Skip to main content
PMC home page
Cancer Science logoLink to Cancer Science
. 2023 Mar 13;114(6):2584–2595. doi: 10.1111/cas.15766

Sugar intake and colorectal cancer risk: A prospective Japanese cohort study

Rieko Kanehara 1, Ryoko Katagiri 1,2,✉, Atsushi Goto 3,4, Taiki Yamaji 3, Norie Sawada 1, Motoki Iwasaki 1,3, Manami Inoue 1,5, Shoichiro Tsugane 1,2; JPHC Study Group †
PMCID: PMC10236631  PMID: 36851860

Abstract

The influence of sugar consumption on the risk of colorectal cancer (CRC) remains controversial. Prospective cohort studies focusing on total and specific types of sugar intake among the Asian population who have different patterns of sugar intake sources than American and European populations are scarce. We intended to examine the association of sugar intake with CRC risk among middle‐aged adults in a Japanese large‐scale population‐based cohort study. The participants (42,405 men and 48,600 women) who were 45–74 years old and answered the questionnaire in 1995–1999 in the Japan Public Health Center‐based Prospective Study were followed up until December 2013. Total sugars, total fructose, and specific types of sugar intake were estimated using a validated 147‐item food frequency questionnaire and divided into quintiles (Q1–Q5). We used Cox proportional hazard regression models adjusted for potential confounders to estimate hazard ratios (HRs) and 95% confidence intervals (CIs). During the follow‐up, 2118 CRC cases (1226 men and 892 women) were identified. We did not observe any clear association between all types of sugar intake and an increased risk of CRC. Analyses by tumor sites yielded a positive association of total sugar consumption with rectal cancer in women (1.75 [1.07–2.87] for Q1 vs. Q5; p linear trend = 0.03), but no statistically significant trend was detected among men. Sugar intake was not associated with CRC risk in middle‐aged Japanese adults. However, for rectal cancer, the probability of an increased risk among women with a higher total sugar intake cannot be excluded.

Keywords: colorectal cancer, dietary sugar, fructose, middle‐aged adult, prospective cohort study

Abbreviations

BMI

body mass index

CI

confidence intervals

CRC

colorectal cancer

FFQ

food frequency questionnaire

HR

hazard ratio

JPHC Study

Japan Public Health Center‐based prospective study

METs

metabolic equivalents

PUFA

polyunsaturated fatty acid

SFA

saturated fatty acid

1. INTRODUCTION

The incidence of CRC was the third highest and CRC was the second leading cause of cancer mortality globally in 2020. 1 Processed meat and alcohol consumption, body fat, height, and insufficient physical activity are known CRC risk factors. 2 However, the impact of dietary carbohydrate or carbohydrate‐containing food intake on the risk of CRC remains controversial, except for whole‐grain intake. 2

Several studies among the American population have assessed the association of sugar consumption with the risk of CRC. Nevertheless, evidence from prospective cohort studies is lacking for the Asian population whose dietary habits and prevalence of obesity (Western Pacific: 6.4%, South‐East Asia: 4.7%, Americas: 28.6%, and Europe: 23.3% in 2016) 3 are distinct from those of American and European populations. Thus, sugar intake has not been established as a factor for estimating the burden of CRC in the Japanese population. 4 Previous studies on the American population have reported that total sugar, 5 , 6 glucose, 7 and sucrose 6 , 7 , 8 , 9 , 10 intake showed no association with CRC risk. Two previous studies on fructose intake reported a positive association, 8 , 10 whereas three studies reported no association. 6 , 7 , 9 Previous reports on Asian populations have been limited to case–control studies. 11 , 12 , 13 Moreover, the percentage of individuals who consume free sugar for over 10% of their daily energy intake, which is the upper limit of intake recommended by World Health Organization guidelines, 14 is lower among the Japanese population (approximately 10%) 15 , 16 than among populations in American (>70%) or European countries (>40%). 17 , 18 , 19 , 20 Meanwhile, Japan is the only Asian country among the top 10 countries with the highest incidence of CRC worldwide. 21 Accordingly, studies of populations with different patterns of sources of sugar intake and high rates of CRC incidence will provide evidence for the distinct distribution of sugar consumption among populations and may have significant implications for public health.

Comprehensive investigations of both total and specific types of simple sugars (glucose, fructose, galactose, sucrose, maltose, and lactose) are essential to clarify their health impact on CRC risk and its underlying mechanisms. In particular, fructose (both monosaccharides and components of sucrose) evokes hepatic de novo lipogenesis that is unregulated by energy intake, unlike glucose metabolism. 22 , 23 Excessive de novo lipogenesis can induce insulin resistance which has been reported to be a risk factor for colorectal neoplasia. 24 , 25 Therefore, previous studies have focused on the potential role of fructose in health and chronic diseases. 23 , 26 , 27 , 28

Here, we aimed to investigate the association of the intake of total sugars (total mono‐ and di‐saccharides), total fructose (sum of fructose as monosaccharides and half of the sucrose), and each type of mono‐ and disaccharide with CRC risk in a large‐scale prospective cohort study in Japan to address the current gaps in the literature.

2. MATERIALS AND METHODS

2.1. Study population

This study involved participants of the JPHC study, which was launched in 1990 (Cohort I) and 1993 (Cohort II). 29 At the baseline survey, individuals aged 40–69 years who lived in 11 public health center areas were recruited. Participants reported their medical and family histories and health‐related lifestyles via self‐administered questionnaire surveys. The questionnaire surveys were conducted three times every 5 years from the baseline survey.

As the second (5‐year follow‐up) questionnaire survey asked for more detailed information on nutrient and food intakes compared with the first survey, we considered data from the second survey in 1995–1999 as the baseline data in this analysis. Participants were informed of the objectives of the JPHC study and that completion of the second questionnaire was regarded as providing consent to participate. Of individuals who participated in the second survey (n = 98,469), we excluded those who answered that they were diagnosed with cancer before the second survey (n = 1614), those with missing values for nutrients (n = 1063), and those with an extremely high or low intake of total energy (above the 97.5th percentile or below the 2.5th percentile of total energy intake according to sex) (n = 4787). A final total of 91,005 participants (men: n = 42,405 and women: n = 48,600) were included in the current analysis.

This study was approved by the Institutional Review Board of the National Cancer Center of Japan (approval no. 2015‐085) and was conducted following the ethical guidelines for medical research in Japan.

2.2. Dietary assessment

In the second survey, an FFQ of 147 items was used to estimate the habitual consumption of food and beverages over the previous year. 30 Details of the questionnaire for dietary assessment are described elsewhere. 30 , 31 We calculated the average daily sugar consumption (each saccharide, total sugars, and total fructose) and other foods and nutrients using the 7th revised edition of the Standard Tables of Food Composition in Japan 32 and the tables for available carbohydrates. 33 We calculated the sum of sucrose, glucose, fructose, lactose, galactose, and maltose consumption as total sugar intake. Because sucrose is a disaccharide binding glucose and fructose with the same molecular weight, we used the following formula: (fructose intake) + ½(sucrose intake) 34 to calculate total fructose intake. Energy‐adjusted nutrient intake was computed by the density method to express the nutrient composition of their diet and to coordinate with the measure in the World Health Organization guidelines for sugar intake. Sugars, protein, fat, carbohydrate, and starch were expressed as % energy. Other nutrients and foods were presented as “per 1000 kcal.” Sugar intake estimates from the FFQ have been validated elsewhere. 16 In brief, Spearman's correlation coefficients (CCs) of total sugar intake (% energy/day) were 0.34–0.57 in Cohorts I and II according to sex, between dietary records and the FFQ. Regarding reproducibility, Spearman's CCs were 0.55–0.66 between the two FFQs with a yearly interval.

