1 Background

Digestive system cancers are significant contributors to the global cancer burden [1]. Specifically, colorectal cancer (CRC) is a highly frequent digestive tract cancer. By 2040, CRC incidence and mortality is projected to substantially increase across the globe [2]. Identifying drivers of CRC risk and changes in modifiable risk factors is acknowledged among preventive approaches for CRC [2]. Lifestyle factors such as overnutrition and obesity that stimulate insulin secretion are also risk factors for CRC [2,3,4,5]. Hyperinsulinemia and insulin resistance (IR) are common in overnutrition and obesity, which has led to the hypothesis that IR is related to cancer development [6].

Epidemiological studies have reported associations between IR and colorectal cancer [7,8,9,10,11,12]. Gold-standard techniques for measuring insulin sensitivity are expensive, time-consuming, and therefore difficult to implement in large-scale epidemiological studies in resource-limited settings [13]. Accordingly, studies on IR and CRC risk used the homeostasis model of insulin resistance (HOMA-IR), circulating C-peptide, or insulin-like growth factor 1 (IGF1-) as measures of IR and insulin production [7,8,9,10,11,12].

Surrogates of IR have been developed based on biochemical measurements routinely assessed in clinical settings. These include the triglyceride glucose index (TyG) computed as ln [(triglycerides, mg/dl) × (fasting glucose, mg/dl)/2] [14]. The TyG index is a simple, non-invasive surrogate marker that shows good prediction of IR measured by the glucose clamp technique, the gold standard technique for measuring IR [15]. Considering that the gold standard method is labor-and time-intensive, TyG is a more favorable marker of IR. [16]

Few epidemiological studies have evaluated the association between TyG index and CRC risk. Fritz and colleagues reported positive associations of TyG with obesity related gastrointestinal cancers including CRC [17]. A retrospective cohort study in Japan reported that TyG was associated with an increased risk of colorectal cancer [18]. To date, no large-scale prospective cohort study has comprehensively evaluated the TyG index in relation to CRC risk in the Korean population [17]. Using the Korean Genome and Epidemiology Study, we evaluated the association of the TyG index with CRC risk and examined the joint effects of a high TyG and selected lifestyle factors on CRC risk.

2 Methods

2.1 Study Participants

We derived an analytical sample from the Korean Genome and Epidemiology Study-Health Examinees cohort (KoGES-HEXA) of 173,202 participants who were recruited between 2004 and 2013 at 38 health examination centers and training hospitals located in eight regions of Korea [19, 20] and linked to the National Cancer Center (NCC) registry. After applying the exclusion criteria described in Fig. 1 and Supplementary Methods, 98,800 participants were analyzed.

Fig. 1
figure 1

Illustration of the selection of study participants in the KoGES-HEXA cohort [21]

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2.2 Exposure Variables

2.2.1 Biochemical Measurements

The methods used to process and measure biochemical parameters in the KoGES have been described elsewhere [22]. Baseline fasting plasma glucose and triglyceride levels were measured using enzymatic calorimetric methods with automatic analyzers (ADVIA 1650 and 1800; Siemens, Tarrytown, NY, USA). Using triglyceride and glucose measurements, the TyG index was calculated as In[(triglycerides, mg/dl) × (fasting glucose, mg/dl)/2] [14].

2.3 Outcome Measures

Incident cases of CRC, colon, and rectal cancer were ascertained using the 10th revision of the International Classification of Diseases codes (ICD 10): colon (C18), rectum (C19-C21). Cases were ascertained from 2004 to December 31, 2020, via linkage to the NCC. The follow-up time for each participant was computed from the interview date to the date of cancer diagnosis, death, or until December 31, 2020, whichever occurred first.

2.4 Assessment of Covariates

The KoGES used a standard interviewer-administered questionnaire to collect data on age, sex, educational level, household income, and lifestyle variables such as alcohol consumption, smoking, and physical exercise. Current alcohol drinkers were those who reported that they had consumed alcohol and were still drinkers alcohol at the time of the interview. Daily alcohol intake (g/day) was calculated for each participant using detailed alcohol assessment information from the questionnaire. Current smokers were defined as participants who had smoked > 400 cigarettes in their lifetime and were still smokers at the time of the interview [23]. Fruit, vegetable, and red meat intakes were computed from a semi-quantitative food frequency questionnaire and expressed as g/1000Kcal/day. Regular physical exercise was defined as engaging in activities that caused body sweating at least five times a week for at least 30 min per session. Height (m) and body weight (kg) were measured to the nearest 0.1 cm and 0.1 kg, respectively, while participants were wearing light clothes and barefooted. Body mass index (BMI) was calculated as body weight divided by height in meters squared (kg/m2).

