Joseph BV, Alessa FM, Al-Mssallem MQ. Flavonoids as Potential Anti-Inflammatory Molecules: A Review. Molecules (Basel, Switzerland). 2022;27(9). Okarter N, Liu RH. Health benefits of whole grain phytochemicals. Critical reviews in food science and nutrition. 2010;50(3):193-208. Nierengarten MB. Calcium may help to protect against colorectal cancer. Cancer. 2025;131(9):e35827. . !!! INVALID CITATION !!! [36, 37]. Ungvari Z, Fekete M, Varga P, Lehoczki A, Munkácsy G, Fekete JT, et al. Association between red and processed meat consumption and colorectal cancer risk: a comprehensive meta-analysis of prospective studies. GeroScience. 2025. Benarba B. Red and processed meat and risk of colorectal cancer: an update. EXCLI journal. 2018;17:792-7. Ling P, Lei J, Ju H. Nanoscaled Porphyrinic Metal-Organic Frameworks for Electrochemical Detection of Telomerase Activity via Telomerase Triggered Conformation Switch. Analytical chemistry. 2016;88(21):10680-6. Sinha R, Cross AJ, Graubard BI, Leitzmann MF, Schatzkin A. Meat intake and mortality: a prospective study of over half a million people. Archives of internal medicine. 2009;169(6):562-71. Health effects of dietary risks in 195 countries, 1990-2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet (London, England). 2019;393(10184):1958-72. Zhang F, Debras C, Matta J, Wang DJPotNS. Consumption of a milk low in lactose high in intrinsic fiber is associated with improved nutrient intake adequacies in Chinese adults: a diet modelling study. 2024;83(OCE4). 陈宣承, 李红领, 食品与发酵工业 李J. 膳食血红素铁促进结直肠癌变机制初探. 2022;48(1):7. Jiang R, Botma A, Rudolph A, Hüsing A, Chang-Claude J. Phyto-oestrogens and colorectal cancer risk: a systematic review and dose-response meta-analysis of observational studies. The British journal of nutrition. 2016;116(12):2115-28. Zhang X, Zhang X, Li R, Lin M, Ou T, Zhou H, et al. Global, regional, and national analyses of the burden of colorectal cancer attributable to diet low in milk from 1990 to 2019: longitudinal observational study. Frontiers in nutrition. 2024;11:1431962. Vernia F, Longo S, Stefanelli G, Viscido A, Latella G. Dietary Factors Modulating Colorectal Carcinogenesis. Nutrients. 2021;13(1). Wang Y, Huang J, Tong H, Jiang Y, Jiang Y, Ma X. Nutrient Acquisition of Gut Microbiota: Implications for Tumor Immunity. Seminars in cancer biology. 2025. Ocvirk S, O'Keefe SJD. Dietary fat, bile acid metabolism and colorectal cancer. Seminars in cancer biology. 2021;73:347-55. Ghatak S, Mehrabi SF, Mehdawi LM, Satapathy SR, Sjölander A. Identification of a Novel Five-Gene Signature as a Prognostic and Diagnostic Biomarker in Colorectal Cancers. International journal of molecular sciences. 2022;23(2). Okaniwa F, Yoshida H. Evaluation of Dietary Management Using Artificial Intelligence and Human Interventions: Nonrandomized Controlled Trial. JMIR formative research. 2022;6(6):e30630. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7242118","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":510345199,"identity":"96ebcbdf-cb38-4543-8ecb-93dba65232f1","order_by":0,"name":"Yining Lai","email":"","orcid":"","institution":"Karamay Central Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yining","middleName":"","lastName":"Lai","suffix":""},{"id":510345208,"identity":"5245e196-f96c-4fc8-b768-6c54ace1a775","order_by":1,"name":"Hongliang Diao","email":"","orcid":"","institution":"Karamay Central Hospital","correspondingAuthor":false,"prefix":"","firstName":"Hongliang","middleName":"","lastName":"Diao","suffix":""},{"id":510345212,"identity":"78e4de53-6a4e-4b59-b5cb-8eb3ba7bcf07","order_by":2,"name":"Xiaoyan Zhu","email":"","orcid":"","institution":"Karamay Central Hospital","correspondingAuthor":false,"prefix":"","firstName":"Xiaoyan","middleName":"","lastName":"Zhu","suffix":""},{"id":510345214,"identity":"612f473e-ab0e-4f95-ac21-36ec8dda2316","order_by":3,"name":"Min Zhu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAyklEQVRIiWNgGAWjYFAC5gOHf/6pseNnZj78gEgtbImPGRuOJUu2s6UZEKmFx9iYsYGZccN5HgUJojTITztjJl24g43Z+DAPgwFDjU00QS2Ms9PKpGeekeEzO8x74AHDsbTcBkJamKWTt0nwsLExmx3mSzBgbDhMWAubdIIZUAsz4+ZmHgMJorTwSKcYG/O2Ab3PTKwWCem0xIczzhxLljgMDOQEYvwiPzv5wIEPFcCo7D98+MGHGhvCWlBBAmnKR8EoGAWjYBTgAgDRUTukhyAujQAAAABJRU5ErkJggg==","orcid":"","institution":"Karamay Central Hospital","correspondingAuthor":true,"prefix":"","firstName":"Min","middleName":"","lastName":"Zhu","suffix":""}],"badges":[],"createdAt":"2025-07-29 10:23:21","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7242118/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7242118/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":91076169,"identity":"18e5fcb1-0698-4d64-adfb-23711643d5ef","added_by":"auto","created_at":"2025-09-11 11:09:06","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":595909,"visible":true,"origin":"","legend":"\u003cp\u003eTrends of death and DALYs rate of CRC in Global,EMR, and China: (a)Global death rate, (b)EMR death rate(c)China death rate(d)Global DALYs rate(e)EMR DALYs rate(f)China DALYs rate\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7242118/v1/c40b926bfb93f2d054c97f29.png"},{"id":91076170,"identity":"5ab52948-bee5-48a7-acfa-de7550332f10","added_by":"auto","created_at":"2025-09-11 11:09:06","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":442233,"visible":true,"origin":"","legend":"\u003cp\u003eThe Death and DALYs rates and number of CRC due to dietary factors in different Genders and age groups. (a)\u003cstrong\u003eGlobal\u003c/strong\u003e death rate and number. (b)Death rate and number in EMR. (c)Death rate and number in \u003cstrong\u003eChina\u003c/strong\u003e. (d)\u003cstrong\u003eGlobal \u003c/strong\u003eDALYs rate and number. (e)DALYs rate and number in EMR (f)\u003cstrong\u003e \u003c/strong\u003eDALYs rate and number in \u003cstrong\u003eChina\u003c/strong\u003e.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7242118/v1/43c9a56df6776152b7ab62c0.png"},{"id":91076173,"identity":"c36b2ed8-7ac8-4bf8-a9cb-40cf76e7ff9a","added_by":"auto","created_at":"2025-09-11 11:09:06","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":202709,"visible":true,"origin":"","legend":"\u003cp\u003eThe death rate of six dietary risk factors related to CRC in Global, EMR and China in 2021.\u003c/p\u003e\n\u003cp\u003eThe vertical axis is the death rate (per 100,000), and the horizontal axis represents different age groups.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7242118/v1/471c86733343b0f0986b19e9.png"},{"id":91076174,"identity":"a7543445-7e3d-434c-a997-5deb05fe8047","added_by":"auto","created_at":"2025-09-11 11:09:06","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":217058,"visible":true,"origin":"","legend":"\u003cp\u003eThe DALYs rate of six dietary risk factors related to CRC in Global,EMR,and China in 2021.\u003c/p\u003e\n\u003cp\u003eThe vertical axis is the DALYs rate (per 100,000), and the horizontal axis represents different age groups.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7242118/v1/e27e957dc833a7f5a7b75b81.png"},{"id":91077864,"identity":"e35593ad-8c5a-4ddf-a4a5-9a562da6b44d","added_by":"auto","created_at":"2025-09-11 11:17:06","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":885146,"visible":true,"origin":"","legend":"\u003cp\u003eGlobal Projection of diet-related CRC (2022–2050)\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-7242118/v1/ecc30a5a5bc3a6ad03439aab.png"},{"id":91076178,"identity":"96072164-83ac-4bb4-be0f-9ac1cfc2ba3e","added_by":"auto","created_at":"2025-09-11 11:09:06","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":829969,"visible":true,"origin":"","legend":"\u003cp\u003eProjection of diet-related CRC burden in EMR (2022–2050)\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-7242118/v1/c61d47042a957866f1dabb06.png"},{"id":91077867,"identity":"2b128126-d6bc-4283-9500-b8468c4c2bd8","added_by":"auto","created_at":"2025-09-11 11:17:06","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":846357,"visible":true,"origin":"","legend":"\u003cp\u003eProjection of diet-related CRC burden in China (2022–2050)\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-7242118/v1/7057aa71f0fddc70fb34fcfd.png"},{"id":99314608,"identity":"780aff52-95a9-4556-9b0a-21eae05fdd65","added_by":"auto","created_at":"2025-12-31 16:22:00","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5756624,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7242118/v1/1ad092a4-7343-43fc-aac9-f08304bfc96d.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Burden and Trends of Diet-Related Colorectal Cancer in Global, East Mediterranean, and China ：Systematic Analysis Based on Global Burden of Disease Study 1990-2021 with Projections to 2050","fulltext":[{"header":"Background","content":"\u003cp\u003eCRC is the third most common cancer and the second most frequent cause of cancer death worldwide \u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. The incidence of CRC is increasing, and it has been estimated that there will be 2.5\u0026nbsp;million new cases by the year 2035.\u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e As an important piece of information, 70–75 percent of CRC patients where it develops sporadically which is predominantly influenced by lifestyle and dietary habits.\u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e In this GBD, The 11 common risk factors include: low dietary fiber intake, insufficient milk and calcium consumption, high red meat and processed meat intake, smoking, alcohol consumption, physical inactivity, as well as overweight/obesity and high fasting blood glucose levels.\u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eIn the context of CRC, the dietary factors is a key risk factor. Research shows that adherence to a Mediterranean diet characterized by olive oil, fish, plant-based foods, and moderate wine consumption offers protective CRC benefits\u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e.There is quite a difference in region-specific adherence to this eating pattern. In EMR, traditional diets coexist with Westernized dietary patterns rich in refined carbohydrates and added sugars.EMR country case-control studies demonstrate this divergence: Moroccan research has shown that risk mitigation is consistent with adherence to the Mediterranean diet\u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e, and Tunisian data revealing over two-fold increased CRC risk (95% CI,1.22–3.87) associated with high processed meat consumption (\u0026gt; 100g/day)\u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e.Dietary patterns are shifting,Turkey and Greece retain traditional eating habits. Such habits may explain why their CRC mortality is lower than Western-diet nations\u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e.This diversity makes the EMR indispensable for studying the link between diet and CRC.\u003c/p\u003e\u003cp\u003eChina is experiencing a swift change in food consumption, as its CRC burden has assumed the global top position\u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e.The socio-economic advancements in China have triggered a movement from lower meat consumption and relying on plants and vegetables to high processed foods and red meat intake which might contribute to CRC rates\u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e.Chinese dietary patterns fundamentally differ from Mediterranean diets in core protective components (fiber density, seafood/olive oil use) and culinary techniques (low-temperature cooking versus high-temperature processing)\u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e.In view of the role of dietary transition in promoting the incidence of CRC and China's unique nutritional context (a large population base and a dietary transition speed far exceeding that of Western developed countries), studying the association between dietary risk factors and colorectal cancer burden can not only provide a reference for global public health regarding disease evolution during the dietary transition period, but also enable the formulation of precise prevention strategies based on the dietary characteristics of the Chinese population.\u003c/p\u003e\u003cp\u003eObservational cohort studies and mechanistic research have clearly shown that diet plays a key role in the progression of CRC. Understanding trends in the EMR, China, and globally—where dietary patterns are changing significantly—is therefore of particular importance. Our study has three main objectives. First, we will outline the burden of Diet-related CRC in global, EMR, and China from 1990 to 2021. Second, we will analyze differences in disease burden across sex and age groups, and will also examine how dietary burdens vary across different SDI levels. Finally, we will forecast CRC deaths and DALYs attributable to dietary factors from 2022 to 2050. By meeting these objectives, we seek to shed light on the temporal epidemiological effects of dietary factors on chronic diseases such as CRC, while providing a solid foundation for multi-regional public health interventions.