2.3. Confirmation of colorectal cancer

Newly diagnosed CRC cases were confirmed using population‐based cancer registries and hospital records in the study areas. Participants were followed up for cancer incidence, relocating out of the study areas, and death from 1995 (Cohort I) and 1998 (Cohort II) (the second survey) to December 2013. CRC cases (C18–C20) were defined according to the third edition of the International Classification of Diseases for Oncology, and were grouped by site, such as proximal colon (C18.0–C18.5), distal colon (C18.6 and C18.7), and rectal (C19 and C20) cancer. Proximal, distal, overlapping sites (C18.8), and unspecified (C18.9) colon cancer were combined and defined as colon cancer.

2.4. Statistical analysis

Participants were divided into quintiles of each type of sugar (total sugar, total fructose, glucose, fructose, galactose, sucrose, maltose, or lactose) consumption according to sex. The mean, SD, and proportion of participant characteristics were calculated according to quintiles of total sugar intake. For the comparison of the characteristics among the quintiles, p‐values were computed using analysis of variance for continuous variables and chi‐squared statistics for categorical variables.

We performed Cox proportional hazards regression analysis to assess the HRs and 95% CIs of CRC risk for the quintiles of sugar consumption according to sex. The lowest category was used as the reference. We categorized sugars into total sugars, total fructose, and specific types of sugars. Potential confounders were included based on their clinical and biological plausibilities. Model 1 was adjusted for study area and age, and Model 2 was additionally adjusted for BMI (kg/m2, quintiles), alcohol drinking status (none, occasionally, <150, 150–299, 300–449, or ≥450 g ethanol/week in men; none, occasionally, <150 or ≥150 g ethanol/week in women), smoking status (never, past, <20 or ≥20 cigarettes/day), MET‐h/day (quintiles), a history of diabetes (yes or no), family history of CRC (yes or no), CRC screening in the past year (fecal occult blood test, barium enema examination, and colonoscopy; yes or no), postmenopausal status (yes or no in women only), exogenous female hormone use (yes or no in women only), total energy intake (kcal/d, quintiles), and category of quintiles for energy‐adjusted nutrient intake using the density method. The nutrients included saturated fatty acids (SFAs), n‐3 polyunsaturated fatty acids (PUFAs), magnesium, vitamin D, vitamin B6, vitamin B12, calcium, folate, and dietary fiber. To examine the linear and quadratic trends across quintiles, we modeled the median sugar intake for each category as a continuous variable.

We applied stratification analysis for the association between total sugar or total fructose intake and CRC risk according to smoking status, alcohol drinking status, BMI, and a history of diabetes (without diabetes). For sensitivity analyses, participants who had cancer onset during the first 3 years of follow‐up were excluded.

The overall proportion of participants with complete data for the analyses was 68.1% and 65.3% in men and women, respectively. To handle missing data on BMI, alcohol drinking status, smoking status, METs, CRC family history, postmenopausal status, and use of exogenous female hormones, 50 rounds of multiple imputations were computed using a fully conditional specification according to sex, using the MI in the SAS procedure. 35 To account for missing data, we included the total sugar intake for saccharides, CRC incidence, person‐years, and the confounders described above. We then combined estimations of HRs from each imputed dataset using Rubin's rules and MIANALYZE in the SAS procedure. The level of statistical significance was set at a two‐sided p‐value < 0.05 level. All statistical analyses were performed using SAS (ver 9.4; SAS Institute).

3. RESULTS

We identified 2118 cases of CRC incidence (1226 for men and 892 for women) over 1,367,197.1 person‐years and an average of approximately 15.0 years of follow‐up. Regardless of sex, participants in the highest quintile of total sugar intake tended to comprise lower proportions of current alcohol drinkers and smokers and had a lower prevalence of a history of diabetes. In contrast, the proportion of participants who underwent CRC screening and the intake of total energy, carbohydrates, dietary fiber, calcium, magnesium, and folic acid were higher in the highest quintile than in the lowest quintile (Table 1). Additionally, both sexes had lower proportions of participants with complete data in the lowest quintile than in the other quintiles (Table 1). For free sugar intake, the proportion of participants who consumed over 10% of daily energy intake was 8.1% overall, 9.7% for men, and 6.8% for women (data not shown in the tables).

TABLE 1.

Baseline characteristics of participants according to quintiles of total sugar intake.