2.5 Statistical Analysis

For sample description, we categorized participants into fourths of TyG index. Missing data on income (8.7%) was assigned “unknown” while missing data on other categorical and continuous covariates (< 5%) was replaced by the mode and median respectively. We described participants’ characteristics according to fourths of the TyG using general linear models and chi-squared tests for continuous and categorical variables respectively. Continuous variables were described as mean (standard error), while categorical variables were described as percentages.

We useda SAS macro developed by Loic et al. [24] to construct restricted cubic splines (RCS) and examine the nonlinear association of TyG and CRC incidence, adjusting for age, sex, educational level, household income, alcohol consumption, smoking, physical exercise, BMI, fruit, vegetable, and total red meat intake. RCS were fitted with three knots at the 5th, 50th, and 95th percentiles of each index using the median of each index as a reference value. We then constructed Cox proportional hazard models and computed hazard ratios (HR) and 95% confidence intervals (CI) for a 1-unit increment in the TyG index adjusted for the same covariates as above, overall and stratified by sex.

We examined the joint effects of the TyG index (< median (8.4) and ≥ 8.4) and BMI (< 25.0 and ≥ 25.0 kg/m2), drinking (never/past drinkers and current drinkers); smoking (never/past smokers and current smokers); physical exercise (yes, no); fruit and vegetable intake, fiber intake, and total red meat intake (below and above median). We performed sensitivity analyses by excluding participants who were diagnosed with CRC in the first 2 years of follow to account for latent period bias/reverse causation. All analyses were performed using SAS software (version 9.4; SAS Institute Inc., Cary, NC, USA), and statistical significance was defined as P < 0.05.

3 Results

Mean (SD) age was 53.2 (8.3) years. Participants contributed a total of 1,217,002 person-years (mean [SE]) of 10.6 (2.0) years, during which 699 CRC cancers (422 colon cancer cases) were observed. Participants with the highest TyG index were more likely to be older, male, middle school graduates, and low-income earners. A high TyG index positively correlated with current smoking, drinking, sedentary lifestyle, high BMI, and low fruit and vegetable intake (Table 1).

Table 1 Baseline characteristics of participants according to fourths of TyG index in the KoGES-HEXA cohort
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The HR and 95% CI for colorectal, colon, and rectal cancers per unit increase in the TyG index are shown in Table 2. TyG index was associated with an increased risk of colorectal (HR, 1.28; 95% CI, 1.12–1.46). colon (1.29, 1.10–1.54); and rectal (1.24, 1.01–1.52) cancers. The associations of TyG index and CRC risk persisted after excluding cases diagnosed within 2 years post-recruitment (Table 2).

Table 2 Hazard ratios and 95% CI of colorectal cancer risk per unit change in TyG index in the KoGES-HEXA cohort
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Dose response analyses showed a linear increase in the risk of colorectal and colon cancer with increasing TyG index (P for nonlinear association, CRC, 0.27; colon, 0.756), but a non-linear association was observed for rectal cancer (P for nonlinear association = 0.033) (Fig. 2).

Fig. 2
figure 2

Dose-response associations between TyG index and colorectal cancer in the KoGES-HEXA cohort. The model was fitted using regression splines with three knots at the 5th, 50th, and 95th percentiles of TyG. The red line indicates hazard ratios (HR), and the dotted lines indicate the 95% CI. The model was adjusted for age, sex, educational level, monthly income, smoking, drinking, regular physical exercise, BMI, fruit and vegetable intake, and total red meat intake

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The association of TyG index with colorectal cancer incidence were generally consistent in men and women (Table 3).