\u003c/p\u003e\n\n\n\n\n\n"},{"header":"Methods","content":"\u003ch3\u003e1. Data sources\u003c/h3\u003e\u003cp\u003eThis study drew on data from GBD Study 2021, which offers comprehensive estimates of CRC mortality, DALYs, and related risk factors from 1990 to 2021. Specifically, we extracted the following information: CRC mortality and DALYs data categorized by age (in 5-year groups), sex, and region (global, EMR, and China); exposure data on diet-related risk factors, such as low in milk, fiber,calcium, and whole-grains, as well as high in red meat and processed meat; and population data along with the world standard population for age standardization.\u003c/p\u003e\u003cp\u003eThe WHO groups countries into six regional, including: (1)African Region,(2)Eastern Mediterranean Region,(3)European Region,(4)Region of the Americas,(5)South-East Asia Region,(6)Western Pacific Region;The Eastern Mediterranean Region comprises 22 countries: Afghanistan, Algeria, Bahrain, Djibouti, Egypt, Iran (Islamic Republic of), Iraq, Jordan, Kuwait, Lebanon, Libya, Morocco, Oman, Pakistan, Palestine, Qatar, Saudi Arabia, Somalia, Sudan, Syrian Arab Republic, Tunisia, United Arab Emirates, and Yemen.\u003c/p\u003e\u003cp\u003eSDI is a composite indicator of total fertility rate under 25 years old, average years of schooling, and lag distributed income percapita\u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e. We used the reference SDI quantile to classify EMR by their SDIs in 2021 into five groups including low, low-middle, middle, high-middle, and high\u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003ch3\u003e2.Statistical analysis\u003c/h3\u003e\u003cp\u003eCalculation of Age-Standardized Rates (ASR): Mortality rates and DALYs rates were age-standardized using the World Standard Population to eliminate the impact of differences in population age structures\u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThe Joinpoint 4.2.0.1 software was used to calculate the estimated annual percentage changes (EAPCs) and 95% confidence intervals (CIs) for age-standardized mortality rates (ASDRs) and DALYs rates, so as to assess the trend changes during 1990–2021. When the Joinpoint model detected significant trend turning points, piecewise linear regression was employed for fitting; otherwise, a single linear trend was fitted\u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003ch3\u003e3. Future projection models\u003c/h3\u003e\u003cp\u003eLog-linear Age-Period-Cohort Model: We used the FORECAST package in R to apply the log-linear age-period-cohort model for predicting sex-specific mortality rates from 2021 to 2050. This model works by removing exponential growth elements and limiting predictions to linear trends to fit recent data patterns, which makes it especially well-suited for forecasting cancer mortality.\u003c/p\u003e\u003cp\u003eAutoregressive Integrated Moving Average (ARIMA) Model for Validation: For time-series data on certain risk factors (such as high red meat consumption and low whole grain intake), we used an ARIMA model to fit trends and produce short-term projections. The results were cross-validated against those from the age-period-cohort model to ensure the predictions are consistent and reliable.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cb\u003e1. Burden of CRC due to dietary risks factors: a comparative analysis of Global, EMR and China\u003c/b\u003e\u003c/p\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e1.1 Dietary factors are the primary determinants affecting CRC mortality\u003c/h2\u003e\u003cp\u003eAccording to GBD Study conducted between 1990\u0026ndash;2021, there are eleven modifiable risk factors with metabolic disorders, diet imbalance, and Low Physical Activity which greatly impact the burden of CRC in terms of disease economics (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Epidemiological data demonstrate an unabated upward trend in CRC mortality and DALYs associated with these risk factors. Furthermore, dietary risks contributed to approximately 38.90% of global CRC deaths and 38.76% of CRC-related DALYs in the year 2021, signifying an imbalance in diet as the foremost preventable cause influencing mortality due to CRC.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eGlobal CRC burden of associated with 11 risk factors (1990\u0026ndash;2021)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"11\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\"\u0026minus;\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\"\u0026minus;\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e\u003cp\u003e1990\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"4\" nameend=\"c9\" namest=\"c6\"\u003e\u003cp\u003e2021\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u003cp\u003eAttributable fraction in 2021\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDeath number\u003c/p\u003e\u003cp\u003e(95% UI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAge-standardised rate of\u003c/p\u003e\u003cp\u003edeaths (95% UI) per\u003c/p\u003e\u003cp\u003e100,000\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDALYs (95% UI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eAge-standardised rate of\u003c/p\u003e\u003cp\u003eDALYs (95% UI) per 100,000\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eDeath number\u003c/p\u003e\u003cp\u003e(95% UI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eAge-standardised rate of\u003c/p\u003e\u003cp\u003edeaths (95% UI) per\u003c/p\u003e\u003cp\u003e100,000\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eDALYs (95% UI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eAge-standardised rate of\u003c/p\u003e\u003cp\u003eDALYs (95% UI) per 100,000\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003eDeaths\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c11\"\u003e\u003cp\u003eDALYs\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eGlobal Burden\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e570318(536544\u0026ndash;597668)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e15.56 (14.49\u0026ndash;16.31)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e\u003cp\u003e14396657.72 (13568749.36-15166575.84)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e357.33 (336.62-375.74)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1044072(950187\u0026thinsp;\u0026minus;\u0026thinsp;112016)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e12.40 (11.24\u0026ndash;13.31)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c8\"\u003e\u003cp\u003e24401100.18 (22689368.55-26161517.73)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e283.24 (263.11-303.33)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDietary factors\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e231758(83612\u0026ndash;346321)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6.33(2.26\u0026ndash;9.47)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e\u003cp\u003e5810277.38(2145982.22-8636186.62)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e144.88(53.10-215.52)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e406099(138065\u0026ndash;628056)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e4.82(1.64\u0026ndash;7.46)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c8\"\u003e\u003cp\u003e9458464.22(3251667.63-14521174.19)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e109.71(37.68-168.52)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e38.90%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e38.76%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAlcohol use\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5569(3961\u0026ndash;7269)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.65(0.46\u0026ndash;0.85)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e\u003cp\u003e181730.46(127132.86-236829.45)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e18.89(13.29\u0026ndash;24.64)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e14936(10532\u0026ndash;20494)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.72(0.51\u0026ndash;0.99)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c8\"\u003e\u003cp\u003e421730.92(299867.94-578279.62)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e20.34(14.46\u0026ndash;27.96)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1.43%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e1.73%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDiet high in red meat\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e84263(-26-168170)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.31(-0.00-4.61)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e\u003cp\u003e2094732.37(-690.76-4190753.33)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e52.39(-0.02-104.82)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e152985(-51-314249)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.82(-0.00-3.74)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c8\"\u003e\u003cp\u003e3552238.54(-1303.11-7213991.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e41.19(-0.02-83.67)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e14.65%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e14.56%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDiet low in whole grains\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e101812(42588\u0026ndash;151170)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.79(1.17\u0026ndash;4.15)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e\u003cp\u003e2540867.41(1050794.36-3754415.59)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e63.47(26.35\u0026ndash;93.84)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e186256(76126\u0026ndash;284803)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e2.21(0.91\u0026ndash;3.38)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c8\"\u003e\u003cp\u003e4327218.86(1754865.24-6578232.30)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e50.19(20.37\u0026ndash;76.30)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e17.84%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e17.73%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDiet low in fibre\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9689(4409\u0026ndash;14808)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.27(0.12\u0026ndash;0.42)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e\u003cp\u003e247015.19(112606.13-380299.24)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6.17(2.80\u0026ndash;9.47)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e13144(5761\u0026ndash;20265)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.16(0.07\u0026ndash;0.24)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c8\"\u003e\u003cp\u003e305675.95(135088.79-469863.48)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e3.58(1.58\u0026ndash;5.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1.26%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e1.25%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDiet low in calcium\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e57363(42914\u0026ndash;71257)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.54(1.15\u0026ndash;1.92)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e\u003cp\u003e1512762.13(1132026.04-1881666.67)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e37.04(27.74\u0026ndash;46.14)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e89089(65018\u0026ndash;112297)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.06(0.77\u0026ndash;1.33)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c8\"\u003e\u003cp\u003e2128938.58(1565530.27-2672450.44)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e24.70(18.17\u0026ndash;31.02)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e8.53%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e8.72%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDiet low in milk\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e81405(22743\u0026ndash;133923)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.22(0.62\u0026ndash;3.65)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e\u003cp\u003e2074171.90(575525.07-3392723.24)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e51.52(14.33\u0026ndash;84.17)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e157562(42973\u0026ndash;262535)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.87(0.51\u0026ndash;3.12)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c8\"\u003e\u003cp\u003e3706461.02(1010584.13-6138022.35)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e42.99(11.73\u0026ndash;71.23)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e15.09%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e15.19%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDiet high in processed meat\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e37083(-9050-75308)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.03(-0.25-2.09)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e\u003cp\u003e886133.12(-218150.41-1802022.13)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e22.49(-5.52-45.73)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e57148(-13411-117694)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.68(-0.16-1.40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c8\"\u003e\u003cp\u003e1301644.06(-310251.07-2664056.37)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e15.11(-3.60-30.93)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e5.47%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e5.33%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHigh body-mass index [BMI]\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e41535(17665-67379.