Men (n = 42,405) Women (n = 48,600)
Q1 Q3 Q5 p‐value Q1 Q3 Q5 p‐Value
Median (interquartile ranges) of total sugar intake (% energy/day) 5.6 (4.5–6.4) 10.3 (9.8–10.9) 16.8 (15.4–19.2) 7.8 (6.5–8.8) 12.9 (12.4–13.4) 19.1 (17.7–21.4)
Number of subjects 8481 8481 8481 9720 9720 9720
Number without missing data 5278 5912 5858 5523 6524 6572
Age (years) 51.3 (7.6) a 51.7 (7.8) 51.6 (8.1) <0.001 52.4 (8.1) 51.9 (7.8) 51.8 (7.8) <0.001
Body mass index (kg/m2) 23.7 (3.2) 23.6 (3.0) 23.6 (3.2) <0.001 23.7 (3.6) 23.5 (3.4) 23.4 (3.7) <0.001
Missing data (%) 3.0 2.1 2.2 4.1 2.1 2.4
Alcohol drinking status
Current drinker (≥1 day/week) (%) 79.9 68.4 45.0 <0.001 15.5 11.8 9.6 <0.001
Occasional drinker (%) 5.0 7.6 11.5 5.6 6.9 7.1
Nondrinker (%) 13.0 21.9 40.7 74.3 78.3 80.3
Missing data (%) 2.1 2.1 2.7 4.6 2.9 3.1
Smoking status
Current smoker (%) 50.0 43.9 46.0 <0.001 8.0 4.0 4.7 <0.001
Past smoker (%) 18.4 19.1 17.0 1.7 1.0 0.8
Never smoked (%) 28.9 34.6 34.6 82.6 89.1 88.4
Missing data (%) 2.7 2.4 2.4 7.8 5.9 6.2
METs (MET‐h/day) 33.3 (6.7) 33.7 (6.7) 33.9 (6.8) <0.001 32.3 (5.6) 32.7 (5.7) 32.9 (5.7) <0.001
Missing data (%) 23.1 16.4 16.5 25.2 16.5 15.0
Family history of CRC (%) 1.3 1.5 1.3 <0.001 0.9 1.3 1.3 <0.001
Missing data (%) 15.8 13.2 13.4 12.6 10.7 10.8
History of diabetes (%) 7.8 6.6 5.5 <0.001 4.7 3.2 2.1 <0.001
CRC screening, yes (%) 27.4 34.0 32.7 <0.001 25.2 31.9 32.5 <0.001
Postmenopausal status (%) ‐ ‐ ‐ ‐ 68.1 72.9 74.5 <0.001
Missing data (%) ‐ ‐ ‐ ‐ 10.6 5.0 4.9
Current use of exogenous female hormones (%) ‐ ‐ ‐ ‐ 2.6 2.5 2.7 <0.001
Missing data (%) ‐ ‐ ‐ ‐ 9.8 4.7 4.7
Food and nutrient intake
Total energy (kcal/day) 2024 (632) 2173 (632) 2260 (665) <0.001 1615 (529) 1873 (525) 2053 (598) <0.001
Protein (% energy/day) 12.6 (3.1) 13.9 (2.5) 13.8 (2.4) <0.001 14.7 (2.9) 15.4 (2.3) 14.7 (2.3) <0.001
Fat (% energy/day) 20.2 (8.3) 23.8 (6.6) 24.2 (6.5) <0.001 26.1 (9.1) 27.9 (6.3) 26.7 (6.2) <0.001
SFAs (% energy/day) 5.9 (2.8) 7.2 (2.5) 7.7 (2.9) <0.001 7.8 (3.3) 8.6 (2.6) 8.4 (3.1) <0.001
n‐3 PUFAs (% energy/day) 0.9 (0.5) 1.1 (0.4) 1.0 (0.4) <0.001 1.2 (0.5) 1.3 (0.4) 1.2 (0.4) <0.001
Carbohydrate (% energy/day) 49.6 (12.0) 52.4 (8.9) 57.3 (8.4) <0.001 55.5 (11.0) 55.2 (7.8) 58.5 (7.9) <0.001
Starch (% energy/day) 41.3 (12.5) 38.8 (9.4) 35.3 (8.4) <0.001 46.1 (11.4) 39.4 (7.7) 33.9 (7.6) <0.001
Dietary fiber (g/1000 kcal) 4.2 (1.6) 5.6 (1.8) 6.7 (2.6) <0.001 5.8 (2.1) 7.4 (2.1) 9.0 (2.8) <0.001
Calcium (mg/1000 kcal) 158.7 (64.6) 233.2 (84.7) 295.3 (132.0) <0.001 217.5 (81.1) 303.3 (101.1) 359.8 (146.0) <0.001
Magnesium (mg/1000 kcal) 132.9 (41.7) 152.5 (35.1) 163.7 (37.1) <0.001 156.4 (45.6) 174.2 (37.7) 187.1 (39.6) <0.001
Vitamin D (μg/1000 kcal) 4.8 (4.0) 5.4 (3.2) 5.1 (3.1) <0.001 5.6 (4.6) 6.3 (3.6) 5.7 (3.2) <0.001
Vitamin B6 (mg/1000 kcal) 0.7 (0.2) 0.7 (0.1) 0.7 (0.2) <0.001 0.7 (0.2) 0.8 (0.2) 0.8 (0.2) <0.001
Vitamin B12 (μg/1000 kcal) 4.2 (2.8) 4.7 (2.4) 4.4 (2.3) <0.001 4.7 (3.0) 5.2 (2.7) 4.8 (2.4) <0.001
Folate (μg/1000 kcal) 146.9 (59.1) 180.0 (62.1) 197.5 (77.8) <0.001 187.0 (72.1) 224.0 (71.2) 255.9 (86.7) <0.001
Vegetable (g/1000 kcal) 63.6 (40.2) 95.4 (54.2) 112.8 (77.4) <0.001 92.0 (52.1) 127.2 (65.7) 153.7 (90.8) <0.001
Fruits (g/1000 kcal) 28.5 (21.8) 75.6 (37.8) 150.9 (101.3) <0.001 52.3 (32.8) 118.9 (47.4) 240.3 (119.7) <0.001
Coffee (mL/1000 kcal) 43.7 (74.5) 71.9 (83.0) 154.8 (170.5) <0.001 52.9 (90.2) 64.4 (79.6) 84.5 (119.7) <0.001
Total sugars b (% energy/day) 5.3 (1.4) 10.4 (0.6) 18.0 (3.8) <0.001 7.4 (1.7) 12.9 (0.6) 20.1 (3.6) <0.001
Free sugars c (% energy/day) 2.0 (1.2) 4.7 (1.9) 9.7 (5.3) <0.001 2.8 (1.5) 5.1 (2.1) 7.9 (4.6) <0.001
Naturally occurring sugars d (% energy/day) 2.5 (1.2) 4.9 (1.9) 7.7 (4.2) <0.001 4.2 (1.6) 7.4 (2.1) 11.9 (4.6) <0.001
Total fructose (fructose + ½ × sucrose) (% energy/day) 1.8 (0.7) 3.9 (0.7) 7.5 (2.2) <0.001 2.8 (0.9) 5.1 (0.8) 8.6 (2.3) <0.001
Glucose (% energy/day) 1.7 (0.7) 2.6 (0.8) 3.5 (1.3) <0.001 1.8 (0.6) 2.8 (0.6) 4.1 (1.3) <0.001
Fructose (% energy/day) 0.8 (0.4) 1.9 (0.6) 3.3 (1.6) <0.001 1.3 (0.5) 2.6 (0.7) 4.4 (1.6) <0.001
Galactose (% energy/day) 0.0 (0.0) 0.0 (0.1) 0.0 (0.1) <0.001 0.0 (0.1) 0.1 (0.1) 0.1 (0.1) <0.001
Sucrose (% energy/day) 2.0 (0.9) 4.3 (1.2) 8.8 (3.6) <0.001 3.0 (1.1) 5.4 (1.3) 8.8 (2.9) <0.001
Maltose (% energy/day) 0.2 (0.1) 0.2 (0.1) 0.2 (0.1) <0.001 0.2 (0.1) 0.3 (0.1) 0.3 (0.1) <0.001
Lactose (% energy/day) 0.6 (0.7) 1.3 (1.2) 2.1 (2.0) <0.001 1.0 (1.0) 1.9 (1.5) 2.4 (2.3) <0.001
Open in a new tab

Note: Baseline characteristics were compared between groups using ANOVA for continuous variables or chi‐squared test for categorical variables. Mean and SD of energy‐adjusted values for total sugar intake (g/day) by the residual method for Q1, Q3, and Q5 were 30.7 (8.0), 58.5 (4.9), and 100.4 (22.3) for men and 38.4 (9.0), 63.0 (6.8), and 95.4 (19.9) for women, respectively. Similarly, the values for free sugar intake (g/day) for Q1, Q3, and Q5 were 11.8 (6.8), 26.5 (11.0), and 54.1 (30.3) for men and 14.6 (7.5), 24.9 (10.8), and 37.5 (23.0) for women; and the values for total fructose (g/day) were 10.6 (4.0), 22.7 (4.4), and 43.1 (13.0) for men and 14.8 (4.7), 25.8 (4.9), and 42.1 (12.1) for women, respectively.

Abbreviations: CRC, colorectal cancer; METs, metabolic equivalents; PUFA, polyunsaturated fatty acid; SD, standard deviation; SFA, saturated fatty acid.

a

Mean (SD) (all such values).

b

“Total sugars” represents the sum of the consumption of the following saccharides: glucose, fructose, galactose, sucrose, maltose, and lactose.

c

“Free sugars” includes monosaccharides and disaccharides added to foods and beverages by the manufacturers, cooks, or consumers; and sugars naturally present in honey, syrups, fruit juices, and fruit juice concentrates. 14

d

“Naturally occurring sugars” represents sugars from foods which do not contain added sweeteners, such as fruits, vegetables, cereals, potatoes, beans, nuts, and dairy products.

No clear association was detected between sugar intake and CRC risk. However, point estimates of multivariable adjusted HRs indicated an inverse association of total fructose with CRC risk among men; in particular, the fourth quintile showed a significant inverse association (Table 2). In contrast with the results in men, point estimates of adjusted HRs indicated positive associations for both total sugars and total fructose in women. Although the association was significant for total sugars with CRC risk, this was limited to the third quintile (1.38 [1.10–1.74] for Q1 vs. Q3; p for quadratic trend = 0.03), and no significant linear trend was noted (p for linear trend = 0.47) (Table 2). Similar results were indicated when we excluded the onset of cancer during the first 3 years of follow‐up. When CRC was classified according to cancer site, no association was detected among men. For women, total sugars were positively associated with rectal cancer risk (1.75 [1.07–2.87] for Q1 vs. Q5; p for linear trend = 0.03) (Table 3).

TABLE 2.

HRs and 95% CIs of colorectal cancer according to quintiles of total sugars and total fructose intake.