Table 3 Hazard ratios and 95% CI of colorectal cancer risk per unit change in TyG index in the KoGES-HEXA cohort
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The joint associations of TyG and selected lifestyle factors with CRC incidence are shown in Table 4. Participants with overweight/obesity and high TyG levels had an elevated risk of CRC (HR, 95% CI: CRC: 1.31, 1.07–1.61; colon, 1.33, 1.03–1.72). Those with a high TyG index and low fruit and vegetable intake (CRC: 1.40, 1.12–1.74; colon, 1.57, 1.18–2.09) or high amounts of red meat (CRC: 1.32, 1.05–1.65; colon: 1.52, 1.15–2.02) had an increased risk of CRC compared to those with a low TyG index and high fruit and vegetable intake or low red meat intake (Table 4).

Table 4 Joint associations of TyG index and lifestyle factors with colorectal cancer risk in the KoGES-HEXA cohort
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4 Discussion

We investigated the association of TyG index and incidence of colorectal, colon and rectal cancers, and the joint effects with lifestyle factors. A high TyG index, meaning IR, was associated with about one-fourth to one-third increased risks of colorectal, colon, and rectal cancers, with linear associations observed for colorectal and rectal cancers, and non-linear association for rectal cancers. A high TyG index, together with overweight/obesity, low fruit intake, or high intake of red meat, jointly elevated the risk of colon cancer.

These findings provide additional epidemiological evidence of the role of IR in CRC carcinogenesis in diverse populations. Hyperinsulinemia and IR promote carcinogenesis via growth promoting and mitogenic effects of insulin and IGF-1 [4]. These ligands activate the 3-phosphorylated inositol-Akt-mammalian target of rapamycin (PI3-Akt-mTOR) and RAS-MAPK signaling pathways [28], which promote cell proliferation and survival, and protein and fatty acid synthesis [4, 16, 28, 29]. Hypertriglyceridemia and hyperglycemia are closely linked to hyperinsulinemia [30], and directly increase CRC risk by promoting fecal secondary bile acid secretion, altering insulin levels [31], and signaling pathways [30]. Elevated fecal secondary bile acids induce oxidative DNA damage, inflammation, NF-κB activation, and increase gut epithelial cell proliferation [32, 33].

The association of hyperinsulinemia and IR with CRC is consistent across studies and populations regardless of the IR measure used. IGF-1 levels were positively associated with CRC in the UK [10, 11], USA [8, 9], and Japan [12]. Plasma C-peptide levels were positively associated with CRC risk the USA and Japan [7, 9, 12]. In a Mendelian randomization study, fasting insulin was associated with an increased risk of CRC, but not of other cancers [34]. Using the TyG index, Fritz et al. reported an increased risk of colon and rectal cancers with a high TyG index in a European cohort [17]. Other studies have reported similar findings in Japan [18] and China [25, 26].

Lifestyle factors that stimulate insulin secretion, such as overweight/obesity, low fruit and vegetable intake, and high red meat consumption, are also risk factors for digestive tract cancers, especially CRC [3, 4]. Our study found that these factors exacerbated the positive association of IR and CRC, in line with their previous independent associations with an increased risk of CRC in the Korean population [35,36,37], and other populations [2, 38]. Our findings are supported by previous studies that reported synergistic effects of metabolic syndrome or insulin resistance and an unfavorable lifestyle on CRC incidence and prognosis [3, 39, 40]. Taken together, lifestyle modifications such as weight loss and optimal intake of fruits, vegetables, and red meat may be important for CRC prevention in individuals with IR. Indeed, large cohort studies have reported that favorable lifestyle patterns are associated with a substantial reduction in CRC risk [39, 41].

The current study is the first to evaluate the association of IR estimated from the TyG index, and the joint association of TyG and selected modifiable lifestyle with CRC risk in the Korean population. We used a large sample size, which enabled the evaluation of specific bowel cancers, employed a cohort study design which empowers causal inferences, and used standardized methods to measure both exposures and outcomes which enabled reduction of measurement errors. We also adjusted for known risk factors of CRC to reduce confounding. Nevertheless, we acknowledge some limitations, such as the use of single measurements of fasting blood glucose and triglycerides. However, these measurements have been shown to remain stable over time. In addition, although we included several covariates in our models, there is a possibility of residual unmeasured confounding.

5 Conclusion

A high TyG index was associated with about one-fourth to one-third increased risk of colorectal, colon, and rectal cancers. Low fruit and vegetable intake, high BMI, and high intake of red meat heightened these risks, particularly for colon cancer. These findings highlight the role of IR in CRC development and suggest that weight management, optimal intake of fruits, vegetables, and red meat might improve CRC risk.