Noninvasive predictive models based on lifestyle analysis and risk factors for early‐onset colorectal cancer

Abstract

BackgroundColorectal cancer (CRC) incidence has increased among patients aged <50 years. Exploring high‐risk factors and screening high‐risk populations may help lower early‐onset CRC (EO‐CRC) incidence. We developed noninvasive predictive models for EO‐CRC and investigated its risk factors.MethodsThis retrospective multicenter study collected information on 1756 patients (811 patients with EO‐CRC and 945 healthy controls) from two medical centers in China. Sociodemographic features, clinical symptoms, medical and family history, lifestyle, and dietary factors were measured. Patients from one cohort were randomly assigned (8:2) to two groups for model establishment and internal validation, and another independent cohort was used for external validation. Multivariable logistic regression, random forest, and eXtreme Gradient Boosting (XGBoost) were performed to establish noninvasive predictive models for EO‐CRC. Some variables in the model influenced EO‐CRC occurrence and were further analyzed. Multivariable logistic regression analysis yielded adjusted odd ratios (ORs) and 95% confidence intervals (CIs).ResultsAll three models showed good performance, with areas under the receiver operator characteristic curves (AUCs) of 0.82, 0.84, and 0.82 in the internal and 0.78, 0.79, and 0.78 in the external validation cohorts, respectively. Consumption of sweet (OR 2.70, 95% CI 1.89–3.86, P < 0.001) and fried (OR 2.16, 95% CI 1.29–3.62, P < 0.001) foods ≥3 times per week was significantly associated with EO‐CRC occurrence.ConclusionWe established noninvasive predictive models for EO‐CRC and identified multiple nongenetic risk factors, especially sweet and fried foods. The model has good performance and can help predict the occurrence of EO‐CRC in the Chinese population.

Age-Based Comparative Analysis of Colorectal Cancer Colonoscopy Screening Findings

“…Moreover, other findings identified within this seminal study were that 70.1% of tumors were located distal to the descending colon, and with 40% being also node-positive, while 20% of EOCRC cases also having microsatellite instabilities [11]. Another highly informative study was conducted by Deng and colleagues earlier in 2023, which focused on developing noninvasive predictive models for EOCRC, based mainly upon lifestyle analyses and other identified risk factors [12]. The results of this particular study, conducted over 811 EOCRC patients and 945 healthy controls, concluded that the consumption of sweet/fried foods items at a frequency of over thrice-weekly, was highly linked to EOCRC manifestation [12].…”

From Algorithms to Clinical Utility: A Systematic Review of Individualized Risk Prediction Models for Colorectal Cancer

“…There are two main categories of risk models for predicting CRC or AN based on their predictor features: (1) conventional risk model, which uses known and well-established risk factors for CRC; and (2) composite model, which combines well-established risk factors with more advanced features, such as test/laboratory findings and genetic factors. Among the 41 included studies, we identified 22 original conventional risk models, [36][37][38]40,45,48,50,51,53,54,58,59,61,[63][64][65][67][68][69][70]72,74] and 13 composite models [39,[41][42][43][44]47,56,57,62,71,73,75,76]. Of these, three conventional risk models have undergone external validation in separate studies: (1) the Asia-Pacific Colorectal Screening (APCS) model [45], validated in four other studies [46,49,66,70];…”

“…The newly developed risk prediction models between 2011 and 2021 predominantly originated from Asian countries (n = 19), including China [37,46,68,74], Korea [47,48,50,51,61,62,66,70,72], Japan [59,65], Thailand [57], Hong Kong [45,49], and Taiwan [56]. Among these novel risk prediction models based on Asian populations, the most common predictors were age (n = 19), smoking history (n = 17), sex (n = 15), BMI (n = 14), family history of CRC (n = 14), alcohol intake (n = 8), and diabetes mellitus (n = 6) [37,[45][46][47][48][49][50][51]56,57,59,61,62,65,66,68,70,72,74,80].…”

“…All included studies, except for that of Jung et al (2018) and Briggs et al (2023), reported AUC values to assess the discriminatory accuracy of the models (Table 3). Among the 44 included models, 23 conventional risk models [37,40,42,[44][45][46][48][49][50][51]54,[58][59][60][61][63][64][65][67][68][69][70]72,74,75] and 10 composite models [39,41,47,56,57,62,71,75,76,80] reported AUC values with a 95% confidence interval (CI), as presented in Figure 5. Meanwhile, the model calibration was assessed in two different metrics: using the expected-to-observed (E/O) ratios, which were only demonstrated in nine of the studies; or by employing the Hosmer-Lemeshow (HL) goodness-of-fit test, as was the case in 12 out of 41 studies.…”

Nitrogen Permease Regulator Like-2 (NPRL2) truncating mutation causes Ohtahara syndrome with incomplete penetrance: expanding the genotype-phenotype correlations

“…The established causative genes include PRRT2, KCNQ2, SCN1A, SCN2A, STXBP1, CDKL5, and ARX. In addition, structural anomalies including neuronal migration disorder or dysgenesis are often associated (Song et al, 2021). Mutations in Nitrogen Permease Regulator Like-2 (NPRL2) are being recognized increasingly as a major player in producing brain structural anomalies (Sun et al, 2021).…”