Quintiles of sugar intake a (% energy/day) p for linear trend p for quadratic trend
Q1 Q2 Q3 Q4 Q5
Men
Total sugars b (% energy/day) 5.6 (0.1–7.1) 8.3 (7.1–9.3) 10.3 (9.3–11.4) 12.7 (11.4–14.3) 16.8 (14.3–62.6)
Person‐years 121,749 123,461 123,701 124,383 124,160
Number of cases 278 247 263 226 212
Age‐ and area‐adjusted c 1.00 (reference) 0.86 (0.73–1.02) 0.91 (0.77–1.07) 0.76 (0.64–0.91) 0.72 (0.60–0.86) <0.001
Multivariable‐adjusted d 1.00 (reference) 0.94 (0.78–1.12) 1.04 (0.86–1.25) 0.90 (0.74–1.10) 0.91 (0.73–1.13) 0.47 0.85
Total fructose (fructose + ½ × sucrose) (% energy/day) 1.7 (0.0–2.4) 2.9 (2.4–3.4) 3.9 (3.4–4.4) 5.0 (4.4–5.8) 7.1 (5.8–32.3)
Person‐years 122,003 123,464 123,308 124,387 124,292
Number of cases 284 264 256 205 217
Age‐ and area‐adjusted c 1.00 (reference) 0.90 (0.76–1.07) 0.87 (0.74–1.03) 0.68 (0.57–0.81) 0.72 (0.60–0.86) <0.001
Multivariable‐adjusted d 1.00 (reference) 0.98 (0.82–1.17) 0.99 (0.82–1.18) 0.81 (0.67–0.99) 0.92 (0.74–1.13) 0.32 0.48
Women
Total sugars b (% energy/day) 7.8 (0.2–9.5) 10.8 (9.5–11.9) 12.9 (11.9–14.0) 15.2 (14.0–16.7) 19.1 (16.7–60.6)
Person‐years 148,921 150,452 149,870 150,458 150,042
Number of cases 160 185 204 171 172
Age‐ and area‐adjusted c 1.00 (reference) 1.19 (0.96–1.47) 1.31 (1.06–1.61) 1.10 (0.88–1.36) 1.09 (0.88–1.36) 0.77
Multivariable‐adjusted d 1.00 (reference) 1.24 (0.99–1.55) 1.38 (1.10–1.74) 1.17 (0.92–1.50) 1.17 (0.90–1.52) 0.47 0.03
Total fructose (fructose + ½ × sucrose) (% energy/day) 2.7 (0.0–3.5) 4.1 (3.5–4.6) 5.1 (4.6–5.6) 6.3 (5.6–7.1) 8.3 (7.1–27.2)
Person‐years 148,631 149,252 150,744 150,222 150,894
Number of cases 169 181 188 170 184
Age‐ and area‐adjusted c 1.00 (reference) 1.10 (0.89–1.36) 1.13 (0.92–1.40) 1.03 (0.83–1.27) 1.09 (0.88–1.35) 0.68
Multivariable‐adjusted d 1.00 (reference) 1.14 (0.92–1.42) 1.18 (0.95–1.48) 1.08 (0.85–1.36) 1.14 (0.89–1.46) 0.48 0.43
Open in a new tab

Abbreviations: CI, confidence interval; CRC, colorectal cancer; HR, hazard ratio; PUFA, polyunsaturated fatty acid; SFA, saturated fatty acid.

a

Participants were categorized into quintiles of total sugar or total fructose intake by sex for the analyses of the corresponding sugar type.

b

“Total sugars” represents the sum of the consumption of the following saccharides: glucose, fructose, galactose, sucrose, maltose, and lactose.

c

Adjusted for age and public health center area.

d

Additionally adjusted for body mass index, alcohol consumption, smoking status, physical activity, family history of CRC, history of diabetes, CRC screening, postmenopausal status (women only), use of exogenous female hormones (women only), total energy intake, and intake of SFAs, n‐3 PUFAs, magnesium, vitamin D, vitamin B6, vitamin B12, calcium, dietary fiber, and folate.

TABLE 3.

HRs and 95% CIs of colon or rectal cancer according to quintiles of sugar intake.

Quintiles of sugar intake a (% energy/day)
Q1 Q2 Q3 Q4 Q5 p for trend
Men
Colon cancer
Total sugars b
Number of cases/person‐years 181/121,749 152/123,461 172/123,701 136/124,383 137/124,160
Multivariable‐adjusted c 1.00 (reference) 0.86 (0.69–1.08) 1.00 (0.79–1.26) 0.79 (0.61–1.02) 0.83 (0.63–1.09) 0.24
Total fructose (fructose + ½ × sucrose)
Number of cases/person‐years 190/122,003 162/123,464 159/123,308 126/124,387 141/124,292
Multivariable‐adjusted c 1.00 (reference) 0.88 (0.71–1.10) 0.89 (0.71–1.12) 0.72 (0.56–0.92) 0.84 (0.65–1.09) 0.20
Proximal colon cancer
Total sugars b
Number of cases/person‐years 78/121,749 58/123,461 68/123,701 66/124,383 60/124,160
Multivariable‐adjusted c 1.00 (reference) 0.71 (0.49–1.01) 0.83 (0.58–1.19) 0.80 (0.55–1.17) 0.75 (0.49–1.13) 0.30
Total fructose (fructose + ½ × sucrose)
Number of cases/person‐years 82/122,003 66/123,464 63/123,308 58/124,387 61/124,292
Multivariable‐adjusted c 1.00 (reference) 0.80 (0.57–1.12) 0.79 (0.55–1.12) 0.74 (0.51–1.07) 0.81 (0.55–1.20) 0.33
Distal colon cancer
Total sugars b
Number of cases/person‐years 94/121,749 91/123,461 93/123,701 63/124,383 72/124,160
Multivariable‐adjusted c 1.00 (reference) 1.04 (0.77–1.41) 1.12 (0.81–1.53) 0.77 (0.53–1.10) 0.93 (0.64–1.36) 0.57
Total fructose (fructose + ½ × sucrose)
Number of cases/person‐years 99/122,003 90/123,464 87/123,308 60/124,387 77/124,292
Multivariable‐adjusted c 1.00 (reference) 0.97 (0.72–1.30) 0.98 (0.72–1.33) 0.69 (0.48–0.97) 0.92 (0.64–1.30) 0.54
Rectal cancer
Total sugars b
Number of cases/person‐years 97/121,749 95/123,461 91/123,701 90/124,383 75/124,160
Multivariable adjusted c 1.00 (reference) 1.09 (0.81–1.47) 1.11 (0.81–1.52) 1.14 (0.82–1.59) 1.05 (0.73–1.52) 0.73
Total fructose (fructose + ½ × sucrose)
Number of cases/person‐years 94/122,003 102/123,464 97/123,308 79/124,387 76/124,292
Multivariable‐adjusted c 1.00 (reference) 1.19 (0.89–1.58) 1.19 (0.88–1.62) 1.02 (0.73–1.41) 1.07 (0.75–1.52) 0.96
Women
Colon cancer
Total sugars b
Number of cases/person‐years 122/148,921 142/150,452 150/149,870 122/150,458 111/150,042
Multivariable‐adjusted c 1.00 (reference) 1.24 (0.96–1.59) 1.32 (1.01–1.72) 1.09 (0.82–1.45) 0.99 (0.73–1.35) 0.63
Total fructose (fructose + ½ × sucrose)
Number of cases/person‐years 129/148,631 140/149,252 132/150,744 124/150,222 122/150,894
Multivariable‐adjusted c 1.00 (reference) 1.16 (0.91–1.49) 1.10 (0.85–1.42) 1.04 (0.79–1.37) 1.02 (0.76–1.36) 0.84
Proximal colon cancer
Total sugars b
Number of cases/person‐years 73/148,921 95/150,452 91/149,870 73/150,458 69/150,042
Multivariable‐adjusted c 1.00 (reference) 1.30 (0.94–1.79) 1.23 (0.87–1.72) 0.96 (0.67–1.40) 0.87 (0.58–1.29) 0.12
Total fructose (fructose + ½ × sucrose)
Number of cases/person‐years 78/148,631 95/149,252 82/150,744 70/150,222 76/150,894
Multivariable‐adjusted c 1.00 (reference) 1.24 (0.91–1.70) 1.06 (0.76–1.47) 0.89 (0.62–1.27) 0.92 (0.63–1.34) 0.19
Distal colon cancer
Total sugars b
Number of cases/person‐years 40/148,921 39/150,452 52/149,870 44/150,458 35/150,042
Multivariable‐adjusted c 1.00 (reference) 1.14 (0.72–1.80) 1.61 (1.02–2.54) 1.50 (0.92–2.44) 1.27 (0.74–2.17) 0.22
Total fructose (fructose + ½ × sucrose)
Number of cases/person‐years 42/148,631 37/149,252 44/150,744 48/150,222 39/150,894
Multivariable‐adjusted c 1.00 (reference) 1.04 (0.66–1.63) 1.28 (0.82–2.02) 1.49 (0.94–2.36) 1.28 (0.77–2.13) 0.14
Rectal cancer
Total sugars b
Number of cases/person‐years 38/148,921 43/150,452 54/149,870 49/150,458 61/150,042
Multivariable‐adjusted c 1.00 (reference) 1.24 (0.79–1.95) 1.57 (1.00–2.47) 1.44 (0.89–2.32) 1.75 (1.07–2.87) 0.03
Total fructose (fructose + ½ × sucrose)
Number of cases/person‐years 40/148,631 41/149,252 56/150,744 46/150,222 62/150,894
Multivariable‐adjusted c 1.00 (reference) 1.07 (0.68–1.67) 1.45 (0.94–2.24) 1.18 (0.74–1.88) 1.50 (0.94–2.40) 0.10
Open in a new tab