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.14(0.48\u0026ndash;1.86)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e\u003cp\u003e1015042.12(429787.23-1631973.77)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e25.54(10.83\u0026ndash;41.20)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e99267(42956\u0026ndash;157948)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.17(0.51\u0026ndash;1.87)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c8\"\u003e\u003cp\u003e2364664.16(1021593.57-3752340.44)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e27.33(11.80-43.37)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e9.51%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e9.69%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHigh fasting plasma glucose\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e31906(16052\u0026ndash;48058)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.89(0.45\u0026ndash;1.34)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e\u003cp\u003e715715.78(358248.80-1089711.73)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e18.46(9.27\u0026ndash;28.03)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e82421(42426\u0026ndash;125402)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.98(0.51\u0026ndash;1.49)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c8\"\u003e\u003cp\u003e1750923.34(900573.43-2657994.92)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e20.31(10.46\u0026ndash;30.81)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e7.89%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e7.18%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eLow physical activity\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5734.54(3368\u0026ndash;8371)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.93(0.55\u0026ndash;1.36)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e\u003cp\u003e128352.60(74846.96-188831.95)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e17.12(10.06\u0026ndash;24.93)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e16698(10065\u0026ndash;24625)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.87(0.53\u0026ndash;1.29)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c8\"\u003e\u003cp\u003e320464.35(192274.95-474069.92)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e15.63(9.47\u0026ndash;22.88)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1.60%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e1.31%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003esmoking\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7774(4960\u0026ndash;10887)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.95(0.60\u0026ndash;1.33)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e\u003cp\u003e232203.99(149570.35-325169.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e25.32(16.29\u0026ndash;35.44)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e17276(10519\u0026ndash;25641)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.82(0.50\u0026ndash;1.21)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c8\"\u003e\u003cp\u003e459249.52(276317.14-684961.95)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e21.44(12.95\u0026ndash;31.98)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1.65%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e1.88%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e1.2 Trends in diet-related CRC burden\u003c/h2\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e illustrate the numbers of Diet-Related CRC deaths and DALYs, as well as the rates of Diet-Related CRC deaths and DALYs in China,EMR and global between 1990 and 2021.Globally, the following metrics demonstrated changes:a 75.23% increase in deaths and a 62.79% increase in DALYs.The estimated annual percentage change (EAPC) for age-standardized mortality rates was \u0026minus;\u0026thinsp;0.95%, and the EAPC for age-standardized DALYs rates was \u0026minus;\u0026thinsp;0.98%. reflecting consistent declines in relative burden despite absolute case growth.In China experienced a 108.08% increase in diet-related CRC deaths, with 37.33% of CRC deaths attributable to dietary risk factors. DALYs increase by 77.12%, and 37.31% of CRC DALYs were diet-related. Age-standardized mortality rates declined EAPC\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.86%, and DALYs rates fell (EAPC\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.94%), aligning with global trends.\u003c/p\u003e\u003cp\u003eIn EMR witnessed a striking 171.37% increase in diet-related CRC deaths, from 4,576 (95% UI: 2,020\u0026ndash;6,784) to 12,420 (95% UI: 4,964\u0026ndash;18,882), comprising 38.65% of all CRC mortality.DALYs followed suit, rising 169.53% from 131,436.78 (95% UI: 58,001.45\u0026ndash;195,770.82) to 354,258.08 (95% UI: 138,866.28\u0026ndash;541,535.80), comprising 38.31% of all CRC DALYs.In 2021, CRC diet-relateddeaths in EMR accounted for 3.06% of the global total and 12.09% of China's CRC deaths.Contrary to global trends, age-standardized mortality rates increased from 2.69 to 2.96 per 100,000 (EAPC\u0026thinsp;=\u0026thinsp;+\u0026thinsp;0.46%,), while DALYs rates rose from 67.07 to 70.85 per 100,000 (EAPC\u0026thinsp;=\u0026thinsp;+\u0026thinsp;0.30%).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eCRC burden of associated with diet-related risk factors in Global,EMR,and China\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"11\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\"\u0026minus;\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\"\u0026minus;\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e\u003cp\u003e1990\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"4\" nameend=\"c9\" namest=\"c6\"\u003e\u003cp\u003e2021\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u003cp\u003eEAPC\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDeath number\u003c/p\u003e\u003cp\u003e(95% UI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAge-standardised rate of\u003c/p\u003e\u003cp\u003edeaths (95% UI) per\u003c/p\u003e\u003cp\u003e100,000\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDALYs (95% UI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eAge-standardised rate of\u003c/p\u003e\u003cp\u003eDALYs (95% UI) per 100,000\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eDeath number\u003c/p\u003e\u003cp\u003e(95% UI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eAge-standardised rate of\u003c/p\u003e\u003cp\u003edeaths (95% UI) per\u003c/p\u003e\u003cp\u003e100,000\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eDALYs (95% UI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eAge-standardised rate of\u003c/p\u003e\u003cp\u003eDALYs (95% UI) per 100,000\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003eDeaths(95% CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c11\"\u003e\u003cp\u003eDALYs(95% CI)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDietary factors(Global)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e231758(83612\u0026ndash;346321)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6.33(2.26\u0026ndash;9.47)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e\u003cp\u003e5810277.38(2145982.22-8636186.62)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e144.88(53.10-215.52)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e406099(138065\u0026ndash;628056)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e4.82(1.64\u0026ndash;7.46)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c8\"\u003e\u003cp\u003e9458464.22(3251667.63-14521174.19)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e109.71(37.68-168.52)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e-0.95 (-0.99\u0026ndash;0.92)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e-0.98 (-1.02\u0026ndash;0.95)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDiet high in processed meat(Global)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e37083(-9050-75308)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.03(-0.25-2.09)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e\u003cp\u003e886133.12(-218150.41-1802022.13)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e22.49(-5.52-45.73)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e57148(-13411-117694)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.68(-0.16-1.40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c8\"\u003e\u003cp\u003e1301644.06(-310251.07-2664056.37)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e15.11(-3.60-30.93)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e-1.39 (-1.45\u0026ndash;1.33)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e-1.33 (-1.39\u0026ndash;1.27)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDiet low in milk(Global)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e81405(22743\u0026ndash;133923)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.22(0.62\u0026ndash;3.65)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e\u003cp\u003e2074171.90(575525.07-3392723.24)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e51.52(14.33\u0026ndash;84.17)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e157562(42973\u0026ndash;262535)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.87(0.51\u0026ndash;3.12)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c8\"\u003e\u003cp\u003e3706461.02(1010584.13-6138022.35)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e42.99(11.73\u0026ndash;71.23)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e-0.62 (-0.68\u0026ndash;0.57)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e-0.66 (-0.73\u0026ndash;0.60)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDiet high in red meat(Global)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e84263(-26-168170)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.31(-0.00-4.61)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e\u003cp\u003e2094732.37(-690.76-4190753.33)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e52.39(-0.02-104.82)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e152985(-51-314249)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.82(-0.00-3.74)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c8\"\u003e\u003cp\u003e3552238.54(-1303.11-7213991.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e41.19(-0.02-83.67)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e-0.84 (-0.87\u0026ndash;0.81)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e-0.85 (-0.88\u0026ndash;0.81)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDiet low in fibre(Global)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9689(4409\u0026ndash;14808)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.27(0.12\u0026ndash;0.42)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e\u003cp\u003e247015.19(112606.13-380299.24)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6.17(2.80\u0026ndash;9.47)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e13144(5761\u0026ndash;20265)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.16(0.07\u0026ndash;0.24)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c8\"\u003e\u003cp\u003e305675.95(135088.79-469863.48)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e3.58(1.58\u0026ndash;5.