Abbreviations: CI, confidence interval; CRC, colorectal cancer; HR, hazard ratio; PUFA, polyunsaturated fatty acid; SFA, saturated fatty acid.

a

Participants were categorized into quintiles of total sugar or total fructose intake by sex for the analyses of the corresponding sugar type.

b

“Total sugars” represent the sum of the consumption of the following saccharides: glucose, fructose, galactose, sucrose, maltose, and lactose.

c

Adjusted for age and public health center area, body mass index, alcohol consumption, smoking status, physical activity, family history of CRC, history of diabetes, CRC screening, postmenopausal status (women only), use of exogenous female hormones (women only), total energy intake, and intake of SFAs, n‐3 PUFAs, magnesium, vitamin D, vitamin B6, vitamin B12, calcium, dietary fiber, and folate.

For all sugar subtypes, no associations were observed among men (Table S1). However, partially positive associations were observed among women for fructose (1.30 [1.04–1.62] for Q1 vs. Q2 and 1.40 [1.11–1.75] for Q1 vs. Q3) (Table S1). The results of stratified analyses according to smoking and alcohol drinking statuses, BMI, and a history of diabetes are presented in Table S2. For smoking status, total sugar intake showed an inverse association among men who had never smoked (HRs [95% CI]: 0.63 [0.42–0.95] for Q1 vs. Q5; p for linear trend = 0.01). For total fructose, the results exhibited the same or a slightly lower range of HRs (0.58 [0.39–0.85] for Q1 vs. Q5; p for linear trend = 0.003). When stratified by BMI and alcohol drinking status, no clear difference was observed among strata in men. The HRs of the third and fourth quintiles were higher for women with BMI ≥ 25 kg/m2 (total sugars: 1.47 [0.97–2.22] for Q1 vs. Q3, 1.75 [1.14–2.67] for Q1 vs. Q4) than among all women. After we excluded participants with diabetes, the results of the associations were similar to those before the exclusion. Additionally, when we analyzed using data of participants who were without missing data, we observed similar results to those from the imputed dataset.

4. DISCUSSION

We did not identify any evident associations of sugar intake with CRC risk in this large‐scale population‐based cohort study. Analysis according to CRC tumor site represented a potential positive association between total sugar intake and rectal cancer in women. This is the first prospective study among Asian adults to investigate the association of the total and specific types of sugar intake with CRC risk. Furthermore, we provide novel data from the perspective that the level of sugar intake is relatively low and food sources are different among the Japanese population compared with the American and European populations. Our findings may contribute to the development of a sugar intake reference that considers the characteristics of diverse populations.

Our findings indicated the highest HRs in the third quintile of total sugar intake among women. This could be influenced by the higher health consciousness among participants in the fourth and highest quintiles. Among the Japanese, the contribution of fruits and vegetables to sugar intake is high. 16 Accordingly, participants who consumed more fruits and vegetables and had other healthy characteristics (never smoked, never drank alcohol, CRC screening, etc.) constituted a higher proportion of the fourth and highest quintiles. Although these variables were adjusted in the models, our results may have been influenced by a healthy background, and the increase in HRs for the fourth and highest quintiles may be attenuated.

Our findings suggest the lack of an association of total sugar intake with CRC risk, in line with previous reports. 5 , 6 However, the increased risk in the middle level of total sugar intake among women in our study was inconsistent with past studies. 5 , 6 We also observed that a moderate level of fructose intake was associated with an increased risk in women, which was inconsistent with previous results. 6 , 7 , 8 , 9 , 10 Findings from the Women's Health Study suggested a positive association with CRC risk in the middle level (the second, third, and fifth quintiles) of total fructose intake, 10 but the findings were not for fructose as a monosaccharide. Of note, these previous studies were conducted among the American population whose total sugar intake was approximately 2‐fold higher, 6 and the contribution of sugar‐sweetened beverages, sweeteners, and confectionaries were 1.5‐fold higher 16 , 36 than that of the population in this study. Additionally, only 8.1% of this study's participants consumed more free sugars than the upper limit defined by the WHO (10% of total energy intake), 14 compared to the studies from the USA (>70%) or the European countries (>40%). 17 , 18 , 19 , 20 The proportion was not much different from the literature among Japanese studies. 15 , 16 Nevertheless, in this study, we observed a partial positive association with CRC risk among women. Further examinations of the presence of a positive association in other Asian populations whose sugar intake is lower than that of the American population are warranted.

The disagreement in the findings between men and women may be influenced by the source of sugar intake. Overall, the directions of the associations between sugar intake and CRC risk were positive in women but were inverse in men, although compared with women, men tend to have more visceral and hepatic adipose tissue and higher insulin resistance. 37 This difference may be explained by the profile that the range of sugar intake and contribution of confectionaries was higher in women than in men according to dietary records among a subsample of the JPHC study. 16 Moreover, given that vegetables and fruits are the main food sources of sugar intake among Japanese men, 16 the preventive effects of antioxidant factors and fiber from vegetables and fruits may be greater. The effects may have at least partially contributed to the inverse association between total sugar and total fructose intake and CRC risk among individuals who had never smoked. Meanwhile, the effects may have been offset by tobacco carcinogens among smokers. Among women, the higher contribution of confectionaries to sugar intake than that observed in men may also weaken the effect, which may be why the inverse associations were not observed.

For proximal colon cancer, our findings indicated lower point estimates of HRs than those for distal colon and rectal cancer, and a possible positive association was observed with rectal cancer among women. These differences in the association with cancer risk might be influenced by the molecular subtype of CRC. The potential mechanisms underlying the effects of sugar intake on colorectal carcinogenesis include weight gain and insulin resistance induced by habitual excessive sugar intake. Hyperinsulinemia facilitated by insulin resistance has been suggested to stimulate the synthesis of insulin‐like growth factor‐1 and to facilitate cell growth and inhibition of apoptosis. 38 , 39 Postprandial hyperglycemia, which has been reported to induce oxidative stress, 40 may constitute another underlying mechanism. Meanwhile, previous findings have suggested that the frequency of BRAF mutations in CRC increased incrementally from the rectum to the ascending colon, 41 and fruit consumption was inversely associated with BRAF‐mutated tumor risk. 42 Therefore, protection against BRAF mutations provided by fruits, which are the main source of sugar intake in the Japanese population, may modify the association of sugar intake with proximal and distal colon cancer. Moreover, the gut microbiome, which influences the association between dietary factors and colorectal carcinogenesis, 43 is differentially distributed across colorectal sites. 44 The heterogeneity in microbiome composition may have also contributed to differences in the results between rectal and colon cancer.

The strengths of this study include its large‐scale and population‐based prospective design, high follow‐up rate, long‐term follow‐up, and the use of validated sugar intake estimates. However, our study has limitations. First, the participants reported their dietary intake in the 1990s, which may not fully reflect that of the contemporary Japanese population. They may have consumed a more westernized diet with more sugars from sugar‐sweetened beverages compared with that of the current study population; the contribution of sugar‐sweetened beverages to total sugar intake was 11.5% in 2013, 15 and 7.1% in 1990. 16 This alteration in the composition of total sugar intake might limit the external validity of our findings. Second, although sugar intake estimates using the FFQ have been validated, some correlation coefficients with dietary records were under 0.50 for several types of sugar. 16 Moreover, correlation coefficients among the FFQ at a yearly interval were also moderate. Therefore, potential measurement errors due to self‐reported intake and unmeasured changing diet were not zero; the errors may have caused misclassification, which may attenuate the associations. Third, we could not exclude potential confounding effects of unmeasured variables, such as nonsteroidal anti‐inflammatory drug use, and residual confounding factors, although we attempted to adjust for potential confounders in the current analyses.

In conclusion, the results of this prospective cohort study in the Japanese population suggested that dietary sugar was not associated with CRC risk, although we cannot exclude the potential association of higher total sugar intake with an increased risk of rectal cancer in women. We provide evidence for the potential role of dietary sugar in CRC risk among populations with different dietary habits to those of the American and European populations.

AUTHOR CONTRIBUTIONS

RK, RK, AG, TY, NS, MI, MI, and ST designed this study and were involved in the interpretation of the results. NS, MI, MI, and ST conducted the research. RK and RK performed the formal analyses and drafted the initial manuscript. RK, RK, AG, TY, NS, MI, MI, and ST reviewed the manuscript for critical content and approved the final version of the manuscript.

FUNDING INFORMATION

This work was supported by the National Cancer Center Research and Development Fund (since 2011), a Grant‐in‐Aid for Cancer Research from the Ministry of Health, Labor, and Welfare of Japan (from 1989 to 2010), the Japan Society for the Promotion of Science (JSPS KAKENHI Grant Number 22 K17382), and the Japan Health Research Promotion Bureau (JH) Research Fund (2019‐(1)‐1).

CONFLICT OF INTEREST STATEMENT

Manami Inoue, Norie Sawada, and Motoki Iwasaki are Editorial Board Members of Cancer Science. The remaining authors have no conflicts of interest concerning this study.

ETHICS STATEMENTS

Approval of the research protocol by an Institutional Reviewer Board: The study was approved by the Institutional Review Board of the National Cancer Center of Japan (approval No. 2015‐085). Informed Consent: All participants were informed of the objectives of the study and that completion of the survey questionnaire was regarded as providing consent to participate. Registry and the Registration No. of the study/trial: N/A. Animal Studies: N/A.

Supporting information

Table S1.

Click here for additional data file. (27.6KB, docx)

Table S2.

Click here for additional data file. (31.5KB, docx)

ACKNOWLEDGMENTS

We appreciate all the participants and members of the survey. We are grateful to Dr. Sarah K. Abe of the National Cancer Center Institute for Cancer Control for her beneficial comments on this study.

APPENDIX A.

Members of the Japan Public Health Center–based Prospective Study Group (JPHC Study, principal investigators: N. Sawada and S. Tsugane) are as follows: N. Sawada, S. Tsugane, M. Iwasaki, M. Inoue, T. Yamaji, R. Katagiri, Y. Miyamoto, H. Ihira, S. K. Abe, S. Tanaka, T. Hanaoka, A. Hidaka, S. Sasazuki, H. Charvat, T. Shimazu, S. Budhathoki, M. Muto and T. Imatoh, National Cancer Center, Tokyo; K. Miyakawa, F. Saito, A. Koizumi, Y. Sano, I. Hashimoto, T. Ikuta, Y. Tanaba, H. Sato, Y. Roppongi, T. Takashima, H. Suzuki, T. Sugie, and T. Moriya, Iwate Prefectural Ninohe Public Health Center, Iwate; Y. Miyajima, N. Suzuki, S. Nagasawa, Y. Furusugi, N. Nagai, Y. Ito, S. Komatsu, and T. Minamizono, Akita Prefectural Yokote Public Health Center, Akita; H. Sanada, Y. Hatayama, F. Kobayashi, H. Uchino, Y. Shirai, T. Kondo, R. Sasaki, Y. Watanabe, Y. Miyagawa, Y. Kobayashi, M. Machida, K. Kobayashi, M. Tsukada, and Y. Shirai, Nagano Prefectural Saku Public Health Center, Nagano; Y. Kishimoto, E. Takara, T. Fukuyama, M. Kinjo, M. Irei, H. Sakiyama, H. Sakiyama, and H. Kuniyoshi, Okinawa Prefectural Chubu Public Health Center, Okinawa; K. Imoto, H. Yazawa, T. Seo, A. Seiko, F. Ito, F. Shoji and R. Saito, Katsushika Public Health Center, Tokyo; A. Murata, K. Minato, K. Motegi, T. Fujieda and S. Yamato, Ibaraki Prefectural Mito Public Health Center, Ibaraki; T. Yoshimi, Ibaraki Prefectural Chuo Public Health Center, Ibaraki; K. Matsui, T. Abe, M. Katagiri and M. Suzuki, Niigata Prefectural Kashiwazaki and Nagaoka Public Health Center, Niigata; H. Sonoda, Niigata Prefectural Nagaoka Public Health Center, Niigata; M. Doi, A. Terao and Y. Ishikawa, and T. Tagami, Kochi Prefectural Chuo‐higashi Public Health Center, Kochi; H. Sueta, H. Doi, M. Urata, N. Okamoto, F. Ide, H. Goto, R. Fujita, Y. Sou, and T. Ando, Nagasaki Prefectural Kamigoto Public Health Center, Nagasaki; H. Sakiyama, N. Onga, H. Takaesu, M. Uehara, T. Nakasone, M. Yamakawa, Y. Miyasato, and T. Kimura, Okinawa Prefectural Miyako Public Health Center, Okinawa; F. Horii, I. Asano, H. Yamaguchi, K. Aoki, S. Maruyama, M. Ichii and M. Takano, Osaka Prefectural Suita Public Health Center, Osaka; J. Ogata, S. Baba, T. Mannami, A. Okayama, and Y. Kokubo, National Cerebral and Cardiovascular Center, Osaka; Y. Honda, S. Sakurai, N. Tsuchiya, and K. Yamagishi, University of Tsukuba, Ibaraki; T. Mizoue, National Center for Global Health and Medicine, Tokyo; K. Nakamura, Niigata University, Niigata; and R. Takachi, Nara Women's University, Nara; J. Ishihara, Azabu University, Kanagawa; T. Sobue, H. Iso, and T. Kitamura, Osaka University, Osaka; I. Saito, Oita University, Oita; N. Yasuda, Kochi University, Kochi; M. Mimura, Keio University, Tokyo; K. Sakata, Iwate Medical University, Iwate; M. Noda, Saitama Medical University; A. Goto, Yokohama City University, Kanagawa; H. Yatsuya, Nagoya university, Aichi; Y. Tsubono, Tohoku University, Miyagi; K. Suzuki, Research Institute for Brain and Blood Vessels Akita, Akita; M. Kabuto, National Institute for Environmental Studies, Ibaraki; M. Yamaguchi, Y. Matsumura, S. Sasaki and S. Watanabe, National Institute of Health and Nutrition, Tokyo; M. Akabane, Tokyo University of Agriculture, Tokyo; T. Kadowaki, The University of Tokyo, Tokyo; Y. Takashima and Y. Yoshida, Kyorin University, Tokyo; S. Matsushima and S. Natsukawa, Saku General Hospital, Nagano; H. Sugimura, Hamamatsu University School of Medicine, Shizuoka; S. Tominaga, Aichi Cancer Center, Aichi; M. Iida, W. Ajiki and A. Ioka, Osaka Medical Center for Cancer and Cardiovascular Disease, Osaka; S. Sato, Chiba Prefectural Institute of Public Health, Chiba; M. Konishi and K. Okada, Ehime University, Ehime; Y. Kawaguchi, Tokyo Medical and Dental University, Tokyo, N. Hamajima, Nagoya University, Aichi; S. Akiba, Kagoshima University, Kagoshima; T. Isobe, Keio University, Tokyo; Y. Sato, Tokyo Gakugei University, Tokyo; H. Shimizu, Sakihae Institute, Gifu; S. Kono, Kyushu University, Fukuoka; E. Maruyama, Kobe University, Hyogo. The members are listed at https://epi.ncc.go.jp/en/jphc/781/8896.html.