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e-1.86 (-1.90\u0026ndash;1.82)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e-1.89 (-1.95\u0026ndash;1.83)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDiet low in whole grains\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e(Global)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e101812(42588\u0026ndash;151170)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.79(1.17\u0026ndash;4.15)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e\u003cp\u003e2540867.41(1050794.36-3754415.59)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e63.47(26.35\u0026ndash;93.84)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e186256(76126\u0026ndash;284803)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e2.21(0.91\u0026ndash;3.38)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c8\"\u003e\u003cp\u003e4327218.86(1754865.24-6578232.30)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e50.19(20.37\u0026ndash;76.30)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e-0.82 (-0.85\u0026ndash;0.78)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e-0.84 (-0.87\u0026ndash;0.81)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDiet low in calcium(Global)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e57363(42914\u0026ndash;71257)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.54(1.15\u0026ndash;1.92)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e\u003cp\u003e1512762.13(1132026.04-1881666.67)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e37.04(27.74\u0026ndash;46.14)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e89089(65018\u0026ndash;112297)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.06(0.77\u0026ndash;1.33)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c8\"\u003e\u003cp\u003e2128938.58(1565530.27-2672450.44)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e24.70(18.17\u0026ndash;31.02)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e-1.33 (-1.37\u0026ndash;1.29)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e-1.45 (-1.50\u0026ndash;1.40)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDietary factors(EMR)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4576(2020\u0026ndash;6784)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.69(1.20\u0026ndash;3.96)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e\u003cp\u003e131436.78(58001.45-195770.82)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e67.07(29.64\u0026ndash;99.54)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e12420(4964\u0026ndash;18882)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e2.96(1.21\u0026ndash;4.47)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c8\"\u003e\u003cp\u003e354258.08(138866.28-541535.80)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e70.85(28.14-108.15)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.46 (0.40\u0026ndash;0.52)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.30 (0.24\u0026ndash;0.35)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDiet high in processed meat(EMR)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e308(-74-649)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.18(-0.04-0.37)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e\u003cp\u003e9228.81(-2236.14-19439.06)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.59(-1.11-9.65)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e972(-223-2060)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.22(-0.05-0.47)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c8\"\u003e\u003cp\u003e29561.54(-6858.08-62271.55)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e5.63(-1.30-11.94)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.76 (0.68\u0026ndash;0.85)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.71 (0.62\u0026ndash;0.80)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDiet low in milk(EMR)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1995(528\u0026ndash;3229)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.17(0.32\u0026ndash;1.89)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e\u003cp\u003e57656.11(15160.60-94132.17)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e29.33(7.75\u0026ndash;47.51)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e5638(1518\u0026ndash;9309)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.35(0.36\u0026ndash;2.23)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c8\"\u003e\u003cp\u003e161039.48(43328.26-267574.28)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e32.19(8.68\u0026ndash;53.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.61 (0.55\u0026ndash;0.67)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.42 (0.38\u0026ndash;0.47)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDiet high in red meat(EMR)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1520(-0-3097)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.88(-0.00-1.80)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e\u003cp\u003e44353.55(-4.98-90404.71)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e22.42(-0.00-45.55)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4401(-0-9018)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.04(-0.00-2.13)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c8\"\u003e\u003cp\u003e126970.79(-19.74-258279.13)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e25.19(-0.00-51.46)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.72 (0.64\u0026ndash;0.80)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.54 (0.47\u0026ndash;0.61)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDiet low in fibre(EMR)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e143(64\u0026ndash;222)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.09(0.04\u0026ndash;0.14)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e\u003cp\u003e4067.11(1842.60-6320.38)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.09(0.94\u0026ndash;3.23)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e350(160\u0026ndash;565)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.08(0.04\u0026ndash;0.13)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c8\"\u003e\u003cp\u003e10345.60(4786.20-16412.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e2.00(0.91\u0026ndash;3.22)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e-0.31 (-0.52\u0026ndash;0.10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e-0.30 (-0.53\u0026ndash;0.07)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDiet low in whole grains\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e(EMR)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2055(843\u0026ndash;3098)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.21(0.50\u0026ndash;1.81)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e\u003cp\u003e59035.67(24286.61-89512.05)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e30.13(12.36\u0026ndash;45.49)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e6001(2471\u0026ndash;9044)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.43(0.60\u0026ndash;2.17)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c8\"\u003e\u003cp\u003e170688.51(69845.64-259403.60)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e34.23(14.09\u0026ndash;51.51)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.73 (0.65\u0026ndash;0.81)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.56 (0.48\u0026ndash;0.63)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDiet low in calcium(EMR)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1664(1254\u0026ndash;2121)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.00(0.75\u0026ndash;1.27)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e\u003cp\u003e46827.77(34716.31-59974.09)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e24.18(18.19\u0026ndash;30.88)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3416(2500\u0026ndash;4462)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.84(0.61\u0026ndash;1.08)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c8\"\u003e\u003cp\u003e95292.20(69532.17-126036.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e19.33(14.17\u0026ndash;25.30)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e-0.51 (-0.56\u0026ndash;0.47)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e-0.69 (-0.73\u0026ndash;0.66)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDietary factors(China)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e49354(22956\u0026ndash;73117)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6.50(3.09\u0026ndash;9.63)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e\u003cp\u003e1442747.31(666850.39-2133156.24)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e160.95(74.99\u0026ndash;237.30)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e102694(36136\u0026ndash;166536)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e5.08(1.79\u0026ndash;8.21)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c8\"\u003e\u003cp\u003e2555444.51(880336.60-4134442.40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e122.93(42.56-198.49)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e-0.86 (-0.93\u0026ndash;0.80)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e-0.94 (-1.03\u0026ndash;0.85)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDiet high in processed meat(China)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1764(-413-3790)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.23(-0.05-0.48)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e\u003cp\u003e53167.50(-12352.58-115432.45)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5.83(-1.37-12.56)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e6612(-1355-14767)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.32(-0.07-0.71)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c8\"\u003e\u003cp\u003e178186.62(-37002.76-405024.52)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e8.57(-1.78-19.35)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1.46 (1.31\u0026ndash;1.60)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e1.64 (1.44\u0026ndash;1.83)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDiet low in milk(China)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e22540(5848\u0026ndash;37330)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.98(0.78\u0026ndash;4.97)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e\u003cp\u003e652029.04(167977.40-1085153.89)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e73.14(18.96-121.15)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e51030(13916\u0026ndash;86781)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e2.53(0.69\u0026ndash;4.30)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c8\"\u003e\u003cp\u003e1253643.02(337481.84-2128072.72)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e60.25(16.22-102.21)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e-0.63 (-0.70\u0026ndash;0.57)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e-0.73 (-0.82\u0026ndash;0.65)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDiet high in red meat(China)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e17607(-3-36613)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.29(-0.00-4.76)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e\u003cp\u003e518212.88(-104.50-1074174.40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e57.50(-0.01-119.28)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e43579(-16-92082)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e2.15(-0.00-4.55)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c8\"\u003e\u003cp\u003e1091787.60(-508.62-2295778.88)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e52.47(-0.02-110.35)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e-0.24 (-0.30\u0026ndash;0.19)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e-0.33 (-0.41\u0026ndash;0.25)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDiet low in fibre(China)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2100(927\u0026ndash;3451)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.27(0.12\u0026ndash;0.44)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e\u003cp\u003e68081.50(29789.69-111751.10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e7.32(3.23\u0026ndash;11.87)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1738(693\u0026ndash;3023)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.09(0.04\u0026ndash;0.15)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c8\"\u003e\u003cp\u003e48099.59(18866.72-84760.77)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e2.40(0.94\u0026ndash;4.16)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e-3.74 (-3.85\u0026ndash;3.62)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e-3.77 (-3.86\u0026ndash;3.67)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDiet low in whole grains\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e(China)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e21329(8463\u0026ndash;32783)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.79(1.11\u0026ndash;4.29)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e\u003cp\u003e624948.36(248199.78-959563.32)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e69.54(27.62-106.93)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e49990(20099\u0026ndash;79928)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e2.47(0.99\u0026ndash;3.94)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c8\"\u003e\u003cp\u003e1241927.75(503164.76-1978508.33)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e59.70(24.17\u0026ndash;94.95)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e-0.46 (-0.52\u0026ndash;0.41)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e-0.57 (-0.65\u0026ndash;0.50)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDiet low in calcium(China)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e18902(13485\u0026ndash;24551)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.55(1.81\u0026ndash;3.28)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e\u003cp\u003e546368.99(389713.79-718067.19)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e61.59(43.88\u0026ndash;80.49)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e20719(14553\u0026ndash;28271)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.04(0.73\u0026ndash;1.41)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c8\"\u003e\u003cp\u003e500468.35(356218.76-682873.06)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e24.20(17.14\u0026ndash;33.05)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e-3.06 (-3.17\u0026ndash;2.96)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e-3.18 (-3.28\u0026ndash;3.08)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003e2 Genders and ages burden of diet-related CRC\u003c/h3\u003e\n\u003cp\u003eIn 2021, the number of CRC deaths attributable to dietary risk factors varied by age group(Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea),Male mortality peaks at 70\u0026ndash;74 years, while females lag by a decade at 80\u0026ndash;84 years.Males outnumber females in deaths below the age of 80, but surpasses the female population after 80 years.Males exhibit higher rates than females overall under the age of 95, with both sexes showing age-dependant increase,with both sexes showing age-dependant increase.In 2021, the peak number of DALYs due to CRC attributable to dietary risk factors occurred in both sexes aged 65 to 69((Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ed).In addition, the number of DALYs due to CRC attributable to dietary risk factors was higher in males than in females under the age of 80 and higher in females than in males over the age of 80.The DALY rate due to CRC attributable to dietary risk factors was higher in males than in females under the age of 80 ,after 80 years females exhibit higher rates and increased with age in both sexes.\u003c/p\u003e\u003cp\u003eIn EMR, the number of CRC deaths peak at 65\u0026ndash;69 years for both sexes,with male deaths consistently exceeding those of females from the age of 55 onward.Additionally, the CRC mortality rate attributable to dietary risk factors showed minimal differences between males and females below 90 years, and increased with age in both sexes(Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb).The peak number of DALYs due to CRC attributable to dietary risk factors occurred in both sexes aged 60 to 64(Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ee).The trend of DALY rate paralleled that of mortality rate.\u003c/p\u003e\u003cp\u003eIn China,The peak number of deaths due to CRC attributable to dietary risk factors occurred in both sexes aged 70 to 74, with males outnumbering females in deaths under the age of 90(Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec).Male mortality rates were higher than those of females across all age groups, with the disparity being particularly pronounced in individuals aged after 80 years.The peak number of DALYs due to CRC attributable to dietary risk factors occurred in both sexes aged 65 to 69(Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ef).The DALY rate due to CRC attributable to dietary risk factors was higher in males than in females, DALY rate in males decreases after 90 years, while females continue to show upward trends.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\n\u003ch3\u003e3 CRC-related DALYs attributable to dietary risk factors\u003c/h3\u003e\n\u003cp\u003eGlobally, the primary diet-related CRC risk factor for females was inadequate milk intake, contributing to 1.91 deaths per 100,000(95% UI:0.53\u0026ndash;3.17) and 43.69 DALYs per 100,000(95% UI:11.87\u0026ndash;72.18).the primary diet-related CRC risk factor for males was inadequate intake of whole-grain diets, contributing to 2.75 deaths per 100,000(95% UI: 1.11\u0026ndash;4.20)and 62.39 DALYs per 100,000(95% UI:25.20\u0026ndash;95.64).The trend was constant across different age groups.By contrast, diets low in fiber and high in possessed meat had the least share in CRC deaths and DALYs.Globally, in the context of the \"diet low in milk\" factor, Females had higher death and DALY rates than males at all ages, and the gap widened with age.in the case of inadequate whole-grain consumption, Males had higher death and DALY rates than females at all ages.\u003c/p\u003e\u003cp\u003eIn EMR, the primary dietary risk factor for females was inadequate milk intake, contributing to 1.59 deaths per 100,000(95% UI: 0.41\u0026ndash;2.59)and 38.23 DALYs(95% UI: 9.88\u0026ndash;62.04). For males, the primary dietary risk factor was inadequate whole-grain intake, associated with 1.50 deaths per 100,000(95% UI: 0.63\u0026ndash;2.27)and 35.46 DALYs per 100,000(95% UI: 14.81\u0026ndash;53.82).In EMR, the trends in mortality and DALYs associated with the \"diet low in milk\" factor are consistent with global patterns.Below the age of 95, in the context of inadequate whole-grain consumption, males exhibited slightly higher death and DALY rates than females.\u003c/p\u003e\u003cp\u003eIn China, the primary dietary risk factor for females was inadequate milk intake,(2.18 deaths [95% UI: 0.60\u0026ndash;3.73], 50.84 DALYs [95% UI: 13.95\u0026ndash;87.60]);For males, the primary dietary risk factor was whole-grain intake (3.44 deaths [95% UI: 1.36\u0026ndash;5.59], 81.74 DALYs [95% UI: 32.46\u0026ndash;133.71]).Across the six factors, the overall trends of mortality and disability rates among males were consistently greater than those among females across different age stages.\u003c/p\u003e\u003cp\u003eCross-Regional Insights from Figures Key risk factors: Inadequate milk intake、Inadequate whole-grain intake and excessive red meat intake were predominant.Excessive red meat intake:Male mortality/DALYs rates exceeded females\u0026rsquo; across regions; rates increased with age, with steeper male increases after 80 years (most pronounced in China).Inadequate whole-grain intake:Male mortality/DALYs rates were higher than females\u0026rsquo;, consistent with the trends of excessive red meat intake.Inadequate milk intake:Females had higher rates than males globally/Eastern Mediterranean, while China showed reversed patterns.\u003c/p\u003e\n\u003ch3\u003e4. Diet-related CRC DALYs across different SDI countries\u003c/h3\u003e\n\u003cp\u003eEMR countries were categorised into five groups based on SDI quintiles as presented in Table\u0026nbsp;3. There was a considerable variation in age-standardised DALYs rates of CRC across the five groups of the EMR (Table\u0026nbsp;3).Notably, the diet-related CRC DALYs rate were highest in the Middle-low SDI group (ranged from 69.80 to 156.70 per 100,000 persons).Middle-SDI countries show a relatively lower DALYs rate(ranged from 54.86 to 77.27 per 100,000 persons), indicating early successes in dietary health management and food quality regulation.\u003c/p\u003e\u003cp\u003eWhen it comes to the burden of CRC caused by specific dietary factors, different characteristics emerge. In terms of insufficient whole-grain intake, the CRC DALYs rates in middle - high SDI countries are the highest(ranged from 35.33 to 65.60 per 100,000 persons).In the case of insufficient milk intake,the CRC DALYs rates were highest in middle-low SDI countries, with a rate (ranged from 32.73 to 64.65 per 100,000 persons).Regarding insufficient fiber intake, the CRC DALYs rates were highest in low SDI countries, with a rate ranging from 3.31 to 10.97 per 100,000 persons. Moreover, the DALYs values resulting from excessive red meat consumption are comparable across different SDI levels. This phenomenon implies that regardless of the level of economic development, meat-eating habits have universal health implications.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e\u003ccolgroup cols=\"8\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\"\u0026minus;\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\"\u0026minus;\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e\u003cp\u003eTabel 3 DALYs rate of diet-related factors for CRC across different SDI in EMR\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLocation\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDietary factors\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDiet low in whole grains\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDiet low in milk\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eDiet high in red meat\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eDiet low in calcium\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eDiet high in processed meat\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eDiet low in fibre\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh SDI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQatar\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e79.42301314648(21.32774683527,131.5561049587)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e43.65166427012(17.48086307179,69.7933796147)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e38.71126636463(9.64975483083,66.14011377026)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e\u003cp\u003e34.39692722042(-0.00721171035,70.63698894662)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e7.29893236978(4.38430147412,10.9770018894)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c7\"\u003e\u003cp\u003e5.82295261382(-1.18739176824,12.596782744159999)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.1333743912(0.03768194247,0.26083633259)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKuwait\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e66.42981823510999(19.128050112,110.19533808962)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e35.9625003945(14.46090216573,55.8913222857)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e33.79405726224(8.86977321642,55.94402410387)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e\u003cp\u003e29.585903600109997(-0.00528906359999999,61.535143568769996)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e6.32382567627(4.04750437784,8.88792317495)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c7\"\u003e\u003cp\u003e4.42370996746(-0.9491437174,9.64181147152)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1.27836579888(0.53540807876,2.0605528675299998)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUnited Arab Emirates\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e94.01226343076999(28.323281371870003,155.98221484016)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e49.14615906563(19.602442085470003,81.96111851684)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e47.37579154765(12.84789312457,81.09902958194)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e\u003cp\u003e38.50510495749(-0.00604913319,78.37600432162)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e14.1573105252(8.98134422187,21.10321408295)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c7\"\u003e\u003cp\u003e7.76706486831(-1.80175807568,16.35626179217)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1.03973404896(0.39209669599999997,1.83723152291)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMiddle-high SDI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLibya\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e120.35750551192(44.33728275815,197.38395526533)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e62.59821930428(26.20484839142,101.64733467446)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e60.40554034723(16.45518584477,105.42669173435002)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e\u003cp\u003e46.7385798792(-0.01433979135,99.4097535628)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e24.52419957458(15.65981830559,35.1256290203)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c7\"\u003e\u003cp\u003e6.70973821549(-1.56147410224,14.96799050702)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e2.24674210025(0.92692758078,3.78785810621)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTurkey\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e101.22287591536(31.265273342209998,165.67396301436)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e56.19709114359(24.0383863579,87.16195560045)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e32.53014435482(9.23401761871,56.4819190515)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e\u003cp\u003e42.242381607889996(-0.01096803222,88.81219773291)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" 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char=\"\u0026minus;\" colname=\"c5\"\u003e\u003cp\u003e32.68234498073(-0.01049923015,67.11707936503)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e11.50929031636(7.22082990231,16.55014773471)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c7\"\u003e\u003cp\u003e5.00685755751(-1.12739563685,10.94212556978)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.45019960883(0.16628261085,0.80724392856)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSaudi Arabia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e65.99437247821(21.55248774999,107.12787060273)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e35.327740634870004(14.29755254504,56.238842747780005)\u003c/p\u003e\u003c/td\u003e\u003ctd 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colname=\"c2\"\u003e\u003cp\u003e75.22276780444(26.249353037520002,122.16908656345998)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e38.80227445069(15.740080627920001,62.74034200834)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e36.07928172838(9.55158229794,61.94846018078)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e\u003cp\u003e28.99037530394(-0.00864038202,60.5554555107)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e16.00924407192(9.86519638585,24.39858675291)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c7\"\u003e\u003cp\u003e4.2750417587000005(-0.897067248059999,9.62666329113)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" 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char=\"\u0026minus;\" colname=\"c7\"\u003e\u003cp\u003e4.70513543772(-1.16008289029,10.45542633503)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.61920491542(0.24585017064000003,1.09059188961)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMiddle SDI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIraq\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e63.97720545505(31.94650929765,97.71502053539001)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e28.98034714245(11.155779293610001,46.66300722706)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e28.835831980499997(7.49927734735,48.75881974479)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e\u003cp\u003e15.08751645026(-0.00533782398,33.90683068145)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e29.7118588703(20.22451872991,40.57565967262)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c7\"\u003e\u003cp\u003e2.83542997848(-0.6221042655,6.19875170894)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1.3665828839999998(0.5810388859,2.28107791485)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIslamic Republic of Iran\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e64.37057719749(25.7253712386,97.05923110768)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e32.604782969030005(13.59573403334,48.882356157909996)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e30.274581780069997(8.13446737549,49.474889644070004)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e\u003cp\u003e23.072284446610002(-0.00326862446,48.03804889344)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e16.43690567662(11.83824466261,21.13349600176)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c7\"\u003e\u003cp\u003e3.66802203622(-0.83525487118,7.834285184570001)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.62804906275(0.26697665526000003,1.00864980434)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEgypt\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e77.2702882687(25.892744691089998,123.80266740191)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e38.14763152127(14.883870462039999,59.57100785787)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e40.22477302707(10.55312507454,66.01097379408999)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e\u003cp\u003e30.601766710240003(-0.00876105164,63.88935454237)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e15.82551907372(10.85564211518,21.75577396761)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c7\"\u003e\u003cp\u003e6.268030982499999(-1.45414080621,13.165397161)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.1768473515(0.06513642554,0.33395937526999997)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTunisia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e60.89627920209001(17.754091024559997,102.53244383542)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e32.729423903960004(12.008084226800001,52.74074332395)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e23.23462935305(5.65069012827,42.082115465)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e\u003cp\u003e24.30840793075(-0.00811847393999999,51.625659347050004)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e9.58442087387(6.04144422638,14.30520748702)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c7\"\u003e\u003cp\u003e3.93064469934(-0.839520151379999,8.73350670188)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.34686224211(0.13816665726,0.64884697415)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSyrian Arab Republic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e54.86326536622(20.260894730540002,93.39225571745)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e28.251116757210003(11.52286924544,46.80585868742)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e25.39948169592(6.5627845084,45.210352726079996)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e\u003cp\u003e21.196327887960003(-0.00707465135,45.66607661131)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e11.778191739679999(7.57126869772,17.00189645634)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c7\"\u003e\u003cp\u003e3.0289522288(-0.70093675401,6.87663437582)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1.27988843261(0.53947352553,2.22013164597)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMiddle-low SDI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDjibouti\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e120.5180302(48.76704775,202.7889325)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e53.9969666(21.12613369,93.10616129)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e52.75789454(14.16008804,99.64942405)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e\u003cp\u003e42.30323366(-0.00932404,95.11765774)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e40.549992(24.83106644,61.21396927)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c7\"\u003e\u003cp\u003e9.477032733(-2.364577124,21.93238522)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e6.432717997(2.68676764,11.38958248)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePalestine\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e156.7029036(86.01251358,222.4554177)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e64.16814941(26.94094208,98.1300546)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e64.64759462(17.67176106,105.0655625)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e\u003cp\u003e41.29159633(-0.013084919,84.77451682)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e81.01995803(58.04225988,104.6820841)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c7\"\u003e\u003cp\u003e5.471038366(-1.319687683,11.83106778)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e8.901592205(3.911870175,14.80810425)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMorocco\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e75.75619058(29.89058692,121.7852637)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e37.87540507(16.45389597,59.75801324)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e36.12116949(10.03359598,62.40103435)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e\u003cp\u003e28.93398224(-0.009198074,61.7321822)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e18.65942964(11.78502889,26.93477761)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c7\"\u003e\u003cp\u003e3.953154824(-0.920081218,8.791341961)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.092970025(0.030463997,0.190859919)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSudan\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e69.79536625(28.27906892,122.1323225)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e32.63481717(11.95998076,57.44469094)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e32.72579266(8.012918485,61.35161061)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e\u003cp\u003e23.31728291(-0.007111327,55.18519814)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e24.54278929(15.26778569,38.529264)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c7\"\u003e\u003cp\u003e3.180556142(-0.723040131,7.692953566)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e2.225395771(0.846721198,4.048290408)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLow SDI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYemen\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e82.22511758(42.85171535,131.530727)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e32.00640923(13.13757634,55.14832477)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e33.19752155(8.810890672,58.88942243)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e\u003cp\u003e21.20307218(-0.005746259,47.50027185)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e45.8883243(28.09814722,69.26278345)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c7\"\u003e\u003cp\u003e2.725443585(-0.628902941,6.338740968)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e3.306001498(1.420352587,6.178403401)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePakistan\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e60.86098659(20.64748979,98.17081858)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e28.84301299(11.58949054,45.99382385)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e24.38688663(6.538440538,42.71877192)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e\u003cp\u003e21.16604685(-0.004247079,45.4833899)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e14.45177861(9.99979734,19.88434579)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c7\"\u003e\u003cp\u003e9.137234087(-2.188888063,20.2686007)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e3.856962962(1.724541887,6.209863677)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSomalia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e129.3330956(67.55629347,206.9294694)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e44.73812499(17.75630979,77.41221739)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e48.09190063(13.66401944,86.75084512)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e\u003cp\u003e35.4889202(-0.006826,81.68307539)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e77.34327838(46.35413305,116.6838711)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c7\"\u003e\u003cp\u003e7.316963143(-1.705589725,17.33204029)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e10.97073875(4.818097689,19.20149546)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAfghanistan\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e142.4854738(50.61494892,268.7431011)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e60.81900975(20.57220882,116.9155354)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e62.81728936(12.39839868,125.1683047)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e\u003cp\u003e46.99544183(-0.013499085,116.9429898)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e61.18492546(27.33895739,101.2073307)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c7\"\u003e\u003cp\u003e5.395333668(-0.984808578,13.78366839)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e8.013870695(2.936446155,14.87700899)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\n\u003ch3\u003e5. CRC burden attributable to diet death and DALYs rate projections till 2050\u003c/h3\u003e\n\u003cp\u003eAccording to projections, the global age-standardized death rate and age-standardized DALYs for diet-related CRC will continue the downward trend observed during 1990\u0026ndash;2021(3.44[95%UI,3.07\u0026ndash;3.81]per 100,000,77.74[95%UI,68.69\u0026ndash;86.80]per 100,000, respectively), with a more pronounced decline magnitude in females (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003e).In 2050, The trend of China remains relatively stable or shows a slight decline for all factors (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e7\u003c/span\u003e).In EMR(Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003e), the age-standardized mortality rate for diet-related CRC in males is projected to exhibit an upward trend(3.26[95%UI,2.93\u0026ndash;3.58]per 100,000), while that in females tends to stabilize. The age-standardized DALYs for diet-related CRC show a slight decline in both sexes.