Kanehara R, Katagiri R, Goto A, et al. Sugar intake and colorectal cancer risk: A prospective Japanese cohort study. Cancer Sci. 2023;114:2584‐2595. doi: 10.1111/cas.15766

Contributor Information

Ryoko Katagiri, Email: rkatagir@ncc.go.jp.

JPHC Study Group:

Y. Miyamoto, H. Ihira, S. K. Abe, S. Tanaka, T. Hanaoka, A. Hidaka, S. Sasazuki, H. Charvat, T. Shimazu, S. Budhathoki, M. Muto, T. Imatoh, K. Miyakawa, F. Saito, A. Koizumi, Y. Sano, I. Hashimoto, T. Ikuta, Y. Tanaba, H. Sato, Y. Roppongi, T. Takashima, H. Suzuki, T. Sugie, T. Moriya, Y. Miyajima, N. Suzuki, S. Nagasawa, Y. Furusugi, N. Nagai, Y. Ito, S. Komatsu, T. Minamizono, H. Sanada, Y. Hatayama, F. Kobayashi, H. Uchino, Y. Shirai, T. Kondo, R. Sasaki, Y. Watanabe, Y. Miyagawa, Y. Kobayashi, M. Machida, K. Kobayashi, M. Tsukada, Y. Kishimoto, E. Takara, T. Fukuyama, M. Kinjo, M. Irei, H. Sakiyama, H. Kuniyoshi, K. Imoto, H. Yazawa, T. Seo, A. Seiko, F. Ito, F. Shoji, R. Saito, A. Murata, K. Minato, K. Motegi, T. Fujieda, S. Yamato, T. Yoshimi, K. Matsui, T. Abe, M. Suzuki, H. Sonoda, M. Doi, A. Terao, Y. Ishikawa, T. Tagami, H. Sueta, H. Doi, M. Urata, N. Okamoto, F. Ide, H. Goto, R. Fujita, Y. Sou, T. Ando, N. Onga, H. Takaesu, M. Uehara, T. Nakasone, M. Yamakawa, Y. Miyasato, T. Kimura, F. Horii, I. Asano, H. Yamaguchi, K. Aoki, S. Maruyama, M. Ichii, M. Takano, J. Ogata, S. Baba, T. Mannami, A. Okayama, Y. Kokubo, Y. Honda, S. Sakurai, N. Tsuchiya, K. Yamagishi, T. Mizoue, K. Nakamura, R. Takachi, J. Ishihara, T. Sobue, H. Iso, T. Kitamura, I. Saito, N. Yasuda, M. Mimura, K. Sakata, M. Noda, H. Yatsuya, Y. Tsubono, K. Suzuki, M. Kabuto, M. Yamaguchi, Y. Matsumura, S. Sasaki, S. Watanabe, M. Akabane, T. Kadowaki, Y. Takashima, Y. Yoshida, S. Matsushima, S. Natsukawa, H. Sugimura, S. Tominaga, M. Iida, W. Ajiki, A. Ioka, S. Sato, M. Konishi, K. Okada, Y. Kawaguchi, S. Akiba, T. Isobe, Y. Sato, H. Shimizu, S. Kono, E. Maruyama, and N. Hamajima

DATA AVAILABILITY STATEMENT

For data access to JPHC Study data, please follow the instructions at https://epi.ncc.go.jp/en/jphc/805/8155.html.