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003e\u003cb\u003eThe impact of diet-related CRC burden: Trends and the role of screening policies in Global, EMR, and China (1990\u0026ndash;2021)\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThis study analyzed diet-related CRC burden based on the data from GBD 2021. The research found that globally, 38.90% of CRC deaths and 38.76% of CRC DALYs could be attributed to dietary risk factors. From 1990 to 2021, the number of diet-related CRC deaths globally increased by 75.23%, and the DALYs grew by 62.79%, highlighting the significant challenge posed by dietary imbalance to global public health.Notably, the diet-related CRC burden in EMR has shown a particularly remarkable growth. The number of CRC deaths in EMR increased by 171.37%, and DALYs grew by 169.53%, with the growth rates far exceeding the global average. This phenomenon is closely related to the impact of the Western dietary pattern on the traditional Mediterranean dietary pattern (rich in plant-based foods, fish, and olive oil) in the region. Such significant changes in dietary structure have directly led to the sharp rise in CRC burden in this region.Epidemiological evidence shows that the Mediterranean dietary pattern can reduce the risk of CRC by 10\u0026ndash;15% through mechanisms such as regulating the gut microbiota and reducing oxidative stress \u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e. In contrast, the Western dietary pattern (high intake of processed foods and red meat) is positively correlated with the risk of CRC\u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eDuring the same period, diet-related CRC deaths and DALYs in China increased by 108.08% and 77.12% respectively.This upward trend is closely linked to dietary changes brought about by urbanization \u0026ndash; specifically, a notable rise in the consumption of red meat and processed meat, alongside a steady drop in the intake of whole grains and dietary fiber\u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e.This dietary shift drives CRC pathogenesis through dual pathological mechanisms: nitrates in processed meats work to worsen long-term intestinal inflammation\u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e, while iron ions (Fe\u0026sup3;⁺) in red meat impair the body\u0026rsquo;s ability to repair damaged DNA\u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e, collectively advancing disease progression.\u003c/p\u003e\u003cp\u003eGlobal EAPC has shown consistent improvement with an average annual decrease of 0.95%. China's EAPC decline (0.86% per year) aligns with this global trend. Conversely, EMR exhibits a concerning upward trajectory, with EAPC increasing at an average annual rate of 0.46%. This notable difference is closely tied to how dietary changes and cancer screening efforts interact.In China, public health interventions,such as the promotion of the Dietary Guidelines for Chinese Residents and alongside a nationwide CRC screening program initiated in the 1970s, have partially offset the negative impact of dietary Westernization. The screening program evolved into a comprehensive system covering both urban and rural populations: the rural component launched in 2005 achieved coverage across 234 counties in 31 provincial-level administrative divisions by 2016; the urban component, initiated in 2012, expanded to 42 cities within 20 provincial-level divisions by 2021\u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e.In contrast, EMR faces worsening disease burden trends. This stems from the replacement of traditional diets with high-fat dietary patterns during urbanization\u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e, compounded by underdeveloped cancer screening infrastructures\u003csup\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003e\u003cb\u003eDisease burden characteristics and mechanistic interpretation from gender and age dimensions\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe findings showed that males CRC mortality risk was generally higher than females under the age of 95,The peak age for colorectal cancer deaths comes about 10 years earlier in men, at 70\u0026ndash;74 years old, compared to 80\u0026ndash;84 years old in women.A similar pattern of delayed peaks in DALYs were observed (male 65\u0026ndash;69 years, female 70\u0026ndash;74 years), closely linked to males consumed 13.3g/d more red/processed meat and less whole-grains/fiber than females \u003csup\u003e[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e. Notably, females over 80 years exhibited higher disease burden than males, while Chinese females showed inverse trends,Chinese males over 80 had a significantly steeper CRC mortality increase,indicating the need to strengthen healthy diet education and optimize screening protocols for this subgroup to improve early diagnosis rates.\u003c/p\u003e\u003cp\u003e\u003cb\u003eAttributable contributions of key dietary risk factors and regional specificities\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThis study confirms that inadequate whole-grain intake, insufficient milk consumption, and excessive red meat intake constitute the \"core triad\" of dietary risk factors for CRC burden, collectively accounting for 81.61% of global diet-related CRC deaths and 81.64% of DALYs. The whole-grains exert synergistic protective effects through dietary fiber (a 10% increase in daily intake reduces CRC risk by 10%\u003csup\u003e[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e), polyphenols, lignans, and other bioactive components, promoting intestinal peristalsis and exerting antioxidant-anti-inflammatory effects\u003csup\u003e[\u003cspan additionalcitationids=\"CR29\" citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/sup\u003e. Calcium in milk (a 300mg daily increase reduces risk by 17%\u003csup\u003e[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/sup\u003e), along with lactoferrin and conjugated linoleic acid, inhibits carcinogenesis by regulating intestinal cell proliferation and immune responses\u003csup\u003e[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]\u003c/sup\u003e. Excessive red meat intake (50g daily increase elevates risk by 18%\u003csup\u003e[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]\u003c/sup\u003e).Drives carcinogenesis through multiple mechanisms: heme iron-induced formation of N-nitroso compounds, heterocyclic amines/polycyclic aromatic hydrocarbons generated by high-temperature cooking\u003csup\u003e[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]\u003c/sup\u003e, and telomerase activation via iron-Pirin complexes (OR\u0026thinsp;=\u0026thinsp;2.41\u003csup\u003e[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]\u003c/sup\u003e).\u003c/p\u003e\u003cp\u003eRegional analyses reveal distinct patterns: red meat contributes 32.7% to CRC burden in EMR, exceeding the global rate of 28.1%, attributed to high-temperature grilling traditions\u003csup\u003e[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]\u003c/sup\u003e. In China, inadequate whole grain intake accounts for 34.5% (vs. global 30.2%), driven by refined grain dominance (whole-grains\u0026thinsp;\u0026lt;\u0026thinsp;1% of diet per *National Whole Grain Action Plan (2024\u0026ndash;2035)*\u003csup\u003e[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]\u003c/sup\u003e). Combined with rising red meat consumption and milk insufficiency, this exacerbates risk via synergistic induction of intestinal oxidative stress and DNA damage\u003csup\u003e[\u003cspan additionalcitationids=\"CR38\" citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]\u003c/sup\u003e. Notably, milk insufficiency contributes 29.1% and 27.8% to female CRC deaths globally and in EMR, respectively, whereas Chinese females show only 18.3% risk contribution\u0026mdash;likely due to daily isoflavone intake\u0026thinsp;\u0026gt;\u0026thinsp;30mg (vs. \u0026lt;1mg/d in Western populations). Isoflavones in soy products may partially offset milk deficiency, with a 20mg/d increase reducing colorectal tumor risk by 8%\u003csup\u003e[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003e\u003cb\u003eResearch limitations and future research directions\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThis study has three main limitations:GBD study relies on model-based estimations, introducing significant biases in regions with collapsed health systems (e.g., war-torn countries). Inadequate cancer registration systems in low-income nations necessitate estimations based on predictive covariates or neighboring countries' trends, potentially skewing results\u003csup\u003e[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]\u003c/sup\u003e.The research did not differentiate the differential responses of CRC pathological types (e.g., adenocarcinoma vs. squamous cell carcinoma) to dietary factors. A 2024 Cancers study found red meat intake more significantly impacted adenocarcinoma risk (HR\u0026thinsp;=\u0026thinsp;1.21), whereas squamous cell carcinoma showed stronger associations with alcohol consumption (HR\u0026thinsp;=\u0026thinsp;1.15)\u003csup\u003e[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eFuture Directions: (1)Regional Biobank Studies(Leverage biobanks in the Middle East and China to investigate diet-microbiota-CRC associations) \u003csup\u003e[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]\u003c/sup\u003e.(2)Machine Learning Models(Develop personalized risk prediction models by integrating dietary patterns, genetic markers)\u003csup\u003e[\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]\u003c/sup\u003e.(3)Digital Nutritional Interventions: Evaluate the feasibility of mobile APP-based dietary monitoring in resource-constrained regions\u003csup\u003e[\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eFrom 1990 to 2021, dietary risk factors have consistently ranked as the leading preventable cause of the global CRC burden. This burden has shown an overall increasing trend, accompanied by striking disparities across regions and population groupsIn China, despite a downward trend in CRC mortality, inadequate intake of whole grains and dairy products remains a critical issue requiring urgent attention. In the Eastern Mediterranean region, the CRC burden has continued to deteriorate, driven by the influence of Western dietary patterns.The core direction for future global CRC prevention and control should be building multi-dimensional strategies that take into account regional features, dietary patterns, gender differences, and age distributions. Through targeted, precise measures, we can reduce CRC burden effectively.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eCRC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eColorectal cancer\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eEMR\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eEastern Mediterranean Region\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eARIMA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eAutoregressive Integrated Moving Average\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eUI\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003euncertainty interval\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eCI\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003econfidence interval\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eDALYs\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003edisability-adjusted life years\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eGBD\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eGlobal Burden of Disease\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eSDI\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003esocio-demographic index\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003ch2\u003eEthics approval and consent to participate\u003c/h2\u003e\u003cp\u003eThis research made use of publicly accessible, anonymized data sourced from the GBD database. Since it did not entail direct interaction with human subjects nor the gathering of personal health details, there was no need for ethical approval or informed consent. The study conformed to the guidelines set forth in the Declaration of Helsinki, guaranteeing ethical practice in the utilization of secondary data for public health-related research.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003cp\u003eNot applicable\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003eThis study was funded by the National Natural Science Foundation of China (Regional Science Foundation, Grant No. 82260322) and the Young Top Talents Program of the \"Tianshan Talents\" Cultivation Plan of the Xinjiang Uygur Autonomous Region (Grant No. 2023TSYCJC0058).It was also supported by the Xinjiang Uygur Autonomous Region Natural Science Foundation (Grant No. 2023D01F19) and the Science and Technology Project of Karamay Central Hospital (Grant No. YK2023-2).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eThe overall conception and design of this study were developed by Zhu Min. Diao Hongliang analyzed the research data. Zhu Xiaoyan contributed to the interpretation of the data. Lai Yining drafted the manuscript and verified the disease burden data, with other authors conducting rigorous revisions. All authors read and approved the final manuscript.Availability of data and materialsThe data used in this study were obtained from the GBD 2021 database, which is publicly available through the Institute for Health Metrics and Evaluation (IHME) website. All data analyzed during this study are accessible at the GBD Results Tool (http://ghdx.healthdata.org/gbd-results-tool) and can be requested for research purposes in accordance with IHME\u0026rsquo;s data-sharing policies. No additional data were generated or analyzed in this study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eWang J, He S, Cao M, Teng Y, Li Q, Tan N, et al. Global, regional, and national burden of colorectal cancer, 1990-2021: An analysis from global burden of disease study 2021. Chinese journal of cancer research = Chung-kuo yen cheng yen chiu. 2024;36(6):752-67.\u003c/li\u003e\n\u003cli\u003eYao J, Chen G. The global, regional, and national alcohol-related colorectal cancer burden and forecasted trends: results from the global burden of disease study 2021. Frontiers in nutrition. 2024;11:1520852.\u003c/li\u003e\n\u003cli\u003eZhu N, Zhang Y, Mi M, Ding Y, Weng S, Zheng J, et al. The death burden of colorectal cancer attributable to modifiable risk factors, trend analysis from 1990 to 2019 and future predictions. Cancer medicine. 2024;13(7):e7136.\u003c/li\u003e\n\u003cli\u003eGlobal, regional, and national burden of colorectal cancer and its risk factors, 1990-2019: a systematic analysis for the Global Burden of Disease Study 2019. The lancet Gastroenterology \u0026amp; hepatology. 2022;7(7):627-47.\u003c/li\u003e\n\u003cli\u003eLiang Y, Zhang N, Wang M, Liu Y, Ma L, Wang Q, et al. Distributions and Trends of the Global Burden of Colorectal Cancer Attributable to Dietary Risk Factors over the Past 30 Years. Nutrients. 2023;16(1).\u003c/li\u003e\n\u003cli\u003eMattioli AV, Farinetti A, Gelmini R. The beneficial effect of Mediterranean diet on colorectal cancer. International journal of cancer. 2019;145(1):306.\u003c/li\u003e\n\u003cli\u003eMahmod AI, Haif SK, Kamal A, Al-Ataby IA, Talib WH. Chemoprevention effect of the Mediterranean diet on colorectal cancer: Current studies and future prospects. Frontiers in nutrition. 2022;9:924192.\u003c/li\u003e\n\u003cli\u003eElbaylek H, Ammor S. Adherence to the Mediterranean Diet and Colorectal Cancer Risk Among Moroccan Population: Hospital-Based Case Control Study. Asian Pacific journal of cancer prevention : APJCP. 2024;25(8):2853-60.\u003c/li\u003e\n\u003cli\u003eGharbi I, Letaief F, Dadaa Z, Yahyaoui Y, Gabsi A, Maghrebi H, et al. The Consumption of Red and Processed Meat and The Risk of Colorectal Cancer: A Case-Control Study among the Tunisian Population. La Tunisie medicale. 2020;98(10):726-9.\u003c/li\u003e\n\u003cli\u003eGarcia-Closas R, Berenguer A, Gonz\u0026aacute;lez CA. Changes in food supply in Mediterranean countries from 1961 to 2001. Public health nutrition. 2006;9(1):53-60.\u003c/li\u003e\n\u003cli\u003eThomson CA, McCullough ML, Wertheim BC, Chlebowski RT, Martinez ME, Stefanick ML, et al. Nutrition and physical activity cancer prevention guidelines, cancer risk, and mortality in the women\u0026apos;s health initiative. Cancer prevention research (Philadelphia, Pa). 2014;7(1):42-53.\u003c/li\u003e\n\u003cli\u003eLiu A, Zhuang Z, Li J, Wang Q, Liu S, Fang H, et al. Burden and trend of dietary risk-related colorectal cancer in China and its provinces: findings from the Global Burden of Disease Study 2019. Public health. 2024;230:21-8.\u003c/li\u003e\n\u003cli\u003eLiu Y, Zhu C, Song H, Che M, Xu B, An B. Temporal trends in disability adjusted life year and mortality for colorectal cancer attributable to a high red meat diet in China from 1990 to 2021: an analysis of the global burden of disease study 2021. BMC gastroenterology. 2024;24(1):476.\u003c/li\u003e\n\u003cli\u003eDou YFJHiS, Engineering, Technology. The Role of the Mediterranean Diet on Health and Its Inspiration for Chinese Diet. 2023.\u003c/li\u003e\n\u003cli\u003eHaagsma JA, James SL, Castle CD, Dingels ZV, Fox JT, Hamilton EB, et al. Burden of injury along the development spectrum: associations between the Socio-demographic Index and disability-adjusted life year estimates from the Global Burden of Disease Study 2017. Injury prevention : journal of the International Society for Child and Adolescent Injury Prevention. 2020;26(Supp 1):i12-i26.\u003c/li\u003e\n\u003cli\u003eChaulagain P, Poudel A, Aryal S, Sainatham C, Lutfi FJB. Global Trends of Burden of Chronic Myeloid Leukemia Based on Socio-Demographic Index (SDI): A Comparative Epidemiological Study. 2024;144:7918-.\u003c/li\u003e\n\u003cli\u003eTong Z, Xie Y, Li K, Yuan R, Zhang L. The global burden and risk factors of cardiovascular diseases in adolescent and young adults, 1990-2019. BMC public health. 2024;24(1):1017.\u003c/li\u003e\n\u003cli\u003e. !!! INVALID CITATION !!! [7, 18].\u003c/li\u003e\n\u003cli\u003eWang X, Bodirsky BL, M\u0026uuml;ller C, Chen KZ, Yuan C. The triple benefits of slimming and greening the Chinese food system. Nature food. 2022;3(9):686-93.\u003c/li\u003e\n\u003cli\u003eRoss FC, Patangia D, Grimaud G, Lavelle A, Dempsey EM, Ross RP, et al. The interplay between diet and the gut microbiome: implications for health and disease. Nature reviews Microbiology. 2024;22(11):671-86.\u003c/li\u003e\n\u003cli\u003eShanmugam R, Majee P, Shi W, Ozturk MB, Vaiyapuri TS, Idzham K, et al. Iron-(Fe3+)-Dependent Reactivation of Telomerase Drives Colorectal Cancers. Cancer discovery. 2024;14(10):1940-63.\u003c/li\u003e\n\u003cli\u003eChen H, Lu B, Dai M. Colorectal Cancer Screening in China: Status, Challenges, and Prospects - China, 2022. China CDC weekly. 2022;4(15):322-8.\u003c/li\u003e\n\u003cli\u003eMusaiger AO, Al-Hazzaa HM, Takruri HR, Mokhatar N. Change in nutrition and lifestyle in the eastern mediterranean region: health impact. Journal of nutrition and metabolism. 2012;2012:436762.\u003c/li\u003e\n\u003cli\u003eHartley C, Chhachhi N, Khader Y, Farhat GN. Barriers to colorectal cancer screening in the Eastern Mediterranean Region: a scoping review using the theoretical domains framework. Journal of gastrointestinal oncology. 2023;14(3):1576-92.\u003c/li\u003e\n\u003cli\u003eWang Q, Liu S, Wang H, Su C, Liu A, Jiang L. Consumption of aquatic products and meats in Chinese residents: A nationwide survey. Frontiers in nutrition. 2022;9:927417.\u003c/li\u003e\n\u003cli\u003eAbebe Z, Wassie MM, Reynolds AC, Melaku YA. Burden and Trends of Diet-Related Colorectal Cancer in OECD Countries: Systematic Analysis Based on Global Burden of Disease Study 1990-2021 with Projections to 2050. Nutrients. 2025;17(8).\u003c/li\u003e\n\u003cli\u003eAune D, Chan DS, Lau R, Vieira R, Greenwood DC, Kampman E, et al. Dietary fibre, whole grains, and risk of colorectal cancer: systematic review and dose-response meta-analysis of prospective studies. BMJ (Clinical research ed). 2011;343:d6617.\u003c/li\u003e\n\u003cli\u003eHullings AG, Sinha R, Liao LM, Freedman ND, Graubard BI, Loftfield E. Whole grain and dietary fiber intake and risk of colorectal cancer in the NIH-AARP Diet and Health Study cohort. The American journal of clinical nutrition. 2020;112(3):603-12.\u003c/li\u003e\n\u003cli\u003eAl-Khayri JM, Sahana GR, Nagella P, Joseph BV, Alessa FM, Al-Mssallem MQ. Flavonoids as Potential Anti-Inflammatory Molecules: A Review. Molecules (Basel, Switzerland). 2022;27(9).\u003c/li\u003e\n\u003cli\u003eOkarter N, Liu RH. Health benefits of whole grain phytochemicals. Critical reviews in food science and nutrition. 2010;50(3):193-208.\u003c/li\u003e\n\u003cli\u003eNierengarten MB. Calcium may help to protect against colorectal cancer. Cancer. 2025;131(9):e35827.\u003c/li\u003e\n\u003cli\u003e. !!! INVALID CITATION !!! [36, 37].\u003c/li\u003e\n\u003cli\u003eUngvari Z, Fekete M, Varga P, Lehoczki A, Munk\u0026aacute;csy G, Fekete JT, et al. Association between red and processed meat consumption and colorectal cancer risk: a comprehensive meta-analysis of prospective studies. GeroScience. 2025.\u003c/li\u003e\n\u003cli\u003eBenarba B. Red and processed meat and risk of colorectal cancer: an update. EXCLI journal. 2018;17:792-7.\u003c/li\u003e\n\u003cli\u003eLing P, Lei J, Ju H. Nanoscaled Porphyrinic Metal-Organic Frameworks for Electrochemical Detection of Telomerase Activity via Telomerase Triggered Conformation Switch. Analytical chemistry. 2016;88(21):10680-6.\u003c/li\u003e\n\u003cli\u003eSinha R, Cross AJ, Graubard BI, Leitzmann MF, Schatzkin A. Meat intake and mortality: a prospective study of over half a million people. Archives of internal medicine. 2009;169(6):562-71.\u003c/li\u003e\n\u003cli\u003eHealth effects of dietary risks in 195 countries, 1990-2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet (London, England). 2019;393(10184):1958-72.\u003c/li\u003e\n\u003cli\u003eZhang F, Debras C, Matta J, Wang DJPotNS. Consumption of a milk low in lactose high in intrinsic fiber is associated with improved nutrient intake adequacies in Chinese adults: a diet modelling study. 2024;83(OCE4).\u003c/li\u003e\n\u003cli\u003e陈宣承, 李红领, 食品与发酵工业 李J. 膳食血红素铁促进结直肠癌变机制初探. 2022;48(1):7.\u003c/li\u003e\n\u003cli\u003eJiang R, Botma A, Rudolph A, H\u0026uuml;sing A, Chang-Claude J. Phyto-oestrogens and colorectal cancer risk: a systematic review and dose-response meta-analysis of observational studies. The British journal of nutrition. 2016;116(12):2115-28.\u003c/li\u003e\n\u003cli\u003eZhang X, Zhang X, Li R, Lin M, Ou T, Zhou H, et al. Global, regional, and national analyses of the burden of colorectal cancer attributable to diet low in milk from 1990 to 2019: longitudinal observational study. Frontiers in nutrition. 2024;11:1431962.\u003c/li\u003e\n\u003cli\u003eVernia F, Longo S, Stefanelli G, Viscido A, Latella G. Dietary Factors Modulating Colorectal Carcinogenesis. Nutrients. 2021;13(1).\u003c/li\u003e\n\u003cli\u003eWang Y, Huang J, Tong H, Jiang Y, Jiang Y, Ma X. Nutrient Acquisition of Gut Microbiota: Implications for Tumor Immunity. Seminars in cancer biology. 2025.\u003c/li\u003e\n\u003cli\u003eOcvirk S, O\u0026apos;Keefe SJD. Dietary fat, bile acid metabolism and colorectal cancer. Seminars in cancer biology. 2021;73:347-55.\u003c/li\u003e\n\u003cli\u003eGhatak S, Mehrabi SF, Mehdawi LM, Satapathy SR, Sj\u0026ouml;lander A. Identification of a Novel Five-Gene Signature as a Prognostic and Diagnostic Biomarker in Colorectal Cancers. International journal of molecular sciences. 2022;23(2).\u003c/li\u003e\n\u003cli\u003eOkaniwa F, Yoshida H. Evaluation of Dietary Management Using Artificial Intelligence and Human Interventions: Nonrandomized Controlled Trial. JMIR formative research. 2022;6(6):e30630.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Colorectal cancer, Diet-related risk factors, EMR, Burden, Trends","lastPublishedDoi":"10.21203/rs.3.rs-7242118/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7242118/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eColorectal cancer (CRC) is the third most common cancer and the second most frequent cause of cancer death worldwideThis study aimed to systematically analyze Burden and trends of the diet-related CRC in global,the Eastern Mediterranean Region (EMR), and China from 1990 to 2021. so as to provide a basis for region-specific prevention and control strategies.\u003c/p\u003e\u003cp\u003eFrom 1990 to 2021, dietary factors remained the primary preventable cause of CRC globally. While the number of diet-related CRC deaths and DALYs increased significantly in global, the age-standardized rates declined. China showed a consistent trend with the global. In contrast,EMR exhibited an opposite trend to the global CRC burden: the number of deaths increased by 171.37% and DALYs by 169.53%, with rising age-standardized rates (EAPC\u0026thinsp;=\u0026thinsp;0.46% for mortality and 0.30% for DALYs).\u003c/p\u003e\u003cp\u003eDietary factors remain the primary preventable contributor to the global burden of CRC, with an overall increasing trend accompanied by significant regional and population heterogeneities. Developing targeted strategies tailored to regional dietary patterns, gender, and age-specific characteristics is crucial for effectively alleviating the burden of colorectal cancer.\u003c/p\u003e","manuscriptTitle":"Burden and Trends of Diet-Related Colorectal Cancer in Global, East Mediterranean, and China ：Systematic Analysis Based on Global Burden of Disease Study 1990-2021 with Projections to 2050","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-11 11:09:01","doi":"10.21203/rs.3.rs-7242118/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ffd11c7e-20c2-45ab-8123-41cb8dce8944","owner":[],"postedDate":"September 11th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-12-28T08:09:57+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-11 11:09:01","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7242118","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7242118","identity":"rs-7242118","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}