REFERENCES

  • 1. International Agency for Research on Cancer . All cancers, Globocan 2020. Accessed 10, May 2021. https://gco.iarc.fr/today/data/factsheets/cancers/39‐All‐cancers‐fact‐sheet.pdf
  • 2. World Cancer Research Fund, American Institute for Cancer Research . Diet, Nutrition, Pysical Activity and Colorectal Cancer (Continuous Update Project Expert Report 2018). 2018.
  • 3. World Health Organization . Internet. Accessed 15 August 2022 https://www.who.int/data/gho/data/indicators/indicator‐details/GHO/prevalence‐of‐obesity‐among‐adults‐bmi‐=‐30‐(age‐standardized‐estimate)‐(‐)
  • 4. Inoue M, Hirabayashi M, Abe SK, et al. Burden of cancer attributable to modifiable factors in Japan in 2015. Glob. Health Med. 2022;4(1):26‐36. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 5. Kabat GC, Shikany JM, Beresford SA, et al. Dietary carbohydrate, glycemic index, and glycemic load in relation to colorectal cancer risk in the Women's Health Initiative. Cancer Causes Control. 2008;19(10):1291‐1298. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 6. Tasevska N, Jiao L, Cross AJ, et al. Sugars in diet and risk of cancer in the NIH‐AARP diet and health study. Int J Cancer. 2012;130(1):159‐169. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 7. McCarl M, Harnack L, Limburg PJ, Anderson KE, Folsom AR. Incidence of colorectal cancer in relation to glycemic index and load in a cohort of women. Cancer Epidemiol Biomarkers Prev. 2006;15(5):892‐896. [DOI] [PubMed] [Google Scholar]
  • 8. Michaud DS, Fuchs CS, Liu S, Willett WC, Colditz GA, Giovannucci E. Dietary glycemic load, carbohydrate, sugar, and colorectal cancer risk in men and women. Cancer Epidemiol Biomarkers Prev. 2005;14(1):138‐147. [PubMed] [Google Scholar]
  • 9. Makarem N, Bandera EV, Lin Y, Jacques PF, Hayes RB, Parekh N. Consumption of sugars, sugary foods, and sugary beverages in relation to adiposity‐related cancer risk in the Framingham offspring cohort (1991‐2013). Cancer Prev Res (Phila). 2018;11(6):347‐358. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 10. Higginbotham S, Zhang ZF, Lee IM, et al. Dietary glycemic load and risk of colorectal cancer in the Women's health study. J Natl Cancer Inst. 2004;96(3):229‐233. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 11. Seow A, Quah SR, Nyam D, Straughan PT, Chua T, Aw TC. Food groups and the risk of colorectal carcinoma in an Asian population. Cancer. 2002;95(11):2390‐2396. [DOI] [PubMed] [Google Scholar]
  • 12. Wang Z, Uchida K, Ohnaka K, et al. Sugars, sucrose and colorectal cancer risk: the Fukuoka colorectal cancer study. Scand J Gastroenterol. 2014;49(5):581‐588. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 13. Cho H, Budhathoki S, Kanehara R, et al. Association between dietary sugar intake and colorectal adenoma among cancer screening examinees in Japan. Cancer Sci. 2020;111(10):3862‐3872. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 14. World Health Organization . Guideline: Sugars Intake for Adults and Children. WHO document production services; 2015. [PubMed] [Google Scholar]
  • 15. Fujiwara A, Murakami K, Asakura K, et al. Estimation of starch and sugar intake in a Japanese population based on a newly developed food composition database. Nutrients. 2018;10(10):1474. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 16. Kanehara R, Goto A, Kotemori A, et al. Validity and reproducibility of a self‐administered food frequency questionnaire for the assessment of sugar intake in middle‐aged Japanese adults. Nutrients. 2019;11(3):554. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 17. Lei L, Rangan A, Flood VM, Louie JC. Dietary intake and food sources of added sugar in the Australian population. Br J Nutr. 2016;115(5):868‐877. [DOI] [PubMed] [Google Scholar]
  • 18. Lluch A, Maillot M, Gazan R, et al. Individual diet modeling shows how to balance the diet of French adults with or without excessive free sugar intakes. Nutrients. 2017;9(2):162. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 19. Chatelan A, Gaillard P, Kruseman M, Keller A. Total, added, and free sugar consumption and adherence to guidelines in Switzerland: results from the first national nutrition survey menuCH. Nutrients. 2019;11(5):1117. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 20. Yang Q, Zhang Z, Gregg EW, Flanders WD, Merritt R, Hu FB. Added sugar intake and cardiovascular diseases mortality among US adults. JAMA Intern Med. 2014;174(4):516‐524. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 21. International Agency for Research on Cancer . CANCER TODAY, Data visualization tools for exploring the global cancer burden in 2020. Accessed 1st, Feb 2022, https://gco.iarc.fr/today/home
  • 22. Elliott SS, Keim NL, Stern JS, Teff K, Havel PJ. Fructose, weight gain, and the insulin resistance syndrome. Am J Clin Nutr. 2002;76(5):911‐922. [DOI] [PubMed] [Google Scholar]
  • 23. Stanhope KL, Schwarz JM, Keim NL, et al. Consuming fructose‐sweetened, not glucose‐sweetened, beverages increases visceral adiposity and lipids and decreases insulin sensitivity in overweight/obese humans. J Clin Invest. 2009;119(5):1322‐1334. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 24. Yoon YS, Keum N, Zhang X, Cho E, Giovannucci EL. Hyperinsulinemia, insulin resistance and colorectal adenomas: a meta‐analysis. Metabolism. 2015;64(10):1324‐1333. [DOI] [PubMed] [Google Scholar]
  • 25. Giovannucci E. Insulin, insulin‐like growth factors and colon cancer: a review of the evidence. J Nutr. 2001;131(11):3109 S‐3120 S. [DOI] [PubMed] [Google Scholar]
  • 26. Malik VS, Pan A, Willett WC, Hu FB. Sugar‐sweetened beverages and weight gain in children and adults: a systematic review and meta‐analysis. Am J Clin Nutr. 2013;98(4):1084‐1102. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 27. Malik VS, Popkin BM, Bray GA, Després JP, Willett WC, Hu FB. Sugar‐sweetened beverages and risk of metabolic syndrome and type 2 diabetes: a meta‐analysis. Diabetes Care. 2010;33(11):2477‐2483. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 28. Charrez B, Qiao L, Hebbard L. The role of fructose in metabolism and cancer. Horm Mol Biol Clin Investig. 2015;22(2):79‐89. [DOI] [PubMed] [Google Scholar]
  • 29. Tsugane S, Sawada N. The JPHC study: design and some findings on the typical Japanese diet. Jpn J Clin Oncol. 2014;44(9):777‐782. [DOI] [PubMed] [Google Scholar]
  • 30. Sasaki S, Kobayashi M, Ishihara J, Tsugane S. Self‐administered food frequency questionnaire used in the 5‐year follow‐up survey of the JPHC study: questionnaire structure, computation algorithms, and area‐based mean intake. J Epidemiol. 2003;13(1):S13‐S22. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 31. Kanehara R, Goto A, Sawada N, et al. Association between sugar and starch intakes and type 2 diabetes risk in middle‐aged adults in a prospective cohort study. Eur J Clin Nutr. 2022;76(5):746‐755. [DOI] [PubMed] [Google Scholar]
  • 32. Ministry of Education, Culture, Sports, Science and Technology, the Government of Japan . Standard Tables of Food Composition in Japan. 7th Revised ed. Official Gazette Cooperation of Japan; 2015. [Google Scholar]
  • 33. Ministry of Education, Culture, Sports, Science and Technology, the Government of Japan . Available carbohydrates, polyols, and organic acids. Standard Tables of Food Composition in Japan. 7th Revised ed. Official Gazette Cooperation of Japan; 2015. [Google Scholar]
  • 34. Pollock NK, Bundy V, Kanto W, et al. Greater fructose consumption is associated with cardiometabolic risk markers and visceral adiposity in adolescents. J Nutr. 2012;142(2):251‐257. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 35. Liu Y, De A. Multiple imputation by fully conditional specification for dealing with missing data in a large epidemiologic study. Int J Stat Med Res. 2015;4(3):287‐295. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 36. Huth PJ, Fulgoni VL, Keast DR, Park K, Auestad N. Major food sources of calories, added sugars, and saturated fat and their contribution to essential nutrient intakes in the U.S. diet: data from the National Health and nutrition examination survey (2003‐2006). Nutr J. 2013;12:116. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 37. Geer EB, Shen W. Gender differences in insulin resistance, body composition, and energy balance. Gend Med. 2009;6(Suppl 1):60‐75. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 38. Kaaks R, Lukanova A. Energy balance and cancer: the role of insulin and insulin‐like growth factor‐I. Proc Nutr Soc. 2001;60(1):91‐106. [DOI] [PubMed] [Google Scholar]
  • 39. Pollak M. Insulin and insulin‐like growth factor signalling in neoplasia. Nat Rev Cancer. 2008;8(12):915‐928. [DOI] [PubMed] [Google Scholar]
  • 40. Ceriello A, Bortolotti N, Motz E, et al. Meal‐induced oxidative stress and low‐density lipoprotein oxidation in diabetes: the possible role of hyperglycemia. Metabolism. 1999;48(12):1503‐1508. [DOI] [PubMed] [Google Scholar]
  • 41. Yamauchi M, Morikawa T, Kuchiba A, et al. Assessment of colorectal cancer molecular features along bowel subsites challenges the conception of distinct dichotomy of proximal versus distal colorectum. Gut. 2012;61(6):847‐854. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 42. Hidaka A, Harrison TA, Cao Y, et al. Intake of dietary fruit, vegetables, and fiber and risk of colorectal cancer according to molecular subtypes: a pooled analysis of 9 studies. Cancer Res. 2020;80(20):4578‐4590. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 43. Song M, Chan AT, Sun J. Influence of the gut microbiome, diet, and environment on risk of colorectal cancer. Gastroenterology. 2020;158(2):322‐340. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 44. Jiao L, Kourkoumpetis T, Hutchinson D, et al. Spatial characteristics of colonic mucosa‐associated gut microbiota in humans. Microb Ecol. 2022;83(3):811‐821. [DOI] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Table S1.

Click here for additional data file. (27.6KB, docx)

Table S2.

Click here for additional data file. (31.5KB, docx)

Data Availability Statement

For data access to JPHC Study data, please follow the instructions at https://epi.ncc.go.jp/en/jphc/805/8155.html.

Articles from Cancer Science are provided here courtesy of Wiley

